CN116737707A - Data processing method, device, electronic equipment and computer readable storage medium - Google Patents

Data processing method, device, electronic equipment and computer readable storage medium Download PDF

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CN116737707A
CN116737707A CN202310711095.7A CN202310711095A CN116737707A CN 116737707 A CN116737707 A CN 116737707A CN 202310711095 A CN202310711095 A CN 202310711095A CN 116737707 A CN116737707 A CN 116737707A
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data
complement
determining
missing
target
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马晓敏
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Bank of China Ltd
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Bank of China Ltd
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    • G—PHYSICS
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    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The application provides a data processing method, a data processing device, electronic equipment and a computer readable storage medium, which can be used in the field of big data. The method comprises the following steps: acquiring first data to be processed and corresponding target parameters, and performing data complementation on the first data according to the target parameters to obtain second data; determining each complement data missing from the first data relative to the second data, wherein the second data is composed of the first data and each complement data; determining a missing value corresponding to the first data according to each piece of complement data, and determining a target marking mode matched with the missing value; marking each complement data in the second data according to the target marking mode to obtain third data, and displaying the third data. The method can enable the data observer to rapidly determine the severity of the missing data of the first data through the target marking mode of the complement data in the third data, thereby timely adjusting the data acquisition strategy and reducing the severity of the missing data of the data acquired next time.

Description

Data processing method, device, electronic equipment and computer readable storage medium
Technical Field
The present application relates to the field of big data, and in particular, to a data processing method, apparatus, electronic device, and computer readable storage medium.
Background
In the foreign exchange market, the trade data is required to be acquired for construction of a trade trend curve.
In order to accurately construct the transaction trend curve, it is necessary to complement the missing data in the transaction data.
The observer needs to observe the completed transaction data and adjust the data acquisition strategy so that the missing data of the transaction data acquired next time cannot be excessive. And the foreign exchange market has massive transaction data, so that observers need to view the massive transaction data. However, the marking modes of the supplementary data in the transaction data are the same, and the severity of the missing data of the transaction data cannot be quickly determined by the data observer, that is, the data acquisition strategy cannot be quickly adjusted, so that the missing information of the transaction data acquired next time is still serious.
Disclosure of Invention
The application provides a data processing method, a data processing device, electronic equipment and a computer readable storage medium, which are used for solving the problem that the severity of missing data in data cannot be determined quickly.
In a first aspect, the present application provides a data processing method, including:
acquiring first data to be processed and corresponding target parameters, and carrying out data complementation on the first data according to the target parameters to obtain second data, wherein the target parameters comprise time parameters and characteristic values of the first data;
determining each piece of complement data missing from the first data relative to the second data, wherein the second data consists of the first data and each piece of complement data;
determining a missing value corresponding to the first data according to each piece of complement data, and determining a target marking mode matched with the missing value, wherein the missing value is used for indicating the severity of the missing data of the first data;
marking each complement data in the second data according to the target marking mode to obtain third data, and displaying the third data.
In a second aspect, the present application provides a data processing apparatus comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring first data to be processed and corresponding target parameters, and carrying out data complementation on the first data according to the target parameters to obtain second data, and the target parameters comprise time parameters and characteristic values of the first data;
the first determining module is used for determining each piece of complement data missing from the first data relative to the second data, and the second data is composed of the first data and each piece of complement data;
the second determining module is used for determining a missing value corresponding to the first data according to each piece of complement data and determining a target marking mode matched with the missing value, wherein the missing value is used for indicating the severity of the missing data of the first data;
and the processing module is used for marking each complement data in the second data according to the target marking mode to obtain third data and displaying the third data.
In a third aspect, the present application provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory to implement the method as described above.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein computer executable instructions for carrying out the method as described above when executed by a processor.
