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

Abstract

The present invention discloses a human resource scheduling management system and method based on big data. The human resource scheduling management system includes a parking space information acquisition module, a staggered parking management module and a parking space reservation management module; the parking space information acquisition module is used to obtain the time information of employees starting their vehicles, and predict the number of parking spaces within a range set by the enterprise according to the time information. According to the prediction result, the employees adjust the time of starting the vehicles and optimize the time of employees'departure; the staggered parking management module is used to manage the positions of parking spaces within the range set by the enterprise reserved by employees, and screen the vehicles for the next parking space in the reserved parking lot to ensure that the screened vehicles meet the time limit conditions; the parking space reservation management module is used to display the parking space status to the employees according to the driving position of the employees'vehicles; expand the parking space search radius according to the vehicle status to obtain the optimal parking space search radius; so that the employees can increase the possibility of successfully reserving parking spaces.

Description

Human resource scheduling management system and method based on big data

Technical Field

The present invention relates to the field of big data technology, and in particular to a human resources scheduling management system and method based on big data.

Background technique

Scheduling is a way to constrain employees' working hours. Scheduling is closely related to employees' full attendance bonuses or performance. Employees who often drive to and from work need to accurately grasp their departure time to avoid being late due to parking or road problems. Therefore, in order to avoid being late due to parking problems, you can make a parking reservation.

In the prior art, there is a patent document with application number: 201610227896.6 and publication date of October 23, 2018; the patent document discloses: based on the parking lots included in the area to which the driver is going, the parking lots are screened according to the driver's requirements to obtain the optimal parking lot; it solves the problem of selecting the correct way to realize parking space reservation and realizes the regional integrity or reliability of the reserved parking lot; although the above document solves the problem of making reservations for parking lots, this document is selected according to the needs of the driver, so that the parking spaces in the parking lot can be further selected according to the needs of the driver; in this way, the vehicles that arrive first will use the parking spaces, and the parking spaces in the parking lot that are obtained later will have to compete for parking spaces in other areas again; thus, a large number of vehicles will pour into the same area, causing traffic congestion; at the same time, it will also cause the reserved parking spaces to wait for other vehicles to successfully reverse into the garage before they can enter the parking spaces smoothly; this delays the time for employees to go to work smoothly, thus causing the problem of employees being late; therefore, the problem needs to be improved.

Summary of the invention

The purpose of the present invention is to provide a human resource scheduling management system and method based on big data to solve the problems raised in the above background technology.

In order to solve the above technical problems, the present invention provides the following technical solutions: a human resource scheduling management system based on big data, the human resource scheduling management system includes a parking space information acquisition module, a staggered parking management module and a parking space reservation management module;

The parking space information acquisition module is used to obtain the time information of the employee starting the vehicle, and predict the number of parking spaces within the range set by the enterprise according to the time information. According to the prediction result, the employee adjusts the time of starting the vehicle and optimizes the departure time of the employee;

The staggered parking management module is used to manage the parking spaces within the range set by the employee reservation company, and screen the vehicle for the next parking space in the reserved parking lot to ensure that the screened vehicle meets the time limit conditions;

The parking space reservation management module is used to display the parking space status to employees according to the location of their vehicles; expand the parking space search radius according to the vehicle status to obtain the optimal parking space search radius; so that employees can increase the possibility of successfully reserving parking spaces;

The parking space information acquisition module is connected with the staggered parking management module and the parking space reservation management module.

