CN115511251A - Human resource scheduling management system and method applied to big data - Google Patents

Human resource scheduling management system and method applied to big data Download PDF

Info

Publication number
CN115511251A
CN115511251A CN202210914142.3A CN202210914142A CN115511251A CN 115511251 A CN115511251 A CN 115511251A CN 202210914142 A CN202210914142 A CN 202210914142A CN 115511251 A CN115511251 A CN 115511251A
Authority
CN
China
Prior art keywords
parking
time
parking space
vehicle
unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210914142.3A
Other languages
Chinese (zh)
Inventor
彭耀栋
李�杰
杨宝
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuxi Woqu Information Technology Co ltd
Original Assignee
Wuxi Woqu Information Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuxi Woqu Information Technology Co ltd filed Critical Wuxi Woqu Information Technology Co ltd
Priority to CN202210914142.3A priority Critical patent/CN115511251A/en
Publication of CN115511251A publication Critical patent/CN115511251A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a human resource scheduling management system and a human resource scheduling management method applied to 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 a vehicle by a worker, predicting the number of parking spaces in an enterprise set range according to the time information, adjusting the time for starting the vehicle by the worker according to a prediction result, and optimizing the departure time of the worker; the peak-staggering parking management module is used for managing the positions of the parking places in the set range of the employee reservation enterprise, screening the vehicles of the next parking place 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 the worker according to the driving position of the worker vehicle; expanding the parking space searching radius according to the vehicle state to obtain the optimal parking space searching radius; so that the worker can increase the probability of successfully reserving the parking space.

