Battery detection task equipment, personnel scheduling method and device, and electronic equipment

Abstract

The invention provides battery detection task equipment, a personnel scheduling method and device and electronic equipment, and the method comprises the steps: constructing a first integer programming model based on a target battery detection task information set and a detection item information set, and solving to obtain the respective processing duration of N target battery detection tasks; based on the respective processing duration of the N target battery detection tasks and the detection equipment information set, constructing a first mixed integer programming model, and solving to obtain an equipment scheduling result, the equipment scheduling result being used for indicating whether the detection equipment executes the target battery detection tasks or not; and based on the equipment scheduling result and the detection personnel information set, constructing a second mixed integer programming model, and solving to obtain a personnel scheduling result which is used for indicating whether the detection personnel execute the target battery detection task or not. Therefore, automatic and accurate scheduling of equipment and personnel of the battery detection task can be realized, and the scheduling efficiency of the battery detection task is improved.

Description

Technical Field

[0001] The present application relates to the field of energy and power technology, and in particular to a method, device, electronic device and storage medium for scheduling equipment and personnel for battery testing tasks. Background Art

[0002] At present, most batteries need to be tested before and during use to detect abnormalities in the battery and ensure reliable operation of the battery. When performing multiple battery testing tasks, equipment and personnel need to be scheduled. However, in the related art, there is a problem of low efficiency in scheduling battery testing tasks. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first objective of the present application is to propose a method for scheduling equipment and personnel for battery testing tasks.

[0005] The second objective of the present application is to provide a device for scheduling equipment and personnel for battery testing tasks.

[0006] The third objective of the present application is to provide an electronic device.

[0007] A fourth objective of the present application is to provide a computer-readable storage medium.

[0008] A fifth object of the present application is to provide a computer program product.

[0009] To achieve the above-mentioned objectives, the first aspect of the present application proposes a method for scheduling equipment and personnel for battery detection tasks, including: based on a target battery detection task information set and a detection item information set, constructing a first integer programming model, and solving the first integer programming model to obtain the processing time of each of N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection item information set includes the project information of each of the S detection items, and N and S are both positive integers; based on the processing time of each of the N target battery detection tasks and the detection equipment information set, constructing a first mixed integer programming model, and solving the first mixed integer programming model. , obtaining the equipment scheduling result, wherein the detection equipment information set includes the equipment information of each of the M detection equipment, and the equipment scheduling result is used to indicate whether the i-th detection equipment performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N; based on the equipment scheduling result and the detection personnel information set, constructing a second mixed integer programming model, and solving the second mixed integer programming model to obtain the personnel scheduling result, wherein the detection personnel information set includes the personnel information of each of the Q detection personnel, and the personnel scheduling result is used to indicate whether the k-th detection personnel performs the j-th target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

[0010] To achieve the above-mentioned purpose, the second aspect of the present application proposes a device for scheduling equipment and personnel for battery detection tasks, including: a processing module, which is used to construct a first integer programming model based on a target battery detection task information set and a detection item information set, and solve the first integer programming model to obtain the processing time of each of N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection item information set includes the project information of each of the S detection items, and N and S are both positive integers; a first scheduling module, which is used to construct a first mixed integer programming model based on the processing time of each of the N target battery detection tasks and the detection equipment information set, and solve the first mixed integer programming model Solving to obtain equipment scheduling results, wherein the detection equipment information set includes the equipment information of each of M detection equipment, and the equipment scheduling result is used to indicate whether the i-th detection equipment performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N; a second scheduling module is used to construct a second mixed integer programming model based on the equipment scheduling results and the detection personnel information set, and solve the second mixed integer programming model to obtain personnel scheduling results, wherein the detection personnel information set includes the personnel information of each of Q detection personnel, and the personnel scheduling result is used to indicate whether the k-th detection personnel performs the j-th target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

[0011] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect embodiment above.

[0012] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method described in the first aspect embodiment above.

[0013] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0014] The equipment and personnel scheduling method, device, electronic device and storage medium for battery detection tasks provided in the present application can consider the target battery detection task information set and the detection item information set, construct a first integer programming model, and solve the first integer programming model to obtain the processing time of each of the N target battery detection tasks, and consider the processing time of each of the N target battery detection tasks and the detection equipment information set, construct a first mixed integer programming model, and solve the first mixed integer programming model to obtain the equipment scheduling result, and consider the equipment scheduling result and the detection personnel information set, construct a second mixed integer programming model, and solve the second mixed integer programming model to obtain the personnel scheduling result, which can realize automatic and accurate scheduling of equipment and personnel for battery detection tasks, thereby improving the scheduling efficiency of battery detection tasks.

