CN115511251B - Human resource scheduling management system and method based on big data - Google Patents

Human resource scheduling management system and method based on big data Download PDF

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CN115511251B
CN115511251B CN202210914142.3A CN202210914142A CN115511251B CN 115511251 B CN115511251 B CN 115511251B CN 202210914142 A CN202210914142 A CN 202210914142A CN 115511251 B CN115511251 B CN 115511251B
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parking
staff
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CN115511251A (en
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彭耀栋
李�杰
杨宝
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Wuxi Woqu Information Technology Co ltd
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Abstract

The invention discloses a human resource scheduling management system and a human resource scheduling management method based on big data, wherein the human resource scheduling management system comprises a parking space information acquisition module, a peak-shifting parking management module and a parking space reservation management module; the parking space information acquisition module is used for acquiring time information of starting the vehicle by the staff, predicting the number of parking spaces in the enterprise setting range according to the time information, adjusting the time of starting the vehicle by the staff according to the prediction result, and optimizing the departure time of the staff; the peak-shifting parking management module is used for managing the parking space positions in the setting range of the worker reservation enterprise, screening vehicles of the next parking space in the reserved parking lot and ensuring that the screened vehicles meet the condition of time limitation; the parking space reservation management module is used for displaying the parking space state to staff according to the running position of the staff vehicle; expanding the parking space searching radius according to the vehicle state to obtain an optimal parking space searching radius; so that the staff can increase the possibility of successfully reserving the parking space.

Description

Human resource scheduling management system and method based on big data
Technical Field
The invention relates to the technical field of big data, in particular to a human resource scheduling management system and method based on big data.
Background
The scheduling table is a behavior for limiting the working time of the staff, and is closely related to the full work attendance prizes or the performance of the staff; however, staff who drive the motor vehicle to get on and off duty are required to accurately grasp the departure time of the motor vehicle to get on and off duty; the problem of a parking space or the problem on a driving road is avoided, so that staff is delayed; therefore, in order to avoid the late problem caused by the problem of the parking space, the reservation of the parking space can be performed;
In the prior art, there is a patent document with the following application number: 201610227896.6, publication day 2018.10.23; the patent document discloses: screening the parking lots according to the requirements of the driver according to the parking lots contained in the area to be visited by the driver, so as to obtain an optimal parking lot; the parking space reservation method solves the problem that the parking space reservation is realized by selecting a correct mode, and the regional integrity or reliability of the reserved parking lot is realized; although the file solves the problem of reservation of the parking lot, the file is selected according to the needs of a driver, so that the parking space in the parking lot can be further obtained and is also selected according to the needs of the driver; by the method, the parking spaces of the vehicles which come first can be used, and then the parking spaces of the parking lot are obtained and the parking spaces of other areas need to be contended again; further, a large number of vehicles are rushed into the same area, and the problem of traffic jam is caused; meanwhile, the reserved parking space can be smoothly accessed after other vehicles are required to be successfully backed up and put in storage; this delays the time for the worker to go to work smoothly, thereby causing a problem that the worker is late; therefore, there is a need to improve upon the problem.
Disclosure of Invention
The invention aims to provide a human resource scheduling management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the human resource scheduling management system based on big data comprises a parking space information acquisition module, a peak-shifting parking management module and a parking space reservation management module;
The parking space information acquisition module is used for acquiring time information of starting the vehicle by the staff, predicting the number of parking spaces in the enterprise setting range according to the time information, adjusting the time of starting the vehicle by the staff according to the prediction result, and optimizing the departure time of the staff;
The peak-shifting parking management module is used for managing the parking space positions in the setting range of the worker reservation enterprise, screening vehicles in the next parking space in the reserved parking lot and ensuring that the screened vehicles meet the condition of time limitation;
The parking space reservation management module is used for displaying the parking space state to staff according to the running position of the staff vehicle; expanding the parking space searching radius according to the vehicle state to obtain an optimal parking space searching radius; the possibility of successfully reserving the parking space is increased for staff;
The parking space information acquisition module is connected with the peak-shifting parking management module and the parking space reservation management module.
