CN116431711A - Data intelligent acquisition method and system based on data characteristics - Google Patents
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Abstract
The invention relates to the field of data acquisition, and discloses a data intelligent acquisition method and system based on data characteristics, wherein the method comprises the following steps: retrieving source data according to data requirements, calculating data association degree of the source data, and classifying the source data according to the data association degree to obtain classified data; analyzing the data attribute of the classified data, and collecting the data characteristics of the classified data according to the data attribute; calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features; carrying out feature expression on the data requirements to obtain requirement features, creating a feature analysis model of the data requirements according to the requirement features, and carrying out feature fitting operation on the fusion features and the requirement features by using the feature analysis model to obtain fitting values; when the fitting value is not larger than a preset value, taking the fusion characteristic as a target characteristic of the data requirement; and acquiring demand data corresponding to the data demand from the source data according to the target characteristics. The invention can improve the accuracy of data acquisition.
Description
Technical Field
The invention relates to the field of data acquisition, in particular to a data intelligent acquisition method and system based on data characteristics.
Background
The data acquisition is an indispensable part of the modern society, can help users analyze various phenomena and trends, and can effectively promote the rapid identification and efficient utilization of data by carrying out data characteristic acquisition and extraction in mass data along with the arrival of digital age and the increase of internet use frequency.
At present, data are generally collected through a data collector, collection conditions are set according to data requirements, and a large amount of related data are obtained, however, in the process of collecting the data by using the method, due to the setting of the collection conditions, the time is wasted due to overlarge data quantity collected in a large setting range, the data quantity obtained in a small setting range is less, and more valuable data cannot be obtained, so that the collected data are inaccurate.
Disclosure of Invention
In order to solve the technical problems, the invention provides a data intelligent acquisition method and a system based on data characteristics, which can improve the accuracy of data acquisition.
In a first aspect, the present invention provides a data intelligent acquisition method implemented based on data features, including:
acquiring data requirements, retrieving source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data;
analyzing the data attribute of the classified data, and collecting the data characteristics of the classified data according to the data attribute;
calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features;
carrying out feature expression on the data demand to obtain a demand feature, creating a feature analysis model of the data demand according to the demand feature, and carrying out feature fitting operation on the fusion feature and the demand feature by utilizing the feature analysis model to obtain a fitting value;
judging whether the fitting value is larger than a preset threshold value or not;
returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value;
when the fitting value is not larger than the preset value, taking the fusion characteristic as a target characteristic of the data requirement;
And acquiring demand data corresponding to the data demand from the source data according to the target characteristics.
In a possible implementation manner of the first aspect, the retrieving source data according to the data requirement includes:
creating a demand text corresponding to the data demand according to the data demand;
constructing a demand catalog corresponding to the data demand in the demand text;
and adding a search code in the demand catalog to search the source data corresponding to the data demand according to the search code.
In a possible implementation manner of the first aspect, the classifying the source data according to the data association degree to obtain classified data includes:
adding a classification label to the source data according to the data association degree;
creating a classification set of the source data, and loading the source data into the classification set according to the classification label to obtain classification data.
In a possible implementation manner of the first aspect, the collecting, according to the data attribute, the data features of the classification data includes:
converting the classified data into linear data according to the data attribute;
performing dimension reduction processing on the linear data to obtain dimension reduction data;
Mapping the reduced-dimension data into a pre-constructed space matrix, and acquiring data characteristics of the classified data in the space matrix by using a preset matrix algorithm.
In a possible implementation manner of the first aspect, the converting the classification data into linear data according to the data attribute includes:
querying a data structure of the classified data through the data attribute;
inquiring a corresponding linear structure algorithm according to the data structure;
and converting the classified data into linear data according to the linear structure algorithm.
In a possible implementation manner of the first aspect, the calculating the feature weight of the data feature includes:
calculating the feature weight of the data feature by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing feature weights, ++>
Entropy-quenching function representing data features, +.>
Entropy extinction value representing data feature, m represents feature quantity of data feature, ++>
Representing a weight matrix, +.>
And represents the jth data characteristic of class a data.