[0015] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0017] Figure 1 A flowchart of a method for scheduling equipment and personnel for a battery testing task provided in an embodiment of the present application;

[0018] Figure 2 A flowchart of another method for scheduling equipment and personnel for battery testing tasks provided in an embodiment of the present application;

[0019] Figure 3 A flowchart of another method for scheduling equipment and personnel for battery testing tasks provided in an embodiment of the present application;

[0020] Figure 4 A schematic diagram of a method for scheduling equipment and personnel for a battery testing task provided in an embodiment of the present application;

[0021] Figure 5 A schematic diagram of the structure of a battery detection task device and a personnel scheduling device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0023] The following describes the equipment and personnel scheduling method, device, electronic device and storage medium for battery detection tasks in an embodiment of the present application with reference to the accompanying drawings.

[0024] Figure 1 A flowchart of a method for scheduling equipment and personnel for a battery testing task provided in an embodiment of the present application.

[0025] At present, most batteries need to be tested before and during use to detect abnormalities in the battery and ensure reliable operation of the battery. When performing multiple battery testing tasks, equipment and personnel need to be scheduled. However, in the related art, there is a problem of low efficiency in scheduling battery testing tasks.

[0026] To address this problem, an embodiment of the present application provides a method for scheduling equipment and personnel for battery testing tasks. The method can take into account the target battery testing task information set and the testing item information set, construct a first integer programming model, and solve the first integer programming model to obtain the processing time of each of the N target battery testing tasks. The method can also take into account the processing time of each of the N target battery testing tasks and the testing equipment information set, construct a first mixed integer programming model, and solve the first mixed integer programming model to obtain the equipment scheduling result. The method can also take into account the equipment scheduling result and the testing personnel information set, construct a second mixed integer programming model, and solve the second mixed integer programming model to obtain the personnel scheduling result. The method can realize automatic and accurate scheduling of equipment and personnel for battery testing tasks, thereby improving the scheduling efficiency of battery testing tasks.

[0027] like Figure 1 As shown, the method comprises the following steps:

[0028] S101, based on the target battery detection task information set and the detection project information set, construct a first integer programming model, and solve the first integer programming model to obtain the processing time of each of N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection project information set includes the project information of each of the S detection projects, and N and S are both positive integers.

[0029] It should be noted that the target battery detection task refers to the battery detection task to be performed, and there are no excessive restrictions on the battery detection task, for example, it may include detection tasks such as battery capacity, battery internal resistance, battery life, and battery temperature characteristics. There are no excessive restrictions on the task information of the target battery detection task, for example, it may include detection content, detection requirements, etc. There are no excessive restrictions on the project information of each detection item, for example, it may include detection content, processing time of the detection item, etc.

[0030] It should be noted that the first IP (Integer Programming) model may adopt any integer programming model in the related art, and no further limitation is made here. The processing time of a battery detection task is the processing time of executing the battery detection task once, that is, the single processing time.

[0031] In one embodiment, the first integer programming model is solved to obtain the processing time of each of the N target battery detection tasks, including solving the first integer programming model to obtain a project configuration result, wherein the project configuration result is used to indicate whether the j-th target battery detection task includes the p-th detection item, p is a positive integer not greater than S, and based on the project configuration result, the processing time of each of the N target battery detection tasks is obtained. It can be understood that a battery detection task may include at least one detection item, and different battery detection tasks may include different detection items. In this embodiment, the project configuration result is a decision variable of the first integer programming model, such as a binary variable.

[0032] In some examples, based on the project configuration results, the processing time of each of the N target battery detection tasks is obtained, including obtaining the sum of the processing time of each detection item included in the j-th target battery detection task as the processing time of the j-th target battery detection task.

[0033] Exemplarily, obtaining the processing time of the detection item includes determining the processing time of the detection item based on a historical battery detection task information set, wherein the historical battery detection task information set includes task information of each of a plurality of historical battery detection tasks.

[0034] It should be noted that there are no excessive restrictions on the task information of historical battery detection tasks, such as the detection content and detection requirements of historical battery detection tasks, the detection items included in historical battery detection tasks, the historical processing time of historical battery detection tasks, the historical processing time of each of the S detection items, the historical basic time of each of the S detection items, the additional processing time of each of the S detection items, the historical start time and historical end time of the historical battery detection tasks, the historical handover time of the historical battery detection tasks, etc. Among them, the historical processing time of the pth detection item is the sum of the historical basic time of the pth detection item and the historical additional time of the pth detection item.

[0035] For example, based on the historical battery detection task information set, determine the processing time of each detection item included in the j-th target battery detection task, including determining multiple historical processing times of the p-th detection task from the historical battery detection task information set, and obtaining the average of the multiple historical processing times of the p-th detection task as the processing time of the p-th detection item.

