Development data analysis method based on low-code platform

Abstract

The invention relates to the technical field of data analysis, and particularly discloses a development data analysis method based on a low-code platform, which comprises the following steps: S1, calculating a development value according to development data, obtaining a first sequence, and obtaining a standard development value based on the first sequence; s2, calculating the number of people to be determined of the sub-demand, and calculating the difference value of the number of people; calculating the theoretical time of the sub-demand, increasing the number of people to be determined of the sub-demand corresponding to the maximum theoretical time according to the number of people difference, and generating a theoretical time set; s3, adjusting the number of people to be determined according to the maximum time and the minimum time, obtaining a new theoretical time set, and repeating the steps until the maximum time of the theoretical time set meets the requirement; and obtaining the number of target personnel of the sub-demand. The development cycle can be effectively shortened, and project delay is avoided.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a development data analysis method based on a low-code platform. Background Art

[0002] A low-code development platform is a software tool that allows developers to create applications by visually dragging and dropping components, thereby reducing the need for manual coding. Such platforms typically provide a series of preconfigured modules and functions that enable developers to quickly build and deploy applications without having to deeply understand the underlying programming details.

[0003] Development time is a crucial factor in project management. It is not only directly related to the time to market and market competitiveness of the product, but also has a profound impact on the economic benefits and customer satisfaction of the enterprise. In today's rapidly changing business environment, shortening the development time means being able to respond to market demands more quickly and seize opportunities. Although development through low-code platforms has shortened development time to a certain extent, in actual operation, it still faces problems such as unreasonable resource allocation, which leads to longer development time. Therefore, how to reasonably allocate resources, reduce development time, and avoid project delays has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of this invention is to provide a development data analysis method based on a low-code platform to solve the following technical problems: How to allocate resources reasonably, reduce development time and avoid project delays.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A development data analysis method based on a low-code platform includes the following steps: S1: Record the application development requirements as sub-requirements, obtain the development data of the sub-requirements, the development data includes the number of developers a1 and the total development time a2, calculate the development value a=a1*a2, sort the development values ​​in ascending order to obtain a first sort, obtain a standard development value A based on the first sort, and the standard development value is used to represent the average situation of the development value; S2: Calculate the number of pending persons for the sub-demands , A tot represents the total standard development value, dys represents the total number of developers preset, Represents (dys*A) / A tot Round down and calculate the difference in the number of people ΔD = dys-D tot , D tot Indicates the total number of pending persons; Calculate the theoretical time T = A / D of the sub-demand. When the maximum theoretical time is greater than the preset maximum time threshold Tys, increase the number of pending people of the sub-demand q corresponding to the maximum theoretical time by 1, determine the theoretical time of the sub-demand q again, and determine the new maximum theoretical time again. Repeat the above steps until the total number of pending people of all sub-demands is equal to the difference in the number of people, and generate the theoretical time set T jh = (T1, T2, ..., T n ), T n represents the theoretical time of sub-demand n at this time, and n represents the total number of sub-demands; S3: When the maximum time T max =max(T jh )>Tys, let the maximum time T max The number of pending people for the corresponding sub-demand i increases by 1, and the minimum time T min =min(T jh ) The number of pending people for the corresponding sub-demand k is reduced by 1, and the theoretical time of sub-demand i and sub-demand k at this time is calculated respectively, and a new theoretical time set is obtained. The above steps are repeated until the maximum time TY corresponding to the theoretical time set Y is reached. max ≤Tys; The pending number of people for sub-demand x in the theoretical set Y is obtained, and it is used as the target number of personnel. The target personnel corresponding to sub-demand x are responsible for the development of sub-demand x.

[0006] As a further solution of the present invention: in the step S1, the process of obtaining the standard development value specifically includes: Calculate the development value a of the e-th position in the first sort i and the development value a of the e-1th position i-1 Phase difference Δa e-1 =a e -a e-1 , when the phase difference is greater than or equal to a preset phase difference threshold, it is marked as a pending phase difference, and the pending phase difference is sorted according to the order of the first sorting to obtain a second sorting; Take the subtrahend corresponding to the phase difference to be determined as the subtrahend to be determined, start from the phase difference C to be determined that is the first in the second sorting, obtain the sorting position c of the subtrahend of the phase difference C to be determined in the first sorting, calculate the judgment ratio P=c / (nc), when the judgment ratio is greater than or equal to the preset judgment ratio threshold, calculate the average of the first c development values ​​in the first sorting as the standard development value.

