CN113704314A - Data analysis method and device, electronic equipment and storage medium - Google Patents
Data analysis method and device, electronic equipment and storage medium Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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Abstract
The disclosure discloses a data analysis method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of cloud computing and big data. After responding to the data to be processed uploading operation performed on the operation interface, extracting keywords in the data to be processed, searching for a data analysis algorithm with the similarity greater than a similarity threshold value with the keywords, responding to a first confirmation operation aiming at the data analysis algorithm, and analyzing the data to be processed according to the confirmed data analysis algorithm. The butt joint with the cloud server can be realized through simple uploading operation and confirmation operation based on the operation interface, so that the data to be processed can be analyzed based on a data analysis algorithm in the cloud server, and a large amount of manpower and material resources are saved. In addition, a cloud computing mode is adopted, a mass data analysis algorithm is provided for analysis of data to be processed, and the processing speed of mass data is greatly improved.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, particularly to the field of cloud computing and big data technologies, and in particular, to a data analysis method and apparatus, an electronic device, and a storage medium.
Background
With the continuous development of big data technology, big data technology has been widely used.
Data analysis is distinct for the wide application of big data. The current common approach is to custom configure according to different domain features. For example, for a website for analyzing crop monitoring data, multiple latitudes such as an analysis soil latitude and a weather latitude need to be customized and developed for customized configuration, and this customized configuration manner greatly increases development cost and labor cost.
Disclosure of Invention
The disclosure provides a data analysis method and device, an electronic device and a storage medium.
According to an aspect of the present disclosure, there is provided a method for analyzing data, including:
responding to the uploading operation of the data to be processed on an operation interface, and extracting keywords in the data to be processed;
searching a data analysis algorithm with the similarity to the keyword being greater than a similarity threshold, wherein the data analysis algorithm is stored in a cloud server;
and responding to a first confirmation operation aiming at the data analysis algorithm, and analyzing the data to be processed according to the confirmed data analysis algorithm.
According to another aspect of the present disclosure, there is provided an apparatus for analyzing data, including:
the extraction module is used for responding to the uploading operation of the data to be processed on the operation interface and extracting keywords in the data to be processed;
the searching module is used for searching a data analysis algorithm with the similarity greater than a similarity threshold value with the keyword, and the data analysis algorithm is stored in the cloud server;
and the analysis module is used for responding to a first confirmation operation aiming at the data analysis algorithm and analyzing the data to be processed according to the confirmed data analysis algorithm.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the preceding aspect.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the preceding aspect.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method according to the preceding aspect.
According to the data analysis method, the data analysis device, the electronic equipment and the storage medium, after responding to the data to be processed uploading operation performed on the operation interface, the keywords in the data to be processed are extracted, the data analysis algorithm with the similarity degree larger than the similarity degree threshold value with the keywords is searched, and the data to be processed is analyzed according to the confirmed data analysis algorithm in response to the first confirmation operation aiming at the data analysis algorithm. The butt joint with the cloud server can be realized through simple uploading operation and confirmation operation based on the operation interface, so that the data to be processed can be analyzed based on a data analysis algorithm in the cloud server, and a large amount of manpower and material resources are saved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flow chart of a data analysis method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of another method for analyzing data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an apparatus for analyzing data according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of another data analysis device provided in the embodiment of the present disclosure;
fig. 5 is a schematic block diagram of an example electronic device 500 provided by embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
A method, an apparatus, an electronic device, and a storage medium for analyzing data according to embodiments of the present disclosure are described below with reference to the accompanying drawings.
In the related art, in order to analyze big data, personalized configurations are performed according to different domain features, for example: crop monitoring data website contains, soil latitude and weather dimension, and the soil dimension includes again: temperature, humidity, pH, metal element content, nonmetal element content, and the like, and weather latitude also includes temperature, humidity, ultraviolet intensity, climate type, and the like. The two dimensions are illustrated, and in practical application, for different fields, the corresponding analysis dimensions are more, a large amount of manpower and a large amount of cost are consumed in the customized configuration mode, the calculation capability of the customized configuration is limited, analysis can be performed only by relying on a single algorithm, and a large error is easily caused in an analysis result.