[0036] For example, the processing time of the p-th detection item is the sum of the basic time of the p-th detection item and the additional time of the p-th detection item.

[0037] Exemplarily, obtaining the processing time of the detection item includes determining the processing time of the detection item from the detection item information set. It is understandable that the detection item information set may include the processing time of each of the S detection items.

[0038] In one embodiment, a first integer programming model is constructed based on the target battery detection task information set, including taking the number of detection items included in the j-th target battery detection task to be greater than or equal to the first set threshold as the j-th first constraint condition, taking the minimum processing time of the j-th target battery detection task as the j-th first optimization goal, and constructing the first integer programming model based on the target battery detection task information set, the historical battery detection task information set, the first constraint condition and the first optimization goal, wherein the historical battery detection task information set includes the task information of each of the multiple historical battery detection tasks. Thus, the number of detection items included in the target battery detection task to be greater than or equal to the first set threshold can be taken as the first constraint condition, so that the detection items included in the determined target battery detection task meet the constraint of a larger number of detection items, and taking the minimum processing time of the target battery detection task as the first optimization goal, which helps to obtain a smaller processing time of the target battery detection task and improve the battery detection efficiency.

[0039] It should be noted that the number of first constraint conditions is ≤ N, and the number of first optimization targets is ≤ N. The first set thresholds corresponding to different target battery detection tasks may be different.

[0040] In some examples, a first integer programming model is solved to obtain the processing time of each of N target battery detection tasks, including constructing a first objective function between the project configuration result and the processing time of the j-th target battery detection task, wherein the independent variable of the first objective function includes the project configuration result, and the dependent variable of the first objective function includes the processing time of the j-th target battery detection task. The first objective function is solved under the first constraint condition to obtain the project configuration result when the processing time of the j-th target battery detection task is the smallest, as the final project configuration result.

[0041] For example, the first integer programming model is as follows:

[0042]

[0043] Among them, t j is the processing time of the jth target battery detection task, b p is the basic duration of the pth test item, e p is the additional duration of the pth test item, zp =1 is used to indicate that the jth target battery detection task contains the pth detection item, z p =0 is used to indicate that the j-th target battery detection task does not include the p-th detection item, and h is the first set threshold.

[0044] in, is the first objective function, is the jth first constraint.

[0045] It should be noted that the first integer programming model can be solved by using any integer programming model solving method in the related art, which is not limited here. Among them, the integer programming model solving method includes a linear programming solver, a gradient descent method, etc.

[0046] S102, based on the processing time of each of the N target battery detection tasks and the detection equipment information set, construct a first mixed integer programming model, and solve the first mixed integer programming model to obtain an equipment scheduling result, wherein the detection equipment information set includes the equipment information of each of the M detection equipment, and the equipment scheduling result is used to indicate whether the i-th detection equipment performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N.

[0047] It should be noted that the testing equipment is used for battery testing, and there are no excessive restrictions on the testing equipment, such as capacity testers, internal resistance testers, cycle life testers, temperature characteristics testers, etc. There are no excessive restrictions on equipment information, such as equipment capacity, equipment testing capabilities, equipment model, equipment availability, etc.

[0048] It should be noted that the first MIP (Mixed Integer Programming) model can adopt any mixed integer programming model in the relevant technology, and no further limitation is made here. The equipment scheduling result is a decision variable of the first mixed integer programming model, for example, the equipment scheduling result is a binary variable.

[0049] It should be noted that the first mixed integer programming model can be solved by using any mixed integer programming model solving method in the related art, which is not limited here. The mixed integer programming model solving method includes a linear programming solver, a gradient descent method, and the like.

[0050] S103, based on the equipment scheduling result and the detection personnel information set, construct a second mixed integer programming model, and solve the second mixed integer programming model to obtain the personnel scheduling result, wherein the detection personnel information set includes the personnel information of Q detection personnel respectively, and the personnel scheduling result is used to indicate whether the k-th detection personnel performs the j-th target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

[0051] It should be noted that the testing personnel refers to the personnel used for battery testing, and there is no excessive limitation on the personnel information. For example, it may include the testing personnel's qualifications, capabilities, attendance (such as duty, vacation), etc.

[0052] It should be noted that the second mixed integer programming model can adopt any mixed integer programming model in the related art, and no further limitation is made here. The staff scheduling result is a decision variable of the second mixed integer programming model, for example, the staff scheduling result is a binary variable.

[0053] It should be noted that the second mixed integer programming model can be solved by using any mixed integer programming model solving method in the related art, which is not limited here. The mixed integer programming model solving method includes a linear programming solver, a gradient descent method, and the like.

