CN116303614A - Method, system, equipment and storage medium for processing raw and cooked data - Google Patents
Method, system, equipment and storage medium for processing raw and cooked data Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a storage medium for processing raw and cooked data. According to the method, raw data with a preset period is obtained from at least one piece of original data, cooked data with a history period is obtained from the cooked data, the history period is related to the preset period, the cooked data and the raw data are subjected to data fusion to obtain fusion data to be corrected, the fusion data to be corrected comprise the cooked data with the history period and the raw data with a new period, the fusion data to be corrected is displayed, and the raw data with the new period is subjected to correction processing in response to correction operation of the raw data with the new period. The repeated workload of repeatedly inputting correction and supplementing perfect content on the data integration result (raw data) of each period by a user is avoided, the comparison query function of raw and cooked data is provided, and the working efficiency of data check and data perfection of the user is improved.
Description
Technical Field
The embodiment of the invention relates to the field of power grid enterprise informatization, in particular to a method, a system, equipment and a storage medium for processing raw and cooked data.
Background
The support of the informatization system is needed for the power grid enterprises to develop the planning work of the power distribution network.
At present, the planning data of the power distribution network is obtained from equipment related data (namely data integration) of each informatization and automation system in a data center through a program, is stored in a database after being automatically processed, is displayed to planning service personnel through a human-computer interface, and allows the planning service personnel to carry out manual correction and supplement improvement.
The method generally executes the data integration process according to the period (according to the year or the quarter), the result of each data integration is taken as an independent data section, a user can only select to check or modify a certain data section in a database, the seen data cannot be distinguished into a data integration result or a result which is manually corrected and supplemented after the data integration, when each period carries out the data integration, a planning user needs to repeatedly correct and supplement the data integration result again, the system cannot copy the content which is manually corrected and supplemented in the previous period to the data integration result of the period, and the workload of repeated checking and recording is great.
Disclosure of Invention
The invention provides a method, a system, equipment and a storage medium for processing raw and cooked data, which are used for avoiding repeated work in the process of processing power grid enterprise data.
In a first aspect, an embodiment of the present invention provides a method for processing raw and cooked data, including:
acquiring raw data of a preset period from at least one piece of original data, and acquiring cooked data of a history period from the cooked data, wherein the history period is associated with the preset period;
performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the fusion data to be corrected comprises the cooked data of the history period and the raw data of the newly added period;
displaying the fusion data to be corrected;
and in response to the correction operation of the new period of raw data, performing correction processing on the new period of raw data.
Optionally, the raw data of the preset period includes raw data of a history period and raw data of a newly added period;
and performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the data fusion comprises the following steps: performing corresponding data coverage processing on raw data of a history period based on cooked data of the history period;
or the raw data of the preset period comprises raw data of a new period;
and performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the data fusion comprises the following steps: and splicing the raw data of the history period and the raw data of the newly added period based on a time relation.
Optionally, after acquiring the raw data of the preset period from the at least one raw data, the method further includes:
setting data types for each field in the raw data according to data sources, wherein the data types comprise a data integration type, a filling type and a problem backflow type; the field of the fill type is configured with data modification rights.
Optionally, the data fusion of the cooked data and the raw data to obtain fusion data to be corrected includes:
and carrying out corresponding coverage processing on the field of the filling type of the raw data of the history period based on the field of the filling type in the cooked data of the history period.
Optionally, the method further comprises:
setting a raw data identifier of the raw data, and correcting the raw data to obtain a cooked data identifier of cooked data, wherein the cooked data identifier is associated with the raw data identifier;
storing the raw data and the corresponding raw data identification in a raw database, storing the cooked data and the corresponding cooked data identification in a cooked database, and storing the cooked data identification and the raw data identification in a physical table.
Optionally, the raw data identifier is determined based on raw data association information, and the cooked data identifier is determined based on cooked data association information and corresponding raw data association information.
Optionally, the method further comprises one or more of the following:
responding to the raw data query operation, and querying and displaying corresponding raw data in the raw database;
responding to the operation of inquiring the cooked data, inquiring the corresponding cooked data in the cooked database and displaying the corresponding cooked data;
and responding to the data comparison operation, comparing the queried raw data with the queried cooked data, and displaying the difference data in a distinguishing way.
