Overhaul contract human resource allocation prediction system and method

Abstract

The invention relates to the technical field of nuclear power overhaul, and aims to solve the problem that human resource configuration cannot meet the implementation requirement of an overhaul plan task during overhaul. The invention provides an overhaul contract human resource configuration prediction system comprising a data acquisition module used for acquiring data, regional personnel access time record data and external overhaul work order outsourcing man-hour data from external work order management software; the data calculation module is used for calculating a coefficient relationship between historical overhaul total outsourcing working hours and human resource configuration, and calculating current overhaul human resource configuration data according to coefficient prediction; the total human resource coefficient configuration module is used for combining a plurality of historical overhaul human resource configuration coefficients to calculate a human resource configuration value coefficient for the current overhaul; and the data storage module is used for storing data of the data acquisition module, the data calculation module and the total human resource configuration module. The method can predict the number of human resources needing to be configured when the current overhaul is not executed.

Description

Technical Field

[0001] The invention relates to the technical field of nuclear power overhaul, and in particular to a human resource allocation prediction system and method for an overhaul contract. Background Art

[0002] During the current nuclear power plant overhaul, the execution of on-site work needs to rely on the work leader to lead the maintenance personnel. The work arrangement of the work leader is directly related to whether the planned work can be completed on schedule.

[0003] Normally, the on-site contractor will allocate the total number of personnel resources based on the contract amount and the overhaul plan. This method often results in the situation where the human resource allocation does not meet the overhaul plan task requirements in short overhauls with concentrated overhaul workloads. This problem can be solved by predicting human resource requirements based on the overhaul task workload combined with the overhaul plan task arrangement method. Summary of the invention

[0004] The purpose of the present invention is to provide a human resource allocation prediction system and method for an overhaul contract, so as to solve the problem that the human resource allocation during the overhaul period does not meet the requirements for implementing the overhaul plan tasks.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides a human resource allocation forecasting system for overhaul contracts, comprising:

[0007] Data acquisition module, used to obtain data from external work order management software, area personnel entry and exit time record data, and external overhaul work order outsourcing work time data;

[0008] The data calculation module is used to calculate the coefficient relationship between the total outsourced man-hours of historical overhaul and human resource allocation, and calculate the current overhaul human resource allocation data based on the coefficient prediction;

[0009] The total human resource coefficient configuration module is used to combine the human resource configuration coefficients of multiple historical overhauls to calculate the human resource configuration value coefficient for the current overhaul;

[0010] The data storage module is used to store data from the data acquisition module, the data calculation module, and the total human resource allocation module.

[0011] In some embodiments, the overhaul data includes the working time of the overhaul site personnel, the actual start time of the overhaul work order, the actual completion time, the planned start time, the planned completion time, the name of the person in charge of the work, the work order task number, the person in charge of the work order, and the outsourced work time code data of the work order.

[0012] In some embodiments, the data calculation module calculates the coefficient relationship between the total outsourced man-hours and the human resource allocation based on the total outsourced man-hours of historical overhaul work orders and the number of people actually involved on site.

[0013] In some embodiments, the data calculation module predicts and calculates the total number of human resources required to be allocated for the current overhaul outsourcing work order based on the total outsourcing work hours of historically completed overhauls, the coefficient relationship of human resource allocation, and the total outsourcing work hours of the current overhaul.

[0014] In some embodiments, the data storage module stores all data acquired by the data acquisition module from the external database, all historical overhaul human resource coefficients calculated by the data calculation module, the human resource coefficients calculated by the total human resource system configuration module, and the current overhaul human resource forecast calculation results obtained based on the configured human resource system.

[0015] On the other hand, the present invention also provides a method for predicting human resource allocation for overhaul contracts, comprising:

[0016] Step 1: Calculate the historical overhaul human resource allocation coefficient;

[0017] Step 2: Configure the human resource coefficient used for forecasting by the current overhaul;

[0018] Step 3: Calculate the estimated number of human resources currently deployed without major repairs.

