A logistics information management system based on big data

Abstract

The present invention discloses a logistics information management system based on big data. The present invention relates to the technical field of task dispatching and solves the problem that the relevant working hours reported by logistics personnel have certain considerations and accuracy. The present invention determines the relevant rate characteristic interval with the most concentrated values ​​by combining the relevant working hours reported by the logistics personnel corresponding to the relevant dispatched tasks with the past task processing data of the corresponding logistics personnel, and then performs relevant confirmation of the working hour interval for such relevant rate characteristic interval. Based on the reported working hours and the working hour interval obtained by analysis, different logistics personnel are calibrated, and the corresponding standard working hour personnel are identified, which facilitates the subsequent specific allocation of relevant workload. Not only can effective management effects be achieved, but also the relevant accuracy of task dispatch can be controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics task dispatching, and in particular to a logistics information management system based on big data. Background Art

[0002] Different projects have different assigned tasks, which need to be assigned to relevant processing personnel in real time to ensure that the tasks can be completed in real time;

[0003] The application with publication number CN111950929A discloses a method and device for balancing the workload of project-type tasks, the method comprising: dividing multiple project nodes based on the task type of the project-type tasks; obtaining historical data of historical project-type tasks with the same task type as the project-type tasks stored in a database; determining the workload of multiple project nodes based on the historical data; determining the total workload of the project-type tasks based on the workload of multiple project nodes, and generating a multi-peak Gaussian distribution map of the project-type tasks; determining the workload of target employees based on a regression random forest algorithm, and generating a Gaussian distribution map of the workload of the target employees; balancing the workload of the target employees based on the multi-peak Gaussian distribution map and the workload Gaussian distribution map, thereby accurately determining the workload of the project-type tasks and balancing the workload of the project-type tasks.

[0004] For different logistics dispatch tasks, logistics personnel need to be allocated based on their corresponding dispatched task quantities. During the actual allocation process, the relevant logistics personnel fill in their working hours, and based on the relevant working hours reported, specific task quantities are allocated. However, the relevant working hours reported by some logistics personnel have certain considerations, and the working hours they fill in are not accurate. Due to competition, some personnel deliberately falsify their working hours, resulting in the inability to deliver relevant tasks in a timely manner later, causing project delays or other situations. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a logistics information management system based on big data, which solves the problem of certain considerations and accuracy of the relevant working hours reported by logistics personnel.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A logistics information management system based on big data, comprising the following steps:

[0007] S1) Based on the different types of assigned tasks included in the relevant logistics project, the relevant operators input the specific task volume of each group of assigned tasks in the task display platform, and the processor obtains the specific task volume of each group of assigned tasks and displays the assigned tasks with the determined task volume on the task display platform;

[0008] S2) The relevant logistics personnel fill in the working hours based on the assigned tasks displayed on the task display platform. The backend processor analyzes the working hours of the relevant logistics personnel who have filled in the working hours, and identifies the personnel with standard working hours and abnormal working hours for the relevant assigned tasks. The abnormal working hours include personnel with mild abnormal working hours and personnel with severe abnormal working hours. The specific sub-steps are as follows:

[0009] S21, select the relevant assigned tasks of a single group, and identify the relevant logistics personnel who have reported working hours for this assigned personnel and mark them as personnel to be processed, and mark the relevant working hours reported as G i , where i represents different persons to be processed;

[0010] S22. Extract the task volume Z of the personnel to be processed who have completed similar assigned tasks from the relevant past data. k And the specific completion time T k , where k represents different similar assigned tasks, using V k =Z k ÷T k Confirm the average completion speed V of a single group of similar assigned tasks k , perform the same process on k different similar assigned tasks, and confirm several average speeds V k Then, according to the time sequence of the corresponding similar assigned tasks, several average speed V k Sort and generate an average speed sorting sequence;

[0011] S23. Based on the average speed ranking sequence of the personnel to be processed, determine the coordinate points of the average speed in a two-dimensional coordinate system, and connect the determined coordinate points to generate a characteristic correlation line of the average speed ranking sequence. The horizontal coordinate axis of the two-dimensional coordinate system represents the ranking position of the average speed, and the vertical coordinate axis represents the average speed value.

