A method and system for automatically reviewing work time entry data

Abstract

The present invention provides a method and system for automatically reviewing man-hour entry data, which belongs to the technical field of man-hour management, and specifically comprises: determining a preset data volume interval of man-hour entry data, and combining the data volume of man-hour entry data of different corresponding objects on different dates, determining the abnormal data entry objects in the corresponding objects, and determining that there is no test phase in the clinical trial project whose data abnormality coefficient does not meet the requirements based on the test phase of the clinical trial project corresponding to the man-hour entry data of the abnormal data entry object, determining the review processing sequence of man-hour entry data of different clinical trial projects based on the data abnormality coefficients and the review processing difficulty in different test phases, and automatically reviewing and processing the man-hour entry data of different clinical trial projects using the review processing sequence, thereby ensuring the reliability of the review processing of the man-hour entry data.

Description

Technical Field

[0001] The present invention belongs to the technical field of work time management, and in particular relates to a method and system for automatically reviewing work time entry data. Background Art

[0002] In the clinical trials of new drugs, it is inevitable to face the review and management of working time data. In order to realize the review and management of working time data in the existing technical solutions, the invention patent application CN201710821967.X "A method for workshop production and processing scheduling based on working time data" realizes the reliable management of working time data through basic data entry, overall measurement, production scheduling, plan adjustment and plan execution. However, the existing technical solutions have the following technical problems:

[0003] When reviewing and processing the working time data, since the amount of data reported for working hours corresponding to different clinical trial projects varies, and the clinical trial projects corresponding to different working time reported data also vary, if the review and processing of working time reported data cannot be conducted based on clinical trial projects, the data reliability of clinical trial projects cannot be guaranteed.

[0004] In view of the above technical problems, the present invention provides a method and system for automatically reviewing work time entry data. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a method for automatically reviewing work time entry data is provided.

[0007] A method for automatically reviewing work time entry data, specifically comprising:

[0008] S1 divides the man-hour entry data into different clinical trial projects based on the corresponding objects of the man-hour entry data, determines the audit data type of the man-hour entry data of the clinical trial project, and determines the audit processing difficulty of the clinical trial project in combination with the daily change in the data volume of the man-hour entry data;

[0009] S2: when the audit processing difficulty of the clinical trial project meets the requirements, determine the preset data volume range of the man-hour input data, and determine the input data abnormal objects among the corresponding objects in combination with the data volume of the man-hour input data of different corresponding objects on different dates;

[0010] S3, when it is determined that there is no trial phase in which the data abnormality coefficient of the clinical trial project does not meet the requirements based on the trial phase of the clinical trial project corresponding to the working time input data of the input data abnormality object, proceed to the next step;

[0011] S4 determines the review and processing order of the labor time entry data of different clinical trial projects based on the data anomaly coefficient and the review and processing difficulty in different trial stages, and uses the review and processing order to automatically review and process the labor time entry data of different clinical trial projects.

[0012] The beneficial effects of the present invention are:

[0013] By determining the abnormal objects in the input data of the corresponding objects, the corresponding objects with data abnormalities are accurately located, which also lays the foundation for determining the data abnormality coefficients of clinical trial projects in different trial stages and improves the processing efficiency of abnormality identification.

[0014] Based on the trial phase of the clinical trial project corresponding to the working time entry data of the object with abnormal data entry, it is determined that there is no trial phase in the clinical trial project where the data abnormality coefficient does not meet the requirements. This realizes accurate assessment of the abnormal situation of the working time entry data of different clinical trial projects divided by the trial phase, improves the accuracy and reliability of the judgment of data abnormality, and also lays a foundation for improving the verification and processing efficiency of the working time entry data of clinical trial projects with more serious data abnormalities.

[0015] The audit and processing order of the working time entry data of different clinical trial projects is determined according to the data anomaly coefficient and the audit and processing difficulty in different trial stages, which realizes the audit and processing of the working time entry data based on the clinical trial projects. At the same time, it also ensures the audit and processing efficiency of the working time entry data of clinical trial projects with high data anomaly levels and low audit and processing difficulty, and ensures the reliability of the working time entry data of clinical trial projects.

[0016] A further technical solution is that the corresponding object is the test subject of the clinical trial project corresponding to the working time entry data, and is specifically determined according to the object corresponding to the working time entry data in the system.

[0017] A further technical solution is to divide the working time input data into different clinical trial projects, specifically including:

[0018] According to the clinical trial project corresponding to the corresponding object, the working time entry data is divided into different clinical trial projects.