Furthermore, the parking space information acquisition module includes a vehicle start trigger unit, a parking space number prediction unit, a vehicle position positioning unit, a tabulation display unit and a shift time acquisition unit;

The vehicle start trigger unit is used to obtain the time information when the employee starts the vehicle;

The parking number prediction unit is used to predict the number of parking spaces within the enterprise set range according to the time when the employee starts the vehicle;

The vehicle position positioning unit is used to obtain the vehicle's driving position information;

The tabulation display unit is used to display the parking space quantity information corresponding to the vehicles started by employees at different times, send the vehicle information to employees for review in the form of a table, and update the table in real time; so that employees can choose the most suitable departure time to make an appointment in advance;

The shift time acquisition unit is used to acquire the employee's shift schedule and obtain the employee's working time period; thereby being able to analyze whether the employee's working time period is a peak time period for reserving parking spaces;

The optimal time departure unit is used to optimize the departure time of the employee according to the number of parking spaces to obtain the optimal departure time;

The output end of the vehicle start trigger unit is connected to the input end of the parking number prediction unit; the output end of the vehicle position positioning unit is connected to the input end of the tabulation display unit; the output end of the parking number prediction unit is connected to the input end of the scheduling time acquisition unit.

Furthermore, the off-peak parking management module includes a vehicle sequence reservation unit, a designated time limit unit and a vehicle screening unit;

The vehicle sequence reservation unit is used to manage the parking spaces reserved by employees to ensure that the reserved parking spaces are arranged in the order set in the parking lot;

The designated time limit unit is used to obtain information about vehicles parked in the parking lot and to limit the time for employees to reserve the next parking space;

The vehicle screening unit is used to screen employees who apply for parking reservations according to the time limit of the reservation platform, and the screening condition is determined according to the location where the employees drive;

The output end of the vehicle sequence reservation unit is connected to the input end of the designated time limit unit; the output end of the designated time limit unit is connected to the input end of the vehicle screening unit.

Further, the parking space reservation management module includes a parking space status 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 status display unit is used to display the parking space status within a set radius with the enterprise as the core to the user, and obtain the parking space status result within the radius;

The parking space reservation time locking unit is used to obtain the distance information between the employee and the enterprise, and display the parking space information that can be reserved to the employee according to the distance information;

The parking space information sending unit is used to send the parking space information successfully reserved by the employee to the employee receiving port;

The reservation radius expansion unit is used to expand the parking space reservation circle outward with the enterprise as the core to increase the radius of the reserved parking space when verifying that the employee has not successfully reserved a parking space within the set time period;

The optimal radius expansion unit is used to obtain the optimal parking space search radius in the parking space search radius that expands outward; and to reserve a parking space within the optimal parking space search radius;

The output end of the parking space status display unit is connected to the input end of the parking space reservation time locking unit; the output end of the parking space reservation time locking unit is connected to the input end of the parking space information sending unit; the reservation radius expansion unit is connected to the input ends of the scheduling time acquisition unit and the optimal radius expansion unit.

Furthermore, a human resource scheduling management method based on big data is implemented by performing the following steps:

Z01: Obtain the time information of the employee starting the vehicle and the employee's shift schedule, and predict the number of parking spaces within the enterprise's set range based on the time information of the employee starting the vehicle; optimize the employee's departure time based on the number of vehicles; if the employee departs at the optimized departure time, obtain the location of the employee's vehicle when it is traveling, and jump to step Z02; if the employee does not depart at the optimized departure time, jump to step Z03;

Z02: If the distance between the employee's vehicle and the enterprise is less than the preset distance, the parking status information of the parking lot within the set radius is set with the enterprise as the core; if there are vacant parking spaces in the parking lot within the set radius, the parking space information to be reserved is sent to the reservation platform in the order of the vehicles parked in the parking lot; the time limit for reserving the next parking space is obtained, and the vehicles for reserving the parking space are screened according to the time limit to obtain the screening result;

Z03: With the enterprise as the core, increase the search radius of the reserved parking space to obtain the optimal outward-expanding parking space search radius; and screen the vehicles that have reserved the parking space to obtain the screening results.

