CN117785868A - Data storage method and system applied to preparation of glass sand inclusion pipe - Google Patents

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

Data storage method and system applied to the preparation of glass rigid sand-filled pipes

Technical field

The present invention relates to the field of data storage technology, and in particular to a data storage method and system applied in the preparation of glass rigid sand-filled pipes.

Background technique

The glass-reinforced corundum tube is a commonly used device in laboratories, mainly used for filtering, drying, vacuum extraction and other operations. It consists of a glass tube and two glass joints. The characteristic of the glass-reinforced corundum tube is that there is a finer sand-filled area in the middle of the glass tube, which can filter out solid particles while allowing liquids to pass through. When quality problems occur in the glass-reinforced corundum tube later, relevant preparation data needs to be dispatched for analysis. Therefore, it is particularly important to store the preparation data of the glass-reinforced corundum tube.

The existing preparation data storage method of glass rigid sand-filled pipes is mainly as follows: obtain the preparation process of glass rigid sand-filled pipes, create a file storage system based on the preparation process, package the preparation data of each process in the preparation process, and then package the The data is stored in the file storage system in turn. When encountering abnormal data or additional data, additional data analysis is required to determine the storage location of the data, and the data is stored in an area. When reading the data subsequently The data needs to be traversed in order to determine the final required data. Therefore, the storage performance of this storage method is low, resulting in a reduction in the efficiency of preparing data storage. Therefore, a method that can improve the data storage efficiency of glass rigid sand-filled pipe preparation is needed.

Summary of the invention

The present invention provides a data storage method and system used in the preparation of glass rigid sand-filled pipes, and its main purpose is to improve the data storage efficiency in the preparation of glass rigid sand-filled pipes.

In order to achieve the above objectives, the present invention provides a data storage method applied to the preparation of glass rigid sand-filled pipes, including:

Obtain the preparation data in the preparation scenario of the glass rigid sand-filled pipe, and extract the time series data and non-time series data in the preparation data;

Calculate the data correlation between each data in the time series data, perform data classification processing on the time series data according to the data correlation, obtain correlated sequence data and non-correlated sequence data, and construct the correlated sequence A distributed database corresponding to the data and the non-correlated sequence data;

Access interfaces corresponding to the associated sequence data and the non-associated sequence data are respectively configured in the distributed database to obtain a first access interface and a second access interface, which are combined with the first access interface and the second access interface. Interface, perform parameter adjustment processing on the distributed database to obtain the target database;

Mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data based on the characteristic information, and combining the target database and the storage database to respectively execute the processing of the time series data and the Describe the data storage of non-time series data and obtain the storage results.

Optionally, the extracting time series data and non-time series data in the preparation data includes:

Perform data denoising processing on the preparation data to obtain denoised preparation data, and identify the time index information of each data in the denoising preparation data;

Determining, according to the time index information, a preparation time point of each data in the noise reduction prepared data;

According to the preparation time point, construct a time scatter plot corresponding to the noise reduction preparation data, perform fitting processing on the data points in the time scatter plot, and obtain a fitting curve;

Calculate the curve slope corresponding to the fitting curve, and analyze the linear relationship between each data in the noise reduction preparation data and the preparation time point according to the curve slope;

According to the linear relationship, time series data and non-time series data are extracted from the noise reduction preparation data.

Optionally, calculating the curve slope corresponding to the fitting curve includes:

Calculate the curve slope corresponding to the fitting curve through the following formula:

Among them, A represents the slope of the curve corresponding to the fitting curve, N _t1 and M _t1 represent the coordinates of the corresponding points in the fitting curve when the preparation time point is t1, and N _t2 and M _t2 represent the fitting curve when the preparation time point is t2. The corresponding point coordinates when represents the slope of the curve point when the preparation time point is t2 and the preparation time point is t1, N _tβ and M _tβ represent the corresponding point coordinates in the fitting curve when the preparation time point is tβ, β represents the curve point in the fitting curve quantity.

