A raw data processing method, system, device and storage medium

Abstract

The application discloses a raw and cooked data processing method, system, device and storage medium. The method comprises the following steps: obtaining raw data of a preset period from at least one original data, and obtaining cooked data of a historical period from cooked data, wherein the historical period is associated with the preset period; performing data fusion on the cooked data and the raw data to obtain to-be-corrected fusion data, wherein the to-be-corrected fusion data comprises the cooked data of the historical period and raw data of a new period; displaying the to-be-corrected fusion data; and in response to a correction operation on the raw data of the new period, performing correction processing on the raw data of the new period. The method avoids the repeated workload of repeatedly entering, correcting and supplementing the content on the data integration result (raw data) of each period, provides a comparison query function of raw and cooked data, and improves the working efficiency of data checking and data supplementing of the user.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of information technology in power grid enterprises, and particularly to a method, system, device and storage medium for processing raw and processed data. Background Technology

[0002] Power grid companies need the support of information systems to carry out distribution network planning.

[0003] Currently, power distribution network planning data is obtained from equipment-related data from various information and automation systems in the data center through a program (i.e., data integration). After automatic processing, the data is stored in the database and displayed to planning personnel through a human-machine interface, allowing them to make manual corrections and additions.

[0004] This method typically executes the data integration process on a cyclical basis (annual or quarterly). Each data integration result is presented as an independent data profile in the database. Users can only view or modify a specific data profile. The data they see cannot distinguish whether it is the result of the data integration or the result after manual correction and supplementation. During each cycle of data integration, users need to repeatedly correct and supplement the data integration results. The system cannot automatically copy the manually corrected and supplemented content from the previous cycle to the data integration results of the current cycle, resulting in a large workload of repeated verification and data entry. Summary of the Invention

[0005] This invention provides a method, system, device, and storage medium for processing raw and cooked data, so as to avoid repetitive work in data processing for power grid companies.

[0006] In a first aspect, embodiments of the present invention provide a method for processing raw and cooked data, including:

[0007] Raw data for a preset period is obtained from at least one set of raw data, and mature data for a historical period is obtained from mature data, wherein the historical period is associated with the preset period;

[0008] The mature data and the raw data are fused together to obtain the fused data to be corrected, wherein the fused data to be corrected includes mature data from the historical period and raw data from the newly added period.

[0009] The data to be fused and corrected is then displayed.

[0010] In response to the correction operation on the newly added period's raw data, the raw data of the newly added period is corrected.

[0011] Optionally, the raw data for the preset period includes raw data from historical periods and raw data from newly added periods;

[0012] The step of fusing the mature data and the raw data to obtain the fused data to be corrected includes: overwriting the raw data of the historical period with corresponding data based on the mature data of the historical period.

[0013] Alternatively, the raw data of the preset period may include raw data of the newly added period;

[0014] The step of fusing the mature data and the raw data to obtain the fused data to be corrected includes: splicing the raw data of the historical period and the raw data of the newly added period based on the time relationship.

[0015] Optionally, after obtaining raw data for a preset period from at least one set of raw data, the method further includes:

[0016] Based on the data source, data types are set for each field in the raw data, including data integration type, reporting type, and issue feedback type; the fields of the reporting type are configured with data modification permissions.

[0017] Optionally, the step of fusing the mature data and the raw data to obtain the fused data to be corrected includes:

[0018] Based on the fields of the reporting type in the mature data of the historical period, the corresponding fields of the reporting type in the raw data of the historical period are overwritten.

[0019] Optionally, the method further includes:

[0020] Set a raw data identifier for the raw data and a mature data identifier for the mature data obtained by modifying the raw data, wherein the mature data identifier is associated with the raw data identifier;

[0021] The raw data and its corresponding raw data identifier are associated and stored in the raw database, the mature data and its corresponding mature data identifier are associated and stored in the mature database, and the mature data identifier and the raw data identifier are associated and stored in a physical table.

[0022] Optionally, the raw data identifier is determined based on raw data association information, and the mature data identifier is determined based on mature data association information and corresponding raw data association information.

[0023] Optionally, the method further includes one or more of the following:

[0024] In response to a live data query operation, the corresponding live data is retrieved from the live database and displayed.

[0025] In response to a mature data query operation, the corresponding mature data is retrieved from the mature database and displayed.

[0026] In response to data comparison operations, the system compares the queried raw data and the queried data, and displays the differences accordingly.

[0027] Secondly, embodiments of the present invention also provide a raw and cooked data processing system, comprising:

[0028] A data acquisition module is used to acquire raw data for a preset period from at least one set of raw data, and to acquire mature data for a historical period from mature data, wherein the historical period is associated with the preset period.

