A project management method, device, computer equipment and storage medium

Abstract

The application relates to a project management method, device, computer equipment and storage medium. The project management method comprises the following steps: acquiring project data; splitting the project data, acquiring data segments, mapping the data segments, and acquiring mapping data; according to the type of the mapping data, merging the mapping data of the same type to acquire marking data; according to the type of the marking data, aggregating the marking data of the same type to acquire a first data unit; presetting a second data unit, comparing the first data unit with the second data unit, positioning problems, and acquiring a management result. Through acquisition of a large amount of project data, the project data is classified, aggregated and analyzed, automatic management of the project is realized, the data processing capacity of the system is enhanced, the data has more definite identification, the work efficiency is improved, and the problem positioning accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of data analysis, and in particular to a method, apparatus, computer equipment, and storage medium for project management. Background Technology

[0002] When conducting financial internal controls, enterprises need a large amount of data generated from their production and operation activities, which must be extracted, aggregated, and analyzed from complex business and financial systems. Relying on manual methods to complete data collection and analysis and formulate internal control management methods is not only time-consuming and labor-intensive, but also faces problems such as incomplete data collection and lack of accuracy.

[0003] Therefore, corporate financial internal control faces problems such as low data processing efficiency and weak reference value of data results. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for project management to address the aforementioned technical problems, thereby resolving the inconvenience of handling internal financial management in existing technologies.

[0005] On the one hand, a method for project management is provided, the method comprising:

[0006] Obtain project data;

[0007] The project data is split into data fragments, and these data fragments are then mapped to obtain the mapped data.

[0008] Based on the type of the mapped data, merge the mapped data of the same type to obtain the labeled data;

[0009] Based on the type of the labeled data, aggregate labeled data of the same type to obtain the first data unit;

[0010] A second data unit is preset, and the first data unit is compared with the second data unit to locate the problem and obtain the control results.

[0011] In one embodiment, the project data is split into segments, including: setting a preset size threshold for the project data; when the size of the acquired project data exceeds the size threshold, the project data is split into segments to obtain data fragments.

[0012] In one embodiment, mapping the data fragment includes: parsing the data fragment according to a first preset data format to obtain parsed data; and converting the parsed data according to a second preset data format to obtain mapped data, wherein the mapped data includes key data and value data corresponding to the key data.

[0013] In one embodiment, merging mapping data of the same type includes: partitioning the mapping data according to the key data type to obtain partitioned data, wherein the partitioned data includes key data, value data corresponding to the key data, and partitioning results; storing the partitioned data in a buffer; and when the storage capacity of the buffer exceeds a preset storage capacity, sorting and merging the partitioned data in the buffer to obtain labeled data.

[0014] In one embodiment, after acquiring the tag data, the process includes: sorting the tag data by key data type to obtain sorted tag data; and transferring the sorted tag data to a cache according to the partition result type.

[0015] In one embodiment, aggregating labeled data of the same type further includes: obtaining random data by randomly selecting labeled data, aggregating the random data to obtain a first aggregation result; obtaining a second aggregation result by aggregating the random data again; comparing the first aggregation result with the second aggregation result, and continuing other aggregation tasks when the comparison is consistent, and pausing other aggregation tasks and outputting the result when the comparison is inconsistent.

[0016] In one embodiment, a second data unit is preset, and the first data unit is compared with the second data unit to locate the problem. This includes: setting business rules based on business problems, where business problems include accounting problems and operational problems, setting the business rules as parameters in the second data unit; comparing the parameters of the first data unit and the second data unit, and filtering out the first data unit with the business problem.

[0017] On the other hand, a project management device is provided, the device comprising:

[0018] The business module is used to obtain project data;

[0019] The mapping module is used to split project data, obtain data fragments, and map the data fragments to obtain mapped data.

[0020] The merging module is used to merge mapping data of the same type according to the type of the mapping data to obtain the labeled data;

[0021] The aggregation module is used to aggregate labeled data of the same type according to the type of labeled data to obtain the first data unit;

[0022] The analysis module is used to preset a second data unit, compare the first data unit with the second data unit, locate problems, and obtain control results.

[0023] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0024] Obtain project data;

[0025] The project data is split into data fragments, and these data fragments are then mapped to obtain the mapped data.