Optionally, calculating the data correlation degree between each data in the time series data includes:

Calculate the data correlation between each data in the time series data using the following formula:

Among them, B represents the data correlation degree between each data in the time series data, D _b represents the data vector corresponding to the b-th data in the time series data, and D _b+1 represents the data vector corresponding to the b+1-th data in the time series data. The data vector, μ represents the data dimension, min _b min _b+1 |D _b -D _b+1 | represents the second-level minimum difference between the b-th data and the b+1-th corresponding data vector in the time series data, max _b max _b+1 |D _b -D _b+1 | represents the second-level maximum difference between the b-th data and the b+1-th corresponding data vector in the time series data.

Optionally, the construction of a distributed database corresponding to the associated sequence data and the non-associated sequence data includes:

Extract structural features corresponding to the associated sequence data and the non-associated sequence data respectively to obtain first structural features and second structural features;

Perform feature screening on the first structural features and the second structural features respectively to obtain first target features and second target features;

Construct feature matrices corresponding to the first target feature and the second target feature respectively to obtain a first feature matrix and a second feature matrix;

Perform matrix fusion on the first feature matrix and the second feature matrix respectively to obtain a first fusion matrix and a second fusion matrix;

According to the first fusion matrix and the second fusion matrix, generate fusion structural features corresponding to the associated sequence data and the non-associated sequence data, and obtain first fusion features and second fusion features;

Extract data parameters corresponding to the associated sequence data and the non-associated sequence data to obtain first data parameters and second data parameters;

Set 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;

According to the storage requirements, a distributed database corresponding to the associated sequence data and the non-associated sequence data is constructed.

Optionally, 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 includes:

Perform attribute extraction on the associated sequence data and the non-associated sequence data respectively to obtain first data attributes and second data attributes;

Calculating the support coefficient between each attribute in the first data attribute to obtain a first support coefficient;

Calculate the support coefficient between each attribute in the second data attribute to obtain the second support coefficient;

According to the first support coefficient and the second support coefficient, respectively extract the key attributes in the first data attribute and the second data attribute to obtain the first key attribute and the second key attribute;

Determine, according to the first key attribute and the second key attribute, the interface types corresponding to the associated sequence data and the non-associated sequence data, respectively, to obtain a first interface type and a second interface type;

Analyze the business logic corresponding to the associated sequence data and the non-associated sequence data respectively to obtain the first business logic and the second business logic;

According to the first business logic and the second business logic, formulate interface logic corresponding to the associated sequence data and the non-associated sequence data to obtain a first interface logic and a second interface logic;

In combination with the first interface type, the second interface type, the first interface logic and the second interface logic, access interfaces corresponding to the associated sequence data and the non-associated sequence data are respectively configured in the distributed database to obtain a first access interface and a second access interface.

Optionally, calculating the support coefficient between each attribute in the first data attribute to obtain the first support coefficient includes:

The support coefficient between each attribute in the first data attribute can be calculated by the following formula:

Among them, F represents the support coefficient between each attribute in the first data attribute, q represents the number of attributes of the first data attribute, e represents the attribute sequence number of the first data attribute, and _He represents the e-th attribute in the first data attribute. The attribute probability of the attribute, He _e+1 represents the attribute probability of the e+1th attribute in the first data attribute, G _{e, e+1} represents the vector ratio of the eth attribute and the e+1th attribute.

Optionally, the mining of feature information corresponding to the non-time series data includes:

Use a preset decision tree mining model to perform information mining on the non-time series data to obtain initial data information;

Perform information cleaning on the initial data information to obtain target data information;

Perform text extraction on the target data information to obtain information text, and calculate the text weight corresponding to the information text;

The information entropy value and the text weight are combined to extract feature information from the target data information.

Optionally, calculating the information entropy value corresponding to each information in the target data information includes:

The information entropy value corresponding to each information in the target data information is calculated by the following formula:

Among them, U represents the information entropy value corresponding to each information in the target data information, i represents the information sequence number corresponding to the target data information, δ represents the number of information corresponding to the target data information, E _i represents the i-th information in the target data information, W(E _i ) represents the occurrence probability of the i-th information in the target data information.