[0019] In some embodiments, step 1 comprises:

[0020] Step 1.1: Calculate the converted working days for a historical overhaul work order;

[0021] Step 1.2: Calculate the total load of work orders per day in historical overhauls;

[0022] Step 1.3: Calculate the total manpower coefficient for historical overhauls.

[0023] In some embodiments, the calculation formula for converting working days is as follows:

[0024]

[0025] Among them, Hz is the total working hours converted from the work order, and Mz is the number of people converted from the work order.

[0026] In some embodiments, the coefficient calculation method includes a mean method total manpower coefficient calculation method and a weight method total manpower coefficient calculation method.

[0027] In some embodiments, after the total manpower coefficient Kt of the current overhaul is calculated, the number of human resources required for the overhaul can be predicted based on the total manpower coefficient Ka selected for the current overhaul. The calculation formula is as follows:

[0028] Wa=Ka / Kt;

[0029] The total manpower coefficient is used to predict the minimum number of on-site staff each day under the current planning window scheduling, thereby predicting the minimum number of on-site staff required during the peak period of the entire project workload.

[0030] Compared with the prior art, the overhaul contract human resource allocation prediction system and method provided by the present invention has the following beneficial effects:

[0031] The present invention predicts the number of human resources required for the current overhaul that has not been performed by calculating the ratio management of historical contract working hours and human resource allocation.

[0032] The present invention calculates the total manpower coefficient. After all work order plans have been arranged in the overhaul plan, the total number of people required on site every day during the overhaul can be intuitively seen before the overhaul, so as to find out the total number of human resources during the overhaul, thereby ensuring that there are sufficient personnel on any day during the overhaul.

[0033] By comparing the total human resource prediction value with the actual human resource allocation, the present invention can observe the accuracy of different human resource prediction methods, thereby achieving iterative improvement of the accuracy of human resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for the technical description.

[0035] Figure 1 A schematic diagram of the structure of the overhaul contract human resource allocation prediction system provided by the present invention;

[0036] Figure 2 A flow chart of the method for predicting human resource allocation for overhaul contracts provided by the present invention;

[0037] Figure 3 A schematic diagram of a method for calculating converted working hours for a work order provided by the present invention;

[0038] Figure 4 A schematic diagram of a method for calculating the total daily workload of a person in charge of a job provided by the present invention;

[0039] Figure 5 This is a schematic diagram of the total manpower coefficient iteration method provided by the present invention. DETAILED DESCRIPTION

[0040] The following is further explained in detail through specific implementation methods.

[0041] like Figure 1As shown, the present invention provides a human resource configuration prediction system for overhaul contracts, including a data acquisition module, a data calculation module, a total human resource coefficient configuration module, and a data storage module to achieve overhaul total human resource prediction.

[0042] The data acquisition module is used to obtain the actual start time, actual completion time, planned start time, planned completion time, name of the work supervisor, work order task number, work order work supervisor, and work order outsourced work time code data of the overhaul work order from the external work order management software, and to obtain the time data of the overhaul site personnel entering and leaving the work site from the external personnel entry and exit system.

[0043] The data calculation module is used to calculate the coefficient between the total outsourced hours of historical overhauls and the actual human resource allocation. This coefficient is also used to predict the demand for human resource allocation based on the current overhaul hours. The data calculation module needs to calculate the coefficient relationship between the total outsourced hours and human resource allocation based on the total outsourced hours of historical overhaul work orders and the actual number of people on site. Based on the coefficient relationship between the total outsourced hours and human resource allocation of historical completed overhauls, as well as the total outsourced hours of the current overhaul, the total number of human resources required for the current overhaul outsourced work order is predicted and calculated.

[0044] The total human resource coefficient configuration module is used to selectively call the human resource configuration coefficients calculated by multiple historical overhauls, and combine and calculate the available human resource configuration coefficients for use in the final human resource configuration forecast.

