Big Data-Based Project Progress Monitoring Methods, Devices, and Electronic Equipment

Abstract

This invention discloses a project progress monitoring method, device, and electronic device based on big data, relating to the field of big data technology. The method includes: acquiring multiple historical versions of project data tables for a target project; analyzing the project's achievement of preset requirements based on project cycle data in each project data table to obtain a requirement achievement scale parameter table; determining the percentage of requirement achievement scale parameters for each cycle and identifying the first benchmark data for the target project in each cycle; determining the second benchmark data for the target project in each cycle based on the expected total scale parameter and task detail data in the project data table; monitoring the first and second progress trend data of the target project, and issuing a risk warning when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and more specifically, to a project progress monitoring method, device, and electronic device based on big data. Background Technology

[0002] Currently, software project development involves different versions. Related technologies typically rely on real-time data tracking of the current development version to determine whether each sub-project has met requirements and whether there are any delays in the design, development, and testing progress at this stage. However, there are risks associated with this approach, such as the inability to assess whether there are any unmet requirements in the current version, and the inability to predict whether the planned workload for each stage of design, coding, and testing will cause project delays.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a project progress monitoring method, device, and electronic device based on big data, to at least solve the technical problem in related technologies that it is impossible to predict whether there is a risk of delay in the current design and development progress of a project.

[0005] According to one aspect of the present invention, a project progress monitoring method based on big data is provided, comprising: acquiring project data tables of multiple historical versions of a target project; analyzing the project's achievement of preset requirements based on project cycle data in each project data table to obtain a requirement achievement scale parameter table; determining the percentage of requirement achievement scale parameters for each cycle based on the requirement achievement scale parameter table, and determining a first benchmark data for the target project in each cycle based on the percentage of requirement achievement scale parameters and the expected total scale parameter of the target project in the current version; determining a second benchmark data for the target project in each cycle based on the expected total scale parameter and task detail data in the project data table; monitoring the first progress trend data and the second progress trend data of the target project in the current version based on the task record data table of the target project, and issuing a risk warning when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data.

[0006] Optionally, the step of determining the percentage of demand arrival scale parameters for each period based on the demand arrival scale parameter table includes: calculating the average demand arrival value of the multiple historical versions in each period based on the demand arrival scale parameter table; calculating the percentage of the average demand arrival value in each period relative to the average total scale parameter of the multiple historical versions of the target project, thereby obtaining the percentage of demand arrival scale parameters.

[0007] Optionally, before determining the first benchmark data for the target project in each period based on the percentage of demand fulfillment and the expected total size parameter of the target project in the current version, the method further includes: calculating the per capita size parameter based on the personnel data in the project data table and the total size parameter of the multiple historical versions; and calculating the expected total size parameter based on the per capita size parameter and the current number of employees involved in the target project.

[0008] Optionally, after determining the first benchmark data for the target project in each period, the method further includes: drawing a first benchmark trend chart based on the first benchmark data for each period and the demand availability parameter table.

[0009] Optionally, the step of determining the second benchmark data of the target project in each period based on the expected total scale parameter and the task detail data in the project data table further includes: splitting and accumulating the task detail data based on the project period data in the project data table to obtain a task scale parameter table; calculating the task scale parameter percentage for each period based on the task scale parameter table; and calculating the second benchmark data of the target project in each period based on the task scale parameter percentage and the expected total scale parameter.

[0010] Optionally, after determining the second benchmark data for the target project in each period, the method further includes: drawing a second benchmark trend chart based on the first benchmark data for each period and the task size parameter table.

[0011] Optionally, after obtaining multiple historical versions of the project data table for the target project, the method further includes: determining whether the amount of data in the project data table exceeds a preset data volume threshold; if the amount of data exceeds the preset data volume threshold, synchronizing the multiple historical versions of the project data table to the target server in batches; if the amount of data does not exceed the preset data volume threshold, synchronizing the multiple historical versions of the project data table to the target server all at once.

[0012] According to another aspect of the present invention, a project progress monitoring device based on big data is also provided, comprising: an acquisition unit for acquiring project data tables of multiple historical versions of a target project; a first processing unit for analyzing the project's achievement of preset requirements based on project cycle data in each of the project data tables, to obtain a requirement achievement scale parameter table; a second processing unit for determining the percentage of requirement achievement scale parameters for each cycle based on the requirement achievement scale parameter table, and determining a first benchmark data for the target project in each cycle based on the percentage of requirement achievement scale parameters and the expected total scale parameter of the target project in the current version; a determination unit for determining a second benchmark data for the target project in each cycle based on the expected total scale parameter and task detail data in the project data tables; and a third processing unit for monitoring the first progress trend data and the second progress trend data of the target project based on the task record data table of the target project in the current version, and issuing a risk warning when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data.

