CN118012938A - Data processing method and device, electronic equipment and storage medium - Google Patents

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

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CN118012938A
CN118012938A CN202410183949.3A CN202410183949A CN118012938A CN 118012938 A CN118012938 A CN 118012938A CN 202410183949 A CN202410183949 A CN 202410183949A CN 118012938 A CN118012938 A CN 118012938A
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data
value
level
parent
child
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何建章
伍家成
伍孝赠
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Zhidao Network Technology Beijing Co Ltd
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Zhidao Network Technology Beijing Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G06F40/177—Editing, e.g. inserting or deleting of tables; using ruled lines
    • G06F40/18—Editing, e.g. inserting or deleting of tables; using ruled lines of spreadsheets
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5018—Thread allocation

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  • General Engineering & Computer Science (AREA)
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  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The application provides a data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains a data initial value to be processed and carries a unique virtual identifier; and identifying the parent-child level relation among the plurality of pieces of data according to the virtual identifier, dividing the data with different levels of the parent-child level relation into a group, and carrying out parallel processing on the grouped multiple groups of data to obtain the processed planning data value. The application introduces a parent-child level relation to ensure the accuracy and the integrity of data display; the father-son level relation and the hierarchy relation between the data are accurately identified by using a recursion processing scheme, so that efficient data processing and planning management are realized; the complexity of data insertion is reduced, and the efficiency of data processing is improved. The application provides more comprehensive, accurate and intelligent data processing service for users.

Description

Data processing method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, an electronic device, and a storage medium.
Background
In the process of realizing the Excel data import user platform, it is found that when the Excel data import including the parent-child relationship is processed, the data import process becomes more complicated, because the uniqueness of each piece of data cannot be directly distinguished in this case, and the parent-child relationship cannot be accurately established. Meanwhile, in processing the parent-child relationship, a time range of each parent level needs to be calculated and processed based on the minimum start time and the maximum end time of the child level. In the case where the data hierarchy is 2 or more, one piece of data may be both the parent level and the child level of others, and thus data import becomes more complicated.
Disclosure of Invention
In order to solve the technical defects, the embodiment of the application provides a data processing method and device, electronic equipment and a storage medium.
An embodiment of a first aspect of the present application provides a data processing method, including the steps of:
Acquiring a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains a data initial value to be processed and carries a unique virtual identifier;
and identifying the parent-child level relation among the plurality of pieces of data according to the virtual identifier, dividing the data with different levels of the parent-child level relation into a group, and carrying out parallel processing on the grouped multiple groups of data to obtain the processed planning data value.
In one possible implementation manner, the initial value of the data to be processed includes a set minimum value and a set maximum value of the data; the process of processing the grouped multiple groups of data in parallel and obtaining the processed planning data value comprises the following steps: for each set of data, starting from the lowest child level, the parent level is recursively processed as follows: acquiring a data initial value of a child level, and comparing the data initial value with a data initial value corresponding to a parent level of the child level; if the comparison result meets the preset condition, the next recursion processing is carried out, if the comparison result does not meet the preset condition, the data initial value corresponding to the father level or the son level is modified, and the recursion processing is carried out again until the comparison result meets the preset condition; and acquiring the processed planning data value.
In one possible implementation manner, the preset condition is: the range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level is larger than or equal to the range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
In one possible implementation manner, if the data initial value corresponding to the parent level is selected to be modified, the planning data value corresponding to the parent level is modified as follows: and the union set of the value range between the maximum value and the minimum value in the data initial values corresponding to the parent level and the value range between the maximum value and the minimum value in the data initial values corresponding to all the child levels under the parent level.
In one possible implementation manner, if the data initial value corresponding to the sub-level is selected to be modified, the planning data value corresponding to the sub-level is modified as follows: intersection of the value range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level and the value range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
In one possible implementation manner, the minimum value of the initial values of the data to be processed is set as a planned starting time, and the maximum value is set as a planned ending time; the data with the parent-child hierarchical relationship are Excel table data.