[0054] It should be noted that the first integer programming model, the first mixed integer programming model, and the second mixed integer programming model can be solved jointly or independently, and no excessive restrictions are made here.

[0055] In one implementation, the first integer programming model, the first mixed integer programming model, and the second mixed integer programming model may be jointly solved to obtain the processing duration, equipment scheduling results, and personnel scheduling results of each of the N target battery detection tasks.

[0056] In summary, according to the method for scheduling equipment and personnel for battery detection tasks in the embodiment of the present application, the target battery detection task information set and the detection item information set can be taken into consideration to construct a first integer programming model, and the first integer programming model is solved to obtain the processing time of each of the N target battery detection tasks. In addition, considering the processing time of each of the N target battery detection tasks and the detection equipment information set, a first mixed integer programming model is constructed, and the first mixed integer programming model is solved to obtain the equipment scheduling result. In addition, considering the equipment scheduling result and the detection personnel information set, a second mixed integer programming model is constructed, and the second mixed integer programming model is solved to obtain the personnel scheduling result. Automatic and accurate scheduling of equipment and personnel for battery detection tasks can be realized, thereby improving the scheduling efficiency of battery detection tasks.

[0057] The embodiment of the present application provides another method for scheduling equipment and personnel for battery testing tasks.

[0058] like Figure 2 As shown, the method may include the following steps:

[0059] S201, based on the target battery detection task information set and the detection project information set, construct a first integer programming model, and solve the first integer programming model to obtain the processing time of each of N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection project information set includes the project information of each of the S detection projects, and N and S are both positive integers.

[0060] For the relevant contents of step S201, please refer to the above embodiment, which will not be described again here.

[0061] S202, obtaining equipment scheduling constraints of M detection equipment.

[0062] In one implementation, the equipment scheduling constraints include time constraints for N target battery detection tasks.

[0063] In some examples, time constraints are obtained, including setting the processing end time of the j-th target battery detection task to be no later than the set time as the j-th second constraint, if j≤N-1, setting the processing start time of the j+1-th target battery detection task to be later than the processing end time of the j-th target battery detection task as the j-th third constraint, if j≤N-1, setting the processing start time of the j+1-th target battery detection task to be later than the handover time of the j-th target battery detection task as the j-th fourth constraint, setting the total processing time of N target battery detection tasks to be less than or equal to the second set threshold as the fifth constraint, and setting at least one of the second constraint, the third constraint, the fourth constraint and the fifth constraint as the time constraint.

[0064] It should be noted that the number of the second constraint conditions is ≤N, the number of the third constraint conditions is ≤N-1, and the number of the fourth constraint conditions is ≤N-1. The set time refers to the deadline of the battery detection task. The set time corresponding to different target battery detection tasks may be different. The handover time of the target battery detection task is equal to or later than the processing end time of the target battery detection task.

[0065] In some examples, when the i-th detection device performs the j-th target battery detection task, the device scheduling result also includes the time period during which the i-th detection device performs the j-th target battery detection task.

[0066] In one embodiment, the equipment scheduling constraint condition includes that one target battery detection task is executed by one detection device, and one detection device only executes one target battery detection task at the same time.

[0067] S203, minimizing the total processing time of the N target battery detection tasks is taken as the second optimization goal.

[0068] S204: construct a first mixed integer programming model based on the processing time of each of the N target battery detection tasks, the detection equipment information set, the equipment scheduling constraints, and the second optimization goal.

[0069] For example, the first mixed integer programming model is as follows:

[0070]

[0071] Among them, x ij =1 is used to instruct the i-th detection device to perform the j-th target battery detection task, x ij =0 is used to indicate that the i-th detection device does not perform the j-th target battery detection task, d j is the set time corresponding to the jth target battery detection task, e j-1 is the handover time of the j-1th target battery detection task, Used to characterize the second optimization objective.

[0072] S205, solving the first mixed integer programming model to obtain equipment scheduling results.

[0073] In one embodiment, the first mixed integer programming model is solved to obtain an equipment scheduling result, including constructing a second objective function between the equipment scheduling result and the total processing time of N target battery detection tasks, wherein the independent variable of the second objective function includes the equipment scheduling result, and the dependent variable of the second objective function includes the total processing time of the N target battery detection tasks. The second objective function is solved under the equipment scheduling constraint condition to obtain the equipment scheduling result when the total processing time of the N target battery detection tasks is the smallest, which is used as the final equipment scheduling result.