[0007] As a further solution of the present invention: in the step S2, when the judgment ratio is less than the judgment ratio threshold, the following steps are performed: The first c development values ​​in the first ranking are removed to obtain a new first ranking, and the first undetermined phase difference C in the second ranking is removed to obtain a new second ranking; Calculate the judgment ratio again and repeat the above steps until the first sort CS1 and the second sort CS2 are obtained. The corresponding judgment ratio P dy is greater than or equal to the judgment ratio threshold, obtains the sorting position N of the pending subtrahend of the judgment ratio Pdy in the first sorting CS1, and calculates the average of the first N development values ​​in the first sorting CS1 as the standard development value.

[0008] As a further solution of the present invention: in the step S3, after obtaining the theoretical time set Y, an optimization step is further included: Step 1: Obtain the minimum time TY of the theoretical time set Y min , calculate the time difference ΔTY=TY max -TY min ; Step 2: When the time difference ΔTY is greater than or equal to the preset time difference threshold ΔT ys When the minimum time TY min The corresponding target number of personnel for sub-demand b is reduced by 1, and the maximum time TY max The number of target personnel for the corresponding sub-demand f increases by 1; Calculate the theoretical time of sub-demand b and sub-demand f at this time respectively, and obtain a new theoretical time set; Step 3: Repeat steps 1 and 2 above until the time difference ΔTZ corresponding to the theoretical set Z is less than ΔT ys , determine the target number of personnel based on the theoretical set Z.

[0009] As a further solution of the present invention: in the step S2, in the process of increasing the number of pending persons of sub-demand q, when the new maximum theoretical time is less than or equal to the maximum time threshold Tys after the number of pending persons of sub-demand q is increased for a certain time, a theoretical time set is generated according to the theoretical time of the sub-demand at this time, and the optimization step is executed.

[0010] As a further solution of the present invention: the optimization step also includes the following steps: Let the minimum time TZ of the theoretical set Z be min The corresponding target number of personnel for sub-demand B is reduced by 1, and the maximum time TZ of the theoretical set Z is max The number of target personnel for the corresponding sub-demand F is increased by 1, and the theoretical time of sub-demand B and sub-demand F is calculated, and a new theoretical time set is obtained. If the maximum time of the new theoretical time set is less than the maximum time of the theoretical set Z, the new theoretical time set is retained; Repeat the above steps until the maximum time of the theoretical time set S is greater than or equal to the maximum time of the theoretical set in the previous iteration process, and determine the target number of personnel based on the theoretical time set S.

[0011] As a further solution of the present invention: in step S2, when the minimum time T min >Tys, send information to report an error.

[0012] As a further solution of the present invention: in the step S1, in the process of obtaining the first sorting, when two or more development values ​​are the same, the same development values ​​are sorted in the order of the time axis.