In a possible implementation manner of the first aspect, the performing feature fusion on the data feature according to the feature weight to obtain a fusion feature includes:
And carrying out feature fusion on the data features by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the fusion features, m representing the feature quantity of the data features, +.>
Representing the weight mean>
Weight value representing the xth data feature, < ->
The weight value representing the y-th data feature, e represents the weight error value.
In a possible implementation manner of the first aspect, the collecting, according to the target feature, demand data corresponding to the data demand from the source data includes:
querying a database structure of the source data;
creating an index mode of the source data according to the database structure, and adding a retrieval tag for the target feature;
and acquiring the demand data corresponding to the data demand from the source data according to the index mode and the index label.
In a possible implementation manner of the first aspect, the performing, by using the feature analysis model, a feature fitting operation on the fusion feature and the demand feature to obtain a fitting value includes:
and performing feature fitting operation on the fusion feature and the demand feature by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
represents the fitting value, m represents the number of fitting calculations, +. >
Representing fitting residual,/->
Represents a cosine function, f represents a fitted curve, +.>
I-th feature representing fusion feature, +.>
The j-th feature representing the demand feature.
In a second aspect, the present invention provides an intelligent data acquisition system implemented based on data features, the system comprising:
the data classification module is used for acquiring data requirements, searching source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data;
the characteristic acquisition module is used for analyzing the data attribute of the classified data and acquiring the data characteristic of the classified data according to the data attribute;
the feature fusion module is used for calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features;
the fitting calculation module is used for carrying out feature expression on the data requirements to obtain requirement features, creating a feature analysis model of the data requirements according to the requirement features, and carrying out feature fitting operation on the fusion features and the requirement features by utilizing the feature analysis model to obtain fitting values;
The fitting judgment module is used for judging whether the fitting value is larger than a preset threshold value or not;
the step return module is used for returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value;
the characteristic acquisition module is used for taking the fusion characteristic as a target characteristic of the data requirement when the fitting value is not larger than the preset value;
and the data acquisition module is used for acquiring the demand data corresponding to the data demand from the source data according to the target characteristics.
Compared with the prior art, the technical principle and beneficial effect of this scheme lie in:
according to the scheme, firstly, a large amount of data related to the demand can be acquired by searching source data according to the acquired data demand, so that the required data such as landscape data can be acquired from the large amount of data, the data demand such as information data of the human history, landscape introduction, technological construction and the like of a certain area, the source data is classified according to the data association degree, classified data can be obtained, and the data with the same type or similar functions such as description of the human history information of the certain area or introduction of videos developed in the certain area can be placed in the same data set, so that the large amount of data can be processed in a centralized manner, and the data processing efficiency is improved; secondly, according to the embodiment of the invention, the source data can be divided into data with different functions through the data association degree by adding the classification labels to the source data according to the data association degree, and corresponding function labels are added according to the different functions; and collecting data characteristics of the classified data according to the data attributes to obtain data definitions or identifications of different data, so that complex data can be represented in a simple form, and recognition and search query of a computer language are facilitated; further, according to the embodiment of the invention, the obvious characteristics in the data characteristics can be identified through calculating the characteristic weights of the data characteristics; and the data features are subjected to feature fusion according to the feature weights, so that fusion features can be obtained, obvious features can be used as main features, and features with insignificant features are subjected to weakening fusion, so that the data target searched during subsequent feature searching is more definite; the data demand is characterized to obtain a demand characteristic, the data demand can be expressed in a data characteristic form, and a computer can understand the data characteristic more easily through the analysis of a subsequent characteristic analysis model, so that the acquisition of data is facilitated; performing feature fitting operation on the fusion feature and the demand feature by using the feature analysis model to obtain fitting values so as to obtain calculation parameters related to data features, and further calculating the data features by using the calculation parameters; and collecting the required data corresponding to the data requirement from the source data according to the target characteristics, so that the data can be collected based on the data characteristics, the data target is clear, the collected data are all the data required by the user, cumbersome steps such as verification and screening of conventional data collection are avoided, a large amount of time is saved, and the efficiency and accuracy of data collection are improved. Therefore, the data intelligent acquisition method and system based on the data characteristics can improve the accuracy of data acquisition.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a data intelligent acquisition method implemented based on data features according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a data intelligent acquisition system implemented based on data features according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a data intelligent collection method implemented based on data features according to an embodiment of the present invention.