A data storage system used in the preparation of glass rigid sand-filled pipes, characterized in that the system includes:

The data processing module is used to obtain the preparation data in the preparation scenario of the glass rigid sand-filled pipe, and extract the time series data and non-time series data in the preparation data;

A database building module is used to calculate the data correlation between each data in the time series data, and perform data classification processing on the time series data according to the data correlation to obtain correlated sequence data and non-correlated sequence data. , construct a distributed database corresponding to the associated sequence data and the non-associated sequence data;

A database adjustment module configured to separately configure 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, combined with the first access interface and the second access interface, performing parameter adjustment processing on the distributed database to obtain a target database;

The data storage module is used to mine the characteristic information corresponding to the non-time series data, construct a storage database corresponding to the non-time series data according to the characteristic information, and execute the corresponding processing of all the data in combination with the target database and the storage database. The data storage of the time series data and the non-time series data is performed to obtain the storage results.

By extracting time series data and non-time series data in the preparation data, the present invention can divide the preparation data, thereby obtaining data with time dependence and data without time dependence, and then can use the time Sequence data is used to understand the preparation status of relevant time trends in the scenario of glass rigidly sandwiched with sand pipes, and through the non-time series data, the relevant description information in the scenario of glass rigidly sandwiched with sand pipes is understood. The present invention calculates each data in the time series data The data correlation between each other can be used to understand the correlation between each data in the time series data, which facilitates subsequent data classification processing and provides guarantee for the construction of subsequent distributed databases. The present invention By respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database, subsequent data access efficiency can be improved through the access interface, and relevant data can be quickly retrieved. The present invention combines The target database and the storage database perform data storage of the time series data and the non-time series data respectively, realizing distributed storage of data, thereby improving data storage efficiency. Therefore, the data storage method and system provided by the embodiments of the present invention for use in the preparation of glass rigid sand-filled pipes can improve the data storage efficiency in the preparation of glass rigid sand-filled pipes.

Description of the drawings

Figure 1 is a schematic flow chart of a data storage method applied to the preparation of glass rigid sand-filled pipes provided by an embodiment of the present invention;

Figure 2 is a functional module diagram of a data storage system used in the preparation of glass rigid sand-filled pipes provided by an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of an electronic device implementing the data storage method applied in the preparation of glass rigid sand-filled pipes according to an embodiment of the present invention.

The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.

Detailed ways

It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

The embodiment of the present application provides a data storage method applied in the preparation of glass rigid sand-filled pipes. In the embodiments of the present application, the execution subject of the data storage method applied to the preparation of glass rigid sand-filled pipes includes but is not limited to servers, terminals, etc. that can be configured to execute the method provided by the embodiments of the present application. at least one of the devices. In other words, the data storage method applied to the preparation of glass rigid sand-filled pipes can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server may be an independent server, or may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, and content delivery networks (Content Delivery Network, CDN), and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.

Referring to FIG. 1 , a schematic flow chart of a data storage method applied to the preparation of glass rigid sand-filled pipes is provided according to an embodiment of the present invention. In this embodiment, the data storage method applied to the preparation of glass rigid sand-filled pipes includes steps S1-S4.

S1. Acquire preparation data in a glass fiber reinforced corundum tube preparation scenario, and extract time series data and non-time series data from the preparation data.

By extracting time series data and non-time series data in the preparation data, the present invention can divide the preparation data, thereby obtaining data with time dependence and data without time dependence, and then can use the time Sequence data is used to understand the preparation situation of relevant time trends in the scenario of glass rigid sand-coated pipes, and the non-time series data is used to understand relevant description information in the scenario of glass rigid-coated sand pipes, wherein the glass rigid-coated sand pipes are made of glass. The finished sand-filled pipe is usually composed of two sections of glass tubes with fine sand particles sandwiched in the middle. The glass sand-filled pipe can make the fine sand particles rise or fall by controlling the pressure difference between the upper and lower sections of the pipe. The glass is just sand-filled. Glass tubes are often used for liquid separation, filtration and drying operations in laboratories. By adjusting the upper and lower positions of the sand-filled tubes, the rising or falling speed of the sand particles can be controlled, thereby achieving separation and filtration of liquids. Glass sand-filled tubes are resistant to It has the characteristics of corrosion and high temperature resistance, good transparency, and easy to use. The preparation data is all processing-related data in the preparation scenario of glass rigid sand pipe, such as material stirring, material heating and other data. The time series data is the The time-related data in the preparation data will show a certain pattern of related data as time changes, such as the data of the material reaction during the heating process. The non-time series data is the time-independent data in the preparation data. Data, such as data related to material properties, equipment parameter data, etc.