In a second aspect, an embodiment of the present invention further provides a system for processing raw and cooked data, including:
the data acquisition module is used for acquiring raw data with a preset period from at least one piece of original data and acquiring cooked data with a history period from the cooked data, wherein the history period is associated with the preset period;
the fusion module is used for carrying out data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the fusion data to be corrected comprises the cooked data of the history period and the raw data of the newly added period;
the display module is used for displaying the fusion data to be corrected;
and the correction module is used for responding to the correction operation of the new period of raw data and carrying out correction processing on the new period of raw data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the raw data processing method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing a processor to implement the method for processing raw data of any one of the first aspects when executed.
According to the method, raw data with a preset period are obtained from at least one piece of original data, and cooked data with a history period are obtained from the cooked data, wherein the history period is related to the preset period, the cooked data and the raw data are subjected to data fusion to obtain fusion data to be corrected, the fusion data to be corrected comprises the cooked data with the history period and the raw data with a new period, the fusion data to be corrected is displayed, and the raw data with the new period is subjected to correction processing in response to correction operation of the raw data with the new period. The repeated workload of repeatedly inputting correction and supplementing perfect content on the data integration result (raw data) of each period by a user is avoided, the comparison query function of raw and cooked data is provided, and the working efficiency of data check and data perfection of the user is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for processing raw and cooked data according to an embodiment of the present invention;
FIG. 2 is a flowchart of another method for processing raw and cooked data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system for processing raw and cooked data according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for processing raw data according to a first embodiment of the present invention, where the method may be applied to a case of power grid enterprise data processing, and the method may be performed by a raw data processing apparatus, which may be implemented in the form of hardware and/or software, and the raw data processing apparatus may be configured in an electronic device such as a computer, a server, a mobile terminal, or the like. As shown in fig. 1, the method includes:
s110, acquiring raw data of a preset period from at least one piece of original data, and acquiring cooked data of a history period from the cooked data, wherein the history period is associated with the preset period.
The raw data can be data in a data integration result table of a power distribution network planning system database, the raw data and the cooked data can be included, correspondingly, the raw data and the cooked data are stored in the data integration result table of the power distribution network planning system database, the raw data and the cooked data are distinguished through section IDs, and creation of sections of the raw data and the cooked data can trigger background service through buttons on a power distribution network planning system interface, and corresponding data transmission and copying are performed.
The raw Data can be Data of a Data center Data integration result from a power distribution network planning system, is Data which is not subjected to manual verification and supplement, and the Cooked Data (coded Data) can be Data which is subjected to manual verification and supplement on the basis of the raw Data. The preset period may be a time period set by a power distribution network planning user according to practical situations, including, but not limited to, one month, one quarter, one year, etc., and is not limited herein, for example, the data processing work is started in 2022 and 1, and the data processing result of the section of 2021 and 12 months needs to be obtained to initialize the data in the system, that is, the preset period is one month.
The history period may be one or more periods before a specific time (time when the data processing operation starts), and accordingly, the history period may be a history period or all history periods selected by the user, or the like, which is not particularly limited herein.
Optionally, after acquiring the raw data of the preset period from the at least one raw data, the method may further include: setting data types for each field in raw data according to data sources, wherein the data types comprise a data integration type, a reporting type and a problem backflow type; the field of the fill type is configured with data modification rights.
The data source may be information representing the way the data is generated in the system, for example, the system performs data integration and automatic generation, the user performs manual filling, etc.
The data integration type may refer to a field in which the field is subject to the data integration result and does not allow manual modification. The type of fill may be a field that allows manual modification and replenishment of the perfected field based on the data integration type field. The problem reflow type may refer to a field of whether a problem exists, for example, whether a line is overloaded again, and whether corresponding device data is set according to an approval result in a problem library in the planning system is set as a field, for example, after a problem of overload of 101 lines is approved, a field of "whether overload is excessive" of 101 lines is set as yes, and if the problem is not approved, a field of "whether overload is excessive" is set as no.
The field type of the excel imported or manually filled data storage time-to-date data table is set as a filling type field, the unmodified field type is set as a data integration type field, some fields are uncertain, and the field is returned to be a problem return type field after an approval flow is confirmed after an external system problem library is generated.
By setting the data types for each field in the raw data according to the data source, the subsequent data processing can be performed on the field basis, so that the data processing pressure is reduced.
