CN116257488A - Geotechnical engineering investigation big data archiving method, device, electronic equipment and medium - Google Patents
Geotechnical engineering investigation big data archiving method, device, electronic equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, and discloses a geotechnical engineering investigation big data archiving method, a geotechnical engineering investigation big data archiving device, electronic equipment and a medium, wherein the geotechnical engineering investigation big data archiving method comprises the following steps: carrying out characteristic analysis on geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics; constructing a data identifier of geotechnical engineering investigation data, and carrying out data classification on the geotechnical engineering investigation data to obtain classified investigation data; acquiring metadata of each datum in the classified investigation data, performing functional analysis on the metadata to obtain metadata functions, and determining key data of each datum in the classified investigation data; calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data, and performing level setting on the classified investigation data to obtain a data level; and constructing a data archiving library of the classified investigation data, and archiving and storing the classified investigation data by utilizing the data archiving library. The invention aims at improving the archiving method of the geotechnical engineering investigation big data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a geotechnical engineering investigation big data archiving method, a geotechnical engineering investigation big data archiving device, electronic equipment and a medium.
Background
In the current industry and government regulations, the data storage must meet a certain period so as to be accessed at any time, according to the regulation, the stored data volume increases exponentially, if the data volume is larger, the difficulty of the data storage is increased, for example, in the aspect of geotechnical engineering investigation, a large amount of data can be acquired according to the requirements of construction engineering, and the data needs to be archived and stored so as to be convenient for subsequent direct reference and use.
In the prior art, a data archiving system is established and archiving processing is carried out on data by combining storage equipment, but the data in the archiving method are piled together, the relation among the data is not considered, so that the data in archiving has no hierarchy, and the data is inconvenient to find when related data is needed to be scheduled subsequently, so that the difficulty of scheduling the data is increased, and therefore, the archiving method capable of improving the geotechnical engineering exploration big data is needed.
Disclosure of Invention
The invention provides a geotechnical engineering investigation big data archiving method, a geotechnical engineering investigation big data archiving device, electronic equipment and a medium, and mainly aims to improve the geotechnical engineering investigation big data archiving method.
In order to achieve the above object, the geotechnical engineering investigation big data archiving method provided by the invention comprises the following steps:
acquiring geotechnical engineering investigation data to be archived, performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics;
constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data;
acquiring metadata of each datum in the classified investigation data, performing functional analysis on the metadata to obtain a metadata function, and determining key data of each datum in the classified investigation data according to the metadata function;
calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level;
And constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
Optionally, the performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics includes:
performing attribute analysis on the geotechnical engineering investigation data to obtain data attributes, and performing linear transformation on the data attributes to obtain attribute linear values;
constructing attribute matrixes corresponding to the data attributes according to the attribute linear values, and calculating a matrix average value of each matrix in the attribute matrixes;
and determining characteristic attributes in the data attributes according to the matrix mean value, and obtaining data characteristics of the geotechnical engineering investigation data according to the characteristic attributes.
Optionally, the screening the data characteristic to obtain a target characteristic includes:
respectively utilizing each characteristic in the data characteristics to encode the geotechnical engineering investigation data to obtain a data code;
calculating the corresponding information quantity of each code in the data codes through the following formula;
wherein E represents the information amount corresponding to each code in the data codes, a represents the serial number of the data codes, and D a Representing a set of random variables of an a-th code in data coding, P (D a ) A probability output function representing a set of random variables of an a-th code in the data code;
and calculating information entropy between the data characteristics according to the information quantity, and screening the data characteristics when the information entropy is larger than a preset entropy value to obtain target characteristics.
Optionally, the classifying the geotechnical engineering survey data according to the data identifier to obtain classified survey data includes:
acquiring an identification ID corresponding to the data identification, and performing text detection on the data identification to obtain an identification text;
extracting keywords of the identification text to obtain identification keywords, and calculating association coefficients of each ID in the identification IDs to obtain first association coefficients;
calculating the association coefficient of each keyword in the identification keywords to obtain a second association coefficient;
and carrying out data classification on the geotechnical engineering investigation data by combining the first association coefficient and the second association coefficient to obtain classified investigation data.