As an embodiment of the present invention, extracting time series data and non-time series data in the preparation data includes: performing data denoising processing on the preparation data, obtaining denoised preparation data, and identifying the denoised data. Time index information of each data in the preparation data. According to the time index information, the preparation time point of each data in the noise reduction preparation data is determined. According to the preparation time point, a corresponding corresponding to the noise reduction preparation data is constructed. Time scatter plot, perform fitting processing on the data points in the time scatter plot to obtain a fitting curve, calculate the curve slope corresponding to the fitting curve, and analyze the noise reduction preparation data according to the curve slope There is a linear relationship between each data in the data and the preparation time point. According to the linear relationship, time series data and non-time series data are extracted from the noise reduction preparation data.

Among them, the noise reduction preparation data is the data obtained after the noise interference in the preparation data is suppressed, the time index information is the timestamp information of each data in the noise reduction preparation data, such as the heating time information about the material in the glass reinforced sand-filled tube preparation scenario, the time scatter plot is a scatter plot constructed with the preparation time point and the data variables of the noise reduction preparation data as coordinate axes, and the linear relationship is the direct relationship between each data in the noise reduction preparation data and the preparation time point.

Optionally, data denoising processing on the preparation data can be implemented through a low-pass filter. The time index information can be obtained by identifying the log data of each data in the denoising preparation data to construct the denoising process. The time scatter plot corresponding to the prepared data can be realized through a graphing tool, which is constructed by a script language. The fitting processing of the data points in the time scatter graph can be realized through a linear fitting function, which can be calculated by The ratio between adjacent slopes of the curve slope, the linear relationship between each data in the noise reduction preparation data and the preparation time point is analyzed according to the degree of change of the ratio, and a time series is extracted from the noise reduction preparation data Data and non-time series data can be implemented through extraction functions, such as the left function.

Further, as an optional embodiment of the present invention, calculating the curve slope corresponding to the fitting curve includes:

Calculate the curve slope corresponding to the fitting curve through the following formula:

Among them, A represents the slope of the curve corresponding to the fitting curve, N _t1 and M _t1 represent the coordinates of the corresponding points in the fitting curve when the preparation time point is t1, and N _t2 and M _t2 represent the fitting curve when the preparation time point is t2. The corresponding point coordinates when represents the slope of the curve point when the preparation time point is t2 and the preparation time point is t1, N _tβ and M _tβ represent the corresponding point coordinates in the fitting curve when the preparation time point is tβ, β represents the curve point in the fitting curve quantity.

S2. Calculate the data correlation between each data in the time series data, perform data classification processing on the time series data according to the data correlation, obtain associated sequence data and non-associated sequence data, and construct the A distributed database corresponding to correlated sequence data and the non-correlated sequence data.

By calculating the data correlation between each data in the time series data, the present invention can understand the correlation between each data in the time series data through the data correlation, which facilitates subsequent data classification processing. This provides a guarantee for the subsequent construction of a distributed database, where the data correlation degree represents the degree of correlation between each data in the time series data.

As an embodiment of the present invention, the calculating of the data association degree between each data in the time series data includes:

Calculate the data correlation between each data in the time series data using the following formula:

Among them, B represents the data correlation degree between each data in the time series data, D _b represents the data vector corresponding to the b-th data in the time series data, and D _b+1 represents the data vector corresponding to the b+1-th data in the time series data. The data vector, μ represents the data dimension, min _b min _b+1 |D _b -D _b+1 | represents the second-level minimum difference between the b-th data and the b+1-th corresponding data vector in the time series data, max _b max _b+1 |D _b -D _b+1 | represents the second-level maximum difference between the b-th data and the b+1-th corresponding data vector in the time series data.