And S120, carrying out data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the fusion data to be corrected comprises the cooked data of a history period and the raw data of a new period.
The data fusion may be a process of acquiring new raw data (including the data acquired in the previous period and the information of new equipment added in the previous period) from the power distribution network planning system, and overlaying the cooked data in the previous period onto a new cooked data formed after the raw data, where the cooked data includes the cooked data in the previous period and the newly added data in the previous period.
When the manual correction is continued on the basis of the cooked data, the corrected data in the previous period is not required to be corrected again, and only the correction work of the newly added data part is required to be paid attention to, so that the repeated workload of repeatedly inputting correction and supplementing perfect content on the data integration result (raw data) of each period by a user is avoided.
Alternatively, the acquisition of the fusion data to be corrected may be: when the raw data of the preset period comprises raw data of a history period and raw data of a new period, performing corresponding data coverage processing on the raw data of the history period based on the cooked data of the history period.
Alternatively, the acquisition of the fusion data to be corrected may be: when the raw data of the preset period comprises the raw data of the newly added period, splicing the raw data of the history period and the raw data of the newly added period based on a time relation.
The period of the new addition may be a period newly added after the previous data processing, for example, 2022 is 1 month and data processing is performed, and the current time is 2022 is 2 months, and the period of the new addition is 1 month, which is only illustrated herein and not particularly limited. The overlay process may be to replace raw data corresponding to the profile ID with cooked data, and specifically, see the following table, the white part is raw data, and the gray shaded part is a part where the cooked data is different from the raw data, i.e., a part where the overlay process is completed.
Table 1 raw data table (section)
TABLE 2 fusion data sheet to be modified (part)
For example, when the data is integrated for the first time, that is, when the raw data of the preset period only includes the raw data of the new period, firstly, the raw data (for example, "2021 year 12 month raw data section") is initialized from the raw data of the data integration (for example, "2022 year one quarter raw data"), then the existing collection Excel table is imported, matching is performed according to the section ID and the like, the filling type field in the Excel table is imported into the raw data, the filling type field is updated, the user supplements and perfects the filling type field in the system, after the problem library of the power distribution network planning system completes the problem approval, finally, the well-done data table is searched according to the section ID associated with the problem, and the problem reflux type field of the corresponding record is updated.
When the manual correction is continued on the basis of the cooked data, the corrected data in the previous period is not required to be corrected again, and only the correction work of the newly added data part is required to be paid attention to, so that the repeated workload of repeatedly inputting correction and supplementing perfect content on the data integration result (raw data) of each period by a user is avoided.
Optionally, the obtaining of the fusion data to be corrected may further be that the corresponding coverage processing is performed on the field of the raw data filling type in the history period through the field of the filling type in the cooked data based on the history period.
For example, if the updating of the second quarter-cooked data section is started in 4 months of 2022, firstly, the data integration type field of the data integration (for example, "2022 second quarter-cooked data") is initialized from the latest data integration raw data (for example, "2022 3 month raw data section") of the data center of the power distribution network planning system, then the filling type field is copied from the last-cooked data (for example, "2022 first quarter-cooked data"), matching is performed according to the section ID, inheritance and fusion of the filling type data are realized, the filling type field is manually supplemented and perfected in the system, and finally the problem reflow type field is set according to the checked batch problem and reflow.
The repeated workload of repeatedly inputting correction and supplementing perfect content on the data integration result of each period by a user is further avoided.
And S130, displaying the fusion data to be corrected.
The fusion data to be corrected can be displayed through a display interface of the power distribution network planning system for a user to check.
Optionally, the power distribution network planning system may also display data based on user operations, for example, one or more of the following: responding to the raw data query operation, and querying and displaying corresponding raw data in a raw database; responding to the cooked data query operation, querying corresponding cooked data in a cooked database and displaying the corresponding cooked data; and responding to the data comparison operation, comparing the queried raw data with the queried cooked data, and displaying the difference data in a distinguishing way.
Through the inquiry and display functions of the setting data and the difference result, the user can clearly view the data.
S140, in response to the correction operation of the new period of raw data, the new period of raw data is subjected to correction processing.
The correction operation may be a data processing operation triggered by a user through the power distribution network planning system and based on raw data, and accordingly, the correction process may include, but is not limited to, decompression, organization, analysis, presentation, inheritance, fusion, transmission, copying, and the like of the raw data.