[0019] A further technical solution is that the audit data type of the working time entry data includes the test results of clinical tests in different dimensions and the physical feedback data of the corresponding objects in different dimensions, which is specifically determined according to the type of clinical trial project corresponding to the working time entry data.

[0020] A further technical solution is that the daily variation of the data volume includes the data volume of the working time entry data between different dates and the variation of the data volume.

[0021] A further technical solution is that the method for determining the review processing order of the working time entry data of the clinical trial project is:

[0022] Determine the comprehensive data abnormality coefficient of the clinical trial project based on the data abnormality coefficients at different trial stages;

[0023] Based on the comprehensive data abnormality coefficient and the difficulty of audit processing, the audit processing priority values ​​of the man-hour entry data of different clinical trial projects are determined, and the audit processing order of the man-hour entry data of the clinical trial projects is determined using the audit processing priority values.

[0024] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for automatic review of work time entry data.

[0025] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0028] Figure 1 It is a flow chart of a method for automatically reviewing time entry data;

[0029] Figure 2 It is a flow chart of the method for determining the difficulty of review processing of clinical trial projects;

[0030] Figure 3 is a flow chart of a method for determining an abnormal object of input data in a corresponding object;

[0031] Figure 4 The present invention is a flowchart of a method for determining the review processing sequence of the working time entry data of a clinical trial project. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0033] Example 1

[0034] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for automatically reviewing working time entry data is provided, which specifically includes:

[0035] S1 divides the man-hour entry data into different clinical trial projects based on the corresponding objects of the man-hour entry data, determines the audit data type of the man-hour entry data of the clinical trial project, and determines the audit processing difficulty of the clinical trial project in combination with the daily change in the data volume of the man-hour entry data;

[0036] Furthermore, the corresponding object is a test subject of a clinical trial project corresponding to the working time entry data, and is specifically determined according to the object corresponding to the working time entry data in the system.

[0037] It should be noted that the working hours input data are divided into different clinical trial projects, including:

[0038] According to the clinical trial project corresponding to the corresponding object, the working time entry data is divided into different clinical trial projects.

[0039] Furthermore, the audit data types of the working time entry data include test results of clinical tests in different dimensions and physical feedback data of corresponding objects in different dimensions, which are specifically determined according to the type of clinical trial project corresponding to the working time entry data.

[0040] It can be understood that the daily variation of the data volume includes the data volume of the working time entry data between different dates and the variation of the data volume.

[0041] It should be noted that if Figure 2 As shown, the method for determining the difficulty of review and processing of the clinical trial project is:

[0042] Determine the data dimension of the audit data type of the man-hour entry data of the clinical trial project based on the audit data type of the man-hour entry data of the clinical trial project, and determine the audit difficulty of the data type of the clinical trial project based on the data dimension;

[0043] Determine the amount of man-hour entry data of the clinical trial project on different dates based on the daily variation of the amount of man-hour entry data of the clinical trial project, and determine the difficulty of data volume review of the clinical trial project by the average of the amount of man-hour entry data on different dates;

[0044] The audit processing difficulty of the clinical trial project is determined by the average value of the data volume audit difficulty and the data type audit difficulty.

[0045] It is understandable that when the difficulty of review and processing of the clinical trial project is not within the preset difficulty range, it is determined that the difficulty of review and processing of the clinical trial project does not meet the requirements.

[0046] Specifically, when the difficulty of the review and processing of the clinical trial project does not meet the requirements, the review and processing priority of the clinical trial project is set to the first level, and the review and processing order of the clinical trial project is determined by the total amount of working hour entry data of the clinical trial project.

[0047] In another embodiment, the method for determining the difficulty of the review process of the clinical trial project is:

[0048] Determine the data dimension of the audit data type of the man-hour entry data of the clinical trial project based on the audit data type of the man-hour entry data of the clinical trial project;

[0049] Determine the amount of the man-hour entry data of the clinical trial project on different dates based on the daily variation of the amount of the man-hour entry data of the clinical trial project, and determine the difficulty of object audit processing for different corresponding objects based on the data dimensions of the audit data types and the amount of man-hour entry data of different corresponding objects of the clinical trial project;

[0050] The object review and processing difficulties of different corresponding objects are normalized and then summed up to determine the review and processing difficulty of the clinical trial project.