Detailed Description
It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
The embodiment of the invention provides a data intelligent acquisition method based on data characteristics, and an execution subject of the data intelligent acquisition method based on the data characteristics comprises, 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 by the embodiment of the invention. In other words, the intelligent data collection method implemented based on the data features may be executed by software or hardware installed in a terminal device or a server device, where 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 data intelligent acquisition method based on data features according to an embodiment of the invention is shown. The intelligent data acquisition method based on the data characteristics depicted in fig. 1 comprises the following steps S1-S8:
s1, acquiring data requirements, retrieving source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data.
According to the embodiment of the invention, a large amount of data related to the demand can be acquired by acquiring the data demand and retrieving the source data according to the data demand, so that the data required by acquiring the large amount of data, such as landscape data, can be acquired, and the data demand is information data such as the human history, landscape introduction, technological construction and the like in a certain area.
As an embodiment of the present invention, the retrieving source data according to the data requirement includes: creating a demand text corresponding to the data demand according to the data demand, constructing a demand catalog corresponding to the data demand in the demand text, and adding a retrieval code in the demand catalog to retrieve source data corresponding to the data demand according to the retrieval code.
The text refers to a file for storing data, the directory refers to a list for storing the data in the directory according to a set form, and the search code refers to a special character generated through a binary code, which has identification indication function and uniqueness.
Optionally, the requirement text is created through java language, and the requirement directory is created through sql language.
Furthermore, according to the embodiment of the invention, the data relevance between the source data can be known by calculating the data relevance of the source data, so that the relevant data can be classified, and the data processing efficiency is improved.
As one embodiment of the present invention, the calculating the data association degree of the source data includes:
calculating the data association degree of the source data by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the degree of association of data->
Representing the source when the source data b is retrievedThe probability of occurrence of the data a,
representing the probability of occurrence of source data b when source data a is retrieved,/for example>
Data tag representing source data a +.>
A data tag representing the source data b, n representing the number of times the source data a is retrieved, m representing the number of times the source data b is retrieved, +.>
Representing a tag function;
Furthermore, in the embodiment of the invention, the source data is classified according to the data association degree, so that classified data can be obtained, and data with the same type or similar functions, such as the description of the historical information of people in a certain area or the introduction of videos developed in a certain area, can be put in the same data set, so that a large amount of data can be intensively processed, and the data processing efficiency is improved.
As one embodiment of the present invention, the classifying the source data according to the data association degree to obtain classified data includes: and adding a classification label to the source data according to the data association degree, creating a classification set of the source data, and loading the source data into the classification set according to the classification label to obtain classification data. Wherein the classification label represents an indicator, the identification function can be identified by a computer language, and the classification set refers to a set of data which can store the same type of data.
Alternatively, the class labels are generated from binary code and the class set of source data may be created by a database function in sql language.
Further, in still another alternative embodiment of the present invention, the adding a classification tag to the source data according to the data association degree may divide the source data into data with different functions according to the data association degree, and add corresponding function tags according to the different functions.
S2, analyzing the data attribute of the classified data, and collecting the data characteristics of the classified data according to the data attribute.
According to the embodiment of the invention, the data attribute of the classified data can be analyzed to obtain the most original characteristic of the data, such as the characteristic of the image which can be described after the pixel points of the image are processed, the unprocessed pixel points can be expressed as the attribute of the data, and the attribute has the nominal, binary, ordinal and numerical symbol names, coding parameters, data sequences and the like which can be used for describing things.
As an alternative embodiment of the present invention, the analyzing the data attribute of the classification data may analyze the data attribute of the classification data by querying a source code of the classification data and identifying a code structure of the source code.
Furthermore, according to the embodiment of the invention, the data definition or identification of different data can be obtained by collecting the data characteristics of the classified data according to the data attributes, so that complex data can be represented in a simple form, and the recognition and the search query of a computer language are facilitated.