[0013] Optionally, the second processing unit includes: a first calculation module, which calculates the average demand arrival value of the multiple historical versions in each period based on the demand arrival scale parameter table; and a second calculation module, which calculates the percentage of the average demand arrival value in each period relative to the average total scale parameter of the multiple historical versions of the target project, to obtain the demand arrival scale parameter percentage.

[0014] Optionally, the big data-based project progress monitoring device further includes: a first calculation unit, used to calculate an average scale parameter based on personnel data in the project data table and the total scale parameter of the multiple historical versions before determining the first benchmark data of the target project in each cycle based on the percentage of demand arrival scale parameter and the expected total scale parameter of the target project in the current version; and a second calculation unit, used to calculate the expected total scale parameter based on the average scale parameter and the number of current employees involved in the target project.

[0015] Optionally, the big data-based project progress monitoring device further includes: a drawing unit, which, after determining the first benchmark data of the target project in each period, draws a first benchmark trend chart based on the first benchmark data of each period and the demand arrival scale parameter table.

[0016] Optionally, the determining unit includes: a first processing module, used to split and accumulate the task detail data based on the project cycle data in the project data table to obtain a task scale parameter table; a third calculation module, used to calculate the task scale parameter percentage for each cycle based on the task scale parameter table; and a fourth calculation module, used to calculate the second benchmark data of the target project for each cycle based on the task scale parameter percentage and the expected total scale parameter.

[0017] Optionally, the determining unit further includes a plotting module, used to plot a second benchmark trend chart based on the first benchmark data for each period and the task size parameter table after determining the second benchmark data for the target project in each period.

[0018] Optionally, the big data-based project progress monitoring device further includes: a judgment unit, used to determine whether the amount of data in the project data table exceeds a preset data volume threshold after obtaining multiple historical versions of the project data table for the target project; a first synchronization unit, used to synchronize the multiple historical versions of the project data table to the target server in batches if the amount of data exceeds the preset data volume threshold; and a second synchronization unit, used to synchronize the multiple historical versions of the project data table to the target server all at once if the amount of data does not exceed the preset data volume threshold.

[0019] In this invention, project data tables for multiple historical versions of the target project are first obtained. Then, based on the project cycle data in each project data table, the on-time scale parameters for the project to meet preset requirements are analyzed to obtain a demand on-time scale parameter table. Based on the demand on-time scale parameter table, the percentage of demand on-time scale parameters for each cycle is determined. Based on the percentage of demand on-time scale parameters and the expected total scale parameter of the target project in the current version, the first benchmark data for the target project in each cycle is determined. Then, based on the expected total scale parameter and the task detail data in the project data table, the second benchmark data for the target project in each cycle is determined. Finally, based on the task record data table of the target project in the current version, the first progress trend data and the second progress trend data of the target project are monitored. When the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data, a risk warning is issued.

[0020] In this invention, first benchmark data and second benchmark data are determined by using project data from historical versions of the project. Then, by obtaining the task data of the current version on the server and comparing it with the first and second benchmark data, it is possible to determine whether the current version is lagging behind. This achieves the purpose of monitoring the progress of the current version of the project based on the historical data information of the target project, thereby solving the technical problem in related technologies that it is impossible to analyze whether there is a risk of lag in the current design and development progress of the project. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is an optional flowchart for project progress monitoring based on big data according to an embodiment of the present invention;

[0023] Figure 2 This is a line graph of an optional first benchmark trend according to an embodiment of the present invention;

[0024] Figure 3 This is a line graph of an optional second benchmark trend according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of an optional big data-based project progress monitoring device according to an embodiment of the present invention;

[0026] Figure 5 This is a hardware structure block diagram of an electronic device (or mobile device) for a project progress monitoring method based on big data according to an embodiment of the present invention. Detailed Implementation

[0027] 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. 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.

[0028] 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.