[0026] Based on the type of the mapped data, merge the mapped data of the same type to obtain the labeled data;

[0027] Based on the type of the labeled data, aggregate labeled data of the same type to obtain the first data unit;

[0028] A second data unit is preset, and the first data unit is compared with the second data unit to locate the problem and obtain the control results.

[0029] In another aspect, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, performs the following steps:

[0030] Obtain project data;

[0031] The project data is split into data fragments, and these data fragments are then mapped to obtain the mapped data.

[0032] Based on the type of the mapped data, merge the mapped data of the same type to obtain the labeled data;

[0033] Based on the type of the labeled data, aggregate labeled data of the same type to obtain the first data unit;

[0034] A second data unit is preset, and the first data unit is compared with the second data unit to locate the problem and obtain the control results.

[0035] The aforementioned project management method, apparatus, computer equipment, and storage medium can acquire project data, break it down into data fragments, map the data to obtain mapped data, merge mapped data of the same type to obtain labeled data, aggregate labeled data of the same type to obtain a first data unit, and compare the first data unit and the second data unit to obtain the management result. By acquiring, organizing, classifying, aggregating, and analyzing large amounts of project data, automated project management is achieved, enhancing the system's data processing capabilities, giving the data more explicit identification, improving work efficiency, and increasing the accuracy of problem localization. Attached Figure Description

[0036] Figure 1 This is a diagram illustrating the application environment of a project management method in one embodiment.

[0037] Figure 2 This is a flowchart illustrating a project management method in one embodiment;

[0038] Figure 3 This is a schematic diagram illustrating the splitting of project data in one embodiment;

[0039] Figure 4 This is a schematic diagram of partition mapping data in one embodiment;

[0040] Figure 5 This is a structural block diagram of the project management device in one embodiment;

[0041] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] The project management methods provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 101 via a network. For example, the project management method provided in this application could be that server 101 processes data and then provides unified user services, detailed data display and aggregation, risk data push and early warning to terminal 102 via the network. Alternatively, terminal 102 could send query or export requests to server 101 via the network. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices, and server 101 can be a standalone server or a server cluster consisting of multiple servers.

[0044] When enterprises use traditional project management methods, they rely on manual management. When faced with a large amount of complex data, the processing efficiency is low and the accuracy of problem analysis is difficult to guarantee. Therefore, this solution proposes a method to use big data technology to split, map, integrate and process project data in order to improve the problems existing in internal financial management.

[0045] like Figure 2 As shown, in one embodiment, a method for project management is provided, the method comprising:

[0046] S1. Obtain project data.

[0047] For example, project data includes operational data and accounting data. Operational data refers to data generated during the company's business operations, such as order data and accounting event data. Accounting system data refers to financial data related to the company's accounts, such as sales details and accounting data. The business system can periodically send project data to the big data platform. This can be done by the business system proactively sending project data to the big data platform, or by the big data platform periodically initiating data requests to the business system, which then sends the data to the big data platform based on the received requests. It is understood that periodic sending means that the sending operation can be performed at preset intervals, such as 1 minute, 5 minutes, 10 minutes, etc., which can be flexibly selected according to actual needs.

[0048] S2. Split the project data to obtain data fragments, map the data fragments, and obtain the mapped data.

[0049] To improve data processing capabilities and meet the requirements of processing large volumes of data, the data can be split into smaller parts to reduce the difficulty of data processing. Project data can be split into smaller parts. Since large volumes of data are relatively complex and highly discrete, different types of project data can be transformed. For example, mapping can be used to obtain mapping data of the same format to complete a unified format conversion. For example, data A can be used to calculate data B through a certain preset function relationship.

[0050] S3. Based on the type of the mapping data, merge the mapping data of the same type to obtain the tag data.

[0051] To reduce the amount of data written to disk, mapped data is merged according to the same key data type. For example, data with the format:<key,value> The two data points are respectively<hello,A> ,<hello,A> The key value is the key data of these two pieces of data. The two pieces of data are merged based on the key value to generate a new data set.<hello,2> Data; or data in a format that is<key,value> The two data points are respectively<hello,A> ,<hi,B> The key value is the key data of these two pieces of data. The two pieces of data are merged based on the key value to generate a new data set.<hello,1> Data and a<hi,1> data.

[0052] S4. Based on the type of the labeled data, aggregate the labeled data of the same type to obtain the first data unit.