[0012] Identify the maximum average speed and the minimum average speed of the characteristic correlation line to construct two sets of correlation lines. These correlation lines are perpendicular to the vertical coordinate axis and pass through the point where the maximum average speed or the point where the minimum average speed is located, so that the upper and lower sets of correlation lines move up and down, but the movement rates of the two sets of correlation lines are different and the two sets of correlation lines do not intersect. Based on each different movement process, several sets of average speeds belonging to the range of the two correlation lines are subjected to variance processing to determine the corresponding to-be-compared variance. Each different movement process corresponds to a different to-be-compared variance. The minimum value is selected from the several to-be-compared variances, and the movement process corresponding to this minimum value is calibrated as the standard process.

[0013] From this standard process, the minimum average speed and the maximum average speed within the range are identified, and the speed characteristic interval corresponding to the personnel to be processed is determined [V i min, V imax], where i represents different persons to be processed;

[0014] S24. Based on the specific amount of task R assigned this time, determine the working time interval of the corresponding personnel to be processed: R÷V i min=W i max and R÷V i max=W i min determines its working time interval [W i min,W i max], based on the working hours G of the personnel to be processed i , compare its relevant working hours with the working time interval:

[0015] When G i >W i When max is reached, the personnel to be processed will be marked as personnel with standard working hours;

[0016] When G i ∈[W i min,W i max], the person to be processed is marked as a person with slightly abnormal working hours;

[0017] When G i <W i min, the person to be processed will be marked as a person with severe abnormal working hours;

[0018] S3) Identify whether there is a standard working time person associated with the relevant assigned task. If there is a standard working time person, determine the working hours reported by the corresponding standard working time person, and allocate the task quantity of the corresponding assigned task. Based on the specific assignment result, identify the relevant completion period of the assigned task. If there is no standard working time person, execute step S4, specifically in the following manner:

[0019] S31. Identify the number of standard working hours personnel associated with the relevant assigned task. If there is only one, directly assign the assigned task to the standard working hours personnel, and then determine whether the standard working hours personnel has other unfinished processing tasks:

[0020] If there are other processing tasks, based on the remaining processing volume of this processing task, the same method as in steps S22-S23 is used to determine the rate characteristic interval of the standard working time personnel for this processing task. The minimum rate value is selected from the rate characteristic interval. Based on this remaining processing volume and the minimum rate value, the processing time is determined. Based on this processing time and the reported working hours, the relevant completion period of this assigned task is determined. The relevant completion period = processing time + working hours.

[0021] If there are no other processing tasks, the working hours reported by the standard working hours personnel will be used as the relevant completion period of this assigned task;

[0022] If there are multiple standard working hours personnel associated with the relevant assigned task, the subsequent step S32 is executed, including:

[0023] Prioritize analyzing standard-hour personnel who have other processing tasks. Determine their specific processing time based on the remaining processing volume of other processing tasks and the corresponding minimum rate. Combined with the reported working hours, confirm the modified working hours for these standard-hour personnel: Modified working hours = working hours + processing time.

[0024] The working hours of relevant standard working hours personnel are marked as GS q , and GS q Including modifying the working hours, where q represents different standard working hours for personnel, and then determining the specific task volume R of this assigned task, using R÷GS q =V q Determine the relevant speed V of the corresponding standard working hours personnel q , then perform ratio processing on the speeds of several standard working-hour personnel associated with the assigned task to determine the ratio sequence;

[0025] Based on the determined ratio sequence, confirm the ratio proportion ZB of each different standard working hours personnel in turn q , where ZB q = Correlation ratio ÷ sum of ratios within the ratio sequence, using R×ZB q =FP q Determine the specific allocation quantity FP corresponding to the standard working hours q , and make associated allocations;

[0026] Randomly select a group of standard working hours workers with specific assigned quantities and related speeds, and determine the relevant completion period for this assigned task: the relevant completion period = specific assigned quantity ÷ related speed;

[0027] If there are no personnel with standard working hours, personnel are randomly selected from the personnel with slightly abnormal working hours, and the selected personnel are re-calibrated as standard working hours personnel to allocate the task volume of the corresponding assigned tasks. Based on the specific assignment results, the relevant completion cycle of this assigned task is identified.