[0045] The combination calculation method of the coefficient can select different units, select up to 5 units for calculation according to the unit type or the power plant to which the unit belongs, and use different data processing methods, such as arithmetic mean or weight value calculation. According to the selected unit and calculation method, the overhaul human resource allocation prediction coefficient for the target unit is calculated.

[0046] The data storage module is used to store data from the data acquisition module, the data calculation module, and the total human resource coefficient configuration module. The data storage module can store all data acquired by the data acquisition module from the external database, all historical overhaul human resource coefficients calculated by the data calculation module, the human resource coefficients calculated by the total human resource system configuration module, and the current overhaul human resource forecast calculation results obtained based on the configured human resource system.

[0047] To achieve the prediction of total human resource allocation for comprehensive overhaul contracts, it is necessary to convert the working hours of historical overhaul work orders, calculate the total manpower coefficient of historical overhauls, and finally calculate the number of human resources that may need to be allocated for the current overhaul.

[0048] like Figures 2 to 5As shown, the present invention also provides a method for predicting human resource allocation for overhaul contracts, comprising the following steps:

[0049] Step 1: Calculate the historical overhaul human resource allocation coefficient. To calculate the historical overhaul human resource allocation coefficient, it is necessary to process the working time data of the historical overhaul work order, and then calculate the coefficient based on the actual personnel input. The specific steps are as follows:

[0050] Step 1.1: Calculate the converted working days Dz for a single work order of historical overhaul.

[0051] like Figure 2 As shown, the calculation of the converted working hours of a single work order in the historical overhaul contract needs to be divided into standard working hours and non-standard working hours, and the working hours are converted according to different methods, and finally the converted number of people Mz and converted working days Dz representing the converted workload of the work order converted total working hours Hz are obtained. The calculation formula is Hz = Mz × Dz.

[0052] The calculation method of standard working hours converted into working days Dz adopted in the present invention is as follows:

[0053] In outsourcing contracts for this type of work order, the composition of the work order hours is given by the items in the standard work hour quota manual. The calculation formula for the items in the standard work hour quota manual is as follows:

[0054] S=(w×m)×c×n1×η+n2×(p+r);

[0055] In the formula, S is the maintenance workload; w is the number of maintenance personnel; m is the maintenance man-day; c is the working time coefficient; p is the preparation time; r is the reporting time; n1 is the number of equipment; n2 is the auxiliary coefficient; η is the multiple-unit coefficient.

[0056] If a work order has n work order working time codes, the contract working time Hc of this work order is:

[0057]

[0058] The total working hours Hz converted from a work order is calculated based on the "maintenance personnel w" and "maintenance man-days m" of all the working hour codes used in the work order.

[0059] The total working hours converted from a work order in Hz is:

[0060]

[0061] The number of people converted into work orders is: Mz = max(W1, W2...W n );

[0062] The work order conversion working days are:

[0063] The calculation method of converting non-standard working hours into working days Dz adopted in the present invention is as follows:

[0064] The "converted number of people" and "converted working days" of non-standard working hours work orders are calculated based on the recommended working hours Hc used in the contract. The rules are as follows:

[0065] Take the square root of the converted number of people and round down. And M1≥2;

[0066] The converted working days are:

[0067] Step 1.2: Calculate the total load Ka of a single-day work order in historical overhauls.

[0068] Since each work order has a planned start and completion time given by the planner, the workload for this work order is the number of days required divided by the given planned days. The calculation formula is as follows:

[0069] Single work order load

[0070] Among them, the planning window Tp = Tf-Ts, Tf is the planned completion, and Ts is the planned start;

[0071] After accumulating all work order loads per day during the overhaul period, the total work order load Ka per day can be obtained as follows:

[0072]

[0073] Step 1.3: Calculate the total manpower coefficient for historical overhauls.

[0074] For historical overhaul, obtain the number of human resources invested during the execution of all work orders of the overhaul, and then calculate the total manpower coefficient. The specific method is as follows:

[0075] To calculate the total manpower coefficient for a single overhaul, we first determine the time period with the highest on-site human resource utilization. The staffing during this time period is the time when the headcount margin is the lowest and the staff output working hours efficiency is the highest.