By performing data classification processing on the time series data according to the data correlation degree, the present invention can separate relevant data and non-correlated data in the time series data, thereby improving the time series data. Management efficiency, wherein the associated sequence data is data with an associated relationship in the time series data, and the non-associated sequence data is data without association in the time series data. Optionally, the Data classification processing of time series data can be performed by comparing the data correlation degree with a preset threshold. If the data correlation degree is greater than the preset threshold, the data is associated sequence data. If the data correlation degree is not greater than If the threshold is preset, the data is non-correlated sequence data.

By constructing a distributed database corresponding to the associated sequence data and the non-associated sequence data, the present invention can perform distributed storage of data, thereby improving the flexibility of data storage, and can quickly read and store data during data storage. Response, wherein the distributed database is a virtual memory for data storage of the associated sequence data and the non-associated sequence data.

As an embodiment of the present invention, the construction of a distributed database corresponding to the associated sequence data and the non-associated sequence data includes: respectively extracting the structural features corresponding to the associated sequence data and the non-associated sequence data, Obtain the first structural feature and the second structural feature, perform feature screening on the first structural feature and the second structural feature respectively, obtain the first target feature and the second target feature, and construct the first target feature and the second target feature respectively. The feature matrix corresponding to the second target feature is used to obtain a first feature matrix and a second feature matrix. The first feature matrix and the second feature matrix are respectively matrix fused to obtain a first fusion matrix and a second fusion matrix. Matrix, according to the first fusion matrix and the second fusion matrix, generate the fusion structural features corresponding to the associated sequence data and the non-associated sequence data, obtain the first fusion feature and the second fusion feature, and extract the The data parameters corresponding to the associated sequence data and the non-associated sequence data are obtained to obtain first data parameters and second data parameters. According to the first fusion feature, the second fusion feature, the first data parameter and the The second data parameter sets storage requirements corresponding to the associated sequence data and the non-associated sequence data, and constructs a distributed database corresponding to the associated sequence data and the non-associated sequence data according to the storage requirements.

Wherein, the first structural feature and the second structural feature are respectively the data structure features corresponding to the associated sequence data and the non-associated sequence data, and the first feature matrix and the second feature matrix are respectively A square matrix constructed from feature values corresponding to the first target feature and the second target feature. The first fusion feature and the second fusion feature are respectively corresponding to the associated sequence data and the non-associated sequence data. Comprehensive characteristics of the data structure, 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 the Requirements for performance, capacity, reliability, security and other aspects of the data storage area corresponding to the correlated sequence data and the non-correlated sequence data.

Optionally, the structural features corresponding to the associated sequence data and the non-associated sequence data can be realized by the SURF feature extraction method. The feature values corresponding to the first structural feature and the second structural feature can be calculated according to Feature screening is performed based on the size of the feature value. The feature matrix corresponding to the first target feature and the second target feature can be implemented through a matrix function, such as the zero matrix function, the matrix of the first feature matrix and the second feature matrix. Fusion can be achieved through matrix fusion algorithms, such as matrix addition rules. The fusion structural features corresponding to the associated sequence data and the non-associated sequence data can be achieved through a feature generator. The associated sequence data and the non-associated sequence data The corresponding data parameters can be implemented through a parameter extractor. The storage requirements corresponding to the associated sequence data and the non-associated sequence data can be set through conditional restriction functions. The distributions corresponding to the associated sequence data and the non-associated sequence data Formula databases can be built using scripting languages.

S3. Configure 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, combine the first access interface and the third access interface The second access interface performs parameter adjustment processing on the distributed database to obtain the target database.

By separately configuring the access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database, the present invention can improve subsequent data access efficiency through the access interfaces, and then quickly retrieve relevant data, wherein, The first access interface and the second access interface are respectively the interfaces used by the distributed database to access the associated sequence data and the non-associated sequence data.