An embodiment of the second aspect of the present application further provides a data processing apparatus, including:
The data acquisition module is configured to acquire a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains a data initial value to be processed and carries a unique virtual identifier; the data initial value to be processed comprises a set minimum value and a set maximum value of the data;
The data processing module is configured to identify father-son hierarchy relations among a plurality of pieces of data according to the virtual identification, divide the data with different hierarchies of the father-son hierarchy relations into a group, and process the grouped multiple groups of data in parallel to obtain a processed planning data value; the process of obtaining the processed planning data value comprises the following steps: for each set of data, starting from the lowest child level, the parent level is recursively processed as follows: acquiring a data initial value of a child level, and comparing the data initial value with a data initial value corresponding to a parent level of the child level; if the comparison result meets the preset condition, the next recursion processing is carried out, if the comparison result does not meet the preset condition, the data initial value corresponding to the father level or the son level is modified, and the recursion processing is carried out again until the comparison result meets the preset condition; acquiring a processed planning data value; wherein the preset conditions are as follows: the range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level is larger than or equal to the range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
In one possible implementation manner, if the data processing module selects to modify the initial value of the data corresponding to the parent level, the planning data value corresponding to the parent level is modified as follows: a union set of a value range between a maximum value and a minimum value in the data initial values corresponding to the father level and a value range between a maximum value and a minimum value in the data initial values corresponding to all the child levels under the father level; if the data initial value corresponding to the sub-level is selected to be modified, the planning data value corresponding to the sub-level is modified as follows: intersection of the value range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level and the value range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
An embodiment of the third aspect of the present application further provides an electronic device, including: at least one processor and a memory storing a computer program; the computer program, when read and executed by a processor, causes the electronic device to perform the data processing method as described above.
The fourth aspect embodiment of the present application also provides a readable storage medium storing a computer program which, when read and executed by an electronic device, causes the electronic device to execute the data processing method as above.
According to the data processing method and device provided by the embodiment of the application, a new parent-child level relation is introduced to ensure the accuracy and the integrity of data display; the father-son level relation and the hierarchy relation between the data are accurately identified by using a recursion processing scheme, so that efficient data processing and planning management are realized; the complexity of data insertion is reduced, and the efficiency of data processing is improved. The application provides more comprehensive, accurate and intelligent data processing service for users.
Drawings
FIG. 1 is a flow chart of a data processing method according to one embodiment of the invention.
Fig. 2 is an illustration of Excel data to be processed in an embodiment of the present invention.
FIG. 3 is a diagram showing an example of data after data processing in an embodiment of the present invention.
Fig. 4 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of exemplary embodiments of the present application is provided in conjunction with the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application and not exhaustive of all embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The application provides a data processing method and a data processing device, namely, excel data is imported and the upper and lower relationships of each item are accurately displayed through data import of a father-son plan, so that more comprehensive, accurate and timely information service can be provided, traffic flow is further optimized, road network safety is improved, and support is provided for development of future intelligent traffic.
The data processing method according to the embodiment of the invention comprises the following steps: acquiring a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains a data initial value to be processed and carries a unique virtual identifier; identifying parent-child hierarchical relationships among a plurality of pieces of data according to the virtual identification; dividing the data with the parent-child hierarchical relationship into a group, and carrying out parallel processing on a plurality of groups of grouped data to obtain the processed planning data value.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, and as shown in fig. 1, the method starts in step S10.
In step S10, a plurality of data with a parent-child hierarchical relationship is acquired, wherein each data contains an initial value of data to be processed and carries a unique virtual identifier.
According to the embodiment of the invention, in order to solve the problem in the Excel-level data importing process, a smart scheme is adopted, namely, a data column is added as a virtual unique identifier, and the parent-child level relation is confirmed according to the column data in the importing process. The virtual data column is named ID, which is not repeatable throughout the Excel table. The scheme is very simple to use and effective, and can easily establish the hierarchical relationship between the data, thereby ensuring the importing correctness of the data.
Firstly, in the Excel data importing process, calling an API interface provided by a server to acquire resource data to be imported. For example, excel data may be uploaded by a user at a client, or may be data stored in advance in a database; after the Excel batch data uploaded by the user is obtained, the Excel batch data can be analyzed and read, for example, in practical application, the data in the Excel table can be read one by one; after reading, the actual data in the Excel table is obtained. As shown in fig. 2, wbs numbers are used as a virtual unique identifier, named ID, in these data. The Excel data is imported by using the ID, so that the data can be easily shunted according to the ID, and the accuracy of the importing process is ensured. The minimum value of the initial values of Excel data to be processed may be set as a planned start time, and the maximum value may be set as a planned end time.