[0053] To reduce the amount of data transmitted and make the data more compact, the labeled data is transmitted to the aggregation task corresponding to its partition result type for aggregation according to the partition result type, so as to obtain the first data unit.

[0054] S5. Preset a second data unit, compare the first data unit with the second data unit, locate the problem, and obtain the control results.

[0055] The parameters in the second data unit are pre-set, the first data unit and the second data unit are compared, the problem type of the first data unit in the business rules is located, and the control result is obtained.

[0056] To handle large volumes of data, some implementations split the project data, including: based on a pre-set project data size threshold, if the project data size exceeds the pre-set threshold, the project data is split into data fragments, for example: Figure 3 As shown, with a size threshold of 128MB, when the acquired project data 310 is 256MB, it is split into two 128MB data segments, data segment 311 and data segment 312. When the acquired project data 320 is 200MB, it is split into a 128MB data segment 321 and a 72MB data segment 322. When the acquired project data 330 is 100MB, it does not need to be split and is directly output, resulting in an unsplit data segment 331.

[0057] To improve the uniformity and standardization of mapped project data, in some implementations, data fragments are mapped, including: pre-setting two data formats and a functional relationship, including a first data format, a second data format, and a functional relationship that can convert data in the first data format into data in the second data format; parsing the data fragments according to the first data format to obtain parsed data; and converting the parsed data into data in the second data format through a preset functional relationship, wherein the data in the second data format is the mapped data, and the mapped data includes key data and value data corresponding to the key data.

[0058] To further explain, a mapper file can be pre-set, containing code for converting data formats. Data fragments are parsed according to a first data format, which can be a key-value pair format, including key data and corresponding value data. The data segments with identifying features are set as key data, and the data segments that can be obtained from the identifying features are set as corresponding value data. The parsed data is then processed by the code in the mapper to obtain the converted data in a second data format, which can also be a key-value pair format, including key data and corresponding value data.

[0059] To reduce data dispersion and optimize the standardization of mapping data, some implementations merge mapping data of the same type. This includes partitioning the mapping data according to the key data type. This partitioning can be achieved by executing a partitioning function, where the partitioning function performs a modulo operation on the key data and partitions based on the result. Alternatively, a custom partitioning method can be used as needed. After partitioning, the partitioning result is added to the mapping data and used as a marker for the partitioned mapping data, enabling subsequent data transfer to the corresponding task based on the type of the partitioning result. For example: Figure 4 As shown, there exists a format as<key,value> The mapping data 410 is partitioned using the partition method 420 to obtain mapping data 430 containing the partition results. The partitioned mapping data is then written to a buffer, a pre-defined storage area that allows for batch collection of partitioned mapping data, reducing the impact of disk I / O. When the number of partitioned mapping data in the buffer reaches a pre-defined threshold, the data is sorted according to the key data type, output, and merged to obtain labeled data. To handle complex, large-volume data and optimize system load, MapReduce (mapping reduction) big data technology can be used. This involves first splitting the acquired large-volume data into data fragments, processing each fragment separately, and then gradually aggregating them to achieve balanced processing of the large-volume data.

[0060] To ensure the even distribution of the acquired labeled data to subsequent tasks, some implementations include, after acquiring the labeled data, sorting the labeled data according to the type of its key data, using the partitioning results as labels for the sorted data, and obtaining sorted labeled data; then, transferring the sorted labeled data to a cache according to the type of its partitioning results. The purpose of sorting is to group labeled data with the same key data type together, and the partitioning results of the sorted labeled data can be used to transfer this data to the corresponding subsequent reduce (aggregation) tasks for iterative processing.

[0061] To avoid data anomalies during calculation or data transmission that could lead to incorrect aggregation results, some implementations further include: randomly selecting labeled data as random data; obtaining a first aggregation result after the random data has been aggregated; obtaining a second aggregation result by re-invoking the random data; comparing the first aggregation result with the second aggregation result; continuing other aggregation tasks when the comparison is consistent; and pausing other aggregation tasks and outputting the result when the comparison is inconsistent.