[0029] The fusion module is used to fuse the mature data and the raw data to obtain fused data to be corrected, wherein the fused data to be corrected includes mature data from the historical period and raw data from the newly added period.

[0030] The display module is used to display the fused data to be corrected;

[0031] The correction module is used to perform correction processing on the newly added period's raw data in response to the correction operation on the newly added period's raw data.

[0032] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0033] At least one processor; and

[0034] A memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the raw and cooked data processing method described in any one of the first aspects.

[0036] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute the raw and cooked data processing method described in any one of the first aspects.

[0037] This invention obtains raw data for a preset period from at least one set of raw data and mature data for a historical period from mature data, wherein the historical period is associated with the preset period. The mature and raw data are then fused to obtain fused data to be corrected. This fused data to be corrected includes mature data from the historical period and raw data from a newly added period. The fused data to be corrected is displayed, and in response to a correction operation on the raw data of a newly added period, the raw data of the newly added period is corrected. This avoids the repetitive workload of users repeatedly entering corrections and supplements to the data integration results (raw data) for each period, and provides a comparison and query function for raw and mature data, improving the efficiency of users' data verification and data improvement work. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a raw and cooked data processing method provided in Embodiment 1 of the present invention;

[0040] Figure 2 This is a flowchart of another raw and cooked data processing method provided in Embodiment 1 of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of a raw and cooked data processing system provided in Embodiment 2 of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0044] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0045] Example 1

[0046] Figure 1 This is a flowchart of a raw and cooked data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to data processing in power grid enterprises. The method can be executed by a raw and cooked data processing device, which can be implemented in hardware and / or software. This raw and cooked data processing device can be configured in electronic devices such as computers, servers, and mobile terminals. Figure 1 As shown, the method includes:

[0047] S110. Obtain raw data for a preset period from at least one set of raw data, and obtain mature data for a historical period from mature data, wherein the historical period is associated with the preset period.

[0048] The raw data can be data from the data integration result table of the distribution network planning system database, which can include raw data and mature data. Accordingly, the raw data and mature data are stored separately in the data integration result table of the distribution network planning database. The raw and mature data are distinguished by the section ID. The creation of raw and mature data sections can be triggered by the buttons on the distribution network planning system interface to trigger the background service and perform the corresponding data transmission and copying.

[0049] Raw data can be data from the data integration results of the distribution network planning system's data center; it is data that has not been manually verified and supplemented. Cooked data can be data that has been manually verified and supplemented based on raw data. The preset period can be a time period set by the distribution network planning user according to the actual situation, including but not limited to one month, one quarter, one year, etc. There is no specific limitation here. For example, if data processing work starts in January 2022, and the data processing results of the cross-section in December 2021 are needed to initialize the data in the system, then the preset period is one month.

[0050] The historical period can be one or more periods preceding a specific moment (the moment when data processing begins). Correspondingly, the historical period can also be a historical week or all historical periods selected by the user, etc., without specific limitations here.

[0051] Optionally, after obtaining raw data for a preset period from at least one set of raw data, the process may further include: setting data types for each field in the raw data according to the data source, wherein the data types include data integration type, reporting type, and issue feedback type; and the fields of the reporting type are configured with data modification permissions.

[0052] The data source can be information that characterizes how the data is generated in the system, such as data automatically generated by the system through data integration or manually filled in by the user.

[0053] Data integration type refers to a field whose data integration result is the standard and cannot be manually modified. Reporting type refers to a field that, based on the data integration type field, allows for manual modification and supplementation. Problem feedback type refers to a field indicating whether a problem exists. For example, the "Is the line overloaded?" field is set to "Yes" based on the approval results in the problem database of the planning system. For instance, if the overload problem of line 101 is approved, the "Is the line overloaded?" field for line 101 will be set to "Yes"; if the problem is not approved, the "Is the line overloaded?" field will be set to "No".

[0054] For example, when saving data imported from Excel or manually entered, the field types of the data table are set to the entry type fields, the unmodified field types are set to the data integration type fields, and some uncertain fields are returned as problem return type fields after generating an external system problem database and confirming through the approval process.

[0055] By setting data types for each field in the raw data according to the data source, subsequent data processing can be performed based on these fields, thereby reducing the pressure of data processing.

[0056] S120. Merge the mature data and the raw data to obtain the merged data to be corrected, wherein the merged data to be corrected includes mature data from historical periods and raw data from newly added periods.