[0074] S206. Based on the equipment scheduling result and the detection personnel information set, a second mixed integer programming model is constructed, and the second mixed integer programming model is solved to obtain the personnel scheduling result, wherein the detection personnel information set includes the personnel information of Q detection personnel respectively, and the personnel scheduling result is used to indicate whether the k-th detection personnel performs the j-th target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

[0075] For the relevant contents of steps S205-S206, please refer to the above embodiment and will not be repeated here.

[0076] In summary, according to the equipment and personnel scheduling method for battery detection tasks in the embodiment of the present application, the equipment scheduling constraints of M detection equipment are obtained, and the minimum total processing time of N target battery detection tasks is taken as the second optimization goal. Based on the respective processing time of the N target battery detection tasks, the detection equipment information set, the equipment scheduling constraints and the second optimization goal, a first mixed integer programming model is constructed. In this way, the determined equipment scheduling results can meet the equipment scheduling constraints, and it is helpful to obtain a smaller total processing time of N target battery detection tasks, which can improve the battery detection efficiency.

[0077] The embodiment of the present application provides another method for scheduling equipment and personnel for battery testing tasks.

[0078] like Figure 3 As shown, the method may include the following steps:

[0079] S301, based on the target battery detection task information set and the detection project information set, construct a first integer programming model, and solve the first integer programming model to obtain the processing time of each of N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection project information set includes the project information of each of the S detection projects, and N and S are both positive integers.

[0080] S302, based on the processing time of each of the N target battery detection tasks and the detection equipment information set, construct a first mixed integer programming model, and solve the first mixed integer programming model to obtain an equipment scheduling result, wherein the detection equipment information set includes the equipment information of each of the M detection equipment, and the equipment scheduling result is used to indicate whether the i-th detection equipment performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N.

[0081] For the relevant contents of steps S301 - S302 , please refer to the above embodiments and will not be described again here.

[0082] S303, obtaining the personnel scheduling constraints of Q inspection personnel.

[0083] In one embodiment, the personnel scheduling constraints of Q inspection personnel are obtained, including that the inspection personnel who perform the j-th target battery inspection task have set qualifications as the j-th sixth constraint, the total working hours of the k-th inspection personnel are within the first set range as the k-th seventh constraint, the number of tasks performed by the k-th inspection personnel is within the second set range as the k-th eighth constraint, and at least one of the sixth constraint, the seventh constraint and the eighth constraint is used as a personnel scheduling constraint.

[0084] It should be noted that the number of the sixth constraint conditions is ≤ N, the number of the seventh constraint conditions is ≤ Q, and the number of the eighth constraint conditions is ≤ Q. Different target battery detection tasks may correspond to different set qualifications, different detection personnel may correspond to different first set ranges, and different detection personnel may correspond to different second set ranges.

[0085] In one embodiment, the personnel scheduling constraint condition includes that one target battery detection task is performed by one detection personnel, and one detection personnel only performs one target battery detection task at the same time.

[0086] S304, minimizing the difference between the total working hours of the Q inspectors is taken as the third optimization goal.

[0087] S305: construct a second mixed integer programming model based on the equipment scheduling result, the detection personnel information set, the personnel scheduling constraints and the third optimization goal.

[0088] For example, the second mixed integer programming model is as follows:

[0089]

[0090] Among them, y jk =1 is used to instruct the kth inspector to perform the jth target battery inspection task, y jk =0 is used to indicate that the kth inspector does not perform the jth target battery inspection task.

[0091] S306, solving the second mixed integer programming model to obtain a personnel scheduling result.

[0092] In one embodiment, the second mixed integer programming model is solved to obtain a personnel scheduling result, including constructing a third objective function between the personnel scheduling result and the difference between the total working hours of each of the Q detection personnel, wherein the independent variable of the third objective function includes the personnel scheduling result, and the dependent variable of the third objective function includes the difference between the total working hours of each of the Q detection personnel. The third objective function is solved under the personnel scheduling constraint condition to obtain a personnel scheduling result when the difference between the total working hours of each of the Q detection personnel is minimized, which is used as the final personnel scheduling result.

[0093] For the relevant contents of step S306, please refer to the above embodiment, which will not be repeated here.

[0094] In summary, according to the equipment and personnel scheduling method for the battery detection task of the embodiment of the present application, the personnel scheduling constraints of Q detection personnel are obtained, and the difference between the total working hours of each of the Q detection personnel is minimized as the third optimization goal. Based on the equipment scheduling results, the detection personnel information set, the personnel scheduling constraints and the third optimization goal, a second mixed integer programming model is constructed. In this way, the determined personnel scheduling results can meet the personnel scheduling constraints, and the difference between the total working hours of each of the Q detection personnel is small, that is, the total working hours of different detection personnel are as uniform as possible, which improves the balance and rationality of personnel scheduling.