[0028] The present invention provides a logistics information management system based on big data. Compared with the existing technology, it has the following advantages:

[0029] The present invention determines the relevant rate characteristic interval with the most concentrated values ​​by combining the relevant working hours reported by the logistics personnel corresponding to the relevant assigned tasks with the corresponding logistics personnel's past task processing data. Then, for such relevant rate characteristic interval, the relevant confirmation of the working hour interval is carried out. Based on the reported working hours and the analyzed working hour interval, different logistics personnel are calibrated, and the corresponding standard working hour personnel are identified, which facilitates the subsequent specific allocation of relevant workloads. It can not only achieve effective management effects, but also control the relevant accuracy of task assignments.

[0030] In the subsequent task allocation management process, priority is given to the corresponding standard working hour personnel. Based on their relevant reported working hours and whether the corresponding personnel have remaining processing volume, working hour analysis is carried out to determine their relevant completion cycle, and corresponding task allocation is carried out. In the process of task allocation, the synchronization and timeliness of their completion are guaranteed to achieve better workload management effects and dispatch control measures for logistics personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the process of the present invention;

[0032] Figure 2 This is a schematic diagram of the associated average speed clustering processing of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] Example 1

[0035] See also Figure 1 This application provides a logistics information management system based on big data. Its task dispatching is mainly aimed at a number of dispatching tasks of related logistics projects. When dispatching, each dispatching task is pre-calibrated with the relevant task volume and completion period. It includes the following steps:

[0036] S1), based on the different types of assigned tasks included in the relevant logistics projects, the relevant operators input the specific task volume of each group of assigned tasks in the task display platform, and the processor then obtains the specific task volume of each group of assigned tasks, and displays the assigned tasks determined by the completed task volume on the task display platform. Specifically, the specific task volume is calibrated in advance by the corresponding operator, and each corresponding assigned task is distinguished in advance during processing, and in the process of distinguishing, different tasks have different task volumes, and the task display platform is just a screen display device, which displays the determined assigned tasks. Application scenario: The front end of this background processor has relevant software and hardware such as application APP or corresponding display platform, and the assigned tasks and task volumes can be input on the corresponding platform. The relevant logistics personnel who need to undertake such assigned tasks input the working hours according to the specific processing rate, and the background processing then performs relevant confirmation on the input working hours to identify whether the input working hours are considerate;

[0037] S2) Relevant logistics personnel fill in working hours based on the assigned tasks displayed on the task display platform;

[0038] The backend processor of this task display platform then performs a working time analysis on the relevant logistics personnel who have reported working hours, identifies the standard working time personnel and abnormal working time personnel of the relevant assigned tasks, and the abnormal working time personnel include those with slightly abnormal working hours and those with seriously abnormal working hours. Specifically, the reason for performing working time analysis here is to avoid malicious competition by some logistics personnel. The so-called malicious competition means that some logistics personnel, in order to obtain such assigned tasks, estimate that the completed working hours will be reported before the current processing progress, and delays will occur in the subsequent processing process, thereby causing delays in task completion. For example: A dispatches a task on the display platform In the example above, the deadline is 3 days. Person B reported 2 days of working hours, and Person C reported 1.5 days of working hours. However, in order to analyze the accuracy of their reported working hours, we confirm the average processing speeds of Person B and Person C in the past, and thus determine their corresponding average speed intervals. Then, based on the average speed intervals, we determine the working time intervals [Wimin, Wimax] of Person B and Person C (such intervals are mentioned in the subsequent content). Person B's working time interval is [1.5 days, 1.8 days], and Person C's working time interval is [2.2 days, 2.5 days]. Therefore, it is difficult for Person C to complete the task within 1.5 days, so Person C is an abnormal person. Person B meets the corresponding working time interval, so he is a standard person.

[0039] The specific sub-steps of identification include:

[0040] S21. Select a single group of assigned tasks, and identify the relevant logistics personnel who have reported working hours for this assigned personnel and mark them as personnel to be processed. Mark the reported working hours as G i, where i represents different personnel to be processed, and its working hours are the total time required for the corresponding personnel to complete the total amount of the assigned tasks;