Optionally, the calculating the association coefficient of each ID in the identification IDs to obtain a first association coefficient includes:
Calculating the association coefficient of each ID in the identification IDs through the following formula:
wherein B represents the association coefficient of each ID in the ID, R represents the total number of ID, delta represents the information dimension corresponding to ID, i represents the serial number of ID, minmin () represents the two-stage minimum difference, maxmax () represents the two-stage maximum difference, F i+1 Represents the vector value corresponding to the (i+1) th ID in the ID marks, F i The vector value of the ith ID in the identification IDs is represented, and i epsilon (a, R) represents the value range of the identification IDs.
Optionally, the determining key data of each data in the classified survey data according to the metadata function includes:
extracting functional parameters of the metadata function, and performing variable analysis on the functional parameters to obtain parameter variables;
performing visual processing on the parameter variables and the functional parameters to obtain a visual scatter diagram;
fitting the visual scatter diagram to obtain a fitting image, and calculating the complexity coefficient of the fitting image;
and determining key data of each datum in the classified investigation data according to the complexity coefficient.
Optionally, the calculating the data dependency between the key data includes:
Performing dimension reduction processing on the key data to obtain dimension reduction data, and performing format conversion on the dimension reduction data to obtain target data;
vector conversion is carried out on the target data to obtain target vectors, and the similarity between each vector in the target vectors is calculated;
according to the similarity, determining a dependency vector in the target vector, and measuring the vector length of each vector in the dependency vectors;
and calculating the length ratio between the vector lengths, and determining the data dependency degree between the key data according to the length ratio.
A geotechnical engineering investigation big data filing device, characterized in that the device comprises:
the characteristic screening module is used for acquiring geotechnical engineering investigation data to be archived, carrying out characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics;
the data classification module is used for constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data;
the function analysis module is used for acquiring metadata of each data in the classified investigation data, carrying out function analysis on the metadata to obtain a metadata function, and determining key data of each data in the classified investigation data according to the metadata function;
The setting level module is used for calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level;
and the data archiving module is used for constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the geotechnical survey big data archiving method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the geotechnical engineering investigation big data archiving method.
According to the invention, the geotechnical engineering investigation data to be archived are obtained, characteristic analysis is carried out on the geotechnical engineering investigation data, the data characteristic property of the geotechnical engineering investigation data can be known, and the geotechnical engineering investigation data can be conveniently known; in addition, the dependency or the affiliated relation between the key data can be obtained by calculating the data dependency corresponding to the key data, and convenience is provided for the follow-up determination of the affiliated relation of the data. Therefore, the geotechnical engineering investigation big data archiving method, the geotechnical engineering investigation big data archiving device, the electronic equipment and the medium provided by the embodiment of the invention can be used for improving the geotechnical engineering investigation big data archiving method.
Drawings
FIG. 1 is a schematic flow chart of a geotechnical engineering investigation big data archiving method according to an embodiment of the invention;
FIG. 2 is a functional block diagram of a calibration device for calibrating the installation deviation of a full-automatic nonlinear search pan-tilt camera according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the geotechnical engineering investigation big data archiving method 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 geotechnical engineering investigation big data archiving method. In the embodiment of the present application, the execution body of the geotechnical engineering investigation big data archiving method 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 geotechnical engineering investigation big data archiving method may be performed by software or hardware installed at a terminal device or a server device, and the software may 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 geotechnical engineering investigation big data archiving method according to an embodiment of the invention is shown. In this embodiment, the geotechnical engineering investigation big data archiving method includes steps S1 to S5.
S1, acquiring geotechnical engineering investigation data to be archived, performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics.
According to the invention, the geotechnical engineering investigation data to be archived are obtained, and the characteristic analysis is carried out on the geotechnical engineering investigation data, so that the data characteristic property of the geotechnical engineering investigation data can be known, and the geotechnical engineering investigation data can be more conveniently known.