[0029] It should be noted that the project progress monitoring method and device based on big data in this disclosure can be used in the field of big data for software and hardware project development, and can also be used in any field other than big data for project development, testing and monitoring project progress. This disclosure does not limit the application field of the project progress monitoring method and device.

[0030] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent information from the aforementioned user or organization.

[0031] This invention can be applied to various software products, control systems, and client-side applications (including but not limited to mobile clients and PCs) of financial institutions. Taking software products as an example, by monitoring the development progress of software products installed on mobile clients and providing timely warnings, the software products can be developed on schedule to realize the business functions of financial institutions (including but not limited to: transfer, wealth management, funds, bill payment, account inquiry, advertising, recommendation, etc.).

[0032] This invention determines first and second benchmark data by using historical project data of the target project. Then, it obtains the current version of the task data on the server and compares it with the first and second benchmark data to determine whether the current version is lagging behind. If a lag is found, an alarm is triggered to alert management personnel to pay attention to the risk warning, which can reduce the project schedule delays and delivery risks caused by such risks.

[0033] The present invention will now be described in conjunction with various embodiments.

[0034] Example 1

[0035] According to an embodiment of the present invention, an optional method embodiment for project progress monitoring based on big data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] Figure 1 This is a flowchart of an optional big data-based project progress monitoring method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0037] Step S101: Obtain project data tables for multiple historical versions of the target project;

[0038] Step S102: Based on the project cycle data in each project data table, analyze the on-time scale parameters of the project to meet the preset requirements, and obtain the on-time scale parameter table.

[0039] Step S103: Based on the demand availability scale parameter table, determine the percentage of demand availability scale parameter for each period, and based on the percentage of demand availability scale parameter and the expected total scale parameter of the target project in the current version, determine the first benchmark data of the target project in each period.

[0040] Step S104: Based on the expected total scale parameter and the task details data in the project data table, determine the second benchmark data for the target project in each cycle;

[0041] Step S105: Based on the task record data table of the target project in the current version, monitor the first progress trend data and the second progress trend data of the target project. When the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data, issue a risk warning message.

[0042] Through the above steps, project data tables for multiple historical versions of the target project are obtained; based on the project cycle data in each project data table, the on-time scale parameters for achieving preset requirements are analyzed to obtain a demand on-time scale parameter table; based on the demand on-time scale parameter table, the percentage of demand on-time scale parameters for each cycle is determined, and based on the percentage of demand on-time scale parameters and the expected total scale parameter of the target project in the current version, the first benchmark data for the target project in each cycle is determined; based on the expected total scale parameter and the task detail data in the project data table, the second benchmark data for the target project in each cycle is determined; based on the task record data table of the target project in the current version, the first progress trend data and the second progress trend data of the target project are monitored, and a risk warning is issued when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data. In this embodiment, the first benchmark data and the second benchmark data are determined through the project data of the historical versions of the target project, and then the task data of the current version on the server is obtained and compared with the first benchmark data and the second benchmark data to determine whether there is a lag in the current version. This achieves the purpose of monitoring the progress of the current version of the project based on the historical data information of the target project, thereby solving the technical problem in related technologies that it is impossible to analyze whether there is a risk of lag in the current design and development progress of the project.

[0043] The embodiments of the present invention will be further described below in conjunction with the above implementation steps.

[0044] Step S101: Obtain project data tables for multiple historical versions of the target project.

[0045] The aforementioned target project can be a software or hardware development and testing project. The project data tables for multiple historical versions of this target project can include: Historical Requirements A (Historical Requirements Scale Data Table A), Historical Personnel Scale Data Table B, Historical Project Cycle Data Table C, and Historical Jira (Process Management System) Task Details Data Table D. Table A can include the arrival time and scale of historical requirements. Requirements can refer to design requirements for software functions and layout, and the scale of requirements can refer to the number of people and the time required for each requirement. Table B can include the number of personnel involved in the design, development, and testing phases of each historical version. Table C can include project milestone dates such as historical delivery time and production launch time. Table D can include the completion time of design, coding, and testing tasks in historical projects and the workload of each corresponding phase. These data tables are central, shared data tables, which are the project data tables mentioned above.

[0046] As an optional implementation of this embodiment, after obtaining multiple historical versions of the project data table for the target project, the method further includes: determining whether the amount of data in the project data table exceeds a preset data volume threshold; if the amount of data exceeds the preset data volume threshold, synchronizing the multiple historical versions of the project data table to the target server in batches; if the amount of data does not exceed the preset data volume threshold, synchronizing the multiple historical versions of the project data table to the target server all at once.