The data processing method, the device, the electronic equipment and the computer readable storage medium provided by the application are characterized in that first data are subjected to data complementation to obtain second data, each piece of the complement data is determined in the second data, the missing value of the first data is determined based on each piece of the complement data, then the target marking mode is determined through the missing value for marking the severity of the missing data of the first data, and thus the complement data in the second data are marked in the target marking mode to obtain third data, so that a data observer can quickly determine the severity of the missing data of the first data through the target marking mode of the complement data in the third data, thereby timely adjusting the data acquisition strategy and reducing the severity of the missing data of the data acquired next time.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic view of a data processing method according to the present application;
FIG. 2 is a flowchart illustrating a first embodiment of a data processing method according to the present application;
FIG. 3 is a flowchart illustrating a second embodiment of a data processing method according to the present application;
FIG. 4 is a flowchart illustrating a third embodiment of a data processing method according to the present application;
FIG. 5 is a flowchart illustrating a fourth embodiment of a data processing method according to the present application;
FIG. 6 is a flowchart of a fifth embodiment of a data processing method according to the present application;
FIG. 7 is a flowchart of a sixth embodiment of a data processing method according to the present application;
FIG. 8 is a block diagram of a data processing apparatus according to the present application;
fig. 9 is a schematic structural diagram of an electronic device according to the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
It should be noted that the data processing method, apparatus, electronic device and computer readable storage medium of the present application may be used in the field of big data, and may also be used in any field other than the field of big data, and the application fields of the data processing method, apparatus, electronic device and computer readable storage medium of the present application are not limited.
In some fields, real-time data is acquired for training and updating of the model. For example, in the financial market, the foreign exchange market data fluctuates at any time, and training and updating of the model by acquiring the foreign exchange market data in real time are required. The data needs to be processed before it can be used. Illustratively, the missing value needs to be supplemented to the data so that the data is complete. After the replenishment of the data is completed, the replenished data is marked.
The inventor discovers that observers need to check massive data every day, the marking modes of the supplementary data in the data are the same, and the severity of the missing data of the initial data cannot be quickly determined by the data observers, namely the data acquisition strategy cannot be quickly adjusted, so that the missing information of the data acquired next time is still serious.
The inventor thinks that the first data is subjected to data complementation to obtain the second data, each piece of complement data is determined in the second data, the missing value of the first data is determined based on each piece of complement data, then the target marking mode is determined through the missing value for marking the severity of the missing data of the first data, and the complement data in the second data is marked in the target marking mode to obtain the third data, so that a data observer can quickly determine the severity of the missing data of the first data through the target marking mode of the complement data in the third data, the data acquisition strategy is timely adjusted, and the severity of the missing data of the data acquired next time is reduced.
Referring to fig. 1, fig. 1 is a schematic view of a scenario of a data processing method according to the present application. The data processing apparatus 100 obtains the first data 110 to be processed, and performs data complement on the first data 110 to obtain second data. The data processing apparatus 100 is provided with a plurality of marking means, each marking means being for marking data of a different severity of missing data. The data processing apparatus 100 obtains the third data 120 by matching the severity of the missing data of the first data with the corresponding target marking mode and marking the complement data in the second data by the target marking mode. The data processing apparatus 100 further displays the third data 120, that is, the display interface 130 of the data processing apparatus 100 displays the third data 120. Illustratively, the data marked in the third data 120 is okda, and okda is marked with a bold and oblique marking.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Referring to fig. 2, fig. 2 is a flowchart of a first embodiment of a data processing method according to the present application, where the data processing method includes:
step S201, obtaining first data to be processed and corresponding target parameters, and carrying out data complement on the first data according to the target parameters to obtain second data, wherein the target parameters comprise time parameters and characteristic values of the first data.
In the present embodiment, the execution subject is a data processing apparatus. For ease of description, the device designations data processing device are used below. The apparatus may be any terminal device having data processing capabilities. The device obtains data to be processed, the data being defined as first data. The first data is data with time sequence, and the first data has characteristic values. For example, the first data may be fluctuation data in a foreign exchange market, the first data includes fluctuation data of a plurality of days, and the fluctuation data has a corresponding fluctuation value, and the fluctuation value is a characteristic value. The device obtains a target parameter of the first data, where the target parameter includes a time parameter of the first data, i.e. a time involved in the first data, and a feature value, and the time parameter is, for example, a time of data generation.
The device may perform data complement on the first data by the target parameter. By way of example, the device determines, through the time parameter, that the first data lacks data of the N time period on the a-th day, that is, the first data includes sub-data of the n+1 time period and sub-data of the N-1 time period on the a-th day, but does not include sub-data of the N time period, and then the sub-data of the N time period needs to be completed. The device interpolates the characteristic value of the sub-data in the N+1 time period and the characteristic value of the sub-data in the N-1 time period to obtain the sub-data in the N time period, wherein the sub-data in the N time period is defined as the complement data. The device can perform linear interpolation and completion on intermediate data in the first data, and the device can perform head-to-tail completion on the data. After the device completes the complement of the first data, the second data can be obtained, namely, the second data is formed by the first data and each complement data.