[0076] Then calculate the total workload for each day during the entire overhaul period, and take the three days with the highest load to calculate the average value Ka, as follows:

[0077]

[0078] The average number of people on site during these three days is calculated as Wa, as follows:

[0079]

[0080] The total manpower coefficient Kt is obtained by division as follows:

[0081]

[0082] It should be noted that the total manpower coefficient represents the relationship between a certain amount of workload and the manpower invested on site. The total manpower coefficient is equal to the cumulative converted working hours of the work order during the current overhaul peak period divided by the total number of people invested on site during the current overhaul peak period. The calculation formula is as follows:

[0083] Kt=∑Hz / ∑Mt;

[0084] Step 2: Configure the human resource factor that the current overhaul uses for forecasting.

[0085] This step is used to calculate the available manpower coefficient Ka for the current overhaul. Due to the continuous improvement of overhaul management, equipment management, and maintenance technology, the total manpower coefficient between each overhaul will be slightly different. In theory, the later the overhaul is performed, the closer the data is to the reasonable value. Therefore, when designing the Kt value of the current overhaul from multiple overhaul data, two sets of coefficient calculation methods are provided, such as Figure 5 As shown, the details are as follows:

[0086] (1) Calculation method of total manpower factor using mean value method: Kt average value of the last five overhauls;

[0087] (2) Calculation method of total manpower coefficient using weighted method: The Kt of the last five overhauls are allocated with weights of 30%, 25%, 20%, 15%, and 10%, and are ranked in the order of higher weights as the overhaul is completed later.

[0088] Due to the differences in reactor types of various nuclear power units, which may lead to differences in the total manpower coefficient, two value selection methods are also set for the objects that have undergone the last five overhauls, as follows:

[0089] (1) Reactor type value selection method: All units with the same reactor type are involved in the value selection, and the five most recent overhauls are selected. This value selection method can minimize the problem of excessive deviation in the predicted value caused by the difference in the impact of different reactor types on the coefficient.

[0090] (2) Power plant value selection method: All units in the power plant are involved in the value selection, and the five most recently completed overhauls are selected. This value selection method can use data from other reactor types to make up for the problem of excessive deviation in predicted values ​​caused by insufficient parameters when the number of values ​​for the same reactor type is less than five.

[0091] Through different coefficient value ranges, calculation methods and value methods, 4 different calculation results can be combined as follows:

[0092] (1) Total manpower coefficient value algorithm 1: Same pile type average method. Take the five most recent sets of data of the same pile type to calculate the average value, and each data has the same weight.

[0093] (2) Total manpower coefficient value algorithm 2: Same pile type weight method. Take the five most recent sets of data of the same pile type to calculate the average value. The newer the data, the greater the weight.

[0094] (3) Total manpower coefficient value algorithm 3: Same power plant average method. Take the five most recent data sets of the same power plant to calculate the average value, and each data has the same weight.

[0095] (4) Total manpower coefficient value algorithm 4: Same power plant weight method. Take the five most recent data sets from the same power plant to calculate the average value. The newer the data, the greater the weight.

[0096] The final value selection method needs to be determined based on the amount of accumulated basic data. Since maintenance management requirements, skill improvement, and tool improvement have a certain impact on human investment, the more recent data is, the more referenceable it is. According to this principle, when the amount of data is sufficient, the calculation method of combination 2 is the best choice; but when the amount of data is insufficient, the other three combinations can also be used.

[0097] Step 3: Calculate the estimated number of human resources currently deployed without major repairs.

[0098] This step is used to predict the human resources Wa required for the current overhaul. After the total manpower coefficient Kt of the current overhaul is calculated, the number of human resources required for the overhaul can be predicted based on the total manpower coefficient Ka selected for the current overhaul. The calculation formula is as follows:

[0099] Wa=Ka / Kt;

[0100] The total manpower coefficient is used to predict the minimum number of on-site staff per day under the current planning window scheduling, thereby predicting the minimum number of on-site staff required during the peak workload of the entire project, providing a theoretical basis for project human resource allocation. When the overhaul construction window is shortened, the human resource allocation forecast results can also be updated in the first place to provide human resource allocation management support for overhaul preparation.