In step Z01, the employee's shift schedule is obtained, and the time period when the employee starts the vehicle is obtained. If the time period when the employee starts the vehicle is a peak time period, the number of parking spaces for employees in the time period set U = {u1, u2, ..., um} in the historical data is obtained as E = {1, 2, ..., n}, where n refers to the number of parking spaces in the peak time period; a functional relationship is established based on the time period U and the number of parking spaces E; the number of parking spaces E within the enterprise setting range during the peak time period is obtained, E = V ₁ U + H; if the time period when the employee starts the vehicle is a low-peak time period, if the vehicle leaving the employee's reserved parking space leaves during the peak time period, the number of vehicles leaving the employee's reserved parking space before the non-peak time period is obtained as E' = {1, 2, 3, ..., p}, where p refers to the number of parking spaces before the non-peak time period; a functional relationship is established based on the time period U' and the number of parking spaces E', and the number of parking spaces obtained is obtained. Then the number of parking spaces within the enterprise’s set range during off-peak hours is E-E';

If the vehicles leaving the reserved parking spaces of employees do not leave during the peak time period, the grey prediction method is used to establish a functional relationship between the time period U” and the number of parking spaces E” according to the number of vehicles leaving, and E”＝V ₂ U”+b; the number of parking spaces within the enterprise setting range during the non-peak time period is EE”;

In step Z01, obtain the longest time H1 that employees take to start their vehicles to reach the company, obtain the late time H2 on the employee schedule, and obtain the employee departure time period corresponding to the same late time on the employee schedule in the historical data; analyze whether the employee departure time period is a peak time period or a non-peak time period, then obtain the time period H3 with the most vacant parking spaces within the company's set range during the peak time period or the non-peak time period; then obtain the optimized departure time L<H2-H1-H3-H4; wherein H1=D*H5+H6; D is the number of traffic lights on the way from the employee to the company, H5 refers to the time when the employee is at each intersection with a red light and the average waiting time for each red light; H6 refers to the time when the employee is driving at the maximum speed allowed on the route; H4 refers to the error time.

In step Z02, the time limit for the employee to reserve the next parking space is Y=Y2-Y1;

Among them: Y2 refers to the vehicle that reserves the next parking space, and the condition of the vehicle is: the time to arrive at the parking lot corresponding to the next parking space is faster than the time when other vehicles arrive at the parking lot; Y1 refers to the time when parking space B is successfully occupied by the vehicle.

In step Z03, the location information W of the enterprise where the employee works is obtained, and the original search radius S of the unsuccessful parking reservation is obtained; then the optimal search radius is obtained. If an empty parking space is successfully reserved within the optimal search radius, then: S+i≤β ₁ *(Z0-Z1);

Among them: i refers to the radius expanding outward from the original search radius with the enterprise as the core; _β1 refers to the walking speed of the employee; Z0 refers to the remaining time before the employee is late; Z1 refers to the time it takes for the employee to walk to the enterprise; by calculating i, the optimal search radius is obtained.

Compared with the prior art, the beneficial effects achieved by the present invention are as follows: through the parking space information acquisition module, the number of parking spaces within the range set by the enterprise is predicted according to the time information of the employees starting the vehicle; thereby, it is possible to analyze whether there are any remaining parking spaces for employees to use within the range set by the enterprise in the current time period; in order to ensure that there are still remaining parking spaces during the time period when the employees start the vehicles, the departure time of the employees is optimized to prevent the employees from being late and thus missing the full attendance award; through the staggered parking management module, the vehicle for the next parking space in the reserved parking lot is screened to ensure that the screened vehicles meet the time limit conditions; it can effectively solve the problem of traffic congestion caused by a large number of vehicles appearing in the same parking lot at the same time, and at the same time will not cause the problem of the next vehicle having to wait for a long time due to the previous vehicle reversing into the parking space; through the parking space reservation management module, the possibility of employees successfully reserving parking spaces can be increased within the optimal parking space search radius, ensuring that employees who reserve parking spaces within the optimal search radius will not be late for employees, and the interests of employees are effectively guaranteed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

FIG1 is a schematic diagram of the module composition of the human resources scheduling management system based on big data of the present invention;

FIG2 is a schematic diagram of the steps of the human resource scheduling management method based on big data of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

Please refer to Figures 1 and 2, the present invention provides a technical solution:

A human resource scheduling management system based on big data, which includes a parking space information acquisition module, a staggered parking management module and a parking space reservation management module;