Further, the first data may be obtained from a data text. After the device acquires the data text, the device performs preset operation on the data in the data text to obtain the first data, wherein the preset operation comprises format conversion, deletion of invalid data and filtration of dirty data. The data format can be unified and redundant invalid data can be removed through normal preset operation.
In step S202, each complement data missing from the first data with respect to the second data is determined, and the second data is composed of the first data and each complement data.
After the device completes the first data, each piece of complete data is recorded, and the device determines each piece of complete data which is missing of the first data relative to the second data according to the recorded information.
Step S203, determining a missing value corresponding to the first data according to each piece of complement data, and determining a target marking mode matched with the missing value, wherein the missing value is used for indicating the severity of the missing data of the first data.
After determining each complement data, the device can determine a missing value of the first data pair through each complement data, wherein the missing value is used for indicating the severity of the missing data of the first data. The device can count the number of the complement data, the number and the missing value have a mapping relation, the device can calculate the missing value through the mapping relation and the determined number, and the missing value is larger as the number of the complement data is larger; the greater the missing value, the more severely the first data is missing data.
The device is provided with a plurality of marking modes. Exemplary marking means include color marks, underlined marks, bolded marks, and the like. Different labeling patterns represent different degrees of severity. For example, the underlined marks represent less severe missing data, the bolded marks represent moderate severity of data, and the color marks represent more severe missing data. The corresponding numerical values are set in different marking modes, the device calculates the difference value between the missing value and the numerical value corresponding to the different marking modes, and the marking mode corresponding to the smallest difference value is determined as the target marking mode, namely the target marking mode is the marking mode matched with the missing value.
And S204, marking each complement data in the second data according to the target marking mode to obtain third data, and displaying the third data.
The second data comprises a plurality of second complement data, and the device marks each complement data in the second data according to a target marking mode to obtain third data. The device displays the third data.
In this embodiment, the second data is obtained after the first data is complemented, each complement data is determined in the second data, the missing value of the first data is determined based on each complement data, and then the target marking mode is determined by the missing value for marking the severity of the missing data of the first data, so that the complement data in the second data is marked in the target marking mode to obtain the third data, so that the severity of the missing data of the first data is determined quickly by the data observer through the target marking mode of the complement data in the third data, the data acquisition strategy is adjusted timely, and the severity of the missing data of the data acquired next time is reduced.
Referring to fig. 3, fig. 3 is a flowchart illustrating a second embodiment of a data processing method according to the present application, based on the first embodiment, step S203 includes:
step S301, determining a numerical interval in which the missing value is located.
In this embodiment, the apparatus is provided with a plurality of numerical intervals, each numerical interval is associated with a data loss severity level, and the greater the upper limit value of the numerical interval is, the greater the data loss severity level associated with the data interval is. After obtaining the missing value, the device determines the numerical interval in which the missing value is located.
Step S302, determining a marking mode associated with the data loss severity level associated with the data interval as a target marking mode matched with the loss value.
After the device determines the numerical value interval in which the missing value is located, determining the severity level of the data missing associated with the numerical value interval. Each data loss severity level is associated with a marking mode, and the device determines the marking mode associated with the data loss severity level as a target marking mode matched with the loss value.
In this embodiment, the marking mode is associated with the severity level of the data loss, and the severity level of the data loss is associated with the numerical interval, so that the device can accurately map to the marking mode representing the severity level of the first data loss based on the numerical interval where the loss value is located.
Referring to fig. 4, fig. 4 is a flowchart illustrating a third embodiment of a data processing method according to the present application, based on the first or second embodiment, step S203 includes:
step S401, obtaining a document of a user to be displayed and document annotation information of the document, and determining a plurality of first marking modes of the user to be displayed on the document according to the document annotation information.
In this embodiment, the emphasis degree of the same marking mode is different for different users. For example, the marking mode is color marking, and the user a often marks information with a lower importance level by color marking, and the user B often marks information with a higher importance level by color marking. In this regard, the manner in which the device marks the data needs to be in accordance with the habit of the user viewing the data to mark the information. The user viewing the data is defined as the tape show user.
The device acquires the document annotation information of the document of the user to be displayed. The document of the user to be displayed can be obtained from the cloud of the user, namely the cloud stores the automatically saved document of the user to be displayed. The document comprises a marking mode of the user to be displayed on the document and document annotation information corresponding to the marking mode. Illustratively, somewhere in the document is marked as a yellow background, and the corresponding document annotation information is "what is the principle here? ". The markup manner in the document is defined as a first markup manner.