[0095] Based on any of the above embodiments, the method further includes configuring and / or adjusting the target constraint condition based on the scheduling requirements of the user group. It is understandable that the scheduling requirements of different user groups may be different. Therefore, the target constraint condition can be configured and / or adjusted in consideration of the scheduling requirements of the user group, and personalized configuration and / or personalized adjustment of the target constraint condition can be achieved.

[0096] It should be noted that the target constraint condition includes at least one of the first constraint condition, the equipment scheduling constraint condition, and the personnel scheduling constraint condition.

[0097] In one implementation, a scheduling rule verification library can also be built, and a rule modification interface with higher rule modification authority can be set. The rule verification library can be connected with scheduling functions such as manual scheduling and staff shift changes to achieve seamless connection and fully guarantee the smooth progress of the inspection task. The verification rules include but are not limited to: task time processing time verification, personnel qualification inspection verification, attendance conflict verification, regular retraining qualification verification, equipment maintenance inspection, etc. Each of the above verification rules can be set separately to see if it is effective.

[0098] Based on any of the above embodiments, Figure 4 As shown, a first integer programming model can be constructed based on the target battery detection task information set, the detection item information set, the historical battery detection task information set, the first constraint condition and the first optimization goal, and the first integer programming model can be solved to obtain the processing time of each of the N target battery detection tasks.

[0099] Based on the processing time of each of the N target battery detection tasks, the detection equipment information set, the equipment scheduling constraints and the second optimization goal, a first mixed integer programming model is constructed, and the first mixed integer programming model is solved to obtain the equipment scheduling result.

[0100] Based on the equipment scheduling results, the detection personnel information set, the personnel scheduling constraints and the third optimization goal, a second mixed integer programming model is constructed, and the second mixed integer programming model is solved to obtain the personnel scheduling results.

[0101] In order to implement the above embodiment, the present application also proposes a battery detection task equipment and a personnel scheduling device.

[0102] Figure 5 A schematic diagram of the structure of a battery detection task device and a personnel scheduling device provided in an embodiment of the present application.

[0103] like Figure 5 As shown, the device 100 for scheduling equipment and personnel for the battery detection task includes: a processing module 110 , a first scheduling module 120 and a second scheduling module 130 .

[0104] The processing module 110 is used to construct a first integer programming model based on the target battery detection task information set and the detection project information set, and solve the first integer programming model to obtain the processing time of each of the N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection project information set includes the project information of each of the S detection projects, and N and S are both positive integers;

[0105] A first scheduling module 120 is used to construct a first mixed integer programming model based on the processing time of each of the N target battery detection tasks and the detection equipment information set, and solve the first mixed integer programming model to obtain a device scheduling result, wherein the detection equipment information set includes the device information of each of the M detection devices, and the device scheduling result is used to indicate whether the i-th detection device performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N;

[0106] The second scheduling module 130 is used to construct a second mixed integer programming model based on the equipment scheduling result and the detection personnel information set, and solve the second mixed integer programming model to obtain a personnel scheduling result, wherein the detection personnel information set includes the personnel information of each of Q detection personnel, and the personnel scheduling result is used to indicate whether the kth detection personnel performs the jth target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

[0107] Furthermore, in a possible implementation of an embodiment of the present application, the processing module 110 is also used to: solve the first integer programming model to obtain a project configuration result, wherein the project configuration result is used to indicate whether the j-th target battery detection task includes the p-th detection item, and p is a positive integer not greater than S; based on the project configuration result, obtain the processing time of each of the N target battery detection tasks.

[0108] Furthermore, in a possible implementation of an embodiment of the present application, the processing module 110 is also used to: set the number of detection items included in the j-th target battery detection task to be greater than or equal to a first set threshold as the j-th first constraint condition; set the minimum processing time of the j-th target battery detection task as the j-th first optimization goal; and construct the first integer programming model based on the target battery detection task information set, the detection item information set, the historical battery detection task information set, the first constraint condition and the first optimization goal, wherein the historical battery detection task information set includes task information of each of multiple historical battery detection tasks.

[0109] Furthermore, in a possible implementation of an embodiment of the present application, the first scheduling module 120 is also used to: obtain equipment scheduling constraints for M detection devices; take the minimum total processing time of N target battery detection tasks as the second optimization goal; and construct the first mixed integer programming model based on the processing time of each of the N target battery detection tasks, the detection equipment information set, the equipment scheduling constraints and the second optimization goal.

[0110] Furthermore, in a possible implementation manner of the embodiment of the present application, the equipment scheduling constraint condition includes a time constraint condition of N target battery detection tasks.