[0047] Project progress monitoring for the current version of a target project can also be performed through a target server. This target server can be configured with a database, such as MySQL. A Python runtime environment can be configured on this target server to analyze data from multiple historical versions of the project's tables. Before data analysis, the project data tables from multiple historical versions of the target project need to be synchronized to this server. For tables with large amounts of data, synchronization and data analysis can be performed in batches according to version cycles. For example, if the data volume of tables A and D exceeds a preset data volume threshold, tables A and D can be synchronized to the target server in batches. If the data volume of tables B and C does not exceed the preset data volume threshold, tables B and C can be synchronized to the target server all at once.

[0048] Step S102: Based on the project cycle data in each project data table, analyze the on-time scale parameters of the project to meet the preset requirements, and obtain the on-time scale parameter table.

[0049] Optionally, before determining the first benchmark data for the target project in each cycle based on the percentage of demand availability and the expected total size parameter of the target project in the current version, the following may also be included: calculating the per capita size parameter based on personnel data in the project data table and the total size parameter of multiple historical versions; and calculating the expected total size parameter based on the per capita size parameter and the current number of employees involved in the target project.

[0050] Based on the number of staff on staff in historical versions and the total size of historical versions, the average size per staff in the current version is calculated. The specific calculation formula is: Average size per staff = AVG total size of a single version / number of staff on staff in a single version, where AVG represents the average calculation. To assess the current version's demand fulfillment, the expected total size of the current version can be calculated by combining the current number of staff on staff (corresponding to the current job postings) with the calculated average size per staff in the current version.

[0051] Step S103 determines the percentage of demand availability parameters for each period based on the demand availability parameter table, and determines the first benchmark data for the target project in each period based on the percentage of demand availability parameters and the expected total size parameter of the target project in the current version.

[0052] The aforementioned benchmark data can serve as a reference for the required demand arrival scale that the current version should achieve within the corresponding cycle. Based on the recorded project cycle, the required demand arrival time and scale for each historical version cycle in the data table are broken down by week, generating a phased cumulative scale chart of the required demand arrival status for each historical version—the required demand arrival scale parameter table, as shown in Table 1. It should be noted that the data breakdown of the required demand arrival time and scale for each historical version cycle in the data table can also be done by month / day / day or other unit cycles. The required demand arrival scale parameter table includes cumulative required demand arrival scale parameter data for multiple cycles of each historical version. In Table 1, T represents the delivery time, and each month from July to December represents a historical version of the target project, such as July**, which represents the target project for the July version. The numbers in the table represent the cumulative scale of required demand arrival at each delivery time.

[0053] Table 1 Parameters of Demand Availability

[0054]

[0055] An alternative implementation further includes, after determining the first benchmark data for the target project in each period, plotting a first benchmark trend chart based on the first benchmark data for each period and a demand availability parameter table.

[0056] Figure 2 This is a line graph of an optional first benchmark trend according to an embodiment of the present invention, such as... Figure 2As shown, the vertical axis represents the cumulative scale, and the horizontal axis represents the time from (T-13) to (T+4) periods. The lines of different shades of gray represent the cumulative scale trend lines for each version of the demand arrival scale parameter table from 7 to 12 months. The dashed line (reference benchmark) represents the first benchmark trend line.

[0057] As an optional implementation method in this embodiment, the step of determining the percentage of demand arrival scale parameters for each period based on the demand arrival scale parameter table includes: calculating the average demand arrival value of multiple historical versions in each period based on the demand arrival scale parameter table; calculating the percentage of the average demand arrival value in each period relative to the average total scale parameter of multiple historical versions of the target project, thereby obtaining the percentage of demand arrival scale parameters.

[0058] The average weekly demand arrival scale of each historical version is calculated longitudinally to obtain the above-mentioned average demand arrival scale. The data can also be fine-tuned according to the overall upward trend. For example, in the T+4 period, since only the October version has accumulated scale data, the average demand arrival scale of the T+4 period will deviate from the overall trend. The average demand arrival scale of the T+4 period can be fine-tuned to conform to the overall upward trend, resulting in a row-fitted average demand arrival scale of the historical version. The percentage of the average demand arrival scale of each period relative to the average total scale parameter of multiple historical versions of the target project can also be calculated to obtain the percentage of demand arrival scale parameter.