[0051] Optionally, the review process for clinical trial projects includes the following steps:

[0052] S11 determines the data dimension of the audit data type of the man-hour entry data of the clinical trial project according to the audit data type of the man-hour entry data of the clinical trial project;

[0053] S12 determines the object audit processing difficulty of different corresponding objects based on the data dimensions of the audit data types of different corresponding objects of the clinical trial project and the data volume of the working time entry data. When the object audit processing difficulty does not meet the requirement but the number of corresponding objects meets the requirement, the object audit processing difficulties of different corresponding objects are normalized and then summed to determine the audit processing difficulty of the clinical trial project. When the requirement is not met, it is determined that the audit processing difficulty of the clinical trial project does not meet the requirement.

[0054] S2: when the audit processing difficulty of the clinical trial project meets the requirements, determine the preset data volume range of the man-hour input data, and determine the input data abnormal objects among the corresponding objects in combination with the data volume of the man-hour input data of different corresponding objects on different dates;

[0055] Furthermore, the preset data volume interval is determined according to the audit data type of the working time entry data, wherein the data volume interval corresponding to the audit data type determines the preset data volume interval.

[0056] Specifically, Figure 3 As shown, the method for determining the abnormal object of input data in the corresponding object is:

[0057] Based on the data volume of the working hours input data of the corresponding object on different dates, determine the date that is not in the preset data volume range, and use it as the data deviation date;

[0058] Whether the corresponding object is an input data abnormality object is determined according to the number of data deviation dates.

[0059] It can be understood that when the number of the data deviation dates is not within the preset date number range, the object is determined to be an input data abnormality object.

[0060] In another embodiment, the method for determining the abnormal object of input data in the corresponding object is:

[0061] Based on the data volume of the working hours input data of the corresponding object on different dates, determine the date that is not in the preset data volume range, and use it as the data deviation date;

[0062] Determine the data deviation coefficients for different data deviation dates according to the deviation between the data volume of the man-hour entry data on different data deviation dates and the endpoints of the preset data volume interval;

[0063] The object data anomaly coefficient of the corresponding object is determined according to the number of data deviation dates and the data deviation coefficients of different data deviation dates, and the object data anomaly coefficient is used to determine whether the corresponding object is an input data anomaly object.

[0064] Optionally, determining the abnormal object of input data in the corresponding object includes the following steps S21-S23:

[0065] S21 determines a date that is not in the preset data volume range based on the data volume of the working hours input data of the corresponding object on different dates, and uses it as a data deviation date;

[0066] Optionally, if and only if the number of data deviation dates meets the requirement, proceed to step S22, otherwise the corresponding object can be determined as an input data abnormal object;

[0067] S22 determines the data deviation coefficients of different data deviation dates according to the deviation between the data volume of the working time input data on different data deviation dates and the endpoints of the preset data volume interval, and determines the data deviation period using the interval date data between different data deviation dates;

[0068] S23 determines the period data anomaly coefficients of different data deviation periods based on the number of data deviation dates of different data deviation periods and the data deviation coefficients of different data deviation dates, determines the object data anomaly coefficient of the corresponding object based on the number of data deviation periods, the number of date intervals between the data deviation periods and the period data anomaly coefficients of different data deviation periods, and uses the object data anomaly coefficient to determine whether the corresponding object is an input data anomaly object.

[0069] S3, when it is determined that there is no trial phase in which the data abnormality coefficient of the clinical trial project does not meet the requirements based on the trial phase of the clinical trial project corresponding to the working time input data of the input data abnormality object, proceed to the next step;

[0070] Furthermore, the method for determining the data abnormality coefficient of the trial phase of the clinical trial project is:

[0071] According to the corresponding input time interval of the test phase, determining the working time input data of different input data abnormal objects within the input time interval;

[0072] Determine the number of data deviation dates of different input data abnormal objects based on the deviation of the data volume of the working time input data on different dates within the input time interval from the endpoints of the preset data volume interval, and determine the interval data abnormality coefficients of different input data abnormal objects based on the number of input data deviation dates within the input time interval;

[0073] The data anomaly coefficient of the test phase is determined by the interval data anomaly coefficients of different input data anomaly objects.

[0074] Specifically, the input time interval corresponding to the test phase is determined according to the preset interval corresponding to the test phase.