The parking space information acquisition module is used to obtain the time information of the employee starting the vehicle, and predict the number of parking spaces within the range set by the enterprise according to the time information. According to the prediction result, the employee adjusts the time of starting the vehicle and optimizes the departure time of the employee;

The staggered parking management module is used to manage the parking spaces within the range set by the employee reservation company, and screen the vehicle for the next parking space in the reserved parking lot to ensure that the screened vehicle meets the time limit conditions;

The parking space reservation management module is used to display the parking space status to employees according to the location of their vehicles; expand the parking space search radius according to the vehicle status to obtain the optimal parking space search radius; so that employees can increase the possibility of successfully reserving parking spaces;

The parking space information acquisition module is connected with the staggered parking management module and the parking space reservation management module.

Further, the parking space information acquisition module includes a vehicle start trigger unit, a parking space number prediction unit, a vehicle position positioning unit, a tabulation display unit and a shift time acquisition unit;

The vehicle start trigger unit is used to obtain the time information when the employee starts the vehicle;

The parking number prediction unit is used to predict the number of parking spaces within the enterprise set range according to the time when the employee starts the vehicle;

The vehicle position positioning unit is used to obtain the vehicle's driving position information;

The tabulation display unit is used to display the parking space quantity information corresponding to the vehicles started by employees at different times, send the vehicle information to employees for review in the form of a table, and update the table in real time; so that employees can choose the most suitable departure time to make an appointment in advance;

The shift time acquisition unit is used to acquire the employee's shift schedule and obtain the employee's working time period; thereby being able to analyze whether the employee's working time period is a peak time period for reserving parking spaces;

The optimal time departure unit is used to optimize the departure time of the employee according to the number of parking spaces to obtain the optimal departure time;

The output end of the vehicle start trigger unit is connected to the input end of the parking number prediction unit; the output end of the vehicle position positioning unit is connected to the input end of the tabulation display unit; the output end of the parking number prediction unit is connected to the input end of the scheduling time acquisition unit;

In the above parking space information acquisition module, a shift schedule acquisition unit and a vehicle start trigger unit are set up at the same time. The shift schedule acquisition unit is used to obtain the working time period of employees, and the vehicle start and departure unit is used to obtain the start time of the vehicle. However, in order to ensure that employees are not late, the vehicle start and departure time is often earlier than the shift schedule. Therefore, in order to ensure that there are still parking spaces during the vehicle start time period, it is necessary to optimize the employee departure time (i.e. the vehicle start time) to prevent employees from being late.

Furthermore, the off-peak parking management module includes a vehicle sequence reservation unit, a designated time limit unit and a vehicle screening unit;

The vehicle sequence reservation unit is used to manage the parking spaces reserved by employees to ensure that the reserved parking spaces are arranged in the order set in the parking lot;

The designated time limit unit is used to obtain information about vehicles parked in the parking lot and to limit the time for employees to reserve the next parking space;

The vehicle screening unit is used to screen employees who apply for parking reservations according to the time limit of the reservation platform, and the screening condition is determined according to the location where the employees drive;

The output end of the vehicle sequence reservation unit is connected to the input end of the designated time limit unit; the output end of the designated time limit unit is connected to the input end of the vehicle screening unit.

Further, the parking space reservation management module includes a parking space status 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 status display unit is used to display the parking space status within a set radius with the enterprise as the core to the user, and obtain the parking space status result within the radius;

The parking space reservation time locking unit is used to obtain the distance information between the employee and the enterprise, and display the parking space information that can be reserved to the employee according to the distance information;

The parking space information sending unit is used to send the parking space information successfully reserved by the employee to the employee receiving port;

The reservation radius expansion unit is used to expand the parking space reservation circle outward with the enterprise as the core to increase the radius of the reserved parking space when verifying that the employee has not successfully reserved a parking space within the set time period;

The optimal radius expansion unit is used to obtain the optimal parking space search radius in the parking space search radius that expands outward; and to make a parking space reservation within the optimal parking space search radius;

The output end of the parking space status display unit is connected to the input end of the parking space reservation time locking unit; the output end of the parking space reservation time locking unit is connected to the input end of the parking space information sending unit; the reservation radius expansion unit is connected to the input ends of the scheduling time acquisition unit and the optimal radius expansion unit.