The geotechnical engineering investigation data are obtained by finding out, analyzing and evaluating the geological, environmental characteristics, geotechnical engineering conditions and other data of a construction site according to the requirements of construction engineering, so that the safety of subsequent engineering construction is improved, the data characteristics are specific attributes or properties of the geotechnical engineering investigation data, and further, the geotechnical engineering investigation data to be archived can be obtained through corresponding databases of corresponding geotechnical engineering.
As an embodiment of the present invention, the performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics includes: performing attribute analysis on the geotechnical engineering investigation data to obtain data attributes, performing linear transformation on the data attributes to obtain attribute linear values, constructing attribute matrixes corresponding to the data attributes according to the attribute linear values, calculating matrix average values of each matrix in the attribute matrixes, determining characteristic attributes in the data attributes according to the matrix average values, and obtaining data characteristics of the geotechnical engineering investigation data according to the characteristic attributes.
The data attribute is a data property corresponding to geotechnical engineering investigation data, such as the length, the data format and the like of the data, the attribute linear value is a numerical expression form corresponding to the data attribute, the attribute matrix is a square matrix constructed by the attribute linear value, the matrix mean is an average value corresponding to each matrix in the attribute matrix, and the characteristic attribute is an attribute with characterization in the data attribute.
Further, in an optional embodiment of the present invention, the attribute analysis of the geotechnical engineering survey data may be implemented by an attribute analysis method, for example, a funnel analysis method, the linear transformation of the data attribute may be implemented by a linear function, for example, a linear function, the construction of an attribute matrix corresponding to the data attribute may be implemented by a matrix construction function, the matrix construction function is compiled by a scripting language, the calculation of a matrix average value of each matrix in the attribute matrices may be implemented by an average function, and the feature attribute may be determined according to the size of the matrix average value.
The data characteristics are screened, the attribute with the representativeness in the attribute characteristics can be obtained, and convenience is provided for subsequent processing, wherein the target attribute is the attribute obtained after the data attribute is screened according to the corresponding rule.
As an embodiment of the present invention, the screening the data characteristic to obtain a target characteristic includes: and respectively utilizing each characteristic in the data characteristics to encode the geotechnical engineering investigation data to obtain data codes, calculating information quantity corresponding to each code in the data codes, calculating information entropy between the data characteristics according to the information quantity, and screening the data characteristics when the information entropy is larger than a preset entropy value to obtain target characteristics.
The data codes are code symbols obtained by coding the geotechnical engineering investigation data in sequence with each attribute as a main part, the information quantity is the information quantity of original data contained in each code in the data codes, the information entropy is the difference value of the information quantity between the data characteristics, the larger the information entropy is, the more important the corresponding data characteristics are indicated, and the preset entropy value is a numerical value for comparing the information entropy, which can be 0.8 or can be set according to actual service scenes.
Further, as an alternative embodiment of the present invention, the encoding process of the geotechnical engineering investigation data by using each of the data characteristics may be implemented by a slow-shear encoding method, the information amount corresponding to each of the data codes may be calculated by a hartley formula,
Further, as an optional embodiment of the present invention, the counting the amount of information corresponding to each of the data codes includes:
calculating the corresponding information quantity of each code in the data codes through the following formula:
wherein E represents the information amount corresponding to each code in the data codes, a represents the serial number of the data codes, and D a Representing a set of random variables of an a-th code in data coding, P (D a ) A probability output function representing a set of random variables of an a-th code in data coding.
S2, constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data.
According to the method, the data identification of the geotechnical engineering investigation data is constructed according to the target characteristics, the geotechnical engineering investigation data can be identified through the data identification, and guarantee is provided for data classification of the geotechnical engineering investigation data in the follow-up process, wherein the data identification is an identification mark corresponding to the geotechnical engineering investigation data, the geotechnical engineering investigation data can be identified conveniently, and further the data identification for constructing the geotechnical engineering investigation data can be achieved through an identification generator, such as 3DMAX.