[0057] Data fusion can be a process of obtaining new raw data (including data obtained in the previous cycle and information on new equipment added in this cycle) from the power distribution network planning system, and then overwriting the raw data with the mature data from the previous cycle to form a new mature data. Accordingly, the mature data includes the mature data from the previous cycle and the data added in this cycle.

[0058] When making manual corrections based on the existing data, there is no need to re-correct the data that has already been corrected in the previous period. Users only need to focus on correcting the newly added data, thus avoiding the repetitive workload of repeatedly entering, correcting, and supplementing the data in the data integration results (raw data) of each period.

[0059] Optionally, the acquisition of the fused data to be corrected can be as follows: when the raw data of the preset period includes raw data of the historical period and raw data of the newly added period, the raw data of the historical period is overwritten based on the mature data of the historical period.

[0060] Optionally, the acquisition of the data to be corrected and merged can be as follows: when the raw data of the preset period includes the raw data of the newly added period, the raw data of the historical period and the raw data of the newly added period are spliced ​​together based on the time relationship.

[0061] The newly added period can be a period added after the previous data processing. For example, if data processing was carried out in January 2022 and the current time is February 2022, then the newly added period is one month. This is just an example and not a specific limitation. Overlay processing can replace the raw data of the corresponding section ID with mature data. See the table below for details. The white part is the raw data, and the gray shaded part is the part that is different between the mature data and the raw data, that is, the part that has been overlaid.

[0062] Table 1. Student Data (Partial)

[0063]

[0064] Table 2. Partial list of fused data to be corrected.

[0065]

[0066] For example, during the first data integration, when the raw data for the preset period only includes raw data for the newly added period, the mature data (e.g., "mature data for the first quarter of 2022") is first initialized from the raw data of the data integration (e.g., "raw data section in December 2021"). Then, the existing data collection Excel spreadsheet is imported, and matching is performed according to the section ID, etc. The reporting type fields in the Excel spreadsheet are imported into the mature data, and the reporting type fields are updated. Users supplement and improve the reporting type fields in the system. After the problem approval is completed in the problem database of the power distribution network planning system, the mature data table is finally searched according to the section ID associated with the problem based on the approved problem, and the problem return type field of the corresponding record is updated.

[0067] When making manual corrections based on the existing data, there is no need to re-correct the data that has already been corrected in the previous period. Users only need to focus on correcting the newly added data, thus avoiding the repetitive workload of repeatedly entering, correcting, and supplementing the data in the data integration results (raw data) of each period.

[0068] Optionally, the data to be corrected and merged can also be obtained by overwriting the fields of the reporting type in the raw data of the historical period with the fields of the reporting type in the mature data of the historical period.

[0069] For example, if the update of the second quarter mature data sections begins in April 2022, the data integration type field of the mature data (e.g., "second quarter data of 2022") is first initialized from the latest raw data (e.g., "raw data section in March 2022") in the data center of the distribution network planning system. Then, the reporting type field is copied from the previous mature data (e.g., "mature data in the first quarter of 2022") and matched according to the section ID, etc., to realize the inheritance and fusion of reporting type data. The reporting type field is supplemented and improved by the user in the system. Finally, the return type field is set according to the approved issues and the return settings.

[0070] This further avoids the repetitive workload of users repeatedly entering, correcting, and supplementing the data integration results in each cycle.

[0071] S130. Display the data to be corrected and merged.

[0072] The data to be corrected and integrated can be displayed through the display interface of the power distribution network planning system for users to view.

[0073] Optionally, the power distribution network planning system can also display data based on user operations, such as one or more of the following: in response to a raw data query operation, query the corresponding raw data in the raw database and display it; in response to a mature data query operation, query the corresponding mature data in the mature database and display it; in response to a data comparison operation, compare the queried raw data and mature data, and display the differences separately.

[0074] By setting up query and display functions for data and difference results, users can clearly view the data.

[0075] S140. In response to the correction operation on the newly generated data of the new period, the newly generated data of the new period is corrected.

[0076] Among them, the correction operation can be a data processing operation based on raw data triggered by the user through the distribution network planning system. Accordingly, the correction processing can include, but is not limited to, decompression, organization, analysis, extraction, inheritance, fusion, transmission and copying of raw data.

[0077] Optionally, a raw data identifier is set for the raw data, and a mature data identifier is set for the mature data obtained by modifying the raw data. The mature data identifier is associated with the raw data identifier. The raw data and the corresponding raw data identifier are associated and stored in the raw database, the mature data and the corresponding mature data identifier are associated and stored in the mature database, and the mature data identifier and the raw data identifier are associated and stored in a physical table.

[0078] The identifier can be a cross-section ID of the data. Raw data identifiers can be determined based on raw data association information, while mature data identifiers can be determined based on mature data association information and the corresponding raw data association information. The physical table can be a data table.