Further, the parking space information acquisition module comprises a vehicle starting triggering unit, a parking space number prediction unit, a vehicle position positioning unit, a tabulation display unit and a scheduling time acquisition unit;
the vehicle starting triggering unit is used for acquiring time information when a worker starts the vehicle;
the parking space number prediction unit is used for predicting parking space number information in a set range of an enterprise according to the time of starting the vehicle by workers;
the vehicle position positioning unit is used for acquiring position information of vehicle running;
The tabulation display unit is used for sending the information of the number of parking spaces corresponding to the vehicles started by the staff at different times to the staff for checking in a form mode, and updating the form in real time; so that staff can select the most proper departure time to reserve in advance;
The shift time acquisition unit is used for acquiring a shift time table of the staff and obtaining a shift time period of the staff; therefore, whether the time period of working of the staff is the peak time period of reserved parking spaces can be analyzed;
The optimal time departure unit is used for optimizing the departure time of staff according to the number of parking spaces to obtain optimal departure time;
The output end of the vehicle starting triggering unit is connected with the input end of the parking space number prediction unit; the output end of the vehicle position positioning unit is connected with the input end of the tabulation display unit; the output end of the parking space number prediction unit is connected with the input end of the scheduling time acquisition unit.
Further, the peak-shifting parking management module comprises a vehicle sequence reservation unit, a designated time limiting unit and a vehicle screening unit;
the vehicle sequence reservation unit is used for managing the reserved parking space positions of the staff, and ensuring that the reserved parking space positions are ordered according to the set sequence in the parking lot;
The appointed time limiting unit is used for acquiring information of the parked vehicles in the parking lot and limiting the time of reserving the next parking space by the staff;
The vehicle screening unit is used for screening workers applying for parking space reservation according to the time limited by the reservation platform, and the screening condition is determined according to the driving position of the workers;
The output end of the vehicle sequence reservation unit is connected with the input end of the appointed time limiting unit; the output end of the appointed time limiting unit is connected with the input end of the vehicle screening unit.
Further, the parking space reservation management module comprises a parking space state display unit, a parking space reservation time locking unit, a parking space information sending unit, a shift time acquisition unit, a reservation radius expansion unit and an optimal radius expansion unit;
The parking space state display unit is used for displaying the parking space state in the radius range with the enterprise as a core to a user and obtaining a parking space state result in the radius range;
the parking space reservation time locking unit is used for acquiring distance information between workers and enterprises and displaying parking space information capable of being reserved to the workers according to the distance information;
The parking space information sending unit is used for sending the parking space information successfully reserved by the staff to the staff receiving port;
the reservation radius expansion unit is used for verifying that when a worker does not reserve the parking space successfully within a set time period, the enterprise is taken as a core to expand the parking space reservation circle outwards, and the radius of the reserved parking space is increased;
The optimal radius expansion unit is used for obtaining an optimal parking space searching radius from the parking space searching radii which are expanded outwards; and reserving the parking space from the optimal parking space searching radius;
The output end of the parking space state display unit is connected with the input end of the parking space reservation time locking unit; the output end of the parking space reservation time locking unit is connected with the input end of the parking space information sending unit; the reservation radius expansion unit is connected with the input ends of the scheduling time acquisition unit and the optimal radius expansion unit.
Further, the human resource scheduling management method based on big data executes the following steps:
Z01: acquiring time information of starting vehicles by workers and scheduling time of the workers, and predicting the number of parking spaces in a set range of an enterprise according to the time information of starting the vehicles by the workers; optimizing the departure time of staff according to the number of vehicles; if the employee starts at the optimized departure time, acquiring the position of the employee when the vehicle runs, and jumping to the step Z02; if the employee does not start at the optimized departure time, jumping to the step Z03;
Z02: if the distance between the staff's vehicle and the enterprise is smaller than the preset distance, setting parking state information of the parking lot in the radius range by taking the enterprise as a core; if the parking lot within the set radius range contains vacant parking spaces, the parking space information to be reserved is sent to the reservation platform according to the sequence of parking vehicles in the parking lot; acquiring the time limit of the reserved next parking space, and screening the vehicles reserved with the parking space according to the time limit to obtain a screening result;
z03: the enterprise is taken as a core, the searching radius of the reserved parking space is increased, and the optimal outwardly-expanded parking space searching radius is obtained; and screening vehicles reserved in the parking space to obtain screening results.