[0059] Step S104: Based on the expected total scale parameter and the task details data in the project data table, determine the second benchmark data for the target project in each cycle.

[0060] The aforementioned second benchmark data represents the reference scale of task requirements that the current version should achieve in the corresponding cycle. The detailed task data can be found in the historical version Jira (process management system) task detail data table D within the project data table, which may include the completion time and workload of design, coding, and testing tasks in historical projects. Based on the expected total scale parameter and the detailed task data in the project data table, the second benchmark data for the target project in each cycle can be determined.

[0061] Optionally, the step of determining the second benchmark data of the target project in each period based on the expected total scale parameter and the task detail data in the project data table further includes: splitting and accumulating the task detail data based on the project period data in the project data table to obtain a task scale parameter table; calculating the task scale parameter percentage for each period based on the task scale parameter table; and calculating the second benchmark data of the target project in each period based on the task scale parameter percentage and the expected total scale parameter.

[0062] The development cycle of each development team is calculated. Based on the Jira task cycle of design, coding, and testing in Data Table C, the development cycle data of each development team is organized. The task completion time and scale of each historical version cycle in Data Table D are split by cycle, and a task scale parameter table for each historical version is generated, as shown in Table 2. It should be noted that the data splitting of task completion time and scale for each historical version cycle in Data Table D can also be split by month / day / day or other unit cycles. The task scale parameter table includes the cumulative task scale parameter data for multiple cycles of each historical version. In Table 2, T represents the delivery time, and each month from October to September represents a historical version of the target project. For example, October represents the target project of the October version. The numbers in Table 2 represent the cumulative scale of task completion for each delivery time. Based on the task scale parameter table, the percentage of the average task scale in each cycle to the average total task scale parameter of multiple historical versions is calculated to obtain the task scale parameter percentage, which is the ratio of the task scale of each cycle to the total task scale. Based on the task scale parameter percentage and the expected total scale parameter, the second benchmark data of the target project in each cycle can be calculated.

[0063] Table 2 Task Scale Parameter Table

[0064]

[0065]

[0066] Optionally, after determining the second benchmark data for the target project in each period, the method further includes: drawing a second benchmark trend chart based on the first benchmark data and the task size parameter table for each period.

[0067] Figure 3 This is a line graph of an optional second benchmark trend according to an embodiment of the present invention, that is, the development team's version release benchmark, which represents the benchmark for the development team to complete the corresponding task. The gray line represents the task execution scale trend of different development teams (Group 1, Group 2, Group 3, Group 4, Group 5, Group 6, and Technical Group) over multiple periods. The dashed line represents the second benchmark trend, the vertical axis represents the percentage of task execution scale, and the horizontal axis represents multiple periods.

[0068] Step S105: Based on the task record data table of the target project in the current version, monitor the first progress trend data and the second progress trend data of the target project. When the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data, issue a risk warning message.

[0069] Based on the first and second benchmark data, it is possible to assess whether there are risks to the current project progress. A batch task can be deployed on server A at night to obtain the project progress trend of the current version of the target project and compare it with the first and second benchmark data. If it is lower than the benchmark value, an alarm can be triggered, thereby prompting managers to pay attention to the risk warning.

[0070] By alerting managers to risk warnings, the project schedule delays and delivery risks caused by such risks can be reduced.

[0071] The invention will now be described in conjunction with another alternative embodiment.

[0072] Example 2

[0073] This embodiment provides an optional big data analysis scheme based on historical projects, which can be used to predict whether there are delays in the arrival of project requirements, whether the timeline for each stage of the project is reasonable, and whether there are potential risks. The scheme provided in this embodiment is described in detail below.

[0074] 1. Data tables and hardware configuration required for data modeling.

[0075] The project data tables required for modeling and analysis include: Historical Requirements A, Historical Version Personnel Scale B, Historical Version Project Cycle C, and Historical Version Jira Task Details D.

[0076] Table A must include the arrival time and scale of historical demand.

[0077] Table B should include the number of personnel involved in the design, development, and testing of each historical version.

[0078] Table C should include project milestone dates such as delivery time and commissioning time.

[0079] Table D should include the completion time of design, coding, and testing tasks in historical projects and the workload of the corresponding stages.

[0080] The above data table is a centrally shared data table.