[0041] S22. Extract the task volume Z of the personnel to be processed who have completed similar assigned tasks from the historical completion data (past relevant processing data). k And the specific completion time T k , where k represents different similar assigned tasks, using V k =Z k ÷T k Confirm the average completion speed V of a single group of similar assigned tasks k , perform the same process on k different similar assigned tasks, and confirm several average speeds V k , several average speeds belong to the average speeds generated by the same person to be processed completing the same type of assigned tasks, and then according to the time sequence of the corresponding similar assigned tasks, several average speeds V k The parameters in the processing are all timestamped. Based on the corresponding timestamps, the corresponding time sequence can be clearly known, and the average speed sorting sequence {V1, V2, V3, ..., V n}, the average speed of several of which is not less than 30;

[0042] S23, based on the average speed sorting sequence {V1, V2, V3, ..., V n}, the coordinate points are determined according to the different average speeds corresponding to different sorting positions, so the generated coordinate points are (1, V1), (2, V2), ... (n, V n ), based on the determined coordinate points, relevant points are determined in the two-dimensional coordinate system, and several of the determined coordinate points are connected to generate a characteristic correlation line of this average speed sorting sequence. A line is composed of several points, so a corresponding line can be generated based on the determined points. The horizontal coordinates of the two-dimensional coordinate system are sorted as 1, 2, ..., n, which are corresponding numbers and can be understood as corresponding sorting positions. The sorting is relatively dense. The horizontal coordinate axis of this two-dimensional coordinate system is the sorting position, and its vertical coordinate axis is the related processing average speed;

[0043] Combine Figure 2, identify the maximum average speed and the minimum average speed of the characteristic correlation line, and construct two sets of correlation lines. These correlation lines are perpendicular to the vertical coordinate axis and pass through the point where the maximum average speed is located or the point where the minimum average speed is located (there are two sets of correlation lines, the upper correlation line passes through the point where the maximum average speed is located, and the lower correlation line passes through the point where the minimum average speed is located). The upper and lower sets of correlation lines move up and down, but the movement rates of the two sets of correlation lines are different and the two sets of correlation lines do not intersect (that is, the upper correlation line will not move downward, and the lower correlation line will not move upward). Based on each different movement process, several sets of average speeds belonging to the range of the two correlation lines are subjected to variance processing to determine the corresponding to-be-compared variance. Each different movement process corresponds to a different to-be-compared variance. The minimum value is selected from several to-be-compared variances, and the movement process corresponding to this minimum value is calibrated as the standard process. Each different movement process contains different average speeds, so the confirmed to-be-compared variances are also different. The smaller the variance value, the smaller the data dispersion, and the stronger the characteristic of the data contained between the two correlation lines.

[0044] Specifically, based on the corresponding characteristic correlation line, the highest point and the lowest point of the characteristic correlation line are determined, and then the upper and lower groups of correlation lines are locked based on the vertical construction method. The upper correlation line moves downward and the lower correlation line moves upward, so that the specific average speed between the two correlation lines can be locked. The average speed between the two groups of correlation lines is proposed to be V1, V2, V3, ..., V n , average the speeds of several groups and determine the total mean value JJ. Determine the variance Fc of several groups of average speeds. This Fc is the variance to be compared.

[0045] The running code for this part is:

[0046] import numpy as np

[0047] import matplotlib.pyplot as plt

[0048] def find_min_variance(V):

[0049] n=len(V)

[0050] x = np.arange(1,n+1)

[0051] y = np.array(V)

[0052] #Calculate the maximum and minimum average speeds

[0053] max_speed = max(y)

[0054] min_speed = min(y)

[0055] #Build two sets of correlation lines

[0056] upper_line=lambda x:max_speed

[0057] lower_line=lambda x:min_speed

[0058] # Initialize the variance list

[0059] variances=[]

[0060] #Traverse different movement processes

[0061] for i in range(n):

[0062] #Calculate the difference between the two sides in the current moving process

[0063] current_variance=np.var(y[i:])

[0064] variances.append(current_variance)

[0065] #Find the index corresponding to the minimum variance

[0066] min_variance_index=np.argmin(variances)

[0067] return min_variance_index+1

[0068] #Sample data

[0069] V=[5,3,8,2,6,4]

[0070] result = find_min_variance(V)

[0071] print("Sorting position corresponding to the standard process:",result)

[0072] #Draw feature correlation line

[0073] plt.plot(np.arange(1,len(V)+1),V,label="V")

[0074] plt.axhline(y=max(V),color=′r′,linestyle=′-′,label="Max Speed")

[0075] plt.axhline(y=min(V),color=′g′,linestyle=′-′,label="Min Speed")

[0076] plt.legend()

[0077] plt.show();

[0078] For example: When two correlation lines move up and down, they will be divided. During the division process, the number of average speeds contained between the correlation lines will change. Based on the changed average speeds, variance processing can be performed based on the existing groups of average speeds. Based on the corresponding variance processing formula, the data dispersion of several average speeds within the range can be identified. When the dispersion is the smallest, that is, when the variance to be compared is the smallest, the resulting range belongs to the optimal range, and the corresponding moving process is the optimal standard process.