As one embodiment of the present invention, the collecting the data features of the classified data according to the data attribute includes: and converting the classified data into linear data according to the data attribute, performing dimension reduction processing on the linear data to obtain dimension reduction data, mapping the dimension reduction data into a pre-constructed space matrix, and acquiring the data characteristics of the classified data in the space matrix by using a preset matrix algorithm. The linear data refers to converting data into digital data, different data are represented by the same dimension, the dimension reduction refers to reducing high-dimension data into low-dimension data, for example, converting three-dimensional data into two-dimensional data, so as to reduce the calculated amount of the data, the space matrix refers to a digital matrix, the data are expressed in a digital form, and the matrix has multiple algorithms.
Further, in yet another optional embodiment of the present invention, the converting the classification data into linear data according to the data attribute includes: and inquiring the data structure of the classified data through the data attribute, inquiring a corresponding linear structure algorithm according to the data structure, and converting the classified data into linear data through the linear structure algorithm according to the linear structure algorithm.
Optionally, the dimension reduction processing of the linear data is implemented through linear algebra, the mapping of the dimension reduction data into a pre-constructed space matrix is implemented through a space mapping algorithm, and the space mapping algorithm is generated through training of a deep learning model.
As a further alternative embodiment of the present invention, the collecting the data features of the classification data by using a preset matrix algorithm includes: and acquiring historical features of the classified data, training the historical features by using a training model of feature engineering to obtain a feature algorithm, and performing feature calculation on the classified data by using the feature algorithm to obtain target data features.
And S3, calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features.
According to the embodiment of the invention, the characteristic weight of the data characteristic is calculated to identify the obvious characteristic in the data characteristic, such as a basketball game video, wherein the characteristic can be described as basketball, sport and exercise, however, the characteristic of the basketball is obvious, and the probability of searching the basketball label as a search label is higher.
As an embodiment of the present invention, the calculating the feature weight of the data feature includes:
calculating the feature weight of the data feature by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing feature weights, ++>
Entropy-quenching function representing data features, +.>
Entropy extinction value representing data feature, m represents feature quantity of data feature, ++>
Representing a weight matrix, +.>
And represents the jth data characteristic of class a data.
Furthermore, in the embodiment of the invention, the data features are subjected to feature fusion according to the feature weights, so that the fusion features can take more obvious features as main features, and the features with less obvious features are weakened and fused, so that the data target searched during the subsequent feature searching is more definite.
As an embodiment of the present invention, the feature fusing the data features according to the feature weights to obtain fused features includes:
And carrying out feature fusion on the data features by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the fusion features, m representing the feature quantity of the data features, +.>
Representing the weight mean>
Weight value representing the xth data feature, < ->
The weight value representing the y-th data feature, e represents the weight error value.
And S4, carrying out feature expression on the data requirements to obtain requirement features, creating a feature analysis model of the data requirements according to the requirement features, and carrying out feature fitting operation on the fusion features and the requirement features by using the feature analysis model to obtain fitting values.
According to the embodiment of the invention, the data requirements are expressed in the form of the data characteristics by the characteristic expression, and the data requirements can be expressed in the form of the data characteristics, so that a computer can understand the data characteristics more easily by analyzing the data characteristics through a subsequent characteristic analysis model, and the acquisition of the data is facilitated.
Optionally, the feature expression is performed on the data requirement, and the requirement feature is obtained by generating a requirement parameter according to the data requirement, and mapping the requirement parameter into a feature matrix.
Furthermore, according to the embodiment of the invention, the data demand can be simulated by using the model through the feature analysis model for creating the data demand according to the demand feature, so that a calculation algorithm for the data demand is obtained, and further the data feature meeting the requirements in the source data is calculated according to the calculation algorithm. The feature analysis model may be created by a deep learning algorithm.
According to the embodiment of the invention, the characteristic analysis model is utilized to perform characteristic fitting operation on the fusion characteristic and the demand characteristic, so that a fitting value is obtained, calculation parameters related to data characteristics can be obtained, the calculated parameters are utilized to calculate the data characteristics, and the fitting value can be 1 or can be set according to actual application scenes.
As an embodiment of the present invention, the performing, by using the feature analysis model, feature fitting operation on the fusion feature and the demand feature to obtain a fitting value includes:
and performing feature fitting operation on the fusion feature and the demand feature by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
represents the fitting value, m represents the number of fitting calculations, +.>
Representing fitting residual,/->
Represents a cosine function, f represents a fitted curve, +.>
I-th feature representing fusion feature, +.>
The j-th feature representing the demand feature.