[0075] Optionally, the determination of the data abnormality coefficient of the trial phase of the clinical trial project includes steps S31-S33:

[0076] S31, according to the corresponding input time interval of the test phase, determining the working time input data of different input data abnormal objects within the input time interval;

[0077] Optionally, before entering step S32, it is also necessary to determine whether there are abnormal data entry objects with missing working time entry data within the entry time period. When the number of abnormal data entry objects with missing working time entry data does not meet the requirement, it can be directly determined that the data abnormality coefficient of the test phase does not meet the requirement. When the number of abnormal data entry objects with missing working time entry data meets the requirement, enter the next step;

[0078] S32: determining the number of data deviation dates of different input data abnormal objects based on the deviation of the data volume of the working time input data on different dates from the endpoints of the preset data volume interval within the input time interval, and determining the interval data abnormality coefficients of different input data abnormal objects based on the number of input data deviation dates within the input time interval and the deviation from the endpoints of the preset data volume interval;

[0079] S33 determines the data anomaly objects in the test phase through interval data anomaly coefficients of different input data anomaly objects, and determines the data anomaly coefficients of the test phase according to the number of data deviation objects.

[0080] Optionally, the above step S32 includes steps S321-S322:

[0081] S321 determines the number of data deviation dates of different input data abnormal objects based on the deviation between the data volume of the working time input data of different dates and the endpoints of the preset data volume interval within the input time interval. When the total number of data deviation dates does not meet the requirement, it is directly judged that the data abnormality coefficient of the test phase does not meet the requirement. When the total number of data deviation dates meets the requirement, enter step S322;

[0082] S322 determines the interval data anomaly coefficients of different input data anomaly objects based on the number of input data deviation dates within the input time interval and the deviations from the endpoints of the preset data volume interval. When the number of input data anomaly objects whose interval data anomaly coefficients do not meet the requirements does not meet the requirements, it is directly judged that the data anomaly coefficient of the test phase does not meet the requirements. When the number of input data anomaly objects whose interval data anomaly coefficients do not meet the requirements meets the requirements, proceed to step S33.

[0083] Furthermore, when there is a test period in which the data anomaly coefficient of the clinical trial project does not meet the requirements, the review processing priority of the clinical trial project is set to the second level, and the review processing order of the clinical trial project is determined by the total amount of working hour entry data of the clinical trial project.

[0084] S4 determines the review and processing order of the labor time entry data of different clinical trial projects based on the data anomaly coefficient and the review and processing difficulty in different trial stages, and uses the review and processing order to automatically review and process the labor time entry data of different clinical trial projects.

[0085] It should be noted that if Figure 4 As shown, the method for determining the review processing order of the working time entry data of the clinical trial project is:

[0086] Determine the comprehensive data abnormality coefficient of the clinical trial project based on the data abnormality coefficients at different trial stages;

[0087] Based on the comprehensive data abnormality coefficient and the difficulty of audit processing, the audit processing priority values ​​of the man-hour entry data of different clinical trial projects are determined, and the audit processing order of the man-hour entry data of the clinical trial projects is determined using the audit processing priority values.

[0088] Specifically, the comprehensive data anomaly coefficient is the average value of the data anomaly coefficients in different test stages.

[0089] Optionally, before entering step S12, it is also necessary to determine whether the data dimension of the audit data type of the working time entry data of the clinical trial project is greater than the preset data dimension, then enter step S111; when the data dimension of the audit data type of the working time entry data of the clinical trial project is greater than the preset data dimension, then enter step S12.

[0090] As an embodiment, S111-S112 include the following steps:

[0091] S111 obtains the number of objects corresponding to the man-hour entry data of the clinical trial project. When the number of objects corresponding to the man-hour entry data of the clinical trial project does not meet the requirement, it is determined that the review processing difficulty of the clinical trial project does not meet the requirement. When the number of objects corresponding to the man-hour entry data of the clinical trial project meets the requirement, proceeds to step S112;

[0092] S112 determines the amount of man-hour entry data of the clinical trial project on different dates based on the daily variation in the amount of man-hour entry data of the clinical trial project. When the amount of man-hour entry data does not meet the required number of dates, it returns to step S12. When the amount of man-hour entry data does not meet the required number of dates, it is determined that the difficulty of review and processing of the clinical trial project does not meet the requirements.