Step S402, determining the importance level corresponding to each first marking mode according to the document annotation information.
The device may determine a level of importance corresponding to each first marking mode based on the document annotation information. The device may count the document annotation information corresponding to each first marking mode, and identify the query statement included in the document annotation information. The device calculates the ratio of the number of the query sentences in the first marking mode to the number of times the user to be displayed marks the information in the first marking mode. The device determines the importance level corresponding to the first marking mode through the ratio. For example, if the ratio is greater than the first value, the importance level corresponding to the first marking mode is a third level; if the ratio is smaller than or equal to the second numerical value and larger than the first numerical value, the importance level corresponding to the first marking mode is a second level; if the ratio is smaller than or equal to the second value, the importance level corresponding to the first marking mode is the first level. The higher the importance level is, the more importance the user to be displayed places on the information marked by the first marking mode. By the method, the device can determine the importance level corresponding to each first marking mode.
In step S403, the first marking mode corresponding to the importance level matched with the missing value is determined as the target marking mode matched with the missing value.
The missing value characterizes a severity of the first data missing data, which may be classified as a level of review. Illustratively, the higher severity corresponds to the third level of importance, the lower severity corresponds to the first level of importance, and the middle severity corresponds to the second level of importance. The device can determine the severity of the data deletion based on the magnitude of the deletion value, so as to determine the importance level corresponding to the severity, and the first marking mode associated with the importance level can be used as the target marking mode matched with the deletion value. For example, if the missing value is greater than the first missing value, the severity of the missing value is higher; if the missing value is smaller than or equal to the first missing value and larger than the second missing value, the severity corresponding to the missing value is moderate; if the missing value is smaller than the second missing value, the severity of the missing value is lower.
When the third data is required to be displayed, the device sends the third data to a terminal associated with the user to be displayed for displaying the third data.
In this embodiment, the device determines, based on annotation information and a marking manner of a document by a user to be displayed, a target marking manner matched with a missing value, and the user to be displayed can quickly determine the severity of missing data of the first data based on habits of the user to be displayed marking data.
Referring to fig. 5, fig. 5 is a flowchart illustrating a fourth embodiment of a data processing method according to the present application, based on any of the first to third embodiments, step S203 includes:
in step S501, the sum of the data amounts of the respective complement data is determined.
Step S502, determining a missing value corresponding to the first data according to the ratio between the sum of the data amounts and the data amount of the first data.
In this embodiment, when the device supplements the first data, the data amount of the supplemented data obtained by the supplementation is recorded by the device. The device obtains the data volume corresponding to each piece of complement data based on the recorded data volume, and the device calculates the sum of the data volumes of the complement data.
The apparatus calculates a ratio of the sum of the data amounts to the data amount of the first data. The ratio is defined as a first ratio, the first ratio may characterize a severity of the first data loss data, and the greater the first ratio, the greater the severity of the first data loss data.
The device stores a mapping relation between the first ratio and the missing value, and the device can determine the missing value corresponding to the first data based on the mapping relation and the first ratio.
In this embodiment, the device may accurately determine the missing value corresponding to the first data by using a ratio between the sum of the respective complement data amounts and the data amount of the first data.
Referring to fig. 6, fig. 6 is a flowchart illustrating a fifth embodiment of a data processing method according to the present application, based on any one of the first to third embodiments, step S203 includes:
in step S601, a first number of the respective complement data and a second number of the respective sub-data in the first data are determined, and the complement data is determined according to the plurality of sub-data.
Step S602, determining a missing value corresponding to the first data according to a ratio between the first number and the second number.
In this embodiment, the apparatus determines, after obtaining the second data, the number of each complement data in the second data, the number being defined as the first number. The first data is composed of a plurality of sub-data, and the device acquires the number of each sub-data in the first data, which is defined as the second number. The apparatus calculates a ratio of the first quantity to the second quantity, the ratio being defined as a second ratio, and the second ratio may characterize a severity of the first data loss data, and the greater the second ratio, the greater the severity of the first data loss data. The mapping relation between the second ratio and the missing value is stored in the device, and the missing value can be determined through the mapping relation and the second ratio.