Furthermore, a human resource scheduling management method based on big data is implemented by performing the following steps:

Z01: Obtain the time information of the employee starting the vehicle and the employee's shift schedule, and predict the number of parking spaces within the enterprise's set range based on the time information of the employee starting the vehicle; optimize the employee's departure time based on the number of vehicles; if the employee departs at the optimized departure time, obtain the location of the employee's vehicle when it is traveling, and jump to step Z02; if the employee does not depart at the optimized departure time, jump to step Z03;

Z02: If the distance between the employee's vehicle and the enterprise is less than the preset distance, the parking status information of the parking lot within the set radius is set with the enterprise as the core; if there are vacant parking spaces in the parking lot within the set radius, the parking space information to be reserved is sent to the reservation platform in the order of the vehicles parked in the parking lot; the time limit for reserving the next parking space is obtained, and the vehicles for reserving the parking space are screened according to the time limit to obtain the screening result;

Z03: With the enterprise as the core, increase the search radius of the reserved parking space to obtain the optimal outward-expanding parking space search radius; and screen the vehicles that have reserved the parking space to obtain the screening results.

In step Z01, the employee's shift schedule is obtained, and the time period when the employee starts the vehicle is obtained. If the time period when the employee starts the vehicle is a peak time period, the number of parking spaces for employees in the time period set U = {u1, u2, ..., um} in the historical data is obtained as E = {1, 2, ..., n}, where n refers to the number of parking spaces in the peak time period; a functional relationship is established based on the time period U and the number of parking spaces E; the number of parking spaces E within the enterprise setting range during the peak time period is obtained, E = V ₁ U + H; if the time period when the employee starts the vehicle is a low-peak time period, if the vehicle leaving the employee's reserved parking space leaves during the peak time period, the number of vehicles leaving the employee's reserved parking space before the non-peak time period is obtained as E' = {1, 2, 3, ..., p}, where p refers to the number of parking spaces before the non-peak time period; a functional relationship is established based on the time period U' and the number of parking spaces E', and the number of parking spaces obtained is obtained. Then the number of parking spaces within the enterprise’s set range during off-peak hours is E-E';

If the vehicles leaving the reserved parking spaces of employees do not leave during the peak time period, the grey prediction method is used to establish a functional relationship between the time period U” and the number of parking spaces E” according to the number of vehicles leaving, and E”＝V ₂ U”+b; the number of parking spaces within the enterprise setting range during the non-peak time period is EE”;

The functional relationship between time period U and number of parking lots E and the functional relationship between time period U' and number of parking lots E' are established to predict the number of parking spaces based on the least squares method, while the functional relationship between time period U" and number of parking lots E" is obtained through the grey prediction analysis method; since it is during the off-peak time period, the number of vehicles leaving is uncontrollable, so the grey prediction analysis method is used to obtain the changing relationship between time and vehicles; and if the number within the range set by the enterprise during the off-peak time period is to be obtained, the difference between the vehicles entering the range set by the enterprise during the peak time period and the vehicles leaving during the peak time period is used; if the number of parking spaces during the off-peak time period is not obtained through the difference method, the obtained number of parking spaces will cause errors; if errors occur, it will cause employees to be late for work.

