Data processing method in Git warehouse management

Abstract

The invention discloses a data processing method in Git warehouse management, relates to the technical field of software engineering, and solves the problem that related processing is not carried out based on different feature data input in different time periods so as to carry out specific monitoring on project progress. According to the method, whether the progress of the project has related abnormity or not is identified based on the comprehensive characteristics of the project determined at the current moment, and the specific abnormity identification standard is based on the data characteristics generated by the project in the previous period, so that the abnormal progress can be rapidly and effectively identified; and signal display is carried out based on the identified abnormal progress, so that external related personnel can check the abnormal progress, response measures can be taken in time, and it is guaranteed that each project can complete related work within the controllable progress.

Description

Technical Field

[0001] The present invention relates to the technical field of software engineering, and in particular to a data processing method in Git warehouse management. Background Art

[0002] In the software development process, Git is a popular version control system that is widely used for code management. However, for large projects, how to effectively use Git commit history to evaluate project progress and member work efficiency has become a challenge. Existing tools often lack in-depth analysis and visualization of these data, making it difficult for developers to quickly obtain key information;

[0003] The application with publication number CN115586919A discloses a git-based code management method and management device, specifically: based on the R&D task request, a master branch is created in the centralized library; based on the R&D task of the master branch, multiple independent feature branches are created in the centralized library, and a mapping relationship is established between the R&D tasks on the feature branch and the R&D tasks on the master branch, and they are cloned to the local library; after the R&D task on the feature branch is completed, a dev branch is created in the centralized library, and the feature branch that completes the R&D task is merged into the dev branch, and cloned to the local library at the same time; after the dev branch passes the self-test, this branch is merged into the master branch, and after the merger is successful, this dev branch is deleted, and a tag branch is established and marked; when there is an emergency, a hotfix branch is created in the centralized library.

[0004] At present, in the process of data management, the Git warehouse only adjusts and stores the entered code data based on preset features or specific data features, but does not perform relevant processing based on the different feature data entered in different time periods to monitor the project progress specifically. At the same time, it does not provide real-time feedback on whether the corresponding project progress is abnormal based on the corresponding entry progress. The function of the original Git warehouse needs to be enhanced. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a data processing method in Git repository management, which solves the problem of not performing relevant processing based on different feature data entered in different time periods to specifically monitor the progress of the project.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A data processing method in Git repository management, comprising the following steps:

[0007] Step 1: Limit the monitoring association period, confirm the code data package that needs to be uploaded to the Git repository within this association period, and format the code data package to obtain the input data package based on the format standard associated with the corresponding branch item of the corresponding project. The association period is the preset period. The specific method is:

[0008] S11, based on the relevant mark of the code data packet, determine the branch item to which the code data packet belongs, and then determine the format standard associated with the branch item, different branch items correspond to different format standards, and the format standard is the interval space between adjacent codes;

[0009] S12, marking the relevant area in the code data packet where no code symbol exists as a blank area, and adjusting the blank area based on the determined format standard so that the internal arrangement format of the code data packet is consistent with the format of the code associated with the branch item, and the format standard is the interval space between adjacent codes;

[0010] Step 2: Based on the multiple input data packets confirmed in the association period, based on the number and input capacity of the input data packets of the same branch item, determine the input characteristics of the corresponding branch item, and then based on the input characteristics of multiple branch items within the corresponding item, determine the comprehensive characteristics of the corresponding item, and then based on the comprehensive characteristics of the corresponding item confirmed in the past association period and the comprehensive characteristics determined this time, generate a characteristic change curve belonging to the corresponding item. The specific sub-steps are:

[0011] S21, correlating a number of input data packets confirmed by a branch item within a period, confirming the number of input data packets and marking it as G i-k , where i represents different projects and k represents different branch items, and then confirm the total amount of input data Z of several input data packages i-k The total amount of input data is the sum of the data capacity of several input data packets, using T i-k =G i-k ×C1+Z i-k ×C2 confirms the input feature T of the corresponding branch item i-k , where C1 and C2 are preset fixed coefficient factors, and the input features of other branch items are determined in the same way;