[0101] By comparing the total human resource forecast value with the actual human resource allocation, we can observe the accuracy of different human resource forecasting methods. Choosing an appropriate human resource allocation algorithm can achieve iterative improvement in the accuracy of human resource allocation.

[0102] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A human resource allocation prediction system for overhaul contracts, characterized in that: include: Data acquisition module, used to obtain data from external work order management software, area personnel entry and exit time record data, and external overhaul work order outsourcing work time data; The data calculation module is used to calculate the coefficient relationship between the total outsourced man-hours of historical overhaul and human resource allocation, and calculate the current overhaul human resource allocation data based on the coefficient prediction; The total human resource coefficient configuration module is used to combine the human resource configuration coefficients of multiple historical overhauls to calculate the human resource configuration value coefficient for the current overhaul; The data storage module is used to store data from the data acquisition module, the data calculation module, and the total human resource allocation module.

2. The overhaul contract human resource allocation prediction system according to claim 1, characterized in that: The overhaul work order related data includes the overhaul site personnel's working time, the actual start time of the overhaul work order, the actual completion time, the planned start time, the planned completion time, the name of the person in charge of the work, the work order task number, the person in charge of the work order, and the work order outsourcing working time code data.

3. The overhaul contract human resource allocation prediction system according to claim 1, characterized in that: The data calculation module calculates the coefficient relationship between the total outsourced working hours and human resource allocation based on the total outsourced working hours of historical overhaul work orders and the actual number of people involved on site.

4. The overhaul contract human resource allocation prediction system according to claim 1 or 3, characterized in that: The data calculation module predicts and calculates the total number of human resources required for the current overhaul outsourcing work order based on the total outsourcing work hours of the overhaul that has been completed in the past, the coefficient relationship of human resource allocation, and the total outsourcing work hours of the current overhaul.

5. The overhaul contract human resource allocation prediction system according to claim 1, characterized in that: The data storage module stores all data obtained by the data acquisition module from the external database, all historical overhaul human resource coefficients calculated by the data calculation module, the human resource coefficients calculated by the total human resource system configuration module, and the current overhaul human resource forecast calculation results obtained based on the configured human resource system.

6. A method for predicting human resource allocation for overhaul contracts, characterized in that: include: Step 1: Calculate the historical overhaul human resource allocation coefficient; Step 2: Configure the human resource coefficient used for forecasting by the current overhaul; Step 3: Calculate the estimated number of human resources currently deployed without major repairs.

7. The overhaul contract human resource allocation prediction system according to claim 6, characterized in that: Step 1 includes: Step 1.1: Calculate the converted working days for a single work order of historical overhaul; Step 1.2: Calculate the total load of work orders per day in historical overhauls; Step 1.3: Calculate the total manpower coefficient for historical overhauls.

8. The overhaul contract human resource allocation prediction system according to claim 7, characterized in that: The calculation formula for converted working days is as follows: Among them, Hz is the total working hours converted from the work order, and Mz is the number of people converted from the work order.

9. The overhaul contract human resource allocation prediction system according to claim 6, characterized in that: In step 2, the coefficient calculation method includes the mean method total manpower coefficient calculation method and the weight method total manpower coefficient calculation method.

10. The overhaul contract human resource allocation prediction system according to claim 6, characterized in that: In step 3, after the calculation of the total manpower coefficient Kt of the current overhaul is completed, the number of human resources required for the overhaul can be predicted based on the total manpower coefficient Ka selected for the current overhaul. The calculation formula is as follows: Wa=Ka / Kt; The total manpower coefficient is used to predict the minimum number of on-site staff each day under the current planning window scheduling, thereby predicting the minimum number of on-site staff required during the peak period of the entire project workload.

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