As an embodiment of the present invention, the access interfaces corresponding to the associated sequence data and the non-associated sequence data are respectively configured in the distributed database to obtain a first access interface and a second access interface, including: respectively Perform attribute extraction on the associated sequence data and the non-associated sequence data to obtain the first data attribute and the second data attribute, calculate the support coefficient between each attribute in the first data attribute, and obtain the first support coefficient , calculate the support coefficient between each attribute in the second data attribute, and obtain the second support coefficient. According to the first support coefficient and the second support coefficient, extract the first data attribute and the second support coefficient respectively. The key attribute in the second data attribute is to obtain the first key attribute and the second key attribute. According to the first key attribute and the second key attribute, the correspondence between the associated sequence data and the non-associated sequence data is determined respectively. interface type, obtain the first interface type and the second interface type, respectively analyze the business logic corresponding to the associated sequence data and the non-associated sequence data, obtain the first business logic and the second business logic, according to the first business logic and the second business logic, formulate the interface logic corresponding to the associated sequence data and the non-associated sequence data, obtain the first interface logic and the second interface logic, combine the first interface type, the third Two interface types, the first interface logic and the second interface logic, respectively configure the access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database to obtain the first access interface and Second access interface.

Among them, the first data attribute and the second data attribute are characteristics that describe various aspects of the associated sequence data and the non-associated sequence data respectively, the first support coefficient represents the degree of influence 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 the interface type corresponding to the associated sequence data and the non-associated sequence data, such as RESTfulAPI, SOAP, GraphQL, etc., the business logic is the business requirements and rules corresponding to the associated sequence data and the non-associated sequence data, and the interface logic is the interface access rules corresponding to the associated sequence data and the non-associated sequence data.

Optionally, the attribute extraction of the associated sequence data and the non-associated sequence data can be implemented through an attribute extraction tool. The attribute extraction tool is compiled by the Java language. The second support coefficient and the first support coefficient The calculation principle is the same and will not be described in detail here. The key attributes of the first data attribute and the second data attribute can be extracted according to the numerical values of the first support coefficient and the second support coefficient. The interface type corresponding to the associated sequence data and the non-associated sequence data can be determined by determining the attribute type corresponding to the first key attribute and the second key attribute. Correlate the data service corresponding to the sequence data, analyze the business function corresponding to the data service, and obtain the business logic according to the business function. The access interface corresponding to the associated sequence data and the non-associated sequence data can be respectively configured in the distributed database through Configuration file implementation, such as XML, JSON, YAML and other configuration files.

Optionally, as an optional embodiment of the present invention, calculating the support coefficient between each attribute in the first data attribute to obtain the first support coefficient includes:

The support coefficient between each attribute in the first data attribute can be calculated by the following formula:

Among them, F represents the support coefficient between each attribute in the first data attribute, q represents the number of attributes of the first data attribute, e represents the attribute sequence number of the first data attribute, and _He represents the e-th attribute in the first data attribute. The attribute probability of the attribute, He _e+1 represents the attribute probability of the e+1th attribute in the first data attribute, G _{e, e+1} represents the vector ratio of the eth attribute and the e+1th attribute.

The present invention combines the first access interface and the second access interface to perform parameter adjustment processing on the distributed database, thereby obtaining a highly adaptable database, thereby providing a guarantee for subsequent data storage.

S4. Mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data based on the characteristic information, and combining the target database and the storage database to perform processing of the time series data respectively. and data storage of the non-time series data to obtain storage results.

By mining the characteristic information corresponding to the non-time series data, the present invention can discover the representation information of the data memory in the non-time series data, thereby improving the accuracy of the subsequent storage database. In order to improve the storage of the non-time series data Efficiency is guaranteed, wherein the feature information is representational description information about the non-time series data.

As an embodiment of the present invention, the mining of feature information corresponding to the non-time series data includes: using a preset decision tree mining model to perform information mining on the non-time series data 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 information text, and calculating the text weight corresponding to the information text, and combining the information entropy value and the text weight to extract the feature information in the target data information.

Wherein, the decision tree mining model is a model used to extract information in data, the initial data information is all relevant description information of each data in the non-time series data, and the target data information is the initial The information obtained after removing the total invalid information and duplicate information of the data information. The information entropy value represents the amount of information contained in each information in the target data information. The text weight represents the importance of the information text. .