[0012] S22, based on the different input features of different branch items in the same project T i-k The maximum and minimum values ​​of the input feature T are determined, a set of value ranges are determined, and values ​​are selected within this range, and the selected values ​​are calibrated as the selected value Z. i-k Calibrated as TD i-k , enter the feature T that is less than the selected value i-k Marked as TX i-k, using TD i-k -Z=CD i-k Confirm TD i-k The difference between Z and Z is calibrated as the height difference, using Z-TX i-k =CX i-k Confirm Z and TX i-k The difference between them is marked as low difference;

[0013] When a selected value Z satisfies: the sum of height differences = the sum of low differences, the sum of height differences is obtained by summing up multiple height differences, and the sum of low differences is obtained by summing up multiple low differences, this selected value Z is marked as the comprehensive feature of this project;

[0014] S23. Based on the comprehensive features confirmed by the project in this association cycle, the comprehensive features confirmed by the project in the past 30 association cycles are identified from the historical completion data, and a feature change curve belonging to the project is generated according to the time sequence.

[0015] Step 3: Based on the characteristic change curve confirmed by the corresponding project, perform characteristic analysis on the end point of the characteristic change curve, determine the corresponding change amplitude range based on the change amplitude of the characteristic change curve, and then confirm whether the numerical feature generated by the end point is abnormal, and display the signal. The specific sub-steps are:

[0016] S31, based on the end point of the characteristic change curve, confirming a line segment between a point before the end point and an initial point of the characteristic change curve, and marking it as a past history curve;

[0017] S32, based on the calibrated past historical curve, determine the lowest point and the highest point of the past historical curve, calibrate the range between the lowest point and the highest point as the numerical activity range, randomly select a set of characteristic values ​​within the numerical activity range, and construct an associated vertical line perpendicular to the characteristic value, and the associated vertical line divides the past historical curve into upper and lower curve segments, wherein the upper curve segment is located above the associated vertical line, and the lower curve segment is located below the associated vertical line, and the associated vertical line is moved up and down by changing the specific value of the characteristic value. When the associated vertical line moves, the sum of the area of ​​the upper curve segment and the associated vertical line and the sum of the area of ​​the lower curve segment and the associated vertical line are recorded, and the associated vertical line is stopped when the sum of the upper and lower areas is equal, and the associated vertical line is calibrated as the standard vertical line;

[0018] S33, based on the calibrated standard vertical line, the standard vertical line is moved up and down by X1 characteristic value to lock a set of standard ranges, wherein X1 is a preset value, and the standard vertical line of the characteristic change curve is confirmed in the same manner as in step S32, to identify whether the standard vertical line belongs to the standard range:

[0019] If the standard vertical line is below the standard range, it means that the progress of this project is abnormal, and an abnormal signal is generated for direct display;

[0020] If the standard vertical line is within the standard range, it means that the progress of the corresponding project in the current association period is normal and no processing is required;

[0021] If the standard vertical line is above the standard range, perform progress trend analysis:

[0022] S331, determine the comprehensive feature Z1 of the end point, then determine the comprehensive feature Z2 of the point before the end point, and use Z1-Z2=Bh to obtain the current change value Bh;

[0023] S332, identifying a portion of the curve segment with an upward trend from the past historical curve, and determining the change value H of the endpoint value from the identified portion of the curve segment p , where p represents different partial curve segments, and H p = comprehensive characteristics of the end point of the partial curve segment - comprehensive characteristics of the initial point of the partial curve segment, from several change values ​​H p In the above example, we select the maximum value H p max;

[0024] S333, if Bh>H p max×α, where α is the preset value, it means that the progress trend of this project is abnormally high, and an abnormally high signal is generated for display; if Bh≤H p max×α represents the progress improvement of the corresponding project in the current association period, and generates a progress improvement signal for display.