[0062] To pinpoint specific business issues within massive datasets, some implementations involve setting business rules based on these issues, including accounting and operational problems. These business rules are then set as parameters in a second data unit. The parameters of the first and second data units are compared to identify the data units containing the business issue. To optimize the cumbersome problem identification process, RPA (Robotic Process Automation) technology can be used. An RPA robot is set up, and the business rules are incorporated into its rule engine. The RPA robot compares data units based on these business rules to automate the analysis of business issues. In the aforementioned project management method, project data is acquired; the project data is split to obtain data fragments, and these fragments are mapped to obtain mapped data; based on the type of the mapped data, data of the same type is merged to obtain labeled data; based on the type of the labeled data, data of the same type is aggregated to obtain a first data unit; a second data unit is preset, and the first and second data units are compared to identify the problem and obtain management results.

[0063] For example, the control results include: summary results and trend charts. The system can push the control results to the terminal, or the terminal can actively send a request command to the system.

[0064] By employing the aforementioned project management methods, data is acquired and analyzed from multiple perspectives, increasing the credibility of subsequent analysis results and the accuracy of problem identification. Data processing efficiency is improved through mapping and aggregation. By comparing the acquired data with preset business rules, problem types are identified and their causes are clarified. Automated project management achieves beneficial effects such as financial supervision, cost reduction, and improved work efficiency.

[0065] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0066] In one embodiment, a project management device 500 is provided, such as... Figure 5 As shown, the project management device can be configured in a server or computer device to execute the aforementioned project management method. The device includes:

[0067] Business module 101 is used to acquire project data. Project data includes operational data and accounting data. Analyzing multiple types of data can increase the persuasiveness of subsequent analysis results.

[0068] The mapping module 102 is used to split the project data, obtain data segments, and map the data segments to obtain mapped data. Splitting and mapping the project data enables segmented processing of large volumes of data, reducing system load and facilitating further data processing.

[0069] The merging module 103 is used to merge mapping data of the same type according to the type of the mapping data to obtain labeled data. Merging mapping data of the same type reduces the output to subsequent stages and alleviates disk I / O.

[0070] The aggregation module 104 is used to aggregate the labeled data of the same type according to the type of the labeled data to obtain a first data unit. Aggregating the labeled data of the same type reduces the amount of data transmission and facilitates subsequent comprehensive comparison of the aggregated data.

[0071] The analysis module 105 is used to preset a second data unit, compare the first data unit with the second data unit, locate problems, and obtain control results. Comparing the two data units can pinpoint business problems existing in the first data unit, enabling project control. The project control device 500 can serve as an execution carrier for project management methods. It can acquire project data, split the data into data fragments, map the data to obtain mapped data, merge mapped data of the same type to obtain labeled data, aggregate labeled data of the same type to obtain the first data unit, and compare the first and second data units to obtain control results. By acquiring, organizing, classifying, aggregating, and analyzing large amounts of project data, automated project control is achieved, enhancing the system's data processing capabilities, giving data clearer identification, improving work efficiency, and increasing the accuracy of problem location.

[0072] In some implementations, the project management device 500 can be used to execute the aforementioned project management method, including:

[0073] A preset threshold for the size of project data is set. When the size of the acquired project data exceeds the threshold, the project data is split into data fragments.

[0074] The data fragment is parsed according to a first preset data format to obtain parsed data; the parsed data is converted according to a second preset data format to obtain mapping data, the mapping data including key data and value data corresponding to the key data.

[0075] In some implementations, the mapping data is partitioned according to the key data type to obtain partitioned data, which includes key data, value data corresponding to the key data, and partitioning results; the partitioned data is stored in a buffer; when the storage capacity of the buffer exceeds a preset storage capacity, the partitioned data in the buffer is sorted, output, and merged to obtain labeled data.

[0076] In some implementations, the tag data is sorted according to the type of the key data to obtain sorted tag data; the sorted tag data is then transferred to a cache according to the type of the partitioning result.

[0077] In some implementations, random data is obtained by randomly selecting the labeled data, and the random data is aggregated to obtain a first aggregation result; a second aggregation result is obtained by calling the random data again for aggregation; the first aggregation result and the second aggregation result are compared. If the comparison is consistent, other aggregation tasks are continued; if the comparison is inconsistent, other aggregation tasks are paused and the result is output.

[0078] In some implementations, business rules are set based on business issues, including accounting issues and operational issues. The business rules are set as parameters in the second data unit. The parameters of the first data unit and the second data unit are compared to filter out the first data unit that contains the business issue.