In step Z01, the longest time H1 taken by the employee to start the vehicle to arrive at the enterprise is obtained, the late time H2 on the employee's shift schedule is obtained, and the employee's departure time period corresponding to the same late time on the employee's shift schedule in the historical data is obtained; if the employee's departure time period is a peak time period or a non-peak time period, then the time period H3 with the most vacant parking spaces within the enterprise's set range in the peak time period or the non-peak time period is obtained; then 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 way from the employee to the enterprise, H5 refers to the time when the employee is at a red light at each intersection and the average waiting time for each red light; H6 refers to the time when the employee travels at the maximum speed allowed on the route; H4 refers to the error time;

In the above steps, for example: the employee's late arrival time is 10:00, and the time period with the most vacant parking spaces within the company's setting range is the first 20 minutes; the longest time the employee spends on the road is H1=D*H5+H6=6*40s+15min=4min+15min=19min; therefore, the employee's optimal departure time should be set at 10:00-20-19min=9:21 departure; by calculating the optimal time, the success rate of employees making reservations for parking spaces can be increased.

In step Z02, the vehicles parked in the parking lot are obtained, and the reservation platform arranges the available parking spaces in the order of the vehicles in the parking lot; the available parking space position set is obtained as Q = {q _a ,q _b ,...,q _j }. If it is verified that Fq _a >Fq _b , the platform needs to ensure that parking space a has been successfully reserved before reserving parking space b to the public; when the platform reserves parking space C to the public, it needs to screen the vehicles for reserving parking space C according to the time limit for employees to reserve parking spaces;

Therefore, the time limit for an employee to reserve the next parking space is Y = Y2-Y1;

Among them: Y2 refers to the vehicle that reserves the next parking space, and the condition of the vehicle is: the time to arrive at the parking lot corresponding to the next parking space is faster than the time when other vehicles arrive at the parking lot; Y1 refers to the time when the vehicle successfully enters parking space B; if in the parking lot, the platform needs to recommend to the public according to the order of the remaining vacant parking spaces in the parking lot; if the platform randomly displays the reservation results to the public, employees need to spend a lot of time waiting for the previous vehicle to completely reverse into the parking lot before they can enter the parking space they have reserved on the platform, which delays the employees' time.

In step Z03, the location information W of the enterprise where the employee works is obtained, and the original search radius S of the unsuccessful parking reservation is obtained; then the optimal search radius is obtained. If an empty parking space is successfully reserved within the optimal search radius, then: S+i≤β ₁ *(Z0-Z1);

Among them: i refers to the radius that expands outward from the original search radius with the enterprise as the core; _β1 refers to the walking speed of the employee; Z0 refers to the remaining time before the employee is late; Z1 refers to the time it takes for the employee to walk to the enterprise;

By calculating i, the optimal search radius is obtained;

S+i≤β ₁ *(Z0-Z1) is calculated in the following way: when an employee fails to successfully reserve a parking space on the platform, the search radius needs to be expanded; the probability of employees successfully searching for parking spaces is increased by extending the radius, and at the same time, the location of the reserved parking space must be ensured to be able to arrive at the company before the employee is late; in the process of calculating i, an optimization algorithm is used to obtain the optimal i value, wherein the optimization algorithm is specifically a gradient descent method, which is a prior art content and will not be elaborated in detail; the global optimal solution is obtained by the gradient descent method.

It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (6)