[0079] From this standard process, the minimum average speed and the maximum average speed within the range are identified, and the speed characteristic interval corresponding to the personnel to be processed is determined [V i min, V i max], where i represents different persons to be processed;

[0080] S24. Based on the specific amount of task R assigned this time, determine the working time interval of the corresponding personnel to be processed: R÷V i min=W i max and R÷V i max=W i min determines its working time interval [W i min,W i max], based on the relevant working hours G reported by the personnel to be processed i , compare its relevant working hours with the working time interval:

[0081] When G i >W i Max means that when filling out the form, such personnel still have spare time for themselves. In this case, such personnel can basically complete the assigned tasks, and this person to be processed is marked as a standard working time person;

[0082] When G i ∈[W i min,W i max], indicating that the working hours reported by such personnel fall within this range and may or may not be completed. This person to be processed is marked as a person with slightly abnormal working hours;

[0083] When G i <W i When the time is min, it means that such personnel are likely to be suspected of malicious competition, and the working hours they fill in are likely to be unfinished. The interval is based on the relevant data of the past processing of similar tasks. The data is very useful because the data with large relevant dispersion is eliminated and the more central relevant data is retained. Therefore, the determined relevant working hour interval can be used as the relevant standard. Therefore, this person to be processed is marked as a person with severely abnormal working hours;

[0084] The relevant logistics personnel who fill in the working hours for completing different assigned tasks will be processed one by one and calibrated one by one.

[0085] Example 2

[0086] Example 1 focuses on the specific reporting of working hours by different logistics personnel, and marks different logistics personnel accordingly, so that the corresponding management party can timely understand the relevant status of the corresponding logistics personnel and conduct relevant assessments. This embodiment assesses whether the relevant logistics personnel can complete the relevant processing process on the relevant timeline based on the completion time of different assigned personnel;

[0087] S3) The backend processor identifies whether there is a standard working hour personnel associated with the relevant assigned task. If there is a standard working hour personnel, the backend processor determines the working hours reported by the corresponding standard working hour personnel, allocates the task quantity of the corresponding assigned task, and identifies the relevant completion cycle of the assigned task based on the specific assignment result. If there is no standard working hour personnel, the backend processor executes step S4, wherein the specific method of identifying the relevant completion cycle of the assigned task is as follows:

[0088] S31. Identify the number of standard working hours personnel associated with the relevant assigned task. If there is only one, directly assign the assigned task to the standard working hours personnel, and then determine whether the standard working hours personnel has other unfinished processing tasks:

[0089] If there are other processing tasks, based on the remaining processing volume of this processing task, the same method as in steps S22-S23 is used to determine the rate characteristic interval of the standard working time personnel for this processing task. The minimum rate value is selected from the rate characteristic interval. Based on this remaining processing volume and the minimum rate value, the processing time is determined. Based on this processing time and the reported working hours, the relevant completion period of this assigned task is determined. The relevant completion period = processing time + working hours.

[0090] If there are no other processing tasks, the working hours reported by the standard working hours personnel will be used as the relevant completion period of this assigned task;

[0091] If there are multiple standard working hours personnel, execute step S32;

[0092] S32. Prioritize analyzing standard-hour personnel who have other processing tasks. Based on the remaining processing volume of other processing tasks and the corresponding minimum rate, determine their specific processing time. Combined with the reported working hours, confirm the modified working hours for such standard-hour personnel: Modified working hours = working hours + processing time.