Depending on the IDs in the Excel table data, it may be classified into different levels, i.e., pieces of data with parent-child hierarchical relationships, which may be expressed as 1, 1.2, 1.2.3, 1.2.3.4,2, 2.1, 2.1.1, 2.1.1.1, … …, for example, and may be expressed as 100, 110, 111, 200, 210, 211, 300, 310, 311, … …, for example. Dividing each ID by using decimal points, and regarding the data with the same ID after division as the data with the same level; data with the same ID before splitting is regarded as a child level under the same parent level. The scheme is very efficient, and the complexity and the calculation amount of data import can be greatly reduced.
Next, in step S20, a parent-child hierarchical relationship between the pieces of data is identified according to the virtual identification; in step S30, data of different levels having parent-child level relationships are divided into a group, and the grouped data are processed in parallel to obtain a processed planning data value.
According to the embodiment of the invention, after the Excel data is imported, the relation between the level and the parent-child level of each data can be easily identified according to the segmented IDs, the Excel data is stored in a database, and the data are split in batches in a stream mode. Next, the time range and hierarchical relationship of each parent level data is processed upward from the lowest level using a recursive manner. Therefore, the situation that the data level is more than or equal to 2 can be easily dealt with, and the time range and the parent-child level relation of each data can be accurately recorded. The scheme is simple and efficient, and the accuracy and the completeness of Excel data import can be ensured.
In one implementation, for each set of data, starting from the lowest child level, the parent level is recursively processed as follows: acquiring a data initial value of a child level, and comparing the data initial value with a data initial value corresponding to a parent level of the child level; if the comparison result meets the preset condition, the next recursion processing is carried out, if the comparison result does not meet the preset condition, the data initial value corresponding to the father level or the son level is modified, and the recursion processing is carried out again until the comparison result meets the preset condition; and acquiring the processed planning data value. The preset conditions are as follows: the range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level is larger than or equal to the range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
Stated another way, the data processing process is: after Excel data is imported, the data is split, the bottommost sub-level is obtained, and recursive processing is performed. In the recursive process, the minimum start time and the maximum end time of the child level are obtained and compared with their parent level. If the parent level satisfies a required condition (e.g., > = condition), the next recursion processing is entered, and if not, the minimum start time and the maximum end time of the parent level or the child level are modified, and after satisfied, the recursion processing is performed again.
In one implementation, if the data initial value corresponding to the parent level is selected to be modified, the planning data value corresponding to the parent level is modified as follows: and the union set of the value range between the maximum value and the minimum value in the data initial values corresponding to the parent level and the value range between the maximum value and the minimum value in the data initial values corresponding to all the child levels under the parent level.
In another implementation, if the data initial value corresponding to the modification sub-level is selected, the planning data value corresponding to the sub-level is modified as follows: intersection of the value range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level and the value range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
For example, the planned start time (minimum start time) of the parent level is No. 1, and the planned end time (maximum end time) is No. 10; the planned starting time of one of the child levels is No. 2, the planned ending time is No. 12, and since the range of values between the planned starting time and the planned ending time of the parent level does not include the range of values between the planned starting time and the planned ending time of the child level, the minimum starting time and the maximum ending time of the parent level or the child level can be selectively modified. If the parent level is selected to be modified, and under the condition that other child levels are not available, the planned starting time after the modification of the parent level is number 1, and the planned ending time is number 12; if other child levels exist, the planning starting time of the other child levels is number 3, the planning ending time is number 13, the planning starting time after the modification of the parent level is number 1, and the planning ending time is number 13; i.e. the union of the range of values of the parent and all child levels is taken. If the child level is selected to be modified, the plan starting time after modification of the child level is number 2, and the plan ending time is number 10, namely, the intersection of the value ranges of the parent level and the child level is taken.
This recursive processing scheme is very efficient, and it can very accurately identify parent-child relationships, hierarchical relationships, and temporal relationships between data, thereby enabling efficient data processing and management. In the Excel data importing process, the recursive processing scheme can reduce the complexity of data insertion and improve the efficiency of data processing.