[0013] Beneficial effects of the present invention: In this solution, firstly, by subdividing the demand and calculating the development value of each sub-demand, the workload and resource requirements of each part can be fully understood, which ensures the scientificity and rationality of resource allocation and avoids the waste or shortage of resources. Then, based on the standard development value and the total resource amount, the number of pending people is calculated, which can accurately match the needs of developers and specific tasks, improve the utilization rate of human resources, and ensure that each sub-demand is fully supported by personnel, thereby reducing the development delay caused by uneven personnel distribution; in the further dynamic adjustment stage, by continuously monitoring and adjusting the number of pending people, the theoretical development time of each sub-demand is controlled, so that the overall development time is always kept within the preset threshold, which not only optimizes time management, but also enhances the flexibility and adaptability of the project, can timely respond to various uncertainties in the development process, and ensure that the project progress is not affected by individual task delays. Finally, through reasonable personnel allocation and strict time control, the entire solution effectively shortens the development cycle and improves the on-time delivery rate of the project. This not only improves the market competitiveness of the enterprise, but also enhances customer satisfaction, ensures that the project can be completed efficiently within the specified time, and brings significant economic benefits and good customer feedback. The present invention effectively improves the efficiency of the development process and the effectiveness of project management through scientific resource allocation and sophisticated time management, thereby successfully shortening the development time, avoiding project delays, and ensuring the smooth delivery of the project and the continued competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a flow chart of a development data analysis method based on a low-code platform of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention is a development data analysis method based on a low-code platform, comprising the following steps: S1: Record the application development requirements as sub-requirements, obtain the development data of the sub-requirements, the development data includes the number of developers a1 and the total development time a2, calculate the development value a=a1*a2, sort the development values ​​in ascending order to obtain a first sort, obtain a standard development value A based on the first sort, and the standard development value is used to represent the average situation of the development value; S2: Calculate the number of pending persons for the sub-demands , A tot represents the total standard development value, dys represents the total number of developers preset, Represents (dys*A) / A tot Round down and calculate the difference in the number of people ΔD = dys-D tot , D tot Indicates the total number of pending persons; Calculate the theoretical time T = A / D of the sub-demand. When the maximum theoretical time is greater than the preset maximum time threshold Tys, increase the number of pending people of the sub-demand q corresponding to the maximum theoretical time by 1, determine the theoretical time of the sub-demand q again, and determine the new maximum theoretical time again. Repeat the above steps until the total number of pending people of all sub-demands is equal to the difference in the number of people, and generate the theoretical time set T jh = (T1, T2, ..., T n ), T n represents the theoretical time of sub-demand n at this time, and n represents the total number of sub-demands; S3: When the maximum time T max =max(T jh )>Tys, let the maximum time T max The number of pending people for the corresponding sub-demand i increases by 1, and the minimum time T min =min(T jh ) The number of pending people for the corresponding sub-demand k is reduced by 1, and the theoretical time of sub-demand i and sub-demand k at this time is calculated respectively, and a new theoretical time set is obtained. The above steps are repeated until the maximum time TY corresponding to the theoretical time set Y is reached. max ≤Tys; The pending number of people for sub-demand x in the theoretical set Y is obtained, and it is used as the target number of personnel. The target personnel corresponding to sub-demand x are responsible for the development of sub-demand x.

[0018] It should be noted that, first of all, by breaking down complex development tasks into smaller and more manageable subtasks, the resources and time required for each sub-requirement can be more accurately evaluated. This segmentation method helps to identify the priority and complexity of different sub-requirements, thereby providing data support for subsequent resource allocation. The development value is calculated to quantify the workload of each sub-requirement, which is convenient for subsequent sorting and standardization. This process ensures the scientific nature of resource allocation, makes the allocation of developers more accurate, and avoids resource waste or shortage due to inaccurate task volume assessment; then, by calculating the number of pending people, the development personnel can be reasonably allocated according to the ratio of the development value of each sub-requirement to the total development value. This method ensures the matching degree of resource allocation with task requirements and improves the utilization efficiency of human resources. At the same time, calculating the difference in the number of people helps to discover the redundancy in resource allocation, which is convenient for adjustment in subsequent steps. This process ensures the fairness and rationality of resource allocation through mathematical models and reduces the deviation that may be caused by human allocation; by calculating the theoretical time, The completion time of each sub-requirement under the current resource allocation can be predicted. When the theoretical time of a sub-requirement exceeds the preset maximum time threshold, its development time can be shortened by increasing its number of pending people, thereby controlling the overall project progress. This dynamic adjustment mechanism ensures that the project can flexibly respond to changes during the development process, optimize resource allocation in a timely manner, and avoid affecting the progress of the entire project due to delays in a single sub-requirement; then, the number of pending people is further adjusted to control the maximum theoretical time. By continuously monitoring and adjusting the number of pending people for sub-requirements corresponding to the maximum theoretical time, while reducing the number of pending people for sub-requirements corresponding to the minimum time, the solution can balance resource allocation among multiple sub-requirements. This iterative process aims to gradually optimize the development time of the entire project so that the development time of all sub-requirements is within an acceptable range, thereby ensuring the stability of the overall project progress; finally, the target number of personnel is ultimately obtained and development tasks are assigned to ensure that each sub-requirement has sufficient resource support, thereby further improving the overall development efficiency of the project and the possibility of on-time delivery.