[0025] The present invention provides a data processing method in Git warehouse management. Compared with the prior art, it has the following beneficial effects:

[0026] The present invention monitors and confirms the code input data of each branch item in each project, and then locks the comprehensive features belonging to the corresponding project based on the confirmed associated features of the corresponding branch item, so as to reflect the associated progress of the corresponding project. By adopting this feature determination and reflecting the corresponding associated progress method, the progress monitoring effect of the corresponding project can be effectively completed, which is convenient for relevant operators to review;

[0027] Subsequently, based on the comprehensive characteristics of this project determined at the current moment, it is identified whether there are any related anomalies in the progress of this project. The specific anomaly identification standard is based on the data characteristics generated by this project in the past cycle, so that abnormal progress can be identified quickly and effectively, and signals are displayed based on the identified abnormal progress for external relevant personnel to view and take timely response measures to ensure that each different project can complete related work within a controllable progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the process of the present invention;

[0029] Figure 2 It is a schematic diagram of the process of abnormality determination based on the associated vertical line of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] First embodiment

[0032] See also Figure 1 , the present application provides a data processing method in Git repository management, comprising the following steps:

[0033] Step 1: Limit the monitoring association period, confirm the code data package that needs to be uploaded to the Git warehouse within this association period, and process the code data package in the format of the corresponding branch item of the corresponding project to obtain the input data package. The association period is a preset period, which is prepared in advance by relevant operators based on experience. Specifically, the Git warehouse is for inputting and managing the code generated in real time, and different projects are set up in this warehouse. There are multiple different branch items in a single group of projects. Each branch item needs to input the associated code data to promote the normal implementation of the entire project. At the same time, based on the code input progress of different branch items of the corresponding project, the overall completion progress of the corresponding project can be confirmed, and signals can be displayed to facilitate management by the management party;

[0034] The specific method of formatting the code data packet is as follows:

[0035] S11, based on the relevant mark of the code data packet, determine the branch item to which the code data packet belongs, the relevant mark of which is prepared in advance by the operator, and then determine the format standard associated with the branch item, the format standard of which is also set in advance by the operator, and different branch items correspond to different format standards, and the format standard is the interval space between adjacent codes;

[0036] S12. Mark the relevant area in the code data packet where no code symbol exists as a blank area. Based on the determined format standard, adjust the blank area so that the internal arrangement format of the code data packet is consistent with the format of the code associated with the corresponding branch item. The format standard is the spacing between adjacent codes. Because different branch items have different format requirements, some only need to be separated by two characters, and some need to be divided by blank lines. Therefore, according to the determined format standard, the relevant arrangement format inside the code data packet is adjusted to ensure that the format of the code data entered by the corresponding branch item is consistent;

[0037] Step 2: Based on multiple input data packets confirmed in the associated cycle, based on the number and input capacity of the input data packets of the same branch item, determine the input characteristics of the corresponding branch item, and then determine the comprehensive characteristics of the corresponding project based on the input characteristics of multiple branch items within the corresponding project, and then generate a characteristic change curve belonging to the corresponding project based on the comprehensive characteristics of the corresponding project confirmed in the past associated cycle and the comprehensive characteristics determined this time (the characteristic change can also be understood as the progress change in each cycle, the larger the characteristic value, the faster the progress of this cycle, and the smaller the characteristic value, the slower the progress of this cycle), wherein the specific sub-steps for generating the characteristic change curve of the corresponding project are:

[0038] S21, correlating a number of input data packets (here, several includes one) confirmed by a branch item within a period, confirming the number of input data packets and marking it as G i-k , where i represents different projects and k represents different branch items, and then confirm the total amount of input data Z of several input data packages i-k The total amount of input data is the sum of the data capacity of several input data packets, using T i-k =G i-k ×C1+Z i-k ×C2 confirms the input feature T of the corresponding branch item i-k , where C1 and C2 are preset fixed coefficient factors, whose specific values ​​are determined by the operator based on experience, and the input features of other branch items are determined in the same way;