S5, judging whether the fitting value is larger than a preset threshold value or not.
According to the embodiment of the invention, the final fitting result can be known by judging whether the fitting value is larger than the preset threshold value, and the operation of the next step is performed according to the fitting value.
And S6, returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value.
It should be appreciated that when the fitting value is greater than a preset value, the error of the training test result for the fusion feature and the demand feature in the feature analysis model is larger, and the finally obtained operation result cannot be used as a reference, so that the parameter is adjusted by the step of returning the feature expression for the data demand, so as to ensure that an ideal model analysis result can be obtained.
And S7, taking the fusion characteristic as a target characteristic of the data requirement when the fitting value is not larger than the preset value.
It should be appreciated that, when the fitting value is not greater than the preset value, it indicates that the error of the training test result for the fusion feature and the demand feature in the feature analysis model is within a reasonable range, and the finally obtained analysis result is more reliable and can be used as a final reference basis.
S8, according to the target characteristics, acquiring the demand data corresponding to the data demand from the source data.
According to the embodiment of the invention, the data can be acquired based on the data characteristics by acquiring the demand data corresponding to the data demand from the source data according to the target characteristics, the data target is clear, the acquired data are all data required by users, complicated steps such as verification, screening and the like of conventional data acquisition are avoided, a large amount of time is saved, and the efficiency and accuracy of data acquisition are improved.
As one embodiment of the present invention, the collecting, according to the target feature, the demand data corresponding to the data demand from the source data includes: and inquiring a database structure of the source data, creating an index mode of the source data according to the database structure, adding a retrieval tag for the target feature, and executing acquisition of the required data from the source data according to the index mode and the retrieval tag. The database structure is an organization mode of a database table, such as a table name, a data type, a constraint condition and the like.
Optionally, the database structure of the source data is identified by querying the original database architecture corresponding to the source data, the index mode of the source data is created according to the database table organization mode of the source data, and the search tag java language is added for the target feature.
According to the scheme, firstly, a large amount of data related to the demand can be acquired by searching source data according to the acquired data demand, so that the required data such as landscape data can be acquired from the large amount of data, the data demand such as information data of the human history, landscape introduction, technological construction and the like of a certain area, and the source data is classified according to the data association degree to obtain classified data, and the classified data can be used for putting the data similar in type or function such as the human history information describing the certain area or the video developed in the certain area in the same data set, so that the large amount of data can be intensively processed, and the data processing efficiency is improved; secondly, according to the embodiment of the invention, the source data can be divided into data with different functions through the data association degree by adding the classification labels to the source data according to the data association degree, and corresponding function labels are added according to the different functions; and collecting data characteristics of the classified data according to the data attributes to obtain data definitions or identifications of different data, so that complex data can be represented in a simple form, and recognition and search query of a computer language are facilitated; further, according to the embodiment of the invention, the obvious characteristics in the data characteristics can be identified through calculating the characteristic weights of the data characteristics; and the data features are subjected to feature fusion according to the feature weights, so that fusion features can be obtained, obvious features can be used as main features, and features with insignificant features are subjected to weakening fusion, so that the data target searched during subsequent feature searching is more definite; the data demand is characterized to obtain a demand characteristic, the data demand can be expressed in a data characteristic form, and a computer can understand the data characteristic more easily through the analysis of a subsequent characteristic analysis model, so that the acquisition of data is facilitated; performing feature fitting operation on the fusion feature and the demand feature by using the feature analysis model to obtain fitting values so as to obtain calculation parameters related to data features, and further calculating the data features by using the calculation parameters; and collecting the required data corresponding to the data requirement from the source data according to the target characteristics, so that the data can be collected based on the data characteristics, the data target is clear, the collected data are all the data required by the user, cumbersome steps such as verification and screening of conventional data collection are avoided, a large amount of time is saved, and the efficiency and accuracy of data collection are improved. Therefore, the data intelligent acquisition method based on the data characteristics can improve the accuracy of data acquisition.