In the data analysis method, in order to avoid using a large amount of manpower and material resources to carry out customized analysis on the big data, a modular design is adopted, namely, the big data are butted with the cloud server through simple uploading operation and confirmation operation, and the big data are analyzed through a data analysis algorithm of the cloud server, so that the operation is simple, the data to be processed can be analyzed through matching the algorithm with the highest similarity of the data to be processed in the massive data analysis algorithm, and the correctness of a data analysis result is further ensured.
Fig. 1 is a schematic flow chart of a data analysis method according to an embodiment of the present disclosure.
As shown in fig. 1, the method comprises the following steps:
As a feasible manner of the embodiment of the application, a user can upload data to be processed to the cloud server by using the data upload interface in the browser, and as another feasible manner of the embodiment of the application, after the data to be processed is collected, the data to be processed can be uploaded to the cloud server by using the network request interface.
It should be noted that, according to different scenarios, one of the first possible implementation manner and the second possible implementation manner may be executed, or the second possible implementation manner may be executed in a case where a user operation is not detected in the first possible implementation manner or the data to be processed cannot be uploaded according to the user operation.
Based on the two possible implementation manners, the data to be processed can be manually input by the user, or the manual input by the user is not required, so that the uploading manner of the data to be processed is more flexible, and different requirements of the user can be met.
After the data to be processed is uploaded, the data to be processed is analyzed, and based on any extraction method in the related technology, keywords in the data to be processed are extracted, wherein the number of the keywords can be one or multiple, and the extraction of specific keywords needs to be determined according to the actual content of the data to be processed. In practical application, in order to realize accurate analysis of data to be processed, at least two keywords are preferably extracted, and the number of extracted keywords is not limited in the embodiment of the present application.
And 102, searching a data analysis algorithm with the similarity to the keyword being greater than a similarity threshold, wherein the data analysis algorithm is stored in a cloud server.
Searching a data analysis algorithm with similarity greater than a similarity threshold value with a keyword in a cloud server according to the keyword, wherein the similarity threshold value is an inspected value, for example, the normalized value range of the similarity is [0,1], the minimum value of the similarity is 0, the keyword is completely unmatched with the data analysis algorithm, the maximum value of the similarity is 1, the keyword is completely matched with the data analysis algorithm, when the similarity threshold value is set, the larger the similarity threshold value is, the more accurate the matched data analysis algorithm is in a month, but the smaller the matched data analysis algorithm is, the smaller the similarity threshold value is, the larger the matched data analysis algorithm number is, and a user is required to select the data analysis algorithm according to actual requirements. The setting size of the similarity threshold is not limited, and the similarity threshold can be flexibly configured according to different application scenes.
The data analysis algorithm in the cloud server is an algorithm pool and comprises algorithms for analyzing various types of data, after the data to be processed is received, the corresponding data analysis algorithm is matched with the cloud server according to the specific type of the data to be processed, and when the matching result is returned to an operation interface, the matching result can be displayed according to the similarity ranking of the keywords, so that the user can conveniently check the matching result.
And 103, responding to a first confirmation operation aiming at the data analysis algorithm, and analyzing the data to be processed according to the confirmed data analysis algorithm.
The purpose of this step is to increase the confirmation operation of the user to ensure that the data analysis can be performed on the data to be processed depending on the correct algorithm, thereby ensuring the accuracy of the final analysis of the data and avoiding the time consumption caused by using the wrong data analysis algorithm.
From the user plane, after the cloud server pushes the data analysis algorithm to the operation interface, the user clicks and determines to complete the operation of the user side after selecting the final data analysis algorithm, and the process is simple and convenient. In a machine aspect, after receiving a confirmation instruction triggered based on an operation interface, the cloud server analyzes the data to be processed based on the confirmed data analysis algorithm, and returns a final analysis result to the operation interface or a receiving address receivable by other users.
As an implementation manner of the embodiment of the present invention, after the data to be processed is analyzed, a visualized data display is generated according to the data analysis result, for example, a graph is generated according to the data analysis result; as another implementation manner of the embodiment of the present invention, the trend of data in a certain time in the future may be predicted according to the data analysis result. For example, when waiting
When the processed data is plant growth data in a certain farm, after the data analysis algorithm based on the cloud server is analyzed, the analysis of the dimensions such as maturity, climate, weather, humidity, temperature and the like of the plant is obtained, the growth state of the plant can be predicted according to the analysis results of the dimensions, and the prediction of the growth state is pushed to an operation interface so as to be convenient for a user to check.