In the step Z01, the scheduling time of the staff is obtained, the time period of starting the vehicle by the staff is obtained, if the time period of starting the vehicle by the staff is a peak time period, the historical data is obtained, the staff gathers as U= { U1, U2, & gt, and the parking space quantity information in um } gathers as E= {1,2, & gt, n }, wherein n refers to the parking space quantity in the peak time period; then establishing a functional relation according to the time period U and the number E of vehicles; obtaining the number E of parking spaces in a set range of an enterprise in a peak time period, wherein E=V 1 U+H; if the time period for starting the vehicle by the employee is a low peak time period, if the vehicle leaving the reserved parking space position of the employee leaves in a peak time period, acquiring a vehicle number set leaving the reserved parking space position of the employee before the off-peak time period as E' = {1,2, 3.,. Then a functional relation is established according to the time period U 'and the parking space number E' to obtainObtaining the number of the parking spaces in the enterprise setting range in the off-peak time period as E-E';
If the vehicle leaving the reserved parking space of the employee does not leave in the peak time period, a function relationship is established between the time period U 'and the parking space number E' obtained by a gray prediction method according to the number of the vehicles leaving, and E '=V 2 U' +b is obtained; the number of the parking spaces in the enterprise setting range in the off-peak time period is E-E';
In the step Z01, the longest time H1 for a worker to start a vehicle to reach an enterprise is acquired, the delay time H2 on a worker schedule is acquired, and the worker departure time period corresponding to the same delay time on the schedule in the historical data is acquired; analyzing whether the departure time period of the staff is a peak time period or an off-peak time period, and acquiring a time period H3 with the largest available parking spaces in the enterprise setting range in the peak time period or the off-peak time period; the optimized departure time L < H2-H1-H3-H4 is obtained; wherein h1=d h5+h6; d is the number of traffic lights on the road from the staff to the enterprise, H5 is the red light of the staff at each intersection, and the waiting time of each red light is averaged; h6 refers to the time that the employee is traveling at the maximum speed allowed on the route; h4 refers to error time.
In step Z02, the time limit for the employee to reserve the next parking space is y=y2—y1;
Wherein: y2 refers to a vehicle reserving the next parking space, and the conditions of the vehicle are as follows: the time for reaching the parking lot corresponding to the next parking space is faster than the time for other vehicles to reach the parking lot; y1 refers to the time that parking spot B was successfully parked by the vehicle.
In step Z03, acquiring enterprise position information W of workers, and acquiring an original searching radius S of a parking space which is not reserved successfully; the optimal searching radius is obtained, and if the reservation to the empty car is successful in the optimal searching radius, the following steps are carried out: s+i.ltoreq.β 1 (Z0-Z1);
Wherein: i is a radius which is expanded outwards on the original searching radius by taking an enterprise as a core; beta 1 is the pace of staff walking; z0 is the remaining time from the employee's delay; z1 is the time spent by staff walking to the enterprise; and calculating i to obtain the optimal searching radius.
Compared with the prior art, the invention has the following beneficial effects: the parking space information acquisition module predicts the parking quantity within the set range of the enterprise according to the time information of starting the vehicle by the staff; therefore, whether the remaining parking spaces are available for staff in the set range of the enterprise or not can be analyzed; in order to ensure that the time period for the staff to start the vehicle still has residual parking spaces, the time for the staff to start is optimized, and the full work attendance prize is prevented from being lost due to the fact that the staff is late; the vehicles in the next parking space in the reserved parking lot are screened through the peak-shifting parking management module, so that the screened vehicles are ensured to meet the condition of time limitation; the problem of traffic jam caused by the fact that a large number of vehicles are simultaneously arranged in the same parking lot can be effectively solved, and meanwhile, the problem of long-time waiting of the latter vehicle caused by the fact that the former vehicle is backed up and put into a parking space is avoided; through the parking space reservation management module, the possibility that the reserved parking space is successful can be increased by staff in the optimal parking space searching radius, the staff reserved the parking space in the optimal searching radius is ensured not to be delayed, and the benefits of the staff are practically ensured.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of the module composition of the human resources scheduling management system based on big data of the present invention;
Fig. 2 is a schematic diagram of steps of a human resource scheduling management method based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
the human resource scheduling management system based on big data comprises a parking space information acquisition module, a peak-shifting parking management module and a parking space reservation management module;
The parking space information acquisition module is used for acquiring time information of starting the vehicle by the staff, predicting the number of parking spaces in the enterprise setting range according to the time information, adjusting the time of starting the vehicle by the staff according to the prediction result, and optimizing the departure time of the staff;
The peak-shifting parking management module is used for managing the parking space positions in the setting range of the worker reservation enterprise, screening vehicles in the next parking space in the reserved parking lot and ensuring that the screened vehicles meet the condition of time limitation;
The parking space reservation management module is used for displaying the parking space state to staff according to the running position of the staff vehicle; expanding the parking space searching radius according to the vehicle state to obtain an optimal parking space searching radius; the possibility of successfully reserving the parking space is increased for staff;
The parking space information acquisition module is connected with the peak-shifting parking management module and the parking space reservation management module.