[0081] The hardware configuration includes one server, A. This server must be configured with at least one database; the example uses MySQL. A Python runtime environment is also required on this server. During data analysis, the original shared data tables need to be synchronized to this server. For tables with large datasets, such as A and D, synchronization and data analysis should be performed in batches according to version control cycles.

[0082] 2. Analysis of historical demand data.

[0083] Based on the project cycle recorded in Table C, the time and scale of demand arrival for each historical version cycle in Table A are split into weekly data, and a phased accumulation of demand arrival for each historical version is generated to obtain a demand arrival scale parameter table. The weekly demand arrival scale of the historical versions is averaged vertically, and the data is fine-tuned according to the overall upward trend to obtain a one-row fitted weekly scale arrival cycle chart of the historical versions, and the percentage of the weekly scale to the total scale is used as the demand arrival benchmark.

[0084] 3. Data analysis of per capita demand.

[0085] Based on the number of staff on duty in the historical versions in Table B and the total size of the historical versions, calculate the average size per person for each version using the formula below: Average size per person for version = AVG total size of a single version / number of staff on duty in a single version, where AVG represents the calculation of average data.

[0086] 4. Assess the current version's requirement availability.

[0087] Based on the current number of employees on staff and the average size per employee calculated in step 3, the expected total size of the current version is determined. By combining the weekly demand fulfillment percentage benchmarks, the current version's demand fulfillment benchmark data is determined, and benchmark trend charts for each period of the current version are obtained.

[0088] 5. Calculate the development cycle for each development team.

[0089] Based on the Jira task cycle of the design coding test in Table D, and referring to the process of the benchmark execution of the requirement in step 2, the development cycle data of the development team is organized to obtain the task scale parameter table, and the task benchmark data of the development team is determined.

[0090] 6. Calculate risks based on benchmarks

[0091] Deploy batch tasks on server A at night to obtain the current version of the project progress trend, compare it with the requirement completion benchmark data and the task benchmark data, and issue an alarm if it falls below the corresponding benchmark value.

[0092] By alerting managers to risk warnings, project delays and delivery risks caused by such risks can be reduced.

[0093] The invention will now be described in conjunction with another alternative embodiment.

[0094] Example 3

[0095] This embodiment provides an optional project progress monitoring device based on big data. The implementation units included in this project progress monitoring device correspond to the implementation steps in the above embodiment one.

[0096] Figure 4 This is a schematic diagram of an optional big data-based project progress monitoring device according to an embodiment of the present invention, such as... Figure 4 As shown, the project progress monitoring device includes: an acquisition unit 41, a first processing unit 42, a second processing unit 43, a determination unit 44, and a third processing unit 45, wherein...

[0097] Acquisition unit 41 is used to acquire project data tables of multiple historical versions of the target project;

[0098] The first processing unit 42 is used to analyze the on-site scale parameters of the project to meet the preset requirements based on the project cycle data in each project data table, and to obtain the on-site scale parameter table of the requirements.

[0099] The second processing unit 43 is used to determine the percentage of demand availability parameters for each period based on the demand availability parameter table, and to determine the first benchmark data for the target project in each period based on the percentage of demand availability parameters and the expected total size parameter of the target project in the current version.

[0100] Unit 44 is used to determine the second benchmark data of the target project in each cycle based on the expected total scale parameter and the task details data in the project data table.

[0101] The third processing unit 45 is used to monitor the first progress trend data and the second progress trend data of the target project based on the task record data table of the target project in the current version, and issue risk warning information when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data.

[0102] The aforementioned big data-based project progress monitoring device can acquire multiple historical versions of project data tables for the target project through acquisition unit 41. The first processing unit 42 analyzes the project cycle data in each project data table to determine the required completion scale parameters, resulting in a required completion scale parameter table. The second processing unit 43 determines the percentage of required completion scale parameters for each cycle based on the required completion scale parameter table. Based on this percentage and the expected total scale parameter of the target project in the current version, the first benchmark data for each cycle is determined. The determination unit 44 determines the second benchmark data for each cycle based on the expected total scale parameter and the task detail data in the project data table. The third processing unit 45 monitors the first and second progress trend data of the target project based on the task record data table in the current version. A risk warning is issued when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data. In this embodiment, first benchmark data and second benchmark data are determined by using project data from historical versions of the target project. Then, the task data of the current version is obtained from the server and compared with the first benchmark data and the second benchmark data to determine whether the current version is lagging behind. This achieves the purpose of monitoring the progress of the current version of the project based on the historical data information of the target project, thereby solving the technical problem in related technologies that it is impossible to predict whether there is a risk of lag in the current design and development progress of the project.