According to the data analysis method, after the data to be processed is uploaded on the operation interface, keywords in the data to be processed are extracted, a data analysis algorithm with the similarity greater than a similarity threshold value with the keywords is searched, and the data to be processed is analyzed according to the confirmed data analysis algorithm in response to a first confirmation operation aiming at the data analysis algorithm. The butt joint with the cloud server can be realized through simple uploading operation and confirmation operation based on the operation interface, so that the data to be processed can be analyzed based on a data analysis algorithm in the cloud server, and a large amount of manpower and material resources are saved. The embodiment of the application adopts a cloud computing mode, provides strong computing capability for data analysis, and enables the processing speed of mass data to be improved in multiples.
In a specific application process, although the algorithm pool of the cloud server provides great convenience for data analysis, since the algorithms are algorithms applicable to the public, algorithm parameters of data analysis algorithms used by the algorithms may have differences when analyzing non-typable to-be-processed data, so that in order to meet analysis results of more data types of to-be-processed data, in the embodiment of the present application, a user is allowed to customize the algorithm parameters to meet user requirements, and a specific implementation method is shown in fig. 2, the method further includes:
For the description of step 201, refer to step 101, and no further description is provided herein in this embodiment.
For clarity of explanation of the algorithm parameters described in the embodiments of the present application, the algorithm parameters are described in an exemplary form, for example, for analyzing financial data with high accuracy requirement, the accuracy in the algorithm parameters may be adjusted, for example, to ten digits after decimal point; or, when analyzing the air data, the air humidity amplitude in the algorithm parameter may be adjusted, for example, the air humidity is adjusted to 16 degrees to 35 degrees, and the configuration of the specific algorithm parameter needs to be flexibly configured according to the application scenario of the current data to be processed and the data result requirement. It should be noted that the above description is briefly made by way of example, but the description is not intended to limit the algorithm parameters to include only the above-mentioned parameters.
And 204, pushing a data analysis template aiming at the data to be processed and a corresponding data analysis result display template to the operation interface according to the keywords.
The analysis template of the data to be processed is a visual display mode and a typesetting mode for analyzing the data to be processed in the operation interface, and may include but is not limited to dimensions, measurement units, precision and the like of the data to be processed.
The modular templating mode can simplify the configuration process and reduce the cost of accessing the cloud server for data analysis.
Or responding to the user-defined operation of the operation interface on the data analysis template and the corresponding data analysis result display template, and analyzing and displaying the data to be processed based on the user-defined data analysis template and the corresponding data analysis result display template.
The embodiment of the application also provides a user-defined data analysis template and a user-defined operation of a corresponding data analysis result display template, but the user-defined process also needs to follow the principle that the user cannot have all the lives, for example, if the data to be processed is plant generation data, when the user-defined data analysis template and the corresponding data analysis result display template are used, the user-defined display of PM2.5 cannot be performed, and the data analysis and data display which are irrelevant to plant growth are performed. The above examples are illustrative only, and are not intended to be limiting in any way.
And step 206, analyzing the data to be processed according to the data analysis algorithm.
And step 207, receiving a receiving channel based on the data analysis result sent by the operation interface.
The receiving channel includes but is not limited to mail, local area network sharing, and download buttons, etc.
If the receiving channel is the operating system 1, the operating system does not support the original format of the data analysis result obtained by the cloud server, and the data analysis result can be converted at the cloud server in order to adapt to the reception of each data analysis result.
As an optional mode of the application, when the receiving channel of the data analysis result is sent based on the operation interface, the data or file type of the data analysis result can be specified.
If not, go to step 209, and if yes, go to step 210.
And step 210, sending the analysis result of the data to be processed to a receiving terminal corresponding to the receiving channel.
And step 211, updating the analysis progress state in real time according to the analysis progress state of the data to be processed, and sending the analysis progress state to the operation interface so as to perform visual display in the operation interface.
The step can ensure the safety of the data, namely when the data is wrong, the data processing process can be traced according to the operation log, and even if the data to be processed is tampered in the operation process, the source can be traced.