Further, the parking space information acquisition module comprises a vehicle starting triggering unit, a parking space number prediction unit, a vehicle position positioning unit, a tabulation display unit and a scheduling time acquisition unit;
the vehicle starting triggering unit is used for acquiring time information when a worker starts the vehicle;
the parking space number prediction unit is used for predicting parking space number information in a set range of an enterprise according to the time of starting the vehicle by workers;
the vehicle position positioning unit is used for acquiring position information of vehicle running;
The tabulation display unit is used for sending the information of the number of parking spaces corresponding to the vehicles started by the staff at different times to the staff for checking in a form mode, and updating the form in real time; so that staff can select the most proper departure time to reserve in advance;
The shift time acquisition unit is used for acquiring a shift time table of the staff and obtaining a shift time period of the staff; therefore, whether the time period of working of the staff is the peak time period of reserved parking spaces can be analyzed;
The optimal time departure unit is used for optimizing the departure time of staff according to the number of parking spaces to obtain optimal departure time;
the output end of the vehicle starting triggering unit is connected with the input end of the parking space number prediction unit; the output end of the vehicle position positioning unit is connected with the input end of the tabulation display unit; the output end of the parking space number prediction unit is connected with the input end of the scheduling time acquisition unit;
In the parking space information acquisition module, a scheduling time acquisition unit and a vehicle starting triggering unit are simultaneously arranged, and the scheduling time acquisition unit is used for acquiring the working time period of workers; the starting unit is used for obtaining the starting time of the vehicle; however, in order to ensure that the staff is not late, the time for starting the vehicle is often earlier than the time for scheduling; therefore, in order to ensure that a parking space remains in the period of time when the vehicle starts, it is necessary to optimize the departure time of the employee (i.e., the time when the vehicle starts) so as to prevent the employee from being late.
Further, the peak-shifting parking management module comprises a vehicle sequence reservation unit, a designated time limiting unit and a vehicle screening unit;
the vehicle sequence reservation unit is used for managing the reserved parking space positions of the staff, and ensuring that the reserved parking space positions are ordered according to the set sequence in the parking lot;
The appointed time limiting unit is used for acquiring information of the parked vehicles in the parking lot and limiting the time of reserving the next parking space by the staff;
The vehicle screening unit is used for screening workers applying for parking space reservation according to the time limited by the reservation platform, and the screening condition is determined according to the driving position of the workers;
The output end of the vehicle sequence reservation unit is connected with the input end of the appointed time limiting unit; the output end of the appointed time limiting unit is connected with the input end of the vehicle screening unit.
Further, the parking space reservation management module comprises a parking space state display unit, a parking space reservation time locking unit, a parking space information sending unit, a shift time acquisition unit, a reservation radius expansion unit and an optimal radius expansion unit;
The parking space state display unit is used for displaying the parking space state in the radius range with the enterprise as a core to a user and obtaining a parking space state result in the radius range;
the parking space reservation time locking unit is used for acquiring distance information between workers and enterprises and displaying parking space information capable of being reserved to the workers according to the distance information;
The parking space information sending unit is used for sending the parking space information successfully reserved by the staff to the staff receiving port;
the reservation radius expansion unit is used for verifying that when a worker does not reserve the parking space successfully within a set time period, the enterprise is taken as a core to expand the parking space reservation circle outwards, and the radius of the reserved parking space is increased;
The optimal radius expansion unit is used for obtaining an optimal parking space searching radius from the parking space searching radii which are expanded outwards; and reserving the parking space from the optimal parking space searching radius;
The output end of the parking space state display unit is connected with the input end of the parking space reservation time locking unit; the output end of the parking space reservation time locking unit is connected with the input end of the parking space information sending unit; the reservation radius expansion unit is connected with the input ends of the scheduling time acquisition unit and the optimal radius expansion unit.