[0093] As an embodiment, step S22 also includes steps S221-S222:

[0094] S221 When there is a data deviation date whose data deviation coefficient does not meet the requirements, the corresponding object can be directly judged as an input data abnormal object. When there is no data deviation date whose data deviation coefficient does not meet the requirements, proceed to the next step;

[0095] S222 uses the interval date data between different data deviation dates to determine the data deviation period. When the longest duration of the data deviation period does not meet the requirements, the corresponding object can be directly judged as an input data abnormal object. When the longest duration of the data deviation period meets the requirements, enter step S23;

[0096] Optionally, before determining the object data anomaly coefficient, it is also necessary to determine whether there is a data deviation period in which the period data anomaly coefficient does not meet the requirements. When there is a data deviation period in which the period data anomaly coefficient does not meet the requirements, the corresponding object can be directly determined to be an input data anomaly object. When there is no data deviation period in which the period data anomaly coefficient does not meet the requirements, the object data anomaly coefficient is determined again.

[0097] Example 2

[0098] On the other hand, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, the above-mentioned method of automatic review of work time entry data is executed.

[0099] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0100] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for automatically reviewing work time entry data, characterized in that: Specifically include: Based on the corresponding objects of the working time entry data, the working time entry data is divided into different clinical trial projects, the audit data type of the working time entry data of the clinical trial project is determined, and the audit processing difficulty of the clinical trial project is determined in combination with the daily changes in the data volume of the working time entry data; When the audit processing difficulty of the clinical trial project meets the requirements, determine the preset data volume range of the man-hour entry data, and determine the abnormal entry data objects among the corresponding objects in combination with the data volume of the man-hour entry data of different corresponding objects on different dates; When it is determined that there is no trial phase in which the data abnormality coefficient of the clinical trial project does not meet the requirements based on the trial phase of the clinical trial project corresponding to the working time input data of the input data abnormal object, proceed to the next step; Determine the review and processing sequence of the working time input data of different clinical trial projects based on the data abnormality coefficient and review and processing difficulty at different trial stages, and use the review and processing sequence to automatically review and process the working time input data of different clinical trial projects; The method for determining the difficulty of review and processing of the clinical trial project is: Determine the data dimension of the audit data type of the man-hour entry data of the clinical trial project based on the audit data type of the man-hour entry data of the clinical trial project, and determine the audit difficulty of the data type of the clinical trial project based on the data dimension; Determine the amount of man-hour entry data of the clinical trial project on different dates based on the daily variation of the amount of man-hour entry data of the clinical trial project, and determine the difficulty of data volume review of the clinical trial project by the average of the amount of man-hour entry data on different dates; The audit processing difficulty of the clinical trial project is determined by the average value of the data volume audit difficulty and the data type audit difficulty.

2. The method for automatically reviewing work time entry data according to claim 1, characterized in that: The corresponding object is the test object of the clinical trial project corresponding to the working time entry data, and is specifically determined according to the object corresponding to the working time entry data in the system.

3. The method for automatically reviewing work time entry data according to claim 1, characterized in that: The working hours input data are divided into different clinical trial projects, including: According to the clinical trial project corresponding to the corresponding object, the working time entry data is divided into different clinical trial projects.

4. The method for automatically reviewing work time entry data according to claim 1, characterized in that: The audit data types of the working time entry data include the test results of clinical tests in different dimensions and the physical feedback data of the corresponding objects in different dimensions, which are specifically determined according to the type of clinical trial project corresponding to the working time entry data.

5. The method for automatically reviewing work time entry data according to claim 1, characterized in that: The daily variation of the data volume includes the data volume of the working time input data between different dates and the variation of the data volume.

6. The method for automatically reviewing work time entry data according to claim 1, characterized in that: When the difficulty of reviewing and processing the clinical trial project is not within the preset difficulty range, it is determined that the difficulty of reviewing and processing the clinical trial project does not meet the requirements.

7. The method for automatically reviewing work time entry data according to claim 1, characterized in that: The method for determining the review and processing order of the working time entry data of the clinical trial project is: Determine the comprehensive data abnormality coefficient of the clinical trial project based on the data abnormality coefficients at different trial stages; Based on the comprehensive data abnormality coefficient and the difficulty of audit processing, the audit processing priority values ​​of the man-hour entry data of different clinical trial projects are determined, and the audit processing order of the man-hour entry data of the clinical trial projects is determined using the audit processing priority values.

8. The method for automatically reviewing work time entry data according to claim 7, characterized in that: The comprehensive data anomaly coefficient is the average value of the data anomaly coefficients in different test stages.

9. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes a method for automatically reviewing work time entry data as described in any one of claims 1-8 when running the computer program.

Images