In this embodiment, the device may accurately determine the missing value corresponding to the first data by using a ratio between the number of the respective complement data amounts and the number of the respective sub data amounts in the first data.
Referring to fig. 7, fig. 7 is a flowchart of a sixth embodiment of a data processing method according to the present application, based on any one of the first to sixth embodiments, step S201 includes:
step S701, determining a type of the first data, and determining a data complement mode of the first data according to the type.
In this embodiment, the data complement manners of the first data are related to the types of the first data, that is, the types of the first data are different, and then the data complement manners of the first data are different. The device stores the association relation between the data complement mode and the type. The device determines the type of the first data, and can determine the data complement mode of the first data through the type and the association relation.
Step S702, determining missing data in the first data according to the time parameter of the first data.
The device may determine missing data in the first data based on the time parameter of the first data. For example, the first data includes sub-data of n+1 time period and sub-data of N-1 time period on day a, but does not include sub-data of N time period, and the sub-data of N time period needs to be completed.
In step S703, the feature value of the first data is subjected to a preset operation according to the data complement method to obtain complement data, and the second data is obtained according to the complement data and the first data.
The device performs preset operation on the characteristic value of the first data according to the data complement mode to obtain complement data, and acquires second data based on the complement data and the first data. The data complement mode is linear interpolation, and the device interpolates the characteristic value of the sub-data in the N+1 time period and the characteristic value of the sub-data in the N-1 time period to obtain the sub-data in the N time period. The device inserts the sub data of the N time period between the sub data of the N+1 time period and the sub data of the N-1 time period, and the second data can be obtained.
In this embodiment, the device may determine the data complement mode based on the type of the first data, so as to accurately perform data complement on the first data based on the data complement mode.
The present application also provides a data processing apparatus, referring to fig. 8, a data processing apparatus 800 includes:
the obtaining module 810 is configured to obtain first data to be processed and corresponding target parameters, and perform data complementation on the first data according to the target parameters to obtain second data, where the target parameters include a time parameter and a feature value of the first data;
a first determining module 820, configured to determine each complement data missing from the first data relative to the second data, where the second data is composed of the first data and each complement data;
a second determining module 830, configured to determine, according to each complement data, a missing value corresponding to the first data, and determine a target marking mode matched with the missing value, where the missing value is used to indicate a severity of missing data of the first data;
and the processing module 840 is configured to tag each complement data in the second data according to the target tag mode to obtain third data, and display the third data.
In one embodiment, the second determining module 830 includes:
the first determining unit is used for determining a numerical interval in which the missing value is located;
and the second determining unit is used for determining the marking mode associated with the data deletion severity level associated with the data interval as the target marking mode matched with the deletion value.
In one embodiment, the second determining module 830 includes:
the first acquisition unit is used for acquiring the document of the user to be displayed and the document annotation information of the document, and determining a plurality of first marking modes of the user to be displayed on the document according to the document annotation information;
the third determining unit is used for determining the importance level corresponding to each first marking mode according to the document annotation information;
a fourth determining unit, configured to determine, as a target marking manner matched with the missing value, a first marking manner corresponding to the importance level matched with the missing value;
and the display unit is used for sending the third data to a terminal associated with the user to be displayed for display.
In one embodiment, the second determining module 830 includes:
a fifth determining unit configured to determine a sum of data amounts of the respective complement data;
and a sixth determining unit, configured to determine a missing value corresponding to the first data according to a ratio between the sum of the data amounts and the data amount of the first data.
In one embodiment, the second determining module 830 includes:
a seventh determining unit configured to determine a first number of the respective complement data and a second number of the respective sub-data in the first data, the complement data being determined according to the plurality of sub-data;
and the eighth determining unit is used for determining the missing value corresponding to the first data according to the ratio between the first number and the second number.
In one embodiment, the acquisition module 810 includes:
the second acquisition unit is used for acquiring the data text;
the first processing unit is used for carrying out preset operation on the data in the data text to obtain first data, wherein the preset operation comprises format conversion, deletion of invalid data and filtration of dirty data.
In one embodiment, the first determining module 820 includes:
a ninth determining unit, configured to determine a type of the first data, and determine a data complement mode of the first data according to the type;
a tenth determining unit, configured to determine missing data in the first data according to a time parameter of the first data;
the second processing unit is used for carrying out preset operation on the characteristic value of the first data according to the data complement mode to obtain complement data, and obtaining second data according to the complement data and the first data.