Further, the human resource scheduling management method based on big data executes the following steps:
Z01: acquiring time information of starting vehicles by workers and scheduling time of the workers, and predicting the number of parking spaces in a set range of an enterprise according to the time information of starting the vehicles by the workers; optimizing the departure time of staff according to the number of vehicles; if the employee starts at the optimized departure time, acquiring the position of the employee when the vehicle runs, and jumping to the step Z02; if the employee does not start at the optimized departure time, jumping to the step Z03;
Z02: if the distance between the staff's vehicle and the enterprise is smaller than the preset distance, setting parking state information of the parking lot in the radius range by taking the enterprise as a core; if the parking lot within the set radius range contains vacant parking spaces, the parking space information to be reserved is sent to the reservation platform according to the sequence of parking vehicles in the parking lot; acquiring the time limit of the reserved next parking space, and screening the vehicles reserved with the parking space according to the time limit to obtain a screening result;
z03: the enterprise is taken as a core, the searching radius of the reserved parking space is increased, and the optimal outwardly-expanded parking space searching radius is obtained; and screening vehicles reserved in the parking space to obtain screening results.
In the step Z01, the scheduling time of the staff is obtained, the time period of starting the vehicle by the staff is obtained, if the time period of starting the vehicle by the staff is a peak time period, the historical data is obtained, the staff gathers as U= { U1, U2, & gt, and the parking space quantity information in um } gathers as E= {1,2, & gt, n }, wherein n refers to the parking space quantity in the peak time period; then establishing a functional relation according to the time period U and the number E of vehicles; obtaining the number E of parking spaces in a set range of an enterprise in a peak time period, wherein E=V 1 U+H; if the time period for starting the vehicle by the employee is a low peak time period, if the vehicle leaving the reserved parking space position of the employee leaves in a peak time period, acquiring a vehicle number set leaving the reserved parking space position of the employee before the off-peak time period as E' = {1,2, 3.,. Then a functional relation is established according to the time period U 'and the parking space number E' to obtainObtaining the number of the parking spaces in the enterprise setting range in the off-peak time period as E-E';
If the vehicle leaving the reserved parking space of the employee does not leave in the peak time period, a function relationship is established between the time period U 'and the parking space number E' obtained by a gray prediction method according to the number of the vehicles leaving, and E '=V 2 U' +b is obtained; the number of the parking spaces in the enterprise setting range in the off-peak time period is E-E';
Establishing a functional relation according to a time period U and a vehicle number E and establishing a functional relation according to a time period U 'and a vehicle number E', wherein the vehicle number is predicted according to a least square method, and the functional relation between the time period U 'and the vehicle number E' is obtained through a gray prediction analysis method; because the number of vehicles leaving is uncontrollable during off-peak time, the time and vehicle change relationship is obtained through a gray predictive analysis method; if the quantity in the enterprise setting range under the off-peak time period is required to be obtained, the difference value between the vehicles entering the enterprise setting range in the peak time period and the vehicles leaving the peak time period is obtained; if the number of the parking spaces in the off-peak time period is obtained by a difference value method, the obtained number of the parking spaces can cause errors; if an error occurs, the worker is late to work.
In the step Z01, the longest time H1 for a worker to start a vehicle to reach an enterprise is acquired, the delay time H2 on a worker schedule is acquired, and the worker departure time period corresponding to the same delay time on the schedule in the historical data is acquired; analyzing whether the departure time period of the staff is a peak time period or an off-peak time period, and acquiring a time period H3 with the largest available parking spaces in the enterprise setting range in the peak time period or the off-peak time period; the optimized departure time L < H2-H1-H3-H4 is obtained; wherein h1=d h5+h6; d is the number of traffic lights on the road from the staff to the enterprise, H5 is the red light of the staff at each intersection, and the waiting time of each red light is averaged; h6 refers to the time that the employee is traveling at the maximum speed allowed on the route; h4 refers to error time;
In the above steps, for example: the delay time of the staff is 10.00, and the time period with the maximum available parking spaces in the enterprise setting range is 20 minutes before; the maximum time a worker spends on the road is h1=d×h5+h6=6×40s+15min=4min+15min=19 min; the employee-optimized departure time should therefore be set to 10.00-20-19 min=9.21; by calculating the optimized time, the success rate of reservation of the staff to the parking space can be improved.