[0019] In another preferred embodiment of the present invention, in step S1, the process of obtaining the standard development value specifically includes: Calculate the development value a of the e-th position in the first sort i and the development value a of the e-1th position i-1 Phase difference Δa e-1 =a e -a e-1, when the phase difference is greater than or equal to a preset phase difference threshold, it is marked as a pending phase difference, and the pending phase difference is sorted according to the order of the first sorting to obtain a second sorting; Take the subtrahend corresponding to the phase difference to be determined as the subtrahend to be determined, start from the phase difference C to be determined that is the first in the second sorting, obtain the sorting position c of the subtrahend of the phase difference C to be determined in the first sorting, calculate the judgment ratio P=c / (nc), when the judgment ratio is greater than or equal to the preset judgment ratio threshold, calculate the average of the first c development values ​​in the first sorting as the standard development value.

[0020] It is worth noting that by determining the locations with larger phase differences, this method can effectively distinguish between the main group and a few high-value groups, and calculate the mean of the first c development values, which can ensure that the mean is closer to the development value of most sub-demands, thereby improving the representativeness and practicality of the mean; since the development values ​​of most sub-demands are concentrated in a relatively low range, after removing the high values ​​in the upper part, the mean can better reflect the actual development resource demand. This method avoids the excessive influence of high values ​​on the mean, making the standard development value more accurate and reliable.

[0021] It is understandable that if Δa i-1 is the phase difference to be determined, and the phase difference to be determined Δa i-1 For example, the phase difference Δa to be determined i-1 =a i -a i-1 , subtrahend is a i-1 , then the undetermined subtrahend is a i-1 , then the sorting position of the undetermined subtrahend in the first sorting is i-1.

[0022] In another preferred embodiment of the present invention, in step S2, when the judgment ratio is less than the judgment ratio threshold, the following steps are performed: The first c development values ​​in the first ranking are removed to obtain a new first ranking, and the first undetermined phase difference C in the second ranking is removed to obtain a new second ranking; Calculate the judgment ratio again and repeat the above steps until the first sort CS1 and the second sort CS2 are obtained. The corresponding judgment ratio P dy is greater than or equal to the judgment ratio threshold, obtains the sorting position N of the pending subtrahend of the judgment ratio Pdy in the first sorting CS1, and calculates the average of the first N development values ​​in the first sorting CS1 as the standard development value.

[0023] In another preferred embodiment of the present invention, in the step S3, after obtaining the theoretical time set Y, an optimization step is further included: Step 1: Obtain the minimum time TY of the theoretical time set Ymin , calculate the time difference ΔTY=TY max -TY min ; Step 2: When the time difference ΔTY is greater than or equal to the preset time difference threshold ΔT ys When the minimum time TY min The corresponding target number of personnel for sub-demand b is reduced by 1, and the maximum time TY max The number of target personnel for the corresponding sub-demand f increases by 1; Calculate the theoretical time of sub-demand b and sub-demand f at this time respectively, and obtain a new theoretical time set; Step 3: Repeat steps 1 and 2 above until the time difference ΔTZ corresponding to the theoretical set Z is less than ΔT ys , determine the target number of personnel based on the theoretical set Z.

[0024] It should be noted that by calculating the time difference, the imbalance in time allocation during the development process can be identified, thereby quantifying the time difference between each sub-requirement in the project; when the time difference is large, by reducing the target number of personnel corresponding to the minimum time sub-requirement b and increasing the target number of personnel corresponding to the maximum time sub-requirement f, the development time of each sub-requirement is balanced, thereby releasing excess development resources from tasks that are completed faster and reallocating them to those tasks that take longer to develop, thereby narrowing the time gap between tasks, preventing delays in certain tasks due to insufficient resources, and avoiding waste of resources, ensuring that each sub-requirement can be completed efficiently within a reasonable time; through iterative optimization and continuous fine-tuning of resource allocation, the time of each task is finally balanced, avoiding the impact of the delay of a single task on the progress of the entire project.