[0039] S22, based on the different input features of different branch items in the same project T i-k The maximum and minimum values ​​of the input feature T are determined, a set of value ranges are determined, and values ​​are selected within this range, and the selected values ​​are calibrated as the selected value Z. i-k Calibrated as TD i-k , enter the feature T that is less than the selected value i-k Marked as TX i-k , using TD i-k -Z=CD i-k Confirm TDi-k The difference between Z and Z is calibrated as the height difference, using Z-TX i-k =CX i-k Confirm Z and TX i-k The difference between them is marked as low difference;

[0040] When a selected value Z satisfies: the sum of height differences = the sum of low differences, the sum of height differences is obtained by summing up multiple height differences, and the sum of low differences is obtained by summing up multiple low differences, this selected value Z is marked as the comprehensive feature of this project;

[0041] S23. Based on the comprehensive features confirmed by the project in this association cycle, the comprehensive features confirmed by the project in the past 30 association cycles are identified from the historical completion data, and a feature change curve (also known as a progress change curve) belonging to the project is generated according to the time sequence. The horizontal coordinate axis of the curve is the timeline, and the vertical coordinate axis is the corresponding feature parameter.

[0042] Specifically, since the corresponding association cycle has been defined, when the current association cycle ends, the next group of association cycles will be generated. When there are data packets that have not been fully entered within the association cycle, the entered data capacity will be used as the calculation basis (that is, for 50M code data, 30M is entered, then 30M will be used for the specific calculation of the sum of subsequent data capacities). When the corresponding branch items are entering data, there are corresponding entry numbers and entry capacities, so that the entry features of the corresponding branch items can be locked. In order to identify and confirm the total feature progress data of the corresponding project, it is necessary to lock the features from the entry features of several branch items to select and confirm the comprehensive features of the corresponding project.

[0043] Second embodiment

[0044] In the specific implementation process of this embodiment, compared with the above embodiment, this embodiment mainly focuses on the numerical analysis process of the characteristic change curve, assesses whether the corresponding project has abnormal progress, and issues timely warnings;

[0045] Step 3: Based on the characteristic change curve confirmed by the corresponding project, perform characteristic analysis on the end point of the characteristic change curve, determine the corresponding change amplitude range based on the change amplitude of the characteristic change curve, and then confirm whether the numerical feature generated by the end point is abnormal, and display the signal. The specific sub-steps for confirming whether the numerical feature generated by the end point is abnormal are as follows:

[0046] S31, based on the end point of the characteristic change curve, confirm the line segment between the point before the end point (that is, the point corresponding to the 30th comprehensive feature, whose end point is the 31st point appearing in this curve) and the initial point of the characteristic change curve, and mark it as the past history curve;

[0047] S32. Based on the calibrated past historical curve, determine the lowest point and the highest point of the past historical curve, calibrate the range between the lowest point and the highest point as the numerical activity range, randomly select a set of characteristic values ​​within the numerical activity range, and construct an associated vertical line perpendicular to the characteristic value, and the associated vertical line divides the past historical curve into upper and lower curve segments, wherein the upper curve segment is located above the associated vertical line, and the lower curve segment is located below the associated vertical line. By changing the specific value of the characteristic value, the associated vertical line is moved up and down. When the associated vertical line moves, the area and And the area sum of the lower curve segment and the associated vertical line, so that the associated vertical line moves to the point where the area sum of the upper and lower areas is equal, and stops, and this associated vertical line is calibrated as the standard vertical line. Specifically, the corresponding area sum is confirmed in the following way: after the associated vertical line is confirmed, based on the two front and rear end points of the past historical curve, a perpendicular line between the two front and rear end points and the associated vertical line is constructed, and the corresponding upper and lower areas can be confirmed. After the upper and lower areas are confirmed, the area sum of the upper and lower areas can be confirmed based on the up and down movement process of the associated vertical line. Therefore, when the associated vertical line moves, the area sum of the upper and lower areas can be effectively equalized.