As one embodiment of the present invention, the classifying the geotechnical engineering survey data according to the data identifier to obtain classified survey data includes: acquiring an identification ID corresponding to the data identification, performing text detection on the data identification to obtain an identification text, extracting keywords of the identification text to obtain identification keywords, calculating an association coefficient of each ID in the identification IDs to obtain a first association coefficient, calculating an association coefficient of each keyword in the identification keywords to obtain a second association coefficient, and performing data classification on geotechnical engineering investigation data by combining the first association coefficient and the second association coefficient to obtain classified investigation data.
The identification ID is an identification number corresponding to the data identification, the identification text is text information contained in the data identification, the identification keywords are words which are important in the identification text, the first association coefficient represents the association degree between each ID in the identification ID, whether the relationship exists between each ID can be seen, and the second association coefficient represents the association degree between each keyword in the identification keywords.
Further, obtaining the identification ID corresponding to the data identification may be achieved through an ID query tool, the ID query tool is compiled by a scripting language, text detection of the data identification may be achieved through an OCR text recognition technology, keyword extraction of the identification text may be achieved through a TF-IDF algorithm, and data classification of geotechnical engineering investigation data may be achieved by calculating a sum value of the first association coefficient and the second association coefficient, where the sum value is the largest.
Further, as an optional embodiment of the present invention, the calculating the association coefficient of each ID in the ID to obtain a first association coefficient includes:
calculating the association coefficient of each ID in the identification IDs through the following formula:
wherein B represents the association coefficient of each ID in the ID, R represents the total number of ID, delta represents the information dimension corresponding to ID, i represents the serial number of ID, minmin () represents the two-stage minimum difference, maxmax () represents the two-stage maximum difference, F i+1 Represents the vector value corresponding to the (i+1) th ID in the ID marks, F i The vector value of the ith ID in the identification IDs is represented, and i epsilon (a, R) represents the value range of the identification IDs.
And S3, acquiring metadata of each data in the classified investigation data, performing functional analysis on the metadata to obtain a metadata function, and determining key data of each data in the classified investigation data according to the metadata function.
According to the invention, by acquiring the metadata of each data in the classified investigation data and carrying out functional analysis on the metadata, the corresponding function of the metadata can be obtained so as to facilitate the subsequent determination of key data, wherein the metadata function is the corresponding function of the metadata, and the metadata function is divided into two types, namely description of the classified investigation data and management of the classified investigation data.
Further, as an alternative embodiment of the present invention, the obtaining the metadata of each data in the classified investigation data may be implemented by a metadata adapter, and the functional analysis of the metadata may be implemented by a functional analysis method.
According to the metadata function, the key data of each data in the classified investigation data are determined, so that the important data of each data in the classified investigation data can be obtained, convenience is provided for the follow-up determination of the data dependency relationship corresponding to the classified investigation data, wherein the key data are the important data of each data in the classified investigation data, and the representative effect can be achieved.
As one embodiment of the present invention, the determining key data of each of the classified survey data according to the metadata function includes: extracting functional parameters of the metadata function, performing variable analysis on the functional parameters to obtain parameter variables, performing visualization processing on the parameter variables and the functional parameters to obtain a visual scatter diagram, performing fitting processing on the visual scatter diagram to obtain a fitting image, calculating complex coefficients of the fitting image, and determining key data of each datum in the classified investigation data according to the complex coefficients.
The function parameters are parameter values corresponding to the metadata function, the parameter values are explanations of the metadata function, the parameter values are variable information in the function parameters and are divided into independent variable information and dependent variable information, the visual scatter diagram is a scatter diagram image expression form corresponding to the parameter values and the function parameters, the fitting image is an image obtained by connecting points in the visual scatter diagram through smooth curves, the complexity coefficient represents the complexity degree corresponding to the fitting image, and the higher the complexity coefficient is, the more complex the fitting image is, and the more important the data corresponding to the metadata function is represented.
Further, as an alternative embodiment of the present invention, the extracting of the functional parameters of the metadata function may be implemented by a parameter extracting tool, the parameter extracting tool is compiled by Java language, the variable analysis of the functional parameters may be implemented by a principal component analysis method, the visualization processing of the parameter variables and the functional parameters may be implemented by a visualization tool, such as a dataset visualization tool, and the fitting processing of the visualization scatter diagram may be implemented by a fitting function, such as a linear fitting function.