[0103] Optionally, the second processing unit includes: a first calculation module, which calculates the average demand arrival value of multiple historical versions in each period based on the demand arrival scale parameter table; and a second calculation module, which calculates the percentage of the average demand arrival value in each period relative to the average total scale parameter of multiple historical versions of the target project, to obtain the demand arrival scale parameter percentage.

[0104] Optionally, the big data-based project progress monitoring device also includes: a first calculation unit, used to calculate the per capita scale parameter based on personnel data in the project data table and the total scale parameter of multiple historical versions before determining the first benchmark data of the target project in each cycle based on the percentage of demand arrival scale parameter and the expected total scale parameter of the target project in the current version; and a second calculation unit, used to calculate the expected total scale parameter based on the per capita scale parameter and the current number of employees involved in the target project.

[0105] Optionally, the big data-based project progress monitoring device also includes: a plotting unit, which, after determining the first benchmark data for the target project in each cycle, plots a first benchmark trend chart based on the first benchmark data for each cycle and the demand availability parameter table.

[0106] Optionally, the determining unit includes: a first processing module, used to split and accumulate the task details data based on the project cycle data in the project data table to obtain a task scale parameter table; a third calculation module, used to calculate the percentage of task scale parameters for each cycle based on the task scale parameter table; and a fourth calculation module, used to calculate the second benchmark data of the target project in each cycle based on the percentage of task scale parameters and the expected total scale parameter.

[0107] Optionally, the determining unit also includes a plotting module, used to plot a second benchmark trend chart based on the first benchmark data and the task size parameter table for each period after determining the second benchmark data for the target project in each period.

[0108] Optionally, the big data-based project progress monitoring device further includes: a judgment unit, used to determine whether the amount of data in the project data table exceeds a preset data volume threshold after obtaining multiple historical versions of the project data table for the target project; a first synchronization unit, used to synchronize multiple historical versions of the project data table to the target server in batches if the amount of data exceeds the preset data volume threshold; and a second synchronization unit, used to synchronize multiple historical versions of the project data table to the target server all at once if the amount of data does not exceed the preset data volume threshold.

[0109] The aforementioned project progress monitoring device based on big data may also include a processor and a memory. The aforementioned acquisition unit 41, first processing unit 42, second processing unit 43, determination unit 44, and third processing unit 45 are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0110] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, first and second benchmark data are determined using historical versions of project data from the target project. Then, the current version's task data is retrieved from the server and compared with the first and second benchmark data to determine if the current version is outdated.

[0111] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0112] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-described big data-based project progress monitoring method by executing the executable instructions.

[0113] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute any of the above-mentioned big data-based project progress monitoring methods.

[0114] Figure 5 This is a hardware structure block diagram of an electronic device (or mobile device) for a project progress monitoring method based on big data according to an embodiment of the present invention. Figure 5 As shown, the electronic device may include one or more processors 502 (shown as 1502a, 502b, ..., 502n in the figure) (processor 502 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 504 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 5 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown.

[0115] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0116] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0121] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A big data based project progress monitoring method, characterized in that, The method comprises the following steps: obtaining a plurality of project data tables of historical versions of a target project; based on the project cycle data in each of the project data tables, analyzing the scale parameter of demand reaching of the project to reach a preset requirement to obtain a demand reaching scale parameter table, wherein the demand reaching scale parameter table comprises accumulated demand reaching scale parameter data of a plurality of cycles of each historical version; based on the demand reaching scale parameter table, determining the demand reaching scale parameter percentage of each cycle, and based on the demand reaching scale parameter percentage and the expected total scale parameter of the target project in the current version, determining the first benchmark data of the target project in each cycle, wherein the first benchmark data is the reference data of the demand reaching scale of the target project in the current version that should be reached in each cycle; based on the expected total scale parameter and the task detail data in the project data table, determining the second benchmark data of the target project in each cycle, wherein the second benchmark data is the reference scale data of the task demand reaching of the target project in the current version that should be reached in each cycle; based on the task record data table of the target project in the current version, monitoring the first progress trend data and the second progress trend data of the target project, and when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data, issuing a risk warning information; before determining the first benchmark data of the target project in each cycle based on the demand reaching scale parameter percentage and the expected total scale parameter of the target project in the current version, the method further comprises the following steps: based on the personnel data in the project data table and the total scale parameter of the plurality of historical versions, calculating the per capita scale parameter; based on the per capita scale parameter and the current number of people involved in the target project, calculating the expected total scale parameter; the step of determining the second benchmark data of the target project in each cycle based on the expected total scale parameter and the task detail data in the project data table further comprises the following steps: based on the project cycle data in the project data table, splitting and accumulating the task detail data to obtain a task scale parameter table; based on the task scale parameter table, calculating the task scale parameter percentage of each cycle; based on the task scale parameter percentage and the expected total scale parameter, calculating the second benchmark data of the target project in each cycle.