[0048] S33, based on the calibrated standard vertical line, the standard vertical line is moved up and down by X1 characteristic value to lock a set of standard ranges, wherein X1 is a preset value, and its specific value is determined by the operator based on experience, and the standard vertical line for this characteristic change curve is confirmed in the same way as in step S32 from the past historical curve. Figure 2 , identify whether this standard vertical line belongs to the standard range:

[0049] If the standard vertical line is within the standard range, it means that the progress of the corresponding project in the current association period is normal and no processing is required;

[0050] If the standard vertical line is below the standard range, it means that the progress of the project is abnormal, and an abnormal signal is generated for direct display for external personnel to view. Based on the generated abnormal signal, external personnel can conduct comprehensive management of the actual progress of such projects, so as to achieve better project progress control effects;

[0051] If the standard vertical line is above the standard range, perform progress trend analysis:

[0052] S331, determine the comprehensive feature Z1 of the end point, and then determine the comprehensive feature Z2 of the point before the end point (that is, the point corresponding to the 30th comprehensive feature, whose end point is the 31st point in this curve), and use Z1-Z2=Bh to obtain the current change value Bh (because the standard vertical line is located above the standard range, the comprehensive feature confirmed by the subsequent end point must be higher than the comprehensive feature of the previous point);

[0053] S332, identifying a portion of the curve segment with an upward trend from the past historical curve, and determining the change value H of the endpoint value from the identified portion of the curve segment p , where p represents different partial curve segments, and H p = comprehensive characteristics of the end point of the partial curve segment - comprehensive characteristics of the initial point of the partial curve segment, from several change values ​​H p In the above example, we select the maximum value H p max;

[0054] S333, if Bh>H p max×α, where α is a preset value, which is set by the operator based on experience. Generally, it is set to 2.5, which means that the progress trend of the project is abnormally high, and an abnormally high signal is generated for display and for external personnel to view;

[0055] If Bh≤H p max×α represents the progress improvement of the corresponding project in the current association period, and generates a progress improvement signal for display for external personnel to view.

[0056] Third embodiment

[0057] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0058] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0059] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data processing method in Git repository management, characterized in that: The following steps are involved: Step 1: Define the monitoring association period, confirm the code data package that needs to be uploaded to the Git repository within this association period, and format the code data package to obtain the input data package based on the format standard associated with the corresponding branch item of the corresponding project; Step 2: Based on the multiple input data packets confirmed in the association period, based on the number and input capacity of the input data packets of the same branch item, determine the input characteristics of the corresponding branch item, and then determine the comprehensive characteristics of the corresponding item based on the input characteristics of multiple branch items within the corresponding item, and then generate a characteristic change curve belonging to the corresponding item based on the comprehensive characteristics of the corresponding item confirmed in the past association period and the comprehensive characteristics determined this time; Step 3: Based on the characteristic change curve confirmed by the corresponding project, perform characteristic analysis on the end point of this characteristic change curve, determine the corresponding change amplitude range based on the change amplitude of this characteristic change curve, and then confirm whether the numerical characteristics generated by this end point are abnormal, and display the signal.

2. A data processing method in Git repository management according to claim 1, characterized in that: In step 1, the specific method of formatting the code data packet is as follows: S11, based on the relevant mark of the code data packet, determine the branch item to which the code data packet belongs, and then determine the format standard associated with the branch item, different branch items correspond to different format standards, and the format standard is the interval space between adjacent codes; S12. Mark the relevant area in the code data packet where no code symbols exist as a blank area. Based on the determined format standard, adjust the blank area so that the internal arrangement format of the code data packet is consistent with the format of the code associated with the branch item to which it belongs. The format standard is the spacing between adjacent codes.

3. A data processing method in Git repository management according to claim 1, characterized in that: The association period in step 1 is a preset period.