As an optional embodiment of the invention, the calculating the complexity coefficients of the fitted image comprises:
calculating the complexity coefficients of the fitted image by the following formula:
wherein G represents the complex coefficient of the fitted image, n represents the coordinate value range of the fitted image, w represents the coordinate number in the fitted image, j and j+1 represent the coordinate positions of the image texture in the n x n range, and H (j, j+1) represents the coordinate mean square error of the fitted image corresponding to the (j, j+1) position.
S4, calculating the data dependency between the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level.
According to the method and the device for determining the dependency relationship among the key data, the dependency relationship or the dependency relationship among the key data can be obtained through calculating the data dependency relationship corresponding to the key data, and convenience is provided for the follow-up determination of the dependency relationship among the data, wherein the data dependency relationship represents the dependency relationship among the key data, and if the data dependency relationship is higher, the dependency relationship among the key data is more obvious.
As one embodiment of the present invention, the calculating the data dependency between the key data includes: performing dimension reduction processing on the key data to obtain dimension reduction data, performing format conversion on the dimension reduction data to obtain target data, performing vector conversion on the target data to obtain target vectors, calculating similarity between each vector in the target vectors, determining dependency vectors in the target vectors according to the similarity, measuring vector length of each vector in the dependency vectors, calculating length ratio between the vector lengths, and determining data dependency between the key data according to the length ratio.
The dimension reduction data is data of which the key data is reduced from high dimension to low dimension, the target data is data of which the format in the dimension reduction data is converted into a unified format, the target vector is a vector expression form corresponding to the target data, the similarity is the similarity degree between each vector in the target vector, the dependency vector is a vector with high similarity in the target vector, the vector length represents the size of each vector in the target vector, and the length ratio is the ratio of the vector lengths corresponding to two vectors in the dependency vector.
Further, as an optional embodiment of the present invention, the dimension reduction processing of the key data may be implemented by a low variance filtering method, the format conversion of the dimension reduction data may be implemented by a format converter, the vector conversion of the target data may be implemented by a Word2vec algorithm, the calculation of the similarity between each vector in the target vectors may be implemented by a cosine similarity algorithm, the dependency vectors in the target vectors may be determined by the magnitude of the similarity, and the length ratio between the vector lengths may be obtained by calculating the ratio of two lengths in the vector lengths.
According to the data dependency, the data dependency corresponding to the classified investigation data is determined, and the data with the dependency in the classified investigation data can be obtained, wherein the data dependency is the dependency among the data in the classified investigation data, and the data dependency corresponding to the classified investigation data is determined according to the value of the data dependency.
The invention can obtain the grades among the classified investigation data by carrying out grade setting on the classified investigation data according to the data dependency relationship, provides convenience for the follow-up filing processing of the classified investigation data, improves the data filing method, wherein the data grade is the grade of the classified investigation data, further, the grade setting of the classified investigation data can determine the upper and lower grade of the classified investigation data through the data dependency relationship, counts the auxiliary data quantity of the classified investigation data, and sets the data grade of the classified investigation data according to the quantity of the auxiliary data quantity.
And S5, constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
According to the method, the data archiving library of the classified investigation data is constructed according to the data level and the target attribute, so that the data archiving is hierarchical, the related data can be conveniently and rapidly scheduled in the later period, and convenience is provided for a user.
According to the invention, the classified investigation data is archived and stored by utilizing the data archiving library, so that the classified investigation data is stored and archived, the data can be stored for a long time, and convenience is provided for subsequent use.
According to the invention, the geotechnical engineering investigation data to be archived are obtained, characteristic analysis is carried out on the geotechnical engineering investigation data, the data characteristic property of the geotechnical engineering investigation data can be known, and the geotechnical engineering investigation data can be conveniently known; in addition, the dependency or the affiliated relation between the key data can be obtained by calculating the data dependency corresponding to the key data, and convenience is provided for the follow-up determination of the affiliated relation of the data. Therefore, the geotechnical engineering investigation big data archiving method provided by the embodiment of the invention can be improved.