1. A human resource scheduling management system based on big data, characterized in that: the human resource scheduling management system includes a parking space information acquisition module, a staggered parking management module and a parking space reservation management module; The parking space information acquisition module is used to obtain the time information of the employee starting the vehicle, and predict the number of parking spaces within the range set by the enterprise according to the time information. According to the prediction result, the employee adjusts the time of starting the vehicle and optimizes the departure time of the employee; Get the longest time H1 that the employee takes to start the vehicle to arrive at the enterprise, get the late time H2 on the employee's shift schedule, and get the employee's departure time period corresponding to the same late time on the employee's shift schedule in the historical data; analyze whether the employee's departure time period is a peak time period or a non-peak time period, and then get the time period H3 with the most vacant parking spaces within the enterprise's set range during the peak time period or the non-peak time period; then get the optimal departure time L<H2-H1-H3-H4; where H1=D*H5+H6; D is the number of traffic lights on the way from the employee to the enterprise, H5 refers to the time when the employee is at a red light at each intersection and the average waiting time for each red light; H6 refers to the time when the employee travels at the maximum speed allowed on the route; H4 refers to the error time; The staggered parking management module is used to manage the parking spaces within the range set by the employee reservation company, and screen the vehicle for the next parking space in the reserved parking lot to ensure that the screened vehicle meets the time limit conditions; The time limit for an employee to reserve the next parking space is Y = Y2-Y1; Among them: Y2 refers to the vehicle that reserves the next parking space, and the vehicle's condition is: the time it arrives at the parking lot corresponding to the next parking space is faster than the time other vehicles arrive at the parking lot; Y1 refers to the time when the parking space B is successfully occupied by the vehicle; The parking space reservation management module is used to display the parking space status to employees according to the location of their vehicles; expand the parking space search radius according to the vehicle status to obtain the optimal parking space search radius; The parking space information acquisition module is connected with the staggered parking management module and the parking space reservation management module. 2. According to the big data-based human resource scheduling management system of claim 1, it is characterized in that: the parking space information acquisition module includes a vehicle start trigger 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 start trigger unit is used to obtain the time information when the employee starts the vehicle; The parking number prediction unit is used to predict the number of parking spaces within the enterprise set range according to the time when the employee starts the vehicle; The vehicle location positioning unit is used to obtain the location information of the vehicle; The tabulation and display unit is used to send the parking space quantity information corresponding to the employees starting their vehicles at different times to the employees for viewing in the form of a table, and update the table in real time; so that the employees can choose the most suitable departure time to make an appointment in advance; The shift schedule acquisition unit is used to acquire the employee's shift schedule and obtain the employee's working time period; The optimal time departure unit is used to optimize the departure time of the employee according to the number of parking spaces to obtain the optimal departure time; The output end of the vehicle start trigger unit is connected to the input end of the parking number prediction unit; the output end of the vehicle position positioning unit is connected to the input end of the tabulation display unit; the output end of the parking number prediction unit is connected to the input end of the scheduling time acquisition unit. 3. The human resource scheduling management system based on big data according to claim 1 is characterized in that: the staggered parking management module includes a vehicle sequence reservation unit, a specified time limit unit and a vehicle screening unit; The vehicle sequence reservation unit is used to manage the parking spaces reserved by employees to ensure that the reserved parking spaces are arranged in the order set in the parking lot; The designated time limit unit is used to obtain information about vehicles parked in the parking lot and to limit the time for employees to reserve the next parking space; The vehicle screening unit is used to screen employees who apply for parking reservations according to the time limit of the reservation platform, and the screening condition is determined according to the location where the employees drive; The output end of the vehicle sequence reservation unit is connected to the input end of the designated time limit unit; the output end of the designated time limit unit is connected to the input end of the vehicle screening unit. 4. The human resource scheduling management system based on big data according to claim 1 is characterized in that: the parking space reservation management module includes a parking space status display unit, a parking space reservation time locking unit, a parking space information sending unit, a reservation radius expansion unit and an optimal radius expansion unit; The parking space status display unit is used to display the parking space status within a set radius with the enterprise as the core to the user, and obtain the parking space status result within the set radius; The parking space reservation time locking unit is used to obtain the distance information between the employee and the enterprise, and display the parking space information that can be reserved to the employee according to the distance information; The parking space information sending unit is used to send the parking space information successfully reserved by the employee to