[0025] In another preferred embodiment of the present invention, in the step S2, when the number of pending persons of sub-demand q is increased, when the new maximum theoretical time is less than or equal to the maximum time threshold Tys after the number of pending persons of sub-demand q is increased a certain time, a theoretical time set is generated according to the theoretical time of the sub-demand at this time, and the optimization step is executed.

[0026] In another preferred embodiment of the present invention, the optimization step further includes the following steps: Let the minimum time TZ of the theoretical set Z be min The corresponding target number of personnel for sub-demand B is reduced by 1, and the maximum time TZ of the theoretical set Z is max The number of target personnel for the corresponding sub-demand F is increased by 1, and the theoretical time of sub-demand B and sub-demand F is calculated, and a new theoretical time set is obtained. If the maximum time of the new theoretical time set is less than the maximum time of the theoretical set Z, the new theoretical time set is retained; Repeat the above steps until the maximum time of the theoretical time set S is greater than or equal to the maximum time of the theoretical set in the previous iteration process, and determine the target number of personnel based on the theoretical time set S.

[0027] It can be understood that by reducing the target number of personnel for sub-demand B corresponding to the minimum time in the theoretical set Z and increasing the target number of personnel for sub-demand F corresponding to the maximum time, the purpose is to further optimize resource allocation. By identifying and reallocating the resource differences between tasks with shorter development times and tasks with longer development times, reducing personnel allocation for tasks that are completed faster can avoid excessive concentration and waste of resources; increasing personnel allocation for tasks that are completed slower will help speed up the progress of these tasks and shorten their development time. This dynamic adjustment ensures a more balanced allocation of resources, avoids delays in some tasks due to insufficient resources, and improves overall development efficiency. Afterwards, by comparing whether the new maximum time is less than the previous maximum time, it can be effectively determined whether the resource adjustment is effective. If the adjustment is effective, the new allocation plan is retained and optimization continues. If it is invalid, further adjustments are stopped to avoid wasting resources in invalid adjustments. A stable state of resource allocation is found through iterative optimization.

[0028] In another preferred embodiment of the present invention, in step S2, when the minimum time T min >Tys, send information to report an error.

[0029] In another preferred embodiment of the present invention, in the step S1, in the process of obtaining the first sorting, when two or more development values ​​are the same, the same development values ​​are sorted in timeline order.

[0030] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A development data analysis method based on a low-code platform, characterized in that: The following steps are involved: S1: Record the application development requirements as sub-requirements, obtain the development data of the sub-requirements, the development data includes the number of developers a1 and the total development time a2, calculate the development value a=a1*a2, sort the development values ​​in ascending order to obtain a first sort, obtain a standard development value A based on the first sort, and the standard development value is used to represent the average situation of the development value; S2: Calculate the number of pending persons for the sub-demands , A tot represents the total standard development value, dys represents the total number of developers preset, Represents (dys*A) / A tot Round down and calculate the difference in the number of people ΔD = dys-D tot , D tot Indicates the total number of pending persons; Calculate the theoretical time T = A / D of the sub-demand. When the maximum theoretical time is greater than the preset maximum time threshold Tys, increase the number of pending people of the sub-demand q corresponding to the maximum theoretical time by 1, determine the theoretical time of the sub-demand q again, and determine the new maximum theoretical time again. Repeat the above steps until the total number of pending people of all sub-demands is equal to the difference in the number of people, and generate the theoretical time set T jh = (T1, T2, ..., T n ), T n represents the theoretical time of sub-demand n at this time, and n represents the total number of sub-demands; S3: When the maximum time T max =max(T jh )>Tys, let the maximum time T max The number of pending people for the corresponding sub-demand i increases by 1, and the minimum time T min =min(T jh ) The number of pending people for the corresponding sub-demand k is reduced by 1, and the theoretical time of sub-demand i and sub-demand k at this time is calculated respectively, and a new theoretical time set is obtained. The above steps are repeated until the maximum time TY corresponding to the theoretical time set Y is reached. max ≤Tys; The pending number of people for sub-demand x in the theoretical set Y is obtained, and it is used as the target number of personnel. The target personnel corresponding to sub-demand x are responsible for the development of sub-demand x.