Optionally, information cleaning of the initial data information can be achieved through a cleaning tool, which is compiled by a programming language. Text extraction of the target data information can be achieved through OCR text technology to calculate the information text. The weight value of the corresponding text character in , determine the text weight of the information text according to the weight value.

Optionally, as an optional embodiment of the present invention, the calculating the information entropy value corresponding to each information in the target data information includes:

The information entropy value corresponding to each information in the target data information is calculated by the following formula:

Among them, U represents the information entropy value corresponding to each information in the target data information, i represents the information sequence number corresponding to the target data information, δ represents the number of information corresponding to the target data information, E _i represents the i-th information in the target data information, W(E _i ) represents the occurrence probability of the i-th information in the target data information.

The present invention realizes distributed storage of data by combining the target database and the storage database to respectively perform data storage of the time series data and the non-time series data, thereby improving the data storage efficiency and providing Convenience is provided for subsequent data reading.

By extracting time series data and non-time series data in the preparation data, the present invention can divide the preparation data, thereby obtaining data with time dependence and data without time dependence, and then can use the time Sequence data is used to understand the preparation status of relevant time trends in the scenario of glass rigidly sandwiched with sand pipes, and through the non-time series data, the relevant description information in the scenario of glass rigidly sandwiched with sand pipes is understood. The present invention calculates each data in the time series data The data correlation between each other can be used to understand the correlation between each data in the time series data, which facilitates subsequent data classification processing and provides guarantee for the construction of subsequent distributed databases. The present invention By respectively configuring access interfaces corresponding to the associated sequence data and the non-associated sequence data in the distributed database, subsequent data access efficiency can be improved through the access interface, and relevant data can be quickly retrieved. The present invention combines The target database and the storage database perform data storage of the time series data and the non-time series data respectively, realizing distributed storage of data, thereby improving data storage efficiency. Therefore, the embodiment of the present invention provides a data storage method applied to the preparation of glass rigid sand-filled pipes, which can improve the data storage efficiency in the preparation of glass rigid sand-filled pipes.

As shown in Figure 2, it is a functional module diagram of a data storage system used in the preparation of glass rigid sand-filled pipes provided by an embodiment of the present invention.

The data storage system 100 used in the preparation of glass rigid sand-filled pipes according to the present invention can be installed in electronic equipment. According to the implemented functions, the data storage system 100 used in the preparation of glass rigid sand-filled pipes 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 present invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The data processing module 101 is used to obtain preparation data in the preparation scenario of glass rigid sand-filled pipes, and extract time series data and non-time series data in the preparation data;

The database building module 102 is used to calculate the data correlation between each data in the time series data, perform data classification processing on the time series data according to the data correlation, and obtain the associated sequence data and non-data correlation. Correlate sequence data, and construct a distributed database corresponding to the correlated sequence data and the non-correlated sequence data;

The database adjustment module 103 is configured to separately configure 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, combined with the third access interface. An access interface and the second access interface perform parameter adjustment processing on the distributed database to obtain a target database;

The data storage module 104 is used to mine the characteristic information corresponding to the non-time series data, construct a storage database corresponding to the non-time series data according to the characteristic information, and combine the target database and the storage database respectively. Perform data storage of the time series data and the non-time series data to obtain storage results.

In detail, each module described in the data storage system 100 used in the preparation of glass rigid sand-filled pipes described in the embodiment of the present application adopts the same method as the one described in Figure 1 described above. The data storage method for preparing sand-filled pipes uses the same technical means and can produce the same technical effects, so we will not go into details here.

As shown in FIG. 3 , it is a schematic structural diagram of an electronic device 1 that implements a data storage method applied to the preparation of glass rigid sand-filled pipes provided by an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a data storage method program for preparing glass reinforced corundum tubes.