[0079] For specific limitations regarding a project management device, please refer to the limitations regarding a project management method described above, which will not be repeated here. Each module in the aforementioned project management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0080] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores project management data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a project management method.

[0081] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0082] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0083] Acquire project data; split the project data to obtain data fragments, and map the data fragments to obtain mapped data; merge mapping data of the same type according to the type of mapped data to obtain labeled data; aggregate labeled data of the same type according to the type of labeled data to obtain the first data unit; preset the second data unit, compare the first data unit and the second data unit to locate problems and obtain control results.

[0084] In another aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0085] Acquire project data; split the project data to obtain data fragments, and map the data fragments to obtain mapped data; merge mapping data of the same type according to the type of mapped data to obtain labeled data; aggregate labeled data of the same type according to the type of labeled data to obtain the first data unit; preset the second data unit, compare the first data unit and the second data unit to locate problems and obtain control results.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0087] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for project management, characterized in that, include: Obtain project data; Data fragments are obtained based on the project data, and multiple mapping data are obtained based on the data fragments. Each mapping data includes key data and corresponding value data. The key data is the data portion with an identifier in the data fragment, and the value data is the data portion obtained corresponding to the key data. The mapping data is partitioned according to the key data, and labeled data is obtained according to the partitioned mapping data. The labeled data includes the mapping data and the partitioning result corresponding to the mapping data. The labeled data is aggregated based on the partitioning results to obtain a first data unit; Based on the comparison result between the first data unit and the preset second data unit, the target business problem is determined. The second data unit is set according to the business rules, and the business rules are set according to the business problem. The process of obtaining multiple mapping data based on the data fragment includes: Two data formats and a function relationship are pre-defined, including a first data format, a second data format, and a mapper file that can convert data in the first data format into data in the second data format; the data fragments are parsed according to the first data format to obtain parsed data; the parsed data is processed by the code in the mapper file to convert it into data in the second data format, and the data in the second data format is the mapping data; The first data unit is obtained by aggregating the labeled data based on the partitioning results, and the method further includes: By randomly selecting the labeled data, random data is obtained, and the random data is aggregated to obtain a first aggregation result; By aggregating the random data again, a second aggregation result is obtained; Compare the first aggregation result with the second aggregation result. If the comparison is consistent, continue with other aggregation tasks. If the comparison is inconsistent, pause the other aggregation tasks and output the result.

2. The method according to claim 1, characterized in that, Data fragments are obtained based on the project data, including: If the size of the project data exceeds a preset size threshold, the project data is split into multiple data segments.

3. The method according to claim 1, characterized in that, Different types of partitioning results correspond to different aggregation tasks; The labeled data is aggregated based on the partitioning results to obtain a first data unit, including: Based on the type of the partitioning result, the marked data is transmitted to the corresponding aggregation task for aggregation to obtain the corresponding first data unit.

4. The method according to claim 1, characterized in that, The mapping data is partitioned based on the key data, and labeled data is obtained based on the partitioned mapping data, including: The mapping data is partitioned according to the key data, and the partitioned mapping data is written into the buffer; When the mapping data stored in the buffer exceeds the preset storage size, the mapping data in the buffer is sorted, output, and merged according to the type of the key data to obtain the tag data.

5. The method according to claim 4, characterized in that, After obtaining the labeled data, the following is also included: The labeled data is sorted according to the type of the key data to obtain sorted labeled data; The sorted labeled data is transferred to the cache according to the type of the partition result.

6. An apparatus for implementing project management as described in claim 1, characterized in that, The device includes: The business module is used to obtain project data; A mapping module is used to obtain data fragments based on the project data and to obtain multiple mapping data based on the data fragments. Each mapping data includes key data and corresponding value data. The key data is the data portion with an identifier in the data fragment, and the key data is the data portion obtained corresponding to the key data. The merging module is used to partition the mapping data according to the key data, and obtain the tag data according to the partitioned mapping data; The aggregation module is used to aggregate the marked data according to the partitioning results to obtain a first data unit; The analysis module is used to determine the target business problem based on the comparison result between the first data unit and a preset second data unit, wherein the second data unit is set according to business rules, and the business rules are set according to the business problem.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the project management method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the project management method according to any one of claims 1 to 5.

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