2. According to a low-code platform-based development data analysis method according to claim 1, it is characterized in that: In the step S1, the process of obtaining the standard development value specifically includes: Calculate the development value a of the e-th position in the first sort i and the development value a of the e-1th position i-1 Phase difference Δa e-1 =a e -a e-1 , when the phase difference is greater than or equal to a preset phase difference threshold, it is marked as a pending phase difference, and the pending phase difference is sorted according to the order of the first sorting to obtain a second sorting; Take the subtrahend corresponding to the phase difference to be determined as the subtrahend to be determined, start from the phase difference C to be determined that is the first in the second sorting, obtain the sorting position c of the subtrahend of the phase difference C to be determined in the first sorting, calculate the judgment ratio P=c / (nc), when the judgment ratio is greater than or equal to the preset judgment ratio threshold, calculate the average of the first c development values ​​in the first sorting as the standard development value.

3. According to a development data analysis method based on a low-code platform according to claim 2, it is characterized in that: In the step S2, when the judgment ratio is less than the judgment ratio threshold, the following steps are performed: The first c development values ​​in the first ranking are removed to obtain a new first ranking, and the first undetermined phase difference C in the second ranking is removed to obtain a new second ranking; Calculate the judgment ratio again and repeat the above steps until the first sort CS1 and the second sort CS2 are obtained. The corresponding judgment ratio P dy is greater than or equal to the judgment ratio threshold, obtains the sorting position N of the pending subtrahend of the judgment ratio Pdy in the first sorting CS1, and calculates the average of the first N development values ​​in the first sorting CS1 as the standard development value.

4. According to a development data analysis method based on a low-code platform according to claim 1, it is characterized in that: In the step S3, after obtaining the theoretical time set Y, an optimization step is also included: Step 1: Obtain the minimum time TY of the theoretical time set Y min , calculate the time difference ΔTY=TY max -TY min ; Step 2: When the time difference ΔTY is greater than or equal to the preset time difference threshold ΔT ys When the minimum time TY min The corresponding target number of personnel for sub-demand b is reduced by 1, and the maximum time TY max The number of target personnel for the corresponding sub-demand f increases by 1; Calculate the theoretical time of sub-demand b and sub-demand f at this time respectively, and obtain a new theoretical time set; Step 3: Repeat steps 1 and 2 above until the time difference ΔTZ corresponding to the theoretical set Z is less than ΔT ys , determine the target number of personnel based on the theoretical set Z.

5. A development data analysis method based on a low-code platform according to claim 4, characterized in that: In the step S2, in the process of increasing the number of pending persons of sub-demand q, when the new maximum theoretical time is less than or equal to the maximum time threshold Tys after the number of pending persons of sub-demand q is increased for a certain time, a theoretical time set is generated according to the theoretical time of the sub-demand at this time, and the optimization step is executed.

6. According to a development data analysis method based on a low-code platform according to claim 4, it is characterized in that: The optimization step also includes the following steps: Let the minimum time TZ of the theoretical set Z be min The corresponding target number of personnel for sub-demand B is reduced by 1, and the maximum time TZ of the theoretical set Z is max The number of target personnel for the corresponding sub-demand F is increased by 1, and the theoretical time of sub-demand B and sub-demand F is calculated, and a new theoretical time set is obtained. If the maximum time of the new theoretical time set is less than the maximum time of the theoretical set Z, the new theoretical time set is retained; Repeat the above steps until the maximum time of the theoretical time set S is greater than or equal to the maximum time of the theoretical set in the previous iteration process, and determine the target number of personnel based on the theoretical time set S.

7. The development data analysis method based on a low-code platform according to claim 1 is characterized in that: In step S2, when the minimum time T min >Tys, send information to report an error.

8. The development data analysis method based on a low-code platform according to claim 1 is characterized in that: In the step S1, in the process of obtaining the first sorting, when two or more development values ​​are the same, the same development values ​​are sorted in the order of the time axis.

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