Optionally, setting a raw data identifier of raw data, and correcting the raw data to obtain a cooked data identifier of cooked data, wherein the cooked data identifier is associated with the raw data identifier, the raw data and the corresponding raw data identifier are associated and stored in a raw database, the cooked data and the corresponding cooked data identifier are associated and stored in the cooked database, and the cooked data identifier and the raw data identifier are associated and stored in a physical table.
Wherein the identification may be a profile ID of the data. The raw data identification may be determined based on raw data association information, and the cooked data identification may be determined based on the cooked data association information and the corresponding raw data association information. The physical table may be a data table.
The comparison function of the raw data and the cooked data is provided by setting the corresponding identifications of the raw data and the cooked data and storing the corresponding identifications in a physical table, so that the working efficiency of data check and data perfection of a user is improved.
In a preferred embodiment, referring specifically to fig. 2, the data integration result, i.e. the data integration type in the raw data table, is copied as field set a to the cooked data that needs to be updated, i.e. data processed, to form the latest data integration result. And copying the filling type field in the last approved and solidified cooked data as a field set B into the cooked data which needs to be updated, namely data processing, and covering according to the ID matching rule to realize the data inheritance of the filling type field, namely the data integration of the cooked data. And recording fields of a data integration type and a filling type through a mature data editing interface to cover the data fields obtained through data integration. And (3) approving the problem reflux type field, and refluxing to the cooked data update field set C.
According to the technical scheme, raw data with a preset period is obtained from at least one piece of original data, cooked data with a history period is obtained from the cooked data, the history period is related to the preset period, the cooked data and the raw data are subjected to data fusion to obtain fusion data to be corrected, the fusion data to be corrected comprises the cooked data with the history period and the raw data with a new period, the fusion data to be corrected is displayed, and the raw data with the new period is subjected to correction processing in response to correction operation of the raw data with the new period. The repeated workload of repeatedly inputting correction and supplementing perfect content on the data integration result (raw data) of each period by a user is avoided, the comparison query function of raw and cooked data is provided, and the working efficiency of data check and data perfection of the user is improved.
Example two
Fig. 3 is a schematic structural diagram of a system for processing raw and cooked data according to a second embodiment of the present invention. As shown in fig. 3, the system includes:
a data obtaining module 310, configured to obtain raw data of a preset period from at least one piece of raw data, and obtain cooked data of a history period from the cooked data, where the history period is associated with the preset period;
the fusion module 320 is configured to perform data fusion on the cooked data and the raw data to obtain fusion data to be corrected, where the fusion data to be corrected includes the cooked data of the history period and the raw data of the new period;
the display module 330 is configured to display the fusion data to be corrected;
and the correction module 340 is configured to perform correction processing on the new period of raw data in response to a correction operation on the new period of raw data.
Optionally, the raw data of the preset period includes raw data of a history period and raw data of a newly added period;
accordingly, the fusion module 320 includes:
the first coverage module is used for performing corresponding data coverage processing on raw data of a history period based on cooked data of the history period;
optionally, the raw data of the preset period includes raw data of a new period;
accordingly, the fusion module 320 includes:
and the splicing module is used for carrying out splicing processing on the raw data of the history period and the raw data of the newly added period based on a time relation.
Optionally, the data acquisition module 310 includes:
the system comprises a data type setting module, a data type setting module and a data processing module, wherein the data type setting module is used for setting data types for each field in raw data according to data sources after raw data with a preset period is acquired from at least one piece of original data, and the data types comprise a data integration type, a reporting type and a problem backflow type; the field of the fill type is configured with data modification rights.
Optionally, the fusing module 320 includes:
and the second coverage module is used for carrying out corresponding coverage processing on the field of the filling type of the raw data of the history period based on the field of the filling type in the cooked data of the history period.
Optionally, the raw and cooked data processing system further comprises:
the identification module is used for setting a raw data identification of the raw data and correcting the raw data to obtain a cooked data identification of cooked data, wherein the cooked data identification is associated with the raw data identification;
the storage module is used for storing the raw data and the corresponding raw data identification in a raw database in an associated manner, storing the cooked data and the corresponding cooked data identification in the cooked database in an associated manner, and storing the cooked data identification and the raw data identification in a physical table in an associated manner.