In the step Z02, the parked vehicles in the parking lot are obtained, and the reservation platform discharges the vacant parking spaces according to the sequence of the vehicles in the parking lot; acquiring a set of vacant parking space positions as Q= { Q a,qb,...,qj }, and if F-Q a>F-qb is verified, ensuring that the parking space a is successfully reserved before reserving the parking space b to the public by the platform; when the platform reserves the position of the parking space C to the public, the platform needs to screen the vehicle reserved with the parking space C according to the time limit of the reserved parking space of the staff;
therefore, the time limit for the employee to reserve the next parking space is y=y2-Y1;
Wherein: y2 refers to a vehicle reserving the next parking space, and the conditions of the vehicle are as follows: the time for reaching the parking lot corresponding to the next parking space is faster than the time for other vehicles to reach the parking lot; y1 refers to the time when the parking space B is successfully parked by the vehicle; if in the parking lot, the platform needs to recommend to the public according to the sequence of the remaining vacant parking spaces in the parking lot; if the platform displays the reserved parking space to the public randomly, staff needs to spend a great deal of time waiting for the previous vehicle to completely reverse and put in storage before entering the reserved parking space on the platform; delaying the time of the staff.
In step Z03, acquiring enterprise position information W of workers, and acquiring an original searching radius S of a parking space which is not reserved successfully; the optimal searching radius is obtained, and if the reservation to the empty car is successful in the optimal searching radius, the following steps are carried out: s+i.ltoreq.β 1 (Z0-Z1);
wherein: i is a radius which is expanded outwards on the original searching radius by taking an enterprise as a core; beta 1 is the pace of staff walking; z0 is the remaining time from the employee's delay; z1 is the time spent by staff walking to the enterprise;
Obtaining an optimal searching radius through calculating i;
The calculation mode of S+i is less than or equal to beta 1 (Z0-Z1) is that when staff does not reserve a parking space successfully on a platform, the searching radius needs to be expanded; the success probability of the staff to search the parking space is increased through the prolonged radius, and meanwhile, the position of the reserved parking space is ensured, so that the staff can arrive at the enterprise before arriving; in the process of calculating i, an optimized i value is obtained by using an optimization algorithm, wherein the optimization algorithm is specifically a gradient descent method, and the gradient descent method is the prior art content and is not described; and obtaining a global optimal solution by a gradient descent method.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. Human resource scheduling management system based on big data, its characterized in that: the manpower resource scheduling management system comprises a parking space information acquisition module, a peak-shifting parking management module and a parking space reservation management module;
The parking space information acquisition module is used for acquiring time information of starting the vehicle by the staff, predicting the number of parking spaces in the enterprise setting range according to the time information, adjusting the time of starting the vehicle by the staff according to the prediction result, and optimizing the departure time of the staff;
Acquiring the longest time H1 for a worker to start a vehicle to reach an enterprise, acquiring the delay time H2 on a worker schedule, and acquiring a worker departure time period corresponding to the same delay time of the worker on the schedule in historical data; analyzing whether the departure time period of the staff is a peak time period or an off-peak time period, and acquiring a time period H3 with the largest available parking spaces in the enterprise setting range in the peak time period or the off-peak time period; the optimized departure time L < H2-H1-H3-H4 is obtained; wherein h1=d h5+h6; d is the number of traffic lights on the road from the staff to the enterprise, H5 is the red light of the staff at each intersection, and the waiting time of each red light is averaged; h6 refers to the time that the employee is traveling at the maximum speed allowed on the route; h4 refers to error time;
The peak-shifting parking management module is used for managing the parking space positions in the setting range of the worker reservation enterprise, screening vehicles in the next parking space in the reserved parking lot and ensuring that the screened vehicles meet the condition of time limitation;
The time limit of the employee to reserve the next parking space is Y=Y2-Y1;
wherein: y2 refers to a vehicle reserving the next parking space, and the conditions of the vehicle are as follows: the time for reaching the parking lot corresponding to the next parking space is faster than the time for other vehicles to reach the parking lot; y1 refers to the time when the parking space B is successfully parked by the vehicle;
The parking space reservation management module is used for displaying the parking space state to staff according to the running position of the staff vehicle; expanding the parking space searching radius according to the vehicle state to obtain an optimal parking space searching radius;
The parking space information acquisition module is connected with the peak-shifting parking management module and the parking space reservation management module.