FIG. 2 is a functional block diagram of a geotechnical engineering survey big data archiving apparatus according to an embodiment of the present invention.
The geotechnical engineering investigation big data filing device 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the geotechnical engineering investigation big data archival device 100 may include a characteristic screening module 101, a data classification module 102, a function analysis module 103, a setting level module 104, and a data archival module 105. 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 characteristic screening module 101 is configured to obtain geotechnical engineering investigation data to be archived, perform characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screen the data characteristics to obtain target characteristics;
the data classification module 102 is configured to construct a data identifier of the geotechnical engineering survey data according to the target characteristic, and perform data classification on the geotechnical engineering survey data according to the data identifier to obtain classified survey data;
The function analysis module 103 is configured to obtain metadata of each data in the classified survey data, perform a function analysis on the metadata to obtain a metadata function, and determine key data of each data in the classified survey data according to the metadata function;
the level setting module 104 is configured to calculate a data dependency between the key data, determine a data dependency corresponding to the classified investigation data according to the data dependency, and perform level setting on the classified investigation data according to the data dependency to obtain a data level;
the data archiving module 105 is configured to construct a data archiving base of the classified survey data according to the data level and the target attribute, and archive and store the classified survey data by using the data archiving base.
In detail, each module in the calibration device 100 for fully-automatic nonlinear search pan-tilt camera installation deviation in the embodiment of the present application adopts the same technical means as the geotechnical engineering investigation big data archiving method described in fig. 1, and can generate the same technical effects, which is not repeated here.
Fig. 3 is a schematic structural diagram of an electronic device 1 for implementing a geotechnical engineering investigation big data archiving method according to an embodiment of the present invention.
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 an geotechnical survey big data archiving method program.
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 geotechnical survey big data archiving method programs, 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 types of data, such as codes of geotechnical engineering investigation big data archiving method programs, 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 each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. 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.
The geotechnical engineering investigation big data archiving method program stored in the memory 11 in the electronic device 1 is a combination of a plurality of instructions, which when run in the processor 10, can implement:
acquiring geotechnical engineering investigation data to be archived, performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics;
Constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data;
acquiring metadata of each datum in the classified investigation data, performing functional analysis on the metadata to obtain a metadata function, and determining key data of each datum in the classified investigation data according to the metadata function;
calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level;
and constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
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 device 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 geotechnical engineering investigation data to be archived, performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics;
constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data;
acquiring metadata of each datum in the classified investigation data, performing functional analysis on the metadata to obtain a metadata function, and determining key data of each datum in the classified investigation data according to the metadata function;
calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level;
and constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
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. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. 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 geotechnical engineering investigation big data archiving method, characterized in that the method comprises:
acquiring geotechnical engineering investigation data to be archived, performing characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics;
constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data;
acquiring metadata of each datum in the classified investigation data, performing functional analysis on the metadata to obtain a metadata function, and determining key data of each datum in the classified investigation data according to the metadata function;
calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level;
and constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
2. The geotechnical engineering survey big data archiving method of claim 1, wherein the performing characteristic analysis on the geotechnical engineering survey data to obtain data characteristics comprises:
performing attribute analysis on the geotechnical engineering investigation data to obtain data attributes, and performing linear transformation on the data attributes to obtain attribute linear values;
constructing attribute matrixes corresponding to the data attributes according to the attribute linear values, and calculating a matrix average value of each matrix in the attribute matrixes;
and determining characteristic attributes in the data attributes according to the matrix mean value, and obtaining data characteristics of the geotechnical engineering investigation data according to the characteristic attributes.
3. The geotechnical engineering survey big data archiving method of claim 1, wherein the screening the data characteristics to obtain target characteristics comprises:
respectively utilizing each characteristic in the data characteristics to encode the geotechnical engineering investigation data to obtain a data code;
calculating the corresponding information quantity of each code in the data codes through the following formula;
wherein E represents the information amount corresponding to each code in the data codes, a represents the serial number of the data codes, and D a Representing a set of random variables of an a-th code in data coding, P (D a ) A probability output function representing a set of random variables of an a-th code in the data code;
and calculating information entropy between the data characteristics according to the information quantity, and screening the data characteristics when the information entropy is larger than a preset entropy value to obtain target characteristics.