The specific method for carrying out shunt processing, namely parallel processing on the grouped multiple groups of data comprises the following steps:
Creating a plurality of threads, wherein the threads can be used for processing data in an Excel table according to the data processing process; after a thread is established, a corresponding queue may also be created that corresponds one to the thread, i.e., one queue corresponds to one thread, in other words, the number is also equal. In the process of creating the thread, the kernel number of the CPU can be obtained firstly, the kernel number of the CPU is determined by system hardware, after the system hardware is determined, the current kernel number of the CPU can be determined, and the kernel number of the CPU determines the speed and the efficiency of data processing; then, the number of threads to be built currently can be determined according to the number of CPU cores, and in specific implementation, the number of threads and the number of CPU cores can be equal, and after the threads are built, a queue corresponding to the number of threads can be built.
And dividing the data into a plurality of groups according to the total data quantity of the grouped data, obtaining a plurality of groups of Excel subgroup data, and equally distributing the Excel subgroup data to a queue for processing. The dividing may include dividing the grouped multiple sets of data equally, for example, assuming that the total data size of the grouped multiple sets of data is s and the divided number of sets is set to 5, then dividing the multiple sets of data equally into 5 sets to obtain 5 sets of Excel set data, where the data size of each set of Excel subset data is s/5. After each Excel subgroup data is obtained, the Excel subgroup data are evenly distributed to a queue for processing; in the process of average allocation, the number of times of Excel subgroup data is exactly matched with the number of queues, and even the number of times of Excel subgroup data is not matched with the number of queues, so that the Excel subgroup data can be allocated in sequence according to the sequence of queues in the process of average allocation.
Because the total data amount s of the plurality of groups of data after each acquired grouping is possibly different and the complexity of the data is different, the processing efficiency is also different, the time for complex data processing is longer, and the time for simple data processing is shorter; the number of splits n may be preset, and the number of splits may be related to the complexity of the data, for example, if the data is complex, the value of the number of splits may be set to be a little higher, and if the data is simple, the value of the number of splits may be set to be a little lower.
Further, although the data amount of each task is the same, the data is different, that is, the complexity of the data is different, so the processing speed of each queue is different, and usually after one queue has processed all the tasks, there are tasks waiting to be processed in the other queue, which is disadvantageous to the overall efficiency of the system, so the task processing state of the other queue can be detected after any queue has processed all the tasks, and the task processing state of the other queue can be detected to include detecting whether there are any more tasks waiting to be processed in the other queue, wherein the task waiting to be processed does not include the task being processed, thereby the efficiency of service processing can be improved.
In this embodiment, preferably, after step S30, it may further include: and returning the processed planning data value to the front-end component, and rendering by using the corresponding component. The front-end component may be an item management platform or a financial management platform.
The front-end component is well suited for handling large amounts of structured data and hierarchical data, allowing a user to easily view and analyze the data, thereby improving the readability and usability of the data, and fig. 3 is an effect display.
The following illustrates the data processing procedure in the vehicle-road collaborative project planning:
For example: before importing data, the data to be processed needs to be frequently clicked to be created and stored on a page when the data is added, wherein one simple method is to export the current data after creating a piece of data, copy the current data in Excel, and import the data after simple modification.
After data is imported, a plurality of project groups are processed in parallel, each research and development and test time needs to be serial, if the parallel time can cause work conflict, in the vehicle-road collaborative project planning, after the scheduling of the vehicle-end project groups is determined, the scheduling of the road-end (the research and development, the test and the implementation scheduling) can be determined, so that in the time range of the whole project, the time on a plurality of different project groups has deviation, the delay of the whole project is caused, the maximum time is acquired by the time with minimum granularity preferentially, the time of the whole project can be reflected, and if the time conflicts with the expected time, each project group can adjust the own time, and the project delivery can be completed in the expected time.
The application uses virtual unique Identification (ID) when data is imported, which can help users to better understand the meaning of the field, and can also improve the flexibility and expandability of the importing scheme; the use of the ID is also reflected on typesetting property of data display, so that the data display is more attractive and easier to read; the method adopts a shunt processing mode to process a large amount of data, can obviously improve the speed and efficiency of data import, can ensure the accuracy and the integrity of data processing, and effectively decomposes the data into a plurality of parts, thereby reducing the load and the pressure of a single processor and greatly improving the processing speed.