2. The big data based human resources scheduling management system of claim 1, wherein: the parking space information acquisition module comprises a vehicle starting triggering unit, a parking space number prediction unit, a vehicle position positioning unit, a tabulation display unit, a scheduling time acquisition unit and an optimal time departure unit;
the vehicle starting triggering unit is used for acquiring time information when a worker starts the vehicle;
the parking space number prediction unit is used for predicting parking space number information in a set range of an enterprise according to the time of starting the vehicle by workers;
the vehicle position positioning unit is used for acquiring position information of vehicle running;
The tabulation display unit is used for sending the corresponding parking space quantity information to workers for checking in a form mode when the workers start the vehicles at different time, and updating the form in real time; so that staff can select the most proper departure time to reserve in advance;
the shift time acquisition unit is used for acquiring a shift time table of the staff and obtaining a shift time period of the staff;
The optimal time departure unit is used for optimizing the departure time of staff according to the number of parking spaces to obtain optimal departure time;
The output end of the vehicle starting triggering unit is connected with the input end of the parking space number prediction unit; the output end of the vehicle position positioning unit is connected with the input end of the tabulation display unit; the output end of the parking space number prediction unit is connected with the input end of the scheduling time acquisition unit.
3. The big data based human resources scheduling management system of claim 1, wherein: the peak-shifting parking management module comprises a vehicle sequence reservation unit, a designated time limiting unit and a vehicle screening unit;
the vehicle sequence reservation unit is used for managing the reserved parking space positions of the staff, and ensuring that the reserved parking space positions are ordered according to the set sequence in the parking lot;
The appointed time limiting unit is used for acquiring information of the parked vehicles in the parking lot and limiting the time of reserving the next parking space by the staff;
The vehicle screening unit is used for screening workers applying for parking space reservation according to the time limited by the reservation platform, and the screening condition is determined according to the driving position of the workers;
The output end of the vehicle sequence reservation unit is connected with the input end of the appointed time limiting unit; the output end of the appointed time limiting unit is connected with the input end of the vehicle screening unit.
4. The big data based human resources scheduling management system of claim 1, wherein: the parking space reservation management module comprises a parking space state display unit, a parking space reservation time locking unit, a parking space information sending unit, a reservation radius expanding unit and an optimal radius expanding unit;
The parking space state display unit is used for displaying the parking space state in the set radius range by taking the enterprise as a core to a user and obtaining a parking space state result in the set radius range;
the parking space reservation time locking unit is used for acquiring distance information between workers and enterprises and displaying parking space information capable of being reserved to the workers according to the distance information;
The parking space information sending unit is used for sending the parking space information successfully reserved by the staff to the staff receiving port;
the reservation radius expansion unit is used for verifying that when a worker does not reserve the parking space successfully within a set time period, the enterprise is taken as a core to expand the parking space reservation circle outwards, and the radius of the reserved parking space is increased;
The optimal radius expansion unit is used for obtaining an optimal parking space searching radius from the parking space searching radii which are expanded outwards; and reserving the parking space from the optimal parking space searching radius;
The output end of the parking space state display unit is connected with the input end of the parking space reservation time locking unit; the output end of the parking space reservation time locking unit is connected with the input end of the parking space information sending unit; the reserved radius expansion unit is connected with the input end of the optimal radius expansion unit.
5. The human resource scheduling management method based on big data is characterized by comprising the following steps of: the management method comprises the following steps:
Z01: acquiring time information of starting vehicles by workers and scheduling time of the workers, and predicting the number of parking spaces in a set range of an enterprise according to the time information of starting the vehicles by the workers; optimizing the departure time of staff according to the number of parking spaces; if the employee starts at the optimized departure time, acquiring the position of the employee when the vehicle runs, and jumping to the step Z02; if the employee does not start at the optimized departure time, jumping to the step Z03;