[0111] Furthermore, in a possible implementation method of an embodiment of the present application, the first scheduling module 120 is also used to: set the processing end time of the j-th target battery detection task not later than the set time as the j-th second constraint condition; if j≤N-1, set the processing start time of the j+1-th target battery detection task later than the processing end time of the j-th target battery detection task as the j-th third constraint condition; if j≤N-1, set the processing start time of the j+1-th target battery detection task later than the handover time of the j-th target battery detection task as the j-th fourth constraint condition; set the total processing time of N target battery detection tasks less than or equal to the second set threshold as the fifth constraint condition; set at least one of the second constraint condition, the third constraint condition, the fourth constraint condition and the fifth constraint condition as the time constraint condition.

[0112] Furthermore, in a possible implementation of an embodiment of the present application, when the i-th detection device performs the j-th target battery detection task, the device scheduling result also includes the time period in which the i-th detection device performs the j-th target battery detection task.

[0113] Furthermore, in a possible implementation of an embodiment of the present application, the second scheduling module 130 is also used to: obtain personnel scheduling constraints for Q detection personnel; minimize the difference between the total working hours of the Q detection personnel as the third optimization goal; and construct the second mixed integer programming model based on the equipment scheduling results, the detection personnel information set, the personnel scheduling constraints and the third optimization goal.

[0114] Furthermore, in a possible implementation of the embodiment of the present application, the second scheduling module 130 is also used to: set the inspection personnel who perform the j-th target battery inspection task with the set qualifications as the j-th sixth constraint condition; set the total working hours of the k-th inspection personnel to be within the first set range as the k-th seventh constraint condition; set the number of tasks performed by the k-th inspection personnel to be within the second set range as the k-th eighth constraint condition; and set at least one of the sixth constraint condition, the seventh constraint condition and the eighth constraint condition as the personnel scheduling constraint condition.

[0115] Furthermore, in a possible implementation of the embodiment of the present application, the processing module 110 is also used to: configure and / or adjust the target constraint conditions based on the scheduling requirements of the user group.

[0116] It should be noted that the above explanations of the embodiment of the method for scheduling equipment and personnel for battery detection tasks are also applicable to the device for scheduling equipment and personnel for battery detection tasks of this embodiment, and will not be repeated here.

[0117] In summary, the equipment and personnel scheduling device for the battery detection task of the embodiment of the present application can consider the target battery detection task information set and the detection item information set, construct a first integer programming model, and solve the first integer programming model to obtain the processing time of each of the N target battery detection tasks, and consider the processing time of each of the N target battery detection tasks and the detection equipment information set, construct a first mixed integer programming model, and solve the first mixed integer programming model to obtain the equipment scheduling result, and consider the equipment scheduling result and the detection personnel information set, construct a second mixed integer programming model, and solve the second mixed integer programming model to obtain the personnel scheduling result, which can realize automatic and accurate scheduling of equipment and personnel for battery detection tasks, and improve the scheduling efficiency of battery detection tasks.

[0118] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the equipment and personnel scheduling method for performing the battery detection tasks provided in the above embodiments.

[0119] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the equipment and personnel scheduling method for the battery detection task provided in the above embodiments.

[0120] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which, when executed by a processor, implements the equipment and personnel scheduling method for the battery detection task provided in the above embodiments.

[0121] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0122] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0123] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0124] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0125] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood to control or imply relative importance or implicitly indicate the number of technical features controlled. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0126] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0128] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0129] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0130] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0131] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for scheduling equipment and personnel for battery testing tasks, characterized in that: include: Based on the target battery detection task information set and the detection project information set, a first integer programming model is constructed, and the first integer programming model is solved to obtain the processing time of each of the N target battery detection tasks, wherein the target battery detection task information set includes the task information of each of the N target battery detection tasks, and the detection project information set includes the project information of each of the S detection projects, and N and S are both positive integers; Based on the processing time of each of the N target battery detection tasks and the detection equipment information set, a first mixed integer programming model is constructed, and the first mixed integer programming model is solved to obtain an equipment scheduling result, wherein the detection equipment information set includes the equipment information of each of the M detection equipment, and the equipment scheduling result is used to indicate whether the i-th detection equipment performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N; Based on the equipment scheduling result and the detection personnel information set, a second mixed integer programming model is constructed, and the second mixed integer programming model is solved to obtain a personnel scheduling result, wherein the detection personnel information set includes the personnel information of each of Q detection personnel, and the personnel scheduling result is used to indicate whether the kth detection personnel performs the jth target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

2. The method according to claim 1, characterized in that The solving the first integer programming model to obtain the processing time of each of the N target battery detection tasks includes: Solving the first integer programming model to obtain a project configuration result, wherein the project configuration result is used to indicate whether the j-th target battery detection task includes the p-th detection item, where p is a positive integer not greater than S; Based on the project configuration result, the processing duration of each of the N target battery detection tasks is obtained.