[0093] The working hours of relevant standard working hours personnel are marked as GS q , and GS q Including modifying the working hours, where q represents different standard working hours for personnel, and then determining the specific task volume R of this assigned task, using R÷GS q =V q Determine the relevant speed V of the corresponding standard working hours personnel q , then perform ratio processing on the relevant speeds of several standard working time personnel associated with this assigned task to determine the ratio sequence. For example: there are three groups of standard working time personnel associated with this assigned task, and their generated relevant speeds are 5, 10, and 15 respectively. Then after performing ratio processing on the three relevant speeds, their ratio sequence is: 5:10:15=1:2:3, and the final ratio sequence is 1:2:3;

[0094] Based on the determined ratio sequence, confirm the ratio proportion ZB of each different standard working hours personnel in turn q , where ZB q = correlation ratio ÷ sum of ratios within the ratio sequence (that is, when the ratio sequence is 1:2:3, the first person's ratio share ZB = 1 ÷ (1+2+3), which is one-sixth). Use R×ZB q =FP q Determine the specific allocation quantity FP corresponding to the standard working hours q , and make associated allocations. The reason for adopting this allocation method is to ensure that the completion time of the assigned task is basically consistent when different people are processing it. That is, if the five people basically complete the assigned task at the same time, the effect of task allocation will be the best, and the overall processing of the task can be completed at the same time or in the same period.

[0095] Randomly select the specific assigned quantity and related speed of a group of standard working time personnel, and determine the relevant completion cycle of this assigned task, the relevant completion cycle = specific assigned quantity ÷ related speed.

[0096] Further implementation method:

[0097] When the background processor identifies that there is no standard working time personnel associated with the assigned task, it generates an intervention signal and transmits it to the display terminal of the external manager;

[0098] External management personnel intervene and randomly select personnel from the personnel with slightly abnormal working hours associated with the assigned tasks, and re-calibrate the selected personnel to standard working hours personnel;

[0099] The background processor then uses the same processing method as in step S3 to allocate the task amount corresponding to the assigned task, and identifies the relevant completion cycle of the assigned task based on its specific assignment result.

[0100] The specific processing method of its further implementation method is basically the same as the specific implementation method in step S3. The personnel are selected by the operator, and multiple personnel or a single personnel are selected based on the amount of tasks. Since the personnel selected by the further implementation method may have some errors in the reported working hours, the relevant completion period of the assigned task processed in the further implementation method may also have relevant errors, but the error amount will not be large, because the relevant working hours after its processing belong to the corresponding working hour range.

[0101] Example 3

[0102] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0103] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0104] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A logistics information management system based on big data, characterized in that: The following steps are involved: S1) Based on the different types of assigned tasks included in the relevant logistics projects, obtain the specific task volume of each group of assigned tasks, and display the assigned tasks with the specific task volume determined by the completion on the task display platform; S2) Based on the assigned tasks displayed on the task display platform, the working hours of the corresponding logistics personnel are obtained. The backend processor analyzes the obtained working hours of the logistics personnel, performs average speed clustering and then performs variance processing on the historical completion data of the assigned tasks to determine the rate characteristic interval of the logistics personnel. Then, based on the comparison results of the working hours of the logistics personnel and the rate characteristic interval, standard working hours personnel and abnormal working hours personnel are identified. Among them, abnormal working hours personnel include mildly abnormal working hours personnel and severely abnormal working hours personnel. The specific sub-steps are: S21, select a single group of assigned tasks, mark the logistics personnel corresponding to the assigned tasks as personnel to be processed, and mark the reported working hours as G i , where i represents different persons to be processed; S22. Extract the task volume Z of the pending personnel who have completed similar assigned tasks from the historical completion data. k And the specific completion time T k , where k represents different similar assigned tasks, using V k =Z k ÷T k Confirm the average completion speed V of a single group of similar assigned tasks k , perform the same process on k different similar assigned tasks, and confirm several average speeds V k Then, according to the time sequence of the corresponding similar assigned tasks, several average speed V k Sort and generate an average speed sorting sequence; S23. Based on the average speed ranking sequence of the personnel to be processed, determine the coordinate points of the average speed in a two-dimensional coordinate system, and connect the determined coordinate points to generate a characteristic correlation line of the average speed ranking sequence. The horizontal coordinate axis of the two-dimensional coordinate system represents the ranking position of the average speed, and the vertical coordinate axis represents the average speed value. Identify the maximum average speed and the minimum average speed of the characteristic correlation line to construct two sets of correlation lines. These correlation lines are perpendicular to the vertical coordinate axis and pass through the point where the maximum average speed or the point where the minimum average speed is located, so that the upper and lower sets of correlation lines move up and down, but the movement rates of the two sets of correlation lines are different and the two sets of correlation lines do not intersect. Based on each different movement process, several sets of average speeds belonging to the range of the two correlation lines are subjected to variance processing to determine the corresponding to-be-compared variance. Each different movement process corresponds to a different to-be-compared variance. The minimum value is selected from the several to-be-compared variances, and the movement process corresponding to this minimum value is calibrated as the standard process. From this standard process, the minimum average speed and the maximum average speed within the range are identified, and the speed characteristic interval corresponding to the personnel to be processed is determined [V i min, V i max], where i represents different persons to be processed; S24. Based on the specific amount of task R assigned this time, determine the working time interval of the corresponding personnel to be processed: R÷V i min=W i max and R÷V i max=W i min determines its working time interval [W i min,W i max], the working hours of the personnel to be processed G i Compare with working hours interval: When G i >W i When max is reached, the personnel to be processed will be marked as personnel with standard working hours; S3) If there are standard working hour personnel, the task quantity of the corresponding assigned task is allocated according to the working hours of the standard working hour personnel, and the relevant completion period of the assigned task is identified based on the specific assignment result; If there are no personnel with standard working hours, personnel are randomly selected from the personnel with slightly abnormal working hours, and the selected personnel are re-calibrated as standard working hours personnel to allocate the task volume of the corresponding assigned tasks. Based on the specific assignment results, the relevant completion cycle of this assigned task is identified.