FIG. 2 is a functional block diagram of the intelligent data acquisition system implemented based on data features of the present invention.
The data intelligent acquisition system 200 based on the data characteristics can be installed in electronic equipment. Depending on the functions implemented, the intelligent data collection system implemented based on the data features may include a data classification module 201, a feature collection module 202, a feature fusion module 203, a fitting calculation module 204, a fitting judgment module 205, a step return module 206, a feature acquisition module 207, and a data collection module 208.
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 embodiment of the present invention, the functions of each module/unit are as follows:
the data classification module 201 is configured to obtain a data requirement, retrieve source data according to the data requirement, calculate a data association degree of the source data, and perform data classification on the source data according to the data association degree to obtain classified data;
the feature collection module 202 is configured to analyze data attributes of the classification data, and collect data features of the classification data according to the data attributes;
The feature fusion module 203 is configured to calculate a feature weight of the data feature, and perform feature fusion on the data feature according to the feature weight to obtain a fusion feature;
the fitting calculation module 204 is configured to perform feature expression on the data requirement to obtain a requirement feature, create a feature analysis model of the data requirement according to the requirement feature, and perform feature fitting operation on the fusion feature and the requirement feature by using the feature analysis model to obtain a fitting value;
the fitting judgment module 205 is configured to judge whether the fitting value is greater than a preset threshold
The step return module 206 is configured to return a step of performing feature expression on the data requirement when the fitting value is greater than a preset value;
the feature obtaining module 207 is configured to take the fusion feature as a target feature of the data requirement when the fitting value is not greater than the preset value;
the data collection module 208 is configured to collect, according to the target feature, demand data corresponding to the data demand from the source data.
In detail, the modules in the data feature-based intelligent data acquisition system 200 in the embodiment of the present invention use the same technical means as the data feature-based intelligent data acquisition method and system described in fig. 1, and can produce the same technical effects, which are not described herein.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the data intelligent acquisition method implemented based on data features.
The electronic device may include a processor 30, a memory 31, a communication bus 32, and a communication interface 33, and may also include a computer program, such as a fired lithium slag forging program, stored in the memory 31 and executable on the processor 30.
The processor 30 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 30 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., executing a firing lithium slag forging program, etc.) stored in the memory 31, and calling data stored in the memory 31.
The memory 31 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 31 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 31 may also be an external storage device of the electronic device in other embodiments, for example, 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 31 may also include both an internal storage unit and an external storage device of the electronic device. The memory 31 may be used not only for storing application software installed in an electronic device and various data such as codes of a firing lithium slag forging program, etc., but also for temporarily storing data that has been output or is to be output.
The communication bus 32 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 31 and at least one processor 30 or the like.
The communication interface 33 is used for communication between the electronic device 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, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and the power source may be logically connected to the at least one processor 30 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited in scope by this configuration.
The intelligent data collection program based on the data features stored in the memory 31 in the electronic device is a combination of a plurality of computer programs, which when run in the processor 30 can implement the following methods:
acquiring data requirements, retrieving source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data;
Analyzing the data attribute of the classified data, and collecting the data characteristics of the classified data according to the data attribute;
calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features;
carrying out feature expression on the data demand to obtain a demand feature, creating a feature analysis model of the data demand according to the demand feature, and carrying out feature fitting operation on the fusion feature and the demand feature by utilizing the feature analysis model to obtain a fitting value;
judging whether the fitting value is larger than a preset threshold value or not;
returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value;
when the fitting value is not larger than the preset value, taking the fusion characteristic as a target characteristic of the data requirement;
and acquiring demand data corresponding to the data demand from the source data according to the target characteristics.