3. The method according to claim 1, characterized in that The constructing of a first integer programming model based on the target battery detection task information set and the detection item information set includes: The number of detection items included in the j-th target battery detection task is greater than or equal to a first set threshold, which is used as the j-th first constraint condition; Minimizing the processing time of the j-th target battery detection task is taken as the j-th first optimization goal; Based on the target battery detection task information set, the detection item information set, the historical battery detection task information set, the first constraint condition and the first optimization goal, the first integer programming model is constructed, wherein the historical battery detection task information set includes task information of each of multiple historical battery detection tasks.

4. The method according to claim 1, characterized in that: The first mixed integer programming model is constructed based on the respective processing durations of the N target battery detection tasks and the detection equipment information sets, including: Obtain equipment scheduling constraints for M testing equipment; Minimizing the total processing time of N target battery detection tasks is taken as the second optimization goal; The first mixed integer programming model is constructed based on the processing duration of each of the N target battery detection tasks, the detection equipment information set, the equipment scheduling constraints and the second optimization goal.

5. The method according to claim 4, characterized in that The equipment scheduling constraints include time constraints for N target battery detection tasks.

6. The method according to claim 5, characterized in that The time constraint condition is obtained, including: The processing end time of the j-th target battery detection task is no later than the set time, which is used as the j-th second constraint condition; If j≤N-1, the processing start time of the j+1th target battery detection task is later than the processing end time of the jth target battery detection task, which is used as the jth third constraint condition; If j≤N-1, the processing start time of the j+1th target battery detection task is later than the handover time of the jth target battery detection task, which is used as the jth fourth constraint condition; The total processing time of the N target battery detection tasks is less than or equal to the second set threshold as a fifth constraint condition; At least one of the second constraint condition, the third constraint condition, the fourth constraint condition and the fifth constraint condition is used as the time constraint condition.

7. The method according to claim 5, characterized in that In the case where the i-th detection device performs the j-th target battery detection task, the device scheduling result also includes the time period in which the i-th detection device performs the j-th target battery detection task.

8. The method according to claim 1, characterized in that The constructing of a second mixed integer programming model based on the equipment scheduling result and the detection personnel information set includes: Obtain the scheduling constraints for Q inspection personnel; The third optimization goal is to minimize the difference between the total working hours of the Q inspectors; The second mixed integer programming model is constructed based on the equipment scheduling result, the detection personnel information set, the personnel scheduling constraints and the third optimization goal.

9. The method according to claim 8, characterized in that The obtaining of the personnel scheduling constraints of the Q detection personnel includes: The inspection personnel who perform the j-th target battery inspection task must have the set qualifications as the j-th sixth constraint condition; The total working hours of the kth inspector are within the first set range as the kth seventh constraint condition; The number of tasks performed by the k-th inspector is within the second set range as the k-th eighth constraint condition; At least one of the sixth constraint condition, the seventh constraint condition and the eighth constraint condition is used as the personnel scheduling constraint condition.

10. The method according to any one of claims 3 to 9, characterized in that: The method further comprises: Configure and / or adjust target constraints based on the scheduling needs of the user group.

11. A battery testing task equipment and personnel scheduling device, characterized in that: include: A processing module, configured to construct a first integer programming model based on a target battery detection task information set and a detection project information set, and solve the first integer programming model to obtain a processing time of each of N target battery detection tasks, wherein the target battery detection task information set includes task information of each of the N target battery detection tasks, and the detection project information set includes project information of each of the S detection projects, where N and S are both positive integers; A first scheduling module is used to construct a first mixed integer programming model based on the processing time of each of the N target battery detection tasks and the detection equipment information set, and solve the first mixed integer programming model to obtain an equipment scheduling result, wherein the detection equipment information set includes the equipment information of each of the M detection equipment, and the equipment scheduling result is used to indicate whether the i-th detection equipment performs the j-th target battery detection task, M is a positive integer, i is a positive integer not greater than M, and j is a positive integer not greater than N; The second scheduling module is used to construct a second mixed integer programming model based on the equipment scheduling result and the detection personnel information set, and solve the second mixed integer programming model to obtain a personnel scheduling result, wherein the detection personnel information set includes the personnel information of each of Q detection personnel, and the personnel scheduling result is used to indicate whether the kth detection personnel performs the jth target battery detection task, Q is a positive integer, and k is a positive integer not greater than Q.

12. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 10 when executed by a processor.

14. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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