Fig. 9 is a hardware configuration diagram of an electronic device, which is shown according to an exemplary embodiment.
The electronic device 900 may include: a processor 91, such as a CPU, a memory 92, a transceiver 93. It will be appreciated by those skilled in the art that the structure shown in fig. 9 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components. The memory 92 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
Processor 91 may invoke a computer program or computer-executable instructions stored in memory 92 to perform all or part of the steps of the data processing method described above.
The transceiver 93 is used to receive information transmitted from an external device and transmit information to the external device.
An electronic device, comprising: a processor, a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the data processing method of any of the embodiments above.
A non-transitory computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the data processing method described above.
A computer program product comprising a computer program which, when executed by a processor of an electronic device, enables the electronic device to perform the above-described data processing method.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. A method of data processing, comprising:
acquiring transaction data of a foreign exchange market as first data to be processed, and acquiring target parameters of the first data, wherein the target parameters comprise time parameters and characteristic values of the first data;
performing data complement on the first data according to the target parameters to obtain second data, and determining each complement data missing from the first data relative to the second data, wherein the second data is composed of the first data and each complement data;
determining a missing value corresponding to the first data according to each piece of complement data, and determining a target marking mode matched with the missing value, wherein the missing value is used for indicating the severity of the missing data of the first data;
marking each complement data in the second data according to the target marking mode to obtain third data, and displaying the third data, wherein the third data is used for constructing a transaction trend curve corresponding to the foreign exchange market.
2. The method according to claim 1, wherein the step of determining the target mark pattern to which the missing value matches includes:
determining a numerical interval in which the missing value is located;
and determining a marking mode associated with the data loss severity level associated with the data interval as a target marking mode matched with the loss value.
3. The method according to claim 1, wherein the step of determining the target mark pattern to which the missing value matches includes:
acquiring a document of a user to be displayed and document annotation information of the document, and determining a plurality of first marking modes of the user to be displayed on the document according to each document;
determining the importance level corresponding to each first marking mode according to the document annotation information;
determining a first marking mode corresponding to the importance level matched with the missing value as a target marking mode matched with the missing value;
the step of displaying the third data includes:
and sending the third data to the terminal associated with the user to be displayed for display.
4. The data processing method according to claim 1, wherein the step of determining the missing value corresponding to the first data from each of the complement data includes:
determining the sum of the data amounts of the complement data;
and determining a missing value corresponding to the first data according to the ratio between the sum of the data amounts and the data amount of the first data.
5. The data processing method according to claim 1, wherein the step of determining the missing value corresponding to the first data from each of the complement data includes:
determining a first number of the complement data and a second number of the sub data in the first data, wherein the complement data is determined according to a plurality of the sub data;
and determining a missing value corresponding to the first data according to the ratio between the first quantity and the second quantity.
6. The data processing method according to any one of claims 1 to 5, wherein the step of acquiring transaction data of the foreign exchange market as first data to be processed includes:
acquiring a data text corresponding to the foreign exchange market;
and carrying out preset operation on the transaction data in the data text to obtain the first data, wherein the preset operation comprises format conversion, deletion of invalid data and filtration of dirty data.
7. The method according to any one of claims 1 to 5, wherein the step of performing data complement on the first data according to the target parameter to obtain second data includes:
determining the type of the first data, and determining a data complement mode of the first data according to the type;
determining missing data in the first data according to the time parameter of the first data;
and carrying out preset operation on the characteristic value of the first data according to the data complement mode to obtain the complement data, and acquiring the second data according to the complement data and the first data.
8. A data processing apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring transaction data of a foreign exchange market as first data to be processed and acquiring target parameters of the first data, and the target parameters comprise time parameters and characteristic values of the first data;
the first processing module is used for carrying out data complement on the first data according to the target parameter to obtain second data, determining each complement data of the first data, which is missing relative to the second data, wherein the second data is composed of the first data and each complement data;
the determining module is used for determining a missing value corresponding to the first data according to each piece of complement data and determining a target marking mode matched with the missing value, wherein the missing value is used for indicating the severity of the missing data of the first data;
the second processing module is used for marking each complement data in the second data according to the target marking mode to obtain third data, and displaying the third data, wherein the third data is used for constructing a transaction trend curve corresponding to the foreign exchange market.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 7.
CN202310711095.7A 2023-06-15 2023-06-15 Data processing method, device, electronic equipment and computer readable storage medium Pending CN116737707A (en)

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