[0079] By setting corresponding identifiers for raw and processed data and storing them in a physical table, a comparison function for raw and processed data is provided, thereby improving the efficiency of users' data verification and data improvement work.

[0080] In a preferred embodiment, see details below. Figure 2 The data integration result, i.e., the data integration type in the raw data table, is copied as field set A to the mature data that needs to be updated (i.e., data processing), forming the latest data integration result. The data entry type fields from the previously approved and solidified mature data are copied as field set B to the mature data that needs to be updated (i.e., data processing), and overwritten according to ID matching rules, achieving data inheritance of data entry type fields, i.e., data integration of mature data. Through the mature data editing interface, the data integration type and data entry type fields are entered to overwrite the data fields obtained through data integration. Issue feedback type fields are approved and fed back to the mature data to update field set C.

[0081] The technical solution of this embodiment obtains raw data for a preset period from at least one set of raw data and mature data for a historical period from mature data, wherein the historical period is associated with the preset period. The mature data and raw data are then fused to obtain fused data to be corrected. This fused data to be corrected includes mature data from the historical period and raw data from a newly added period. The fused data to be corrected is displayed, and in response to a correction operation on the raw data of a newly added period, the raw data of the newly added period is corrected. This avoids the repetitive workload of users repeatedly entering corrections and supplements to the data integration results (raw data) for each period, and provides a comparison and query function for raw and mature data, improving the efficiency of users' data verification and data improvement work.

[0082] Example 2

[0083] Figure 3 This is a schematic diagram of the structure of a raw and cooked data processing system provided in Embodiment 2 of the present invention. Figure 3 As shown, the system includes:

[0084] The data acquisition module 310 is used to acquire raw data for a preset period from at least one set of raw data, and to acquire mature data for a historical period from mature data, wherein the historical period is associated with the preset period.

[0085] The fusion module 320 is used to fuse the mature data and the raw data to obtain fused data to be corrected, wherein the fused data to be corrected includes mature data from the historical period and raw data from the newly added period.

[0086] Display module 330 is used to display the fused data to be corrected;

[0087] The correction module 340 is used to perform correction processing on the raw data of the newly added period in response to the correction operation on the raw data of the newly added period.

[0088] Optionally, the raw data for the preset period includes raw data from historical periods and raw data from newly added periods;

[0089] Accordingly, the fusion module 320 includes:

[0090] The first overlay module is used to overlay the raw data of the historical period with corresponding data based on the mature data of the historical period.

[0091] Optionally, the raw data of the preset period includes raw data of the newly added period;

[0092] Accordingly, the fusion module 320 includes:

[0093] The splicing module is used to splice the raw data of the historical period and the raw data of the newly added period based on the time relationship.

[0094] Optionally, the data acquisition module 310 includes:

[0095] The data type setting module is used to set the data type for each field in the raw data according to the data source after obtaining raw data for a preset period from at least one raw data source. The data type includes data integration type, reporting type and issue feedback type; the fields of the reporting type are configured with data modification permissions.

[0096] Optionally, the fusion module 320 includes:

[0097] The second overlay module is used to perform corresponding overlay processing on the fields of the filling type in the raw data of the historical period based on the fields of the filling type in the mature data of the historical period.

[0098] Optionally, the raw / cooked data processing system further includes:

[0099] An identification module is used to set a raw data identifier for the raw data and a mature data identifier for the mature data obtained by modifying the raw data, wherein the mature data identifier is associated with the raw data identifier;

[0100] The storage module is used to associate the raw data and the corresponding raw data identifier in a raw database, associate the mature data and the corresponding mature data identifier in a mature database, and associate the mature data identifier and the raw data identifier in a physical table.

[0101] Optionally, the raw data identifier is determined based on raw data association information, and the mature data identifier is determined based on mature data association information and corresponding raw data association information.

[0102] Optionally, the raw and cooked data processing system may further include one or more of the following:

[0103] The first display module is used to respond to a data query operation by querying the corresponding data in the data database and displaying it.

[0104] The second display module is used to respond to a mature data query operation by querying the corresponding mature data in the mature database and displaying it.

[0105] The third display module is used to respond to data comparison operations, compare the queried raw data and cooked data, and display the differences in the data separately.

[0106] The raw and cooked data processing system provided in this embodiment of the invention can execute the raw and cooked data processing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0107] Example 3

[0108] Figure 4This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0109] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0110] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0111] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with 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 special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as raw and cooked data processing methods.

[0112] In some embodiments, the raw and cooked data processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the raw and cooked data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the raw and cooked data processing method by any other suitable means (e.g., by means of firmware).