Optionally, the raw data identifier is determined based on raw data association information, and the cooked data identifier is determined based on cooked data association information and corresponding raw data association information.
Optionally, the raw and cooked data processing system further comprises one or more of the following:
the first display module is used for responding to the raw data query operation, querying corresponding raw data in the raw database and displaying the raw data;
the second display module is used for responding to the query operation of the cooked data, querying the corresponding cooked data in the cooked database and displaying the corresponding cooked data;
and the third display module is used for responding to the data comparison operation, comparing the queried raw data with the queried cooked data and displaying the difference data in a distinguishing way.
The raw and cooked data processing system provided by the embodiment of the invention can execute the raw and cooked data processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the raw data processing method.
In some embodiments, the raw data processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the raw data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the raw data processing method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program used to implement the raw data processing methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program 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.
Example IV
The fourth embodiment of the present invention also provides a computer readable storage medium storing computer instructions for causing a processor to execute a method for processing raw and mature data, the method comprising:
acquiring raw data of a preset period from at least one piece of original data, and acquiring cooked data of a history period from the cooked data, wherein the history period is associated with the preset period;
performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the fusion data to be corrected comprises the cooked data of the history period and the raw data of the newly added period;
displaying the fusion data to be corrected;
and in response to the correction operation of the new period of raw data, performing correction processing on the new period of raw data.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), 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 an electronic device 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) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically 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 that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of raw and cooked data processing, comprising:
acquiring raw data of a preset period from at least one piece of original data, and acquiring cooked data of a history period from the cooked data, wherein the history period is associated with the preset period;
performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the fusion data to be corrected comprises the cooked data of the history period and the raw data of the newly added period;
displaying the fusion data to be corrected;
and in response to the correction operation of the new period of raw data, performing correction processing on the new period of raw data.
2. The method of claim 1, wherein the pre-set period of raw data comprises historical period of raw data and a new period of raw data;
and performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the data fusion comprises the following steps: performing corresponding data coverage processing on raw data of a history period based on cooked data of the history period;
or the raw data of the preset period comprises raw data of a new period;
and performing data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the data fusion comprises the following steps: and splicing the raw data of the history period and the raw data of the newly added period based on a time relation.
3. The method of claim 2, further comprising, after acquiring the raw data of the preset period from the at least one raw data:
setting data types for each field in the raw data according to data sources, wherein the data types comprise a data integration type, a filling type and a problem backflow type; the field of the fill type is configured with data modification rights.
4. The method of claim 3, wherein the data fusing the cooked data and the raw data to obtain fused data to be corrected comprises:
and carrying out corresponding coverage processing on the field of the filling type of the raw data of the history period based on the field of the filling type in the cooked data of the history period.
5. The method according to claim 1, wherein the method further comprises:
setting a raw data identifier of the raw data, and correcting the raw data to obtain a cooked data identifier of cooked data, wherein the cooked data identifier is associated with the raw data identifier;
storing the raw data and the corresponding raw data identification in a raw database, storing the cooked data and the corresponding cooked data identification in a cooked database, and storing the cooked data identification and the raw data identification in a physical table.
6. The method of claim 5, wherein the raw data identification is determined based on raw data association information, and wherein the cooked data identification is determined based on cooked data association information and corresponding raw data association information.
7. The method of claim 5, further comprising one or more of the following:
responding to the raw data query operation, and querying and displaying corresponding raw data in the raw database;
responding to the operation of inquiring the cooked data, inquiring the corresponding cooked data in the cooked database and displaying the corresponding cooked data;
and responding to the data comparison operation, comparing the queried raw data with the queried cooked data, and displaying the difference data in a distinguishing way.
8. A raw data processing system, comprising:
the data acquisition module is used for acquiring raw data with a preset period from at least one piece of original data and acquiring cooked data with a history period from the cooked data, wherein the history period is associated with the preset period;
the fusion module is used for carrying out data fusion on the cooked data and the raw data to obtain fusion data to be corrected, wherein the fusion data to be corrected comprises the cooked data of the history period and the raw data of the newly added period;
the display module is used for displaying the fusion data to be corrected;
and the correction module is used for responding to the correction operation of the new period of raw data and carrying out correction processing on the new period of raw data.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the raw data processing method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the raw data processing method of any one of claims 1-7 when executed.
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