In particular, the specific implementation method of the processor 30 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a non-volatile computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement the method of:
acquiring data requirements, retrieving source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data;
analyzing the data attribute of the classified data, and collecting the data characteristics of the classified data according to the data attribute;
calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features;
carrying out feature expression on the data demand to obtain a demand feature, creating a feature analysis model of the data demand according to the demand feature, and carrying out feature fitting operation on the fusion feature and the demand feature by utilizing the feature analysis model to obtain a fitting value;
judging whether the fitting value is larger than a preset threshold value or not;
returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value;
When the fitting value is not larger than the preset value, taking the fusion characteristic as a target characteristic of the data requirement;
and acquiring demand data corresponding to the data demand from the source data according to the target characteristics.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The intelligent data acquisition method based on the data characteristics is characterized by comprising the following steps:
acquiring data requirements, retrieving source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data, wherein the calculating the data association degree of the source data comprises the following steps:
calculating the data association degree of the source data by using the following formula:
analyzing the data attribute of the classified data, and collecting the data characteristics of the classified data according to the data attribute;
calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features;
carrying out feature expression on the data demand to obtain a demand feature, creating a feature analysis model of the data demand according to the demand feature, and carrying out feature fitting operation on the fusion feature and the demand feature by utilizing the feature analysis model to obtain a fitting value;
judging whether the fitting value is larger than a preset value or not;
returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value;
when the fitting value is not larger than the preset value, taking the fusion characteristic as a target characteristic of the data requirement;
and acquiring demand data corresponding to the data demand from the source data according to the target characteristics.
2. The method of claim 1, wherein retrieving source data according to the data requirements comprises:
Creating a demand text corresponding to the data demand according to the data demand;
constructing a demand catalog corresponding to the data demand in the demand text;
and adding a search code in the demand catalog to search the source data corresponding to the data demand according to the search code.
3. The method of claim 1, wherein the data classifying the source data according to the data association degree to obtain classified data comprises:
adding a classification label to the source data according to the data association degree;
creating a classification set of the source data, and loading the source data into the classification set according to the classification label to obtain classification data.
4. The method of claim 1, wherein the collecting data features of the classification data based on the data attributes comprises:
converting the classified data into linear data according to the data attribute;
performing dimension reduction processing on the linear data to obtain dimension reduction data;
mapping the dimensionality reduction data into a pre-constructed space matrix, and acquiring data characteristics of the classification data in the space matrix by using a preset matrix algorithm.
5. The method of claim 4, wherein said converting said classification data into linear data according to said data attributes comprises:
querying a data structure of the classified data through the data attribute;
inquiring a corresponding linear structure algorithm according to the data structure;
and converting the classified data into linear data according to the linear structure algorithm.
6. The method of claim 1, wherein said calculating feature weights for the data features comprises:
calculating the feature weight of the data feature by using the following formula:
7. The method according to claim 1, wherein the feature fusing the data features according to the feature weights to obtain fused features includes:
and carrying out feature fusion on the data features by using the following formula:
8. The method according to claim 1, wherein the collecting, according to the target feature, demand data corresponding to the data demand from the source data includes:
querying a database structure of the source data;
creating an index mode of the source data according to the database structure, and adding a retrieval tag for the target feature;
and acquiring the demand data corresponding to the data demand from the source data according to the index mode and the index label.
9. The method according to claim 1, wherein performing a feature fitting operation on the fusion feature and the demand feature by using the feature analysis model to obtain a fitting value includes:
and performing feature fitting operation on the fusion feature and the demand feature by using the following formula:
10. A system for intelligent data collection based on data features, the system comprising:
the data classification module is used for acquiring data requirements, searching source data according to the data requirements, calculating the data association degree of the source data, and carrying out data classification on the source data according to the data association degree to obtain classified data;
the characteristic acquisition module is used for analyzing the data attribute of the classified data and acquiring the data characteristic of the classified data according to the data attribute;
the feature fusion module is used for calculating the feature weight of the data features, and carrying out feature fusion on the data features according to the feature weight to obtain fusion features;
the fitting calculation module is used for carrying out feature expression on the data requirements to obtain requirement features, creating a feature analysis model of the data requirements according to the requirement features, and carrying out feature fitting operation on the fusion features and the requirement features by utilizing the feature analysis model to obtain fitting values;
the fitting judgment module is used for judging whether the fitting value is larger than a preset threshold value or not;
the step return module is used for returning to the step of carrying out characteristic expression on the data requirement when the fitting value is larger than a preset value;
The characteristic acquisition module is used for taking the fusion characteristic as a target characteristic of the data requirement when the fitting value is not larger than the preset value;
and the data acquisition module is used for acquiring the demand data corresponding to the data demand from the source data according to the target characteristics.
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