[0113] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] Computer programs for implementing the raw and cooked data processing methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are performed. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0115] Example 4

[0116] Embodiment 4 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a raw data processing method, the method comprising:

[0117] Raw data for a preset period is obtained from at least one set of raw data, and mature data for a historical period is obtained from mature data, wherein the historical period is associated with the preset period;

[0118] The mature data and the raw data are fused together to obtain the fused data to be corrected, wherein the fused data to be corrected includes mature data from the historical period and raw data from the newly added period.

[0119] The data to be fused and corrected is then displayed.

[0120] In response to the correction operation on the newly added period's raw data, the raw data of the newly added period is corrected.

[0121] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the 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 can be received in any form (including sound input, voice input, or tactile input).

[0123] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0124] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0125] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0126] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing raw and cooked data, characterized in that, include: Raw data for a preset period is obtained from at least one set of raw data, and mature data for a historical period is obtained from mature data, wherein the historical period is associated with the preset period; the raw data for the preset period includes raw data from the historical period and raw data from the newly added period. The mature data and the raw data are fused together to obtain the fused data to be corrected, wherein the fused data to be corrected includes mature data from the historical period and raw data from the newly added period. The data to be fused and corrected is then displayed. In response to the correction operation on the newly added period's raw data, the raw data of the newly added period is corrected. The step of fusing the mature data and the raw data to obtain the fused data to be corrected includes: overwriting the raw data of the historical period with the corresponding data based on the mature data of the historical period. Alternatively, the raw data of the preset period may include raw data of the newly added period; The step of fusing the mature data and the raw data to obtain the fused data to be corrected includes: splicing the raw data of the historical period and the raw data of the newly added period based on the time relationship. The process, after obtaining raw data for a preset period from at least one set of raw data, further includes: Based on the data source, data types are set for each field in the raw data. The data types include data integration type, data entry type, and issue feedback type. The data integration type is a field that is based on the data integration result and cannot be manually modified. The data entry type is a field that allows manual modification and supplementation based on the data integration type fields. The issue feedback type refers to the field that has an issue. Based on the fields of the reporting type in the mature data of the historical period, the corresponding fields of the reporting type in the raw data of the historical period are overwritten.

2. The method according to claim 1, characterized in that, The method further includes: Set a raw data identifier for the raw data and a mature data identifier for the mature data obtained by modifying the raw data, wherein the mature data identifier is associated with the raw data identifier; The raw data and its corresponding raw data identifier are associated and stored in the raw database, the mature data and its corresponding mature data identifier are associated and stored in the mature database, and the mature data identifier and the raw data identifier are associated and stored in a physical table.

3. The method according to claim 2, characterized in that, The raw data identifier is determined based on the raw data association information, and the mature data identifier is determined based on the mature data association information and the corresponding raw data association information.

4. The method according to claim 2, characterized in that, The method further includes one or more of the following: In response to a live data query operation, the corresponding live data is retrieved from the live database and displayed. In response to a mature data query operation, the corresponding mature data is retrieved from the mature database and displayed. In response to data comparison operations, the system compares the queried raw data and the queried data, and displays the differences accordingly.

5. A raw and cooked data processing system, characterized in that, include: A data acquisition module is used to acquire raw data for a preset period from at least one set of raw data, and to acquire mature data for a historical period from mature data, wherein the historical period is associated with the preset period; the raw data for the preset period includes raw data for newly added periods. The fusion module is used to fuse the mature data and the raw data to obtain fused data to be corrected, wherein the fused data to be corrected includes mature data from the historical period and raw data from the newly added period. The display module is used to display the fused data to be corrected; The correction module is used to perform correction processing on the newly added period's raw data in response to the correction operation on the newly added period's raw data; The data acquisition module includes: The data type setting module is used to set the data type for each field in the raw data after obtaining raw data for a preset period from at least one set of raw data, based on the data source. The data type includes a data integration type, a data entry type, and a problem feedback type. The data integration type refers to fields whose data integration results are final and cannot be manually modified. The data entry type refers to fields whose data integration type fields can be manually modified and supplemented. The problem feedback type refers to fields whose problems exist. The fusion module includes: The first overlay module is used to overlay the raw data of the historical period with corresponding data based on the mature data of the historical period. The splicing module is used to splice the raw data of the historical period and the raw data of the newly added period based on the time relationship. The second overlay module is used to perform corresponding overlay processing on the fields of the filling type in the raw data of the historical period based on the fields of the filling type in the mature data of the historical period.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the raw and cooked data processing method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the raw and cooked data processing method according to any one of claims 1-5.

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