4. The data processing method in Git repository management according to claim 1, characterized in that: In step 2, the specific sub-steps of generating the characteristic change curve of the corresponding project are: S21, correlating a number of input data packets confirmed by a branch item within a period, confirming the number of input data packets and marking it as G i-k , where i represents different projects and k represents different branch items, and then confirm the total amount of input data Z of several input data packages i-k The total amount of input data is the sum of the data capacity of several input data packets, using T i-k =G i-k ×C1+Z i-k ×C2 confirms the input feature T of the corresponding branch item i-k , where C1 and C2 are preset fixed coefficient factors, and the input features of other branch items are determined in the same way; S22, based on the different input features of different branch items in the same project T i-k The maximum and minimum values ​​of the input feature T are determined, a set of value ranges are determined, and values ​​are selected within this range, and the selected values ​​are calibrated as the selected value Z. i-k Calibrated as TD i-k , enter the feature T that is less than the selected value i-k Calibrated as TX i-k , using TD i-k -Z=CD i-k Confirm TD i-k The difference between Z and Z is calibrated as the height difference, using Z-TX i-k =CX i-k Confirm Z and TX i-k The difference between them is marked as low difference; When a selected value Z satisfies: the sum of height differences = the sum of low differences, the sum of height differences is obtained by summing up multiple height differences, and the sum of low differences is obtained by summing up multiple low differences, this selected value Z is marked as the comprehensive feature of this project; S23. Based on the comprehensive features confirmed by the project in this correlation cycle, the comprehensive features confirmed by the project in the past 30 groups of correlation cycles are identified from the historical completion data, and the feature change curve belonging to the project is generated according to the time sequence.

5. The data processing method in Git repository management according to claim 1, characterized in that: In step 3, the specific sub-steps for confirming whether the numerical feature generated by the end point is abnormal are: S31, based on the end point of the characteristic change curve, confirming a line segment between a point before the end point and an initial point of the characteristic change curve, and marking it as a past history curve; S32, based on the calibrated past historical curve, determine the lowest point and the highest point of the past historical curve, calibrate the range between the lowest point and the highest point as the numerical activity range, randomly select a set of characteristic values ​​within the numerical activity range, and construct an associated vertical line perpendicular to the characteristic value, and the associated vertical line divides the past historical curve into upper and lower curve segments, wherein the upper curve segment is located above the associated vertical line, and the lower curve segment is located below the associated vertical line, and the associated vertical line is moved up and down by changing the specific value of the characteristic value, and the associated vertical line is recorded when the associated vertical line moves, and the area sum of the upper curve segment and the associated vertical line and the area sum of the lower curve segment and the associated vertical line are recorded, and the associated vertical line is stopped when the area sum of the upper and lower areas is equal, and the associated vertical line is calibrated as the standard vertical line; S33, based on the calibrated standard vertical line, the standard vertical line is moved up and down by X1 characteristic value to lock a set of standard ranges, wherein X1 is a preset value, and the standard vertical line of the characteristic change curve is confirmed in the same manner as in step S32, to identify whether the standard vertical line belongs to the standard range: If the standard vertical line is below the standard range, it means that the progress of the project is abnormal, and an abnormal signal is generated for direct display.

6. A data processing method in Git repository management according to claim 5, characterized in that: The step S33 further comprises: If the standard vertical line is within the standard range, it means that the progress of the corresponding project in the current association period is normal and no processing is required.

7. A data processing method in Git repository management according to claim 5, characterized in that: The step S33 further comprises: If the standard vertical line is above the standard range, perform progress trend analysis: S331, determine the comprehensive feature Z1 of the end point, then determine the comprehensive feature Z2 of the point before the end point, and use Z1-Z2=Bh to obtain the current change value Bh; S332, identifying a portion of the curve segment with an upward trend from the past historical curve, and determining the change value H of the endpoint value from the identified portion of the curve segment p , where p represents different partial curve segments, and H p = comprehensive characteristics of the end point of the partial curve segment - comprehensive characteristics of the initial point of the partial curve segment, from several change values ​​H p In the above example, we select the maximum value H p max; S333, if Bh>H p max×α, where α is the preset value, represents an abnormally high change in the progress trend of this project, and generates an abnormally high signal for display.

8. A data processing method in Git repository management according to claim 7, characterized in that: In step S333, if Bh≤H p max×α represents the progress improvement of the corresponding project in the current association period, and generates a progress improvement signal for display.

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