The application can better meet the demands and expectations of users, improve the readability and usability of data, and can obviously improve the efficiency and accuracy of data processing.
Another embodiment of the present invention further provides a data processing apparatus, as shown in fig. 4, including:
a data acquisition module 410 configured to acquire a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains an initial value of data to be processed and carries a unique virtual identifier; the data initial value to be processed comprises a set minimum value and a set maximum value of the data;
the data processing module 420 is configured to identify a parent-child level relationship among a plurality of pieces of data according to the virtual identifier, divide the data with different levels of the parent-child level relationship into a group, and perform parallel processing on the grouped plurality of groups of data to obtain a processed planning data value; the process of obtaining the processed planning data value comprises the following steps: for each set of data, starting from the lowest child level, the parent level is recursively processed as follows: acquiring a data initial value of a child level, and comparing the data initial value with a data initial value corresponding to a parent level of the child level; if the comparison result meets the preset condition, the next recursion processing is carried out, if the comparison result does not meet the preset condition, the data initial value corresponding to the father level or the son level is modified, and the recursion processing is carried out again until the comparison result meets the preset condition; acquiring a processed planning data value; wherein the preset conditions are as follows: the range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level is larger than or equal to the range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
In this embodiment, preferably, if the data processing module 420 selects to modify the initial value of the data corresponding to the parent level, the planning data corresponding to the parent level is modified as follows: a union set of a value range between a maximum value and a minimum value in the data initial values corresponding to the father level and a value range between a maximum value and a minimum value in the data initial values corresponding to all the child levels under the father level; if the data initial value corresponding to the sub-level is selected to be modified, the planning data value corresponding to the sub-level is modified as follows: intersection of the value range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level and the value range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
In a part of the data processing apparatus according to an embodiment of the present invention, reference is also made to the above detailed description of the method embodiment.
The method of the invention may be performed in an electronic device. The electronic device may be any device having storage and computing capabilities, and may be implemented as a server, a workstation, or the like, or may be implemented as a personal configured computer such as a desktop computer, a notebook computer, or may be implemented as a terminal device such as a mobile phone, a tablet computer, an intelligent wearable device, or an internet of things device, but is not limited thereto.
Fig. 5 shows a schematic diagram of an electronic device according to an embodiment of the invention. As shown in fig. 5, the electronic device may include: processor 510, memory 520, input/output interface 530, communication interface 540, and bus 550. Wherein processor 510, memory 520, input/output interface 530, and communication interface 540 enable a communication connection between each other within an electronic device via bus 550. The processor 510 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided in the embodiments of the present disclosure. The Memory 520 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage, dynamic storage, or the like. Memory 520 may store an operating system and other application programs, and when the embodiments of the present disclosure are implemented in software or firmware, the associated program code is stored in memory 520 and executed by processor 510. The input/output interface 530 is used for connecting with an input/output module to realize information input and output. The input/output module may be configured as a component in an electronic device (not shown in the figure) or may be external to the electronic device to provide corresponding functions. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc. The communication interface 540 is used to connect with a communication module (not shown in the figure) to enable communication interaction between the present electronic device and other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.). Bus 550 includes a path to transfer information between components of the electronic device (e.g., processor 510, memory 520, input/output interface 530, and communication interface 540).
Embodiments of the present invention also provide a non-transitory readable storage medium storing instructions for causing the electronic device to perform a method according to an embodiment of the present invention. The readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be any method or technology for information storage. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of readable storage media include, but are not limited to: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage, and the like.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with examples of the invention. The required structure for a construction of such a system is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It should be appreciated that the teachings of the present invention as described herein may be implemented in a variety of programming languages and that the foregoing descriptions of specific languages are provided for disclosure of preferred embodiments of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is for carrying out the functions performed by the elements for carrying out the objects of the invention.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. A method of data processing comprising the steps of:
Acquiring a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains a data initial value to be processed and carries a unique virtual identifier;
and identifying the parent-child level relation among the plurality of pieces of data according to the virtual identifier, dividing the data with different levels of the parent-child level relation into a group, and carrying out parallel processing on the grouped multiple groups of data to obtain the processed planning data value.