the employee receiving port; The reservation radius expansion unit is used to expand the parking space reservation circle outward with the enterprise as the core to increase the radius of the reserved parking space when verifying that the employee has not successfully reserved a parking space within the set time period; The optimal radius expansion unit is used to obtain the optimal parking space search radius in the parking space search radius that expands outward; and to reserve a parking space within the optimal parking space search radius; The output end of the parking space status display unit is connected to the input end of the parking space reservation time locking unit; the output end of the parking space reservation time locking unit is connected to the input end of the parking space information sending unit; the reservation radius expansion unit is connected to the input end of the optimal radius expansion unit. 5. A human resource scheduling management method based on big data, characterized in that: the management method performs the following steps: Z01: Obtain the time information of the employee starting the vehicle and the employee's shift schedule, and predict the number of parking spaces within the enterprise's set range based on the time information of the employee starting the vehicle; optimize the employee's departure time based on the number of parking spaces; if the employee departs at the optimized departure time, obtain the location of the employee's vehicle when it is traveling, and jump to step Z02; if the employee does not depart at the optimized departure time, jump to step Z03; Z02: If the distance between the employee's vehicle and the enterprise is less than the preset distance, the enterprise is taken as the core to obtain the parking status information of the parking lot within the set radius; if there are vacant parking spaces in the parking lot within the set radius, the parking space information to be reserved is sent to the reservation platform in the order of the vehicles parked in the parking lot; the time limit for reserving the next parking space is obtained, and the vehicles for reserving the parking space are screened according to the time limit to obtain the screening result; Z03: With the enterprise as the core, the search radius of the reserved parking space is increased to obtain the optimal outward-expanded parking space search radius; and the vehicles that have reserved the parking space are screened to obtain the screening results; In step Z01, the employee's shift schedule is obtained to obtain the time period when the employee starts the vehicle. If the time period when the employee starts the vehicle is a peak time period, the employee's parking space quantity information set in the time period set U = {u1, u2, ..., um} is obtained in the historical data as E = {1, 2, ..., n}, where n refers to the number of parking spaces in the peak time period; a functional relationship is established based on the time period U and the number of parking spaces E; the number of parking spaces E within the enterprise setting range during the peak time period is obtained, E = V ₁ U + H; If the time period when the employee starts the vehicle is a low-peak time period, and if the vehicle that leaves the employee's reserved parking space leaves during the peak time period, then the number of vehicles that leave the employee's reserved parking space before the off-peak time period is obtained as E'＝{1,2,3,...,p}, where p refers to the number of parking spaces before the off-peak time period; then a functional relationship is established based on the time period U' and the number of parking spaces E', and E'＝V ₂ U'′+b is obtained; then the number of parking spaces within the enterprise's set range during the off-peak time period is obtained as E-E'; If the vehicles leaving the reserved parking spaces of employees do not leave during the peak time period, the grey prediction method is used to establish a functional relationship between the time period U” and the number of parking spaces E” according to the number of vehicles leaving, and E”＝V ₂ U”+b; the number of parking spaces within the enterprise setting range during the non-peak time period is EE”; In step Z01, the longest time H1 taken by the employee to start the vehicle to arrive at the enterprise is obtained, the late time H2 on the employee's shift schedule is obtained, and the employee's departure time period corresponding to the same late time on the employee's shift schedule in the historical data is obtained; if the employee's departure time period is a peak time period or a non-peak time period, then the time period H3 with the most vacant parking spaces within the enterprise's set range in the peak time period or the non-peak time period is obtained; then 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 way from the employee to the enterprise, H5 refers to the time when the employee is at a red light at each intersection and the average waiting time for each red light; H6 refers to the time when the employee travels at the maximum speed allowed on the route; H4 refers to the error time; In step Z02, the time limit for the employee to reserve the next parking space is Y=Y2-Y1; Among them: Y2 refers to the vehicle that reserves the next parking space, and the condition of the vehicle is: the time to arrive at the parking lot corresponding to the next parking space is faster than the time when other vehicles arrive at the parking lot; Y1 refers to the time when the parking space B is successfully occupied by the vehicle. 6. The method for human resource scheduling management based on big data according to claim 5 is characterized in that: in step Z03, the location information W of the enterprise where the employee works is obtained, and the original search radius S of the unsuccessful parking reservation is obtained, and then the optimal search radius is obtained. If an empty parking space is successfully reserved in the optimal search radius, then: S+i≤β ₁ *(Z0-Z1); Among them: i refers to the radius that expands outward from the original search radius with the enterprise as the core; _β1 refers to the walking speed of the employee; Z0 refers to the remaining time before the employee is late; Z1 refers to the time it takes for the employee to walk to the enterprise; By calculating i, the optimal search radius is obtained.