Z02: if the distance between the staff's vehicle and the enterprise is smaller than the preset distance, the enterprise is taken as a core, and parking state information of the parking lot in the set radius range is obtained; if the parking lot within the set radius range contains vacant parking spaces, the parking space information to be reserved is sent to the reservation platform according to the sequence of parking vehicles in the parking lot; acquiring the time limit of the reserved next parking space, and screening the vehicles reserved with the parking space according to the time limit to obtain a screening result;
Z03: the enterprise is taken as a core, the searching radius of the reserved parking space is increased, and the optimal outwardly-expanded parking space searching radius is obtained; screening vehicles reserved in the parking space to obtain screening results;
In the step Z01, the scheduling time of the staff is obtained, the time period of starting the vehicle by the staff is obtained, if the time period of starting the vehicle by the staff is a peak time period, the historical data is obtained, the staff gathers as U= { U1, U2, & gt, and the parking space quantity information in um } gathers as E= {1,2, & gt, n }, wherein n refers to the parking space quantity in the peak time period; then establishing a functional relation according to the time period U and the number E of vehicles; obtaining the number E of parking spaces in a set range of an enterprise in a peak time period, wherein E=V 1 U+H;
If the time period for starting the vehicle by the employee is a low peak time period, if the vehicle leaving the reserved parking space position of the employee leaves in a peak time period, acquiring a vehicle number set leaving the reserved parking space position of the employee before the off-peak time period as E' = {1,2, 3.,. Then, establishing a functional relation according to the time period U ' and the parking space number E ' to obtain E ' =V 2 U ' ' +b; obtaining the number of the parking spaces in the enterprise setting range in the off-peak time period as E-E';
If the vehicle leaving the reserved parking space of the employee does not leave in the peak time period, a function relationship is established between the time period U 'and the parking space number E' obtained by a gray prediction method according to the number of the vehicles leaving, and E '=V 2 U' +b is obtained; the number of the parking spaces in the enterprise setting range in the off-peak time period is E-E';
In the step Z01, the longest time H1 for a worker to start a vehicle to reach an enterprise is acquired, the delay time H2 on a worker schedule is acquired, and the worker departure time period corresponding to the same delay time on the schedule in the historical data is acquired; analyzing whether the departure time period of the staff is a peak time period or an off-peak time period, and acquiring a time period H3 with the largest available parking spaces in the enterprise setting range in the peak time period or the off-peak time period; the optimized departure time L < H2-H1-H3-H4 is obtained; wherein h1=d h5+h6; d is the number of traffic lights on the road from the staff to the enterprise, H5 is the red light of the staff at each intersection, and the waiting time of each red light is averaged; h6 refers to the time that the employee is traveling at the maximum speed allowed on the route; h4 refers to error time;
in step Z02, the time limit for the employee to reserve the next parking space is y=y2—y1;
Wherein: y2 refers to a vehicle reserving the next parking space, and the conditions of the vehicle are as follows: the time for reaching the parking lot corresponding to the next parking space is faster than the time for other vehicles to reach the parking lot; y1 refers to the time that parking spot B was successfully parked by the vehicle.
6. The human resources scheduling management method based on big data according to claim 5, wherein: in step Z03, the enterprise location information W of the employee is acquired, the original search radius S of the parking space that is not reserved successfully is acquired, an optimal search radius is obtained, and if the parking space is reserved successfully in the optimal search radius, then:
S+i≤β1*(Z0-Z1)；
wherein: i is a radius which is expanded outwards on the original searching radius by taking an enterprise as a core; beta 1 is the pace of staff walking; z0 is the remaining time from the employee's delay; z1 is the time spent by staff walking to the enterprise;
And calculating i to obtain the optimal searching radius.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105469627A (en) * 2015-11-27 2016-04-06 应石磊 A parking stall reservation method, apparatus and system
CN108961104A (en) * 2018-06-29 2018-12-07 深圳春沐源控股有限公司 Scenic spot parking stall management method and scenic spot parking management device
WO2019242832A1 (en) * 2018-06-18 2019-12-26 Bayerische Motoren Werke Aktiengesellschaft Method, device, cloud service, system, and computer program for smart parking a connected vehicle
CN113257034A (en) * 2021-07-05 2021-08-13 江西省湾流港智能科技有限公司 Intelligent parking space reservation method and system
CN114218483A (en) * 2021-12-16 2022-03-22 城云科技（中国）有限公司 Parking recommendation method and application thereof

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105469627A (en) * 2015-11-27 2016-04-06 应石磊 A parking stall reservation method, apparatus and system
WO2019242832A1 (en) * 2018-06-18 2019-12-26 Bayerische Motoren Werke Aktiengesellschaft Method, device, cloud service, system, and computer program for smart parking a connected vehicle
CN108961104A (en) * 2018-06-29 2018-12-07 深圳春沐源控股有限公司 Scenic spot parking stall management method and scenic spot parking management device
CN113257034A (en) * 2021-07-05 2021-08-13 江西省湾流港智能科技有限公司 Intelligent parking space reservation method and system
CN114218483A (en) * 2021-12-16 2022-03-22 城云科技（中国）有限公司 Parking recommendation method and application thereof

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