2. A data processing method according to claim 1, wherein the initial value of the data to be processed includes a set minimum value and a set maximum value of the data; the process of processing the grouped multiple groups of data in parallel and obtaining the processed planning data value comprises the following steps: for each set of data, starting from the lowest child level, the parent level is recursively processed as follows: acquiring a data initial value of a child level, and comparing the data initial value with a data initial value corresponding to a parent level of the child level; if the comparison result meets the preset condition, the next recursion processing is carried out, if the comparison result does not meet the preset condition, the data initial value corresponding to the father level or the son level is modified, and the recursion processing is carried out again until the comparison result meets the preset condition; and acquiring the processed planning data value.
3. A data processing method according to claim 2, wherein the preset condition is: the range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level is larger than or equal to the range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
4. A data processing method according to claim 3, wherein if the data initial value corresponding to the parent level is selected to be modified, the planning data value corresponding to the parent level is modified as follows: and the union set of the value range between the maximum value and the minimum value in the data initial values corresponding to the parent level and the value range between the maximum value and the minimum value in the data initial values corresponding to all the child levels under the parent level.
5. A data processing method according to claim 3, wherein if the data initial value corresponding to the sub-level is selected to be modified, the planning data value corresponding to the sub-level is modified as follows: intersection of the value range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level and the value range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
6. A data processing method according to any one of claims 2 to 5, wherein a minimum value among the initial values of the data to be processed is set as a planned start time and a maximum value is set as a planned end time; the data with the parent-child hierarchical relationship are Excel table data.
7. A data processing apparatus, comprising:
The data acquisition module is configured to acquire a plurality of pieces of data with a parent-child hierarchical relationship, wherein each piece of data contains a data initial value to be processed and carries a unique virtual identifier; the data initial value to be processed comprises a set minimum value and a set maximum value of the data;
The data processing module is configured to identify father-son hierarchy relations among a plurality of pieces of data according to the virtual identification, divide the data with different hierarchies of the father-son hierarchy relations into a group, and process the grouped multiple groups of data in parallel to obtain a processed planning data value; the process of obtaining the processed planning data value comprises the following steps: for each set of data, starting from the lowest child level, the parent level is recursively processed as follows: acquiring a data initial value of a child level, and comparing the data initial value with a data initial value corresponding to a parent level of the child level; if the comparison result meets the preset condition, the next recursion processing is carried out, if the comparison result does not meet the preset condition, the data initial value corresponding to the father level or the son level is modified, and the recursion processing is carried out again until the comparison result meets the preset condition; acquiring a processed planning data value; wherein the preset conditions are as follows: the range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level is larger than or equal to the range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
8. The data processing apparatus according to claim 7, wherein if the data processing module selects to modify the initial value of the data corresponding to the parent level, the planning data value corresponding to the parent level is modified as follows: a union set of a value range between a maximum value and a minimum value in the data initial values corresponding to the father level and a value range between a maximum value and a minimum value in the data initial values corresponding to all the child levels under the father level; if the data initial value corresponding to the sub-level is selected to be modified, the planning data value corresponding to the sub-level is modified as follows: intersection of the value range between the maximum value and the minimum value in the initial value of the data corresponding to the parent level and the value range between the maximum value and the minimum value in the initial value of the data corresponding to the child level.
9. An electronic device, comprising:
A memory;
a processor; and
A computer program;
Wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1 to 6.
10. A computer-readable storage medium, characterized by a computer program stored thereon; the computer program being executed by a processor to implement the method of any one of claims 1 to 6.
CN202410183949.3A 2024-02-19 2024-02-19 Data processing method and device, electronic equipment and storage medium Pending CN118012938A (en)

Priority Applications (1)

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CN202410183949.3A CN118012938A (en) 2024-02-19 2024-02-19 Data processing method and device, electronic equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410183949.3A CN118012938A (en) 2024-02-19 2024-02-19 Data processing method and device, electronic equipment and storage medium

Publications (1)

Publication Number Publication Date
CN118012938A true CN118012938A (en) 2024-05-10

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Country Link
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