The processor 10 may be composed of an integrated circuit in some embodiments, for example, it may be composed of a single packaged integrated circuit, or it may be composed of multiple integrated circuits packaged with the same function or different functions, including one or A combination of multiple central processing units (CPUs), microprocessors, digital processing chips, graphics processors and various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device 1, using various interfaces and lines to connect various components of the entire electronic device, by running or executing programs or modules stored in the memory 11 (for example, executing A data storage method program applied to the preparation of glass rigid sand-filled pipes, etc.), and calls the 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. The readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. . In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital (SD) device equipped on the electronic device. ) card, Flash Card, etc. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in electronic equipment, such as the code of a data storage method program applied to the preparation of glass rigid sand pipes, etc., but can also be used to temporarily store data that has been processed. Output or data to be output.

The communication bus 12 may be a Peripheral Component Interconnect (PCI for short) bus or an Extended Industry Standard Architecture (EISA for short) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. The bus is configured to enable connection communication between the memory 11 and at least one processor 10 and the like.

The communication interface 13 is used for communication between the above-mentioned electronic device 1 and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which are generally used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display (Display) or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, 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, organic light-emitting diode) touch device, or the like. The display may also be appropriately referred to as a display screen or a display unit, and is used for displaying information processed in the electronic device and for displaying a visualized user interface.

FIG. 3 only shows an electronic device with components. Persons skilled in the art can understand that the structure shown in FIG. 3 does not limit the electronic device 1 and may include fewer or more components than shown in the figure. components, or combinations of certain components, or different arrangements of components.

For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) that supplies power to various components. Preferably, the power supply may be logically connected to the at least one processor 10 through a power management system, so that through the power management system The system implements functions such as charging management, discharge management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and any other components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described again here.

It should be understood that the above embodiments are for illustration only, and the scope of the patent application is not limited by this structure.

The memory 11 in the electronic device 1 stores a data storage method program applied to the preparation of glass rigid sand-filled pipes, which is a combination of multiple instructions. When run in the processor 10, it can be implemented:

Obtain the preparation data in the preparation scenario of the glass rigid sand-filled pipe, and extract the time series data and non-time series data in the preparation data;

Calculating the data association degree between each data in the time series data, performing data classification processing on the time series data according to the data association degree to obtain associated sequence data and unassociated sequence data, and constructing a distributed database corresponding to the associated sequence data and the unassociated sequence data;

Access interfaces corresponding to the associated sequence data and the non-associated sequence data are respectively configured in the distributed database to obtain a first access interface and a second access interface, which are combined with the first access interface and the second access interface. Interface, perform parameter adjustment processing on the distributed database to obtain the target database;

Mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data based on the characteristic information, and combining the target database and the storage database to respectively execute the processing of the time series data and the Describe the data storage of non-time series data and obtain the storage results.

Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

Furthermore, if the integrated modules/units of the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Memory).

The present invention also provides a computer-readable storage medium. The readable storage medium stores a computer program. When executed by a processor of an electronic device, the computer program can realize:

Obtain the preparation data in the preparation scenario of the glass rigid sand-filled pipe, and extract the time series data and non-time series data in the preparation data;

Calculate the data correlation between each data in the time series data, perform data classification processing on the time series data according to the data correlation, obtain correlated sequence data and non-correlated sequence data, and construct the correlated sequence A distributed database corresponding to the data and the non-correlated sequence data;

Access interfaces corresponding to the associated sequence data and the non-associated sequence data are respectively configured in the distributed database to obtain a first access interface and a second access interface, which are combined with the first access interface and the second access interface. Interface, perform parameter adjustment processing on the distributed database to obtain the target database;

Mining the characteristic information corresponding to the non-time series data, constructing a storage database corresponding to the non-time series data based on the characteristic information, and combining the target database and the storage database to respectively execute the processing of the time series data and the Describe the data storage of non-time series data and obtain the storage results.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

Therefore, the embodiments should be regarded as illustrative and non-restrictive from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is therefore intended that all claims falling within the claims All changes within the meaning and scope of equivalent elements are included in the present invention. Any accompanying reference signs in the claims shall not be construed as limiting the claim in question.

The embodiments of this application can obtain and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or digital computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

Furthermore, it is clear that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim may also be implemented by one unit or system by software or hardware. The words first, second, etc. are used to indicate names and do not indicate any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified. Modifications or equivalent substitutions may be made 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.