Fig. 3 is a schematic structural diagram of an apparatus for analyzing data according to an embodiment of the present disclosure, as shown in fig. 3, including: an extraction module 301, a search module 302 and an analysis module 303.
The extraction module 301 is configured to, in response to a to-be-processed data uploading operation performed on an operation interface, extract a keyword in the to-be-processed data;
a searching module 302, configured to search for a data analysis algorithm whose similarity to the keyword is greater than a similarity threshold, where the data analysis algorithm is stored in a cloud server;
an analyzing module 303, configured to, in response to a first confirmation operation for the data analysis algorithm, analyze the to-be-processed data according to the confirmed data analysis algorithm.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the apparatus further includes: an extraction module 401, a search module 402, and an analysis module 403. For the extraction module 401, the search module 402, and the analysis module 403, reference may be made to the relevant descriptions of the extraction module 301, the search module 302, and the analysis module 303 shown in fig. 3, which is not described herein again in this embodiment of the present application.
A first updating module 404, configured to, before the analyzing module 403 analyzes the data to be processed according to the confirmed data analysis algorithm, respond to configuration of algorithm parameters in the data analysis algorithm at the operation interface, and update the algorithm parameters in the data analysis algorithm synchronously.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the apparatus further includes:
a pushing module 405, configured to push a data analysis template and a corresponding data analysis result display template for the to-be-processed data to the operation interface according to the keyword before the analysis module 403 analyzes the to-be-processed data according to the confirmed data analysis algorithm;
the first processing module 406 is configured to, in response to a second confirmation operation of the operation interface on the data analysis template and the corresponding data analysis result display template, analyze and display the to-be-processed data based on the confirmed data analysis template and the corresponding data analysis result display template;
or, the apparatus further includes a second processing module 407, configured to respond to a user-defined operation of the operation interface on the data analysis template and the corresponding data analysis result display template, and analyze and display the to-be-processed data based on the user-defined data analysis template and the corresponding data analysis result display template.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the apparatus further includes:
a receiving module 408, configured to receive a receiving channel based on a data analysis result sent by the operation interface;
a confirming module 409, configured to confirm whether a first data format corresponding to the receiving channel is consistent with a second data format of the analyzed to-be-processed data;
a conversion module 410, configured to convert the second data format into the first data format when the confirmation module confirms the inconsistency;
a sending module 411, configured to send the analysis result of the converted to-be-processed data to a receiving terminal corresponding to the receiving channel.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the apparatus further includes:
the second updating module 412 is configured to update the analysis progress state of the to-be-processed data in real time according to the analysis progress state;
the issuing module 413 is configured to issue the analysis progress status to the operation interface, so as to perform visual display in the operation interface.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the apparatus further includes:
a first recording module 414, configured to record a start-stop time for starting to analyze the data to be processed;
a second recording module 415, configured to record the start-stop time and the analysis progress status in a running log.
According to the data analysis device, after the data to be processed is uploaded on the operation interface, keywords in the data to be processed are extracted, a data analysis algorithm with the similarity larger than a similarity threshold value with the keywords is searched, and the data to be processed is analyzed according to the confirmed data analysis algorithm in response to a first confirmation operation aiming at the data analysis algorithm. The butt joint with the cloud server can be realized through simple uploading operation and confirmation operation based on the operation interface, so that the data to be processed can be analyzed based on a data analysis algorithm in the cloud server, and a large amount of manpower and material resources are saved.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of the present embodiment, and the principle is the same, and the present embodiment is not limited thereto.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 into a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An I/O (Input/Output) interface 505 is also connected to the bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing Unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The calculation unit 501 performs the respective methods and processes described above, such as the analysis method of data. For example, in some embodiments, the method of analyzing data may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the aforementioned method of analyzing the data in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, Integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip, System On a Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), internet, and blockchain Network.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (15)
1. A method of analyzing data, comprising:
responding to the uploading operation of the data to be processed on an operation interface, and extracting keywords in the data to be processed;
searching a data analysis algorithm with the similarity to the keyword being greater than a similarity threshold, wherein the data analysis algorithm is stored in a cloud server;
and responding to a first confirmation operation aiming at the data analysis algorithm, and analyzing the data to be processed according to the confirmed data analysis algorithm.