2. The project progress monitoring method according to claim 1, characterized in that, the step of determining the demand reaching scale parameter percentage of each cycle based on the demand reaching scale parameter table comprises the following steps: based on the demand reaching scale parameter table, calculating the demand reaching average of the plurality of historical versions in each cycle; calculating the percentage of the demand reaching average in each cycle to the total scale parameter average of the plurality of historical versions of the target project to obtain the demand reaching scale parameter percentage.

3. The project progress monitoring method according to claim 1, characterized in that, after determining the first benchmark data of the target project in each cycle, the method further comprises the following steps: based on the first benchmark data of each cycle and the demand reaching scale parameter table, drawing a first benchmark trend chart.

4. The project progress monitoring method according to claim 1, characterized by, after determining the second benchmark data of the target project in each cycle, the method further comprises the following steps: Drawing a second benchmark trend chart based on the first benchmark data and the task size parameter table of each cycle.

5. The project progress monitoring method according to claim 1, characterized in that, After obtaining the project data table of the multiple historical versions of the target project, further comprising: determining whether the data amount in the project data table exceeds a preset data amount threshold; in the case that the data amount exceeds the preset data amount threshold, synchronizing the project data table of the multiple historical versions to the target server in batches; in the case that the data amount does not exceed the preset data amount threshold, synchronizing the project data table of the multiple historical versions to the target server at one time. 6.A big data based project progress monitoring device, characterized in that, comprising: an acquisition unit, configured to acquire a project data table of multiple historical versions of a target project; a first processing unit, configured to analyze a demand-to-site size parameter of a project reaching a preset demand based on project cycle data in each of the project data tables, and obtain a demand-to-site size parameter table, wherein the demand-to-site size parameter table comprises accumulated demand-to-site size parameter data of multiple cycles of each historical version; a second processing unit, configured to determine a demand-to-site size parameter percentage of each cycle based on the demand-to-site size parameter table, and determine first benchmark data of each cycle of the target project based on the demand-to-site size parameter percentage and an expected total size parameter of the target project in a current version, wherein the first benchmark data is demand-to-site size reference data that the target project in the current version should reach in each cycle; a determination unit, configured to determine second benchmark data of each cycle of the target project based on the expected total size parameter and task detail data in the project data table, wherein the second benchmark data is reference size data of task demand-to-site that the target project in the current version should reach in each cycle; a third processing unit, configured to monitor first progress trend data and second progress trend data of the target project based on a task record data table of the target project in the current version, and issue a risk warning information when the first progress trend data is lower than the first benchmark data or the second progress trend data is lower than the second benchmark data. The device further comprises: a first calculation unit, configured to calculate a per capita size parameter based on personnel data in the project data table and total size parameters of the multiple historical versions before determining the first benchmark data of each cycle of the target project based on the demand-to-site size parameter percentage and the expected total size parameter of the target project in the current version; and a second calculation unit, configured to calculate the expected total size parameter based on the per capita size parameter and a current number of personnel involved in the target project. The determination unit comprises: a first processing module, configured to split and accumulate the task detail data based on project cycle data in the project data table, and obtain a task size parameter table; a third calculation module, configured to calculate a task size parameter percentage of each cycle based on the task size parameter table; and a fourth calculation module, configured to calculate the second benchmark data of each cycle of the target project based on the task size parameter percentage and the expected total size parameter.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the big data-based project progress monitoring method of any one of claims 1 to 5 when the computer program is run.

8. An electronic device, comprising: The computer readable storage medium includes a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the big data-based project progress monitoring method of any one of claims 1 to 5 when the computer program is run.

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