2. A logistics information management system based on big data according to claim 1, characterized in that: In step S24, when G i ∈[W i min,W i max], the person to be processed will be marked as a person with slightly abnormal working hours.

3. The logistics information management system based on big data according to claim 1, characterized in that: In step S24, when G i ∈[W i min,W i max], the person to be processed will be marked as a person with slightly abnormal working hours.

4. The logistics information management system based on big data according to claim 1, characterized in that: In step S3, if there are standard working time personnel, the specific method of identifying the relevant completion period of their assigned tasks is as follows: S31. Identify the number of standard working hours personnel associated with the relevant assigned task. If there is only one, directly assign the assigned task to the standard working hours personnel, and then determine whether the standard working hours personnel has other unfinished processing tasks: If there are other processing tasks, based on the remaining processing volume of this processing task, and using the same method as steps S22-S23, determine the rate characteristic interval of this standard working time personnel for this processing task, select the minimum rate from the rate characteristic interval, and based on this remaining processing volume and the minimum rate, determine the processing time. Based on this processing time and the reported working hours, determine the relevant completion cycle of this assigned task, and its relevant completion cycle = processing time + working hours.

5. A logistics information management system based on big data according to claim 4, characterized in that: In step S31, if the standard working hours personnel does not have other processing tasks, the working hours filled in by the standard working hours personnel are used as the relevant completion period of the assigned task.

6. A logistics information management system based on big data according to claim 4, characterized in that: In step S31, if there are multiple standard working hours personnel associated with the relevant assigned task, the subsequent step S32 is executed, including: Prioritize analyzing standard-hour personnel who have other processing tasks. Determine their specific processing time based on the remaining processing volume of other processing tasks and the corresponding minimum rate. Combined with the reported working hours, confirm the modified working hours for these standard-hour personnel: Modified working hours = working hours + processing time. The working hours of relevant standard working hours personnel are marked as GS q , and GS q Including modifying the working hours, where q represents different standard working hours for personnel, and then determining the specific task volume R of this assigned task, using R÷GS q =V q Determine the relevant speed V of the corresponding standard working hours personnel q , then perform ratio processing on the speeds of several standard working-hour personnel associated with the assigned task to determine the ratio sequence; Based on the determined ratio sequence, confirm the ratio proportion ZB of each different standard working hours personnel in turn q , where ZB q = Correlation ratio ÷ sum of ratios within the ratio sequence, using R×ZB q =FP q Determine the specific allocation quantity FP corresponding to the standard working hours q , and make associated allocations.

7. A logistics information management system based on big data according to claim 6, characterized in that: The step S32 further includes: Randomly select the specific assigned quantity and related speed of a group of standard working time personnel, and determine the relevant completion cycle of this assigned task, the relevant completion cycle = specific assigned quantity ÷ related speed.

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