2. The method of analyzing data of claim 1, wherein prior to said analyzing said data to be processed according to said confirmed data analysis algorithm, said method further comprises:
synchronously updating algorithm parameters in the data analysis algorithm in response to configuration of algorithm parameters in the data analysis algorithm at the operational interface.
3. The method of analyzing data of claim 1, wherein prior to said analyzing said data to be processed according to said confirmed data analysis algorithm, said method further comprises:
pushing a data analysis template and a corresponding data analysis result display template aiming at the data to be processed to the operation interface according to the keywords; responding to a second confirmation operation of the operation interface on the data analysis template and the corresponding data analysis result display template, and analyzing and displaying the data to be processed based on the confirmed data analysis template and the corresponding data analysis result display template;
or responding to the user-defined operation of the operation interface on the data analysis template and the corresponding data analysis result display template, and analyzing and displaying the data to be processed based on the user-defined data analysis template and the corresponding data analysis result display template.
4. A method of analyzing data according to claim 3, wherein the method further comprises:
the receiving channel is used for receiving a data analysis result sent based on the operation interface;
determining whether a first data format corresponding to the receiving channel is consistent with a second data format of the analyzed data to be processed;
if not, converting the second data format into a first data format;
and sending the analysis result of the converted data to be processed to a receiving terminal corresponding to the receiving channel.
5. A method of analyzing data according to claim 1, wherein the method further comprises:
updating the analysis progress state in real time according to the analysis progress state of the data to be processed;
and sending the analysis progress state to the operation interface so as to be visually displayed in the operation interface.
6. The method of analyzing data of claim 5, wherein the method further comprises:
recording the starting and ending time for starting to analyze the data to be processed;
and recording the start-stop time and the analysis progress state in a running log.
7. An apparatus for analyzing data, comprising:
the extraction module is used for responding to the uploading operation of the data to be processed on the operation interface and extracting keywords in the data to be processed;
the searching module is used for searching a data analysis algorithm with the similarity greater than a similarity threshold value with the keyword, and the data analysis algorithm is stored in the cloud server;
and the analysis module is used for responding to a first confirmation operation aiming at the data analysis algorithm and analyzing the data to be processed according to the confirmed data analysis algorithm.
8. The apparatus for analyzing data according to claim 7, wherein the apparatus further comprises:
and the first updating module is used for responding to the configuration of the algorithm parameters in the data analysis algorithm on the operation interface before analyzing the data to be processed according to the confirmed data analysis algorithm, and synchronously updating the algorithm parameters in the data analysis algorithm.
9. The apparatus for analyzing data according to claim 7, wherein the apparatus further comprises:
the pushing module is used for pushing a data analysis template and a corresponding data analysis result display template aiming at the data to be processed to the operation interface according to the keywords before the data to be processed is analyzed according to the confirmed data analysis algorithm; the first processing module is used for responding to a second confirmation operation of the operation interface on the data analysis template and the corresponding data analysis result display template, and analyzing and displaying the data to be processed based on the confirmed data analysis template and the corresponding data analysis result display template;
or, the device further includes a second processing module, configured to respond to a user-defined operation of the operation interface on the data analysis template and the corresponding data analysis result display template, and analyze and display the to-be-processed data based on the user-defined data analysis template and the corresponding data analysis result display template.
10. An apparatus for analyzing data according to claim 9, wherein said apparatus further comprises:
the receiving module is used for receiving a receiving channel based on a data analysis result sent by the operation interface;
the confirming module is used for confirming whether a first data format corresponding to the receiving channel is consistent with a second data format of the analyzed data to be processed;
the conversion module is used for converting the second data format into the first data format when the confirmation module confirms that the data formats are inconsistent;
and the sending module is used for sending the analysis result of the converted data to be processed to a receiving terminal corresponding to the receiving channel.
11. The apparatus for analyzing data according to claim 7, wherein the apparatus further comprises:
the second updating module is used for updating the analysis progress state in real time according to the analysis progress state of the data to be processed;
and the issuing module is used for issuing the analysis progress state to the operation interface so as to carry out visual display in the operation interface.
12. An apparatus for analyzing data according to claim 11, wherein said apparatus further comprises:
the first recording module is used for recording the starting and ending time for starting to analyze the data to be processed;
and the second recording module is used for recording the start-stop time and the analysis progress state in an operation log.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
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