4. The geotechnical engineering survey big data archiving method of claim 1, wherein the classifying the geotechnical engineering survey data according to the data identifier to obtain classified survey data comprises:
acquiring an identification ID corresponding to the data identification, and performing text detection on the data identification to obtain an identification text;
extracting keywords of the identification text to obtain identification keywords, and calculating association coefficients of each ID in the identification IDs to obtain first association coefficients;
calculating the association coefficient of each keyword in the identification keywords to obtain a second association coefficient;
and carrying out data classification on the geotechnical engineering investigation data by combining the first association coefficient and the second association coefficient to obtain classified investigation data.
5. The geotechnical engineering survey big data archiving method of claim 4, wherein the calculating the association coefficient of each ID in the identification IDs to obtain the first association coefficient comprises:
Calculating the association coefficient of each ID in the identification IDs through the following formula:
wherein B represents the association coefficient of each ID in the ID, R represents the total number of ID, delta represents the information dimension corresponding to ID, i represents the serial number of ID, minmin () represents the two-stage minimum difference, maxmax () represents the two-stage maximum difference, F i+1 Represents the vector value corresponding to the (i+1) th ID in the ID marks, F i The vector value of the ith ID in the identification IDs is represented, and i epsilon (a, R) represents the value range of the identification IDs.
6. The geotechnical engineering survey big data archiving method of claim 1, wherein the determining key data of each of the classified survey data according to the metadata function comprises:
extracting functional parameters of the metadata function, and performing variable analysis on the functional parameters to obtain parameter variables;
performing visual processing on the parameter variables and the functional parameters to obtain a visual scatter diagram;
fitting the visual scatter diagram to obtain a fitting image, and calculating the complexity coefficient of the fitting image;
and determining key data of each datum in the classified investigation data according to the complexity coefficient.
7. The geotechnical engineering survey big data archiving method of claim 1, wherein the calculating the data dependency between the key data comprises:
performing dimension reduction processing on the key data to obtain dimension reduction data, and performing format conversion on the dimension reduction data to obtain target data;
vector conversion is carried out on the target data to obtain target vectors, and the similarity between each vector in the target vectors is calculated;
according to the similarity, determining a dependency vector in the target vector, and measuring the vector length of each vector in the dependency vectors;
and calculating the length ratio between the vector lengths, and determining the data dependency degree between the key data according to the length ratio.
8. A geotechnical engineering investigation big data filing device, characterized in that the device comprises:
the characteristic screening module is used for acquiring geotechnical engineering investigation data to be archived, carrying out characteristic analysis on the geotechnical engineering investigation data to obtain data characteristics, and screening the data characteristics to obtain target characteristics;
the data classification module is used for constructing a data identifier of the geotechnical engineering investigation data according to the target characteristics, and carrying out data classification on the geotechnical engineering investigation data according to the data identifier to obtain classified investigation data;
The function analysis module is used for acquiring metadata of each data in the classified investigation data, carrying out function analysis on the metadata to obtain a metadata function, and determining key data of each data in the classified investigation data according to the metadata function;
the setting level module is used for calculating the data dependency among the key data, determining the data dependency corresponding to the classified investigation data according to the data dependency, and performing level setting on the classified investigation data according to the data dependency to obtain a data level;
and the data archiving module is used for constructing a data archiving library of the classified investigation data according to the data level and the target attribute, and archiving and storing the classified investigation data by utilizing the data archiving library.
9. In order to solve the above-mentioned problem, the present invention also provides an electronic device, characterized in that the electronic device includes:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the geotechnical survey big data archiving method of any one of claims 1 to 7.
10. In order to solve the above problems, the present invention also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the geotechnical engineering investigation big data archiving method according to any one of claims 1 to 7.
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