Description

Human resource scheduling management system and method applied to 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 applied to the big data.
Background
The scheduling list is a behavior for restricting the working time of the staff, and is related to the staff's general service award or performance information; when the motor vehicle is started, workers who get on or off duty need to accurately master the departure time of the motor vehicle; the problem that workers are late due to the problems of parking spaces or the problems of driving on roads is avoided; therefore, in order to avoid the late arrival problem caused by the problem of the parking space, the parking space can be reserved;
in the prior art, there is a patent document with application numbers: 201610227896.6, published as 2018.10.23; the patent documents thereof disclose: screening parking lots according to the parking lots contained in an area where a driver wants to go and the requirement of the driver to obtain an optimal parking lot; the parking space reservation method solves the problems that a correct mode is selected to realize parking space reservation, and the regional integrity or reliability of a reserved parking lot is realized; although the reservation of the parking lot is solved, the reservation is selected according to the requirements of the driver, so that the parking spaces in the parking lot can be further selected according to the requirements of the driver; in this way, the first-come vehicle can use the parking space, then, the obtained parking spaces of the parking lot need to compete for parking spaces of other areas again; further causing a large number of vehicles to flow into the same area, and causing traffic jam; meanwhile, the reserved parking space can be smoothly entered into the parking space only after other vehicles are successfully backed up and put in storage; the time that the workers are on duty smoothly is delayed, and the problem that the workers are delayed is caused; therefore, improvement of the problem is required.
Disclosure of Invention
The present invention provides a human resource scheduling management system and method applied to big data to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the human resource scheduling management system is applied to big data and comprises a parking space information acquisition module, a peak-off parking management module and a parking space reservation management module;
the parking space information acquisition module is used for acquiring time information of starting a vehicle by a worker, predicting the number of parking spaces in an enterprise set range according to the time information, adjusting the time for starting the vehicle by the worker according to a prediction result, and optimizing the departure time of the worker;
the off-peak parking management module is used for managing the parking position within the set range of the employee reservation enterprise, screening the vehicle of the next parking position in the reserved parking lot and ensuring that the screened vehicle meets the condition of time limitation;
the parking space reservation management module is used for displaying the parking space state to the worker according to the driving position of the worker vehicle; expanding the parking space searching radius according to the vehicle state to obtain the optimal parking space searching radius; the possibility of successfully reserving the parking spaces can be increased for the employees;
the parking space information acquisition module is connected with the peak-shifting parking management module and the parking space reservation management module.
Furthermore, 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 shift schedule acquisition unit;
the vehicle starting triggering unit is used for acquiring time information when a worker starts a vehicle;
the parking space number prediction unit is used for predicting the information of the number of parking spaces in the set range of the enterprise according to the time for starting the vehicle by the staff;
the vehicle position positioning unit is used for acquiring the position information of the vehicle in running;
the system comprises a tabulation display unit, a data processing unit and a data processing unit, wherein the tabulation display unit is used for sending parking space quantity information corresponding to vehicles started by workers at different times to the workers for checking in a tabular mode and updating the tabular mode in real time; so that the staff can select the most suitable departure time to make advance reservation;
the scheduling time acquiring unit is used for acquiring a scheduling schedule of the staff to obtain the working time period of the staff; whether the time period of the employee on duty is the peak time period of the reserved parking space can be analyzed;
the optimal time starting unit is used for optimizing the starting time of workers according to the number of the parking stalls to obtain the optimal starting 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; and the output end of the parking space number prediction unit is connected with the input end of the scheduling time acquisition unit.
Furthermore, the off-peak parking management module comprises a vehicle sequence reservation unit, a specified time limiting unit and a vehicle screening unit;
the vehicle sequence reservation unit is used for managing the reserved parking positions of the employees and ensuring that the reserved parking positions are sorted according to the set sequence in the parking lot;
the appointed time limiting unit is used for acquiring information of 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 parking stall reservation according to the time limited by the reservation platform, and the screening condition is determined according to the driving positions of the workers;
the output end of the vehicle sequence booking unit is connected with the input end of the appointed time limiting unit; and the output end of the appointed time limiting unit is connected with the input end of the vehicle screening unit.
Furthermore, 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 scheduling 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 which takes an enterprise as a core and is within a set radius range to a user to obtain a parking space state result within the radius range;
the parking space reservation time locking unit is used for acquiring distance information between a worker and an enterprise and displaying reserved parking space information to the worker according to the distance information;
the parking place information sending unit is used for sending the parking place information which is successfully reserved by the staff to the staff receiving port;
the reservation radius extension unit is used for checking that when the parking space is not reserved successfully by the staff in a set time period, the enterprise is used as a core to outwardly extend the parking space reservation circle and increase the radius of the reserved parking space;
the optimal radius expansion unit is used for obtaining an optimal parking space search radius from the parking space search radii expanded outwards; parking space reservation is carried out within 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 appointment time locking unit; the output end of the parking space appointment time locking unit is connected with the input end of the parking space information sending unit; and the reservation radius extension unit is connected with the input ends of the scheduling time acquisition unit and the optimal radius extension unit.
Further, the human resource scheduling management method applied to the big data 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 workers according to the number of vehicles; if the employee starts at the optimized departure time, acquiring the position of the vehicle of the employee when the vehicle is driven, and jumping to the step Z02; if the employee does not start at the optimized departure time, jumping to step Z03;
z02: if the distance between the vehicle of the employee and the enterprise is smaller than the preset distance, setting parking state information of the parking lot within the radius range by taking the enterprise as a core; if the parking lot in the set radius range contains vacant parking places, the parking place 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 reserving the next parking space, and screening the vehicles of the reserved parking space according to the time limit to obtain a screening result;
z03: taking an enterprise as a core, increasing the search radius of the reserved parking space to obtain the optimal parking space search radius which expands outwards; and screening the vehicles at the reserved parking places to obtain a screening result.
In step Z01, obtaining the scheduling time of the employee to obtain a time period for the employee to start the vehicle, and if the time period for the employee to start the vehicle is a peak time period, obtaining information sets of the number of parking spaces in the historical data, wherein the time period set of the employee is U = { U1, U2,.., um }, and the information set of the number of parking spaces in the time period set of the employee is K = {1,2,.., n }, and n refers to the number of parking spaces in the peak time period; establishing a functional relation according to the time period U and the parking space E; obtaining the number U of the parking spaces in the enterprise setting range in the peak time period, wherein U = V 1 K+H；
If the time period for starting the vehicles by the staff is the low peak time period, if the number of the vehicles leaving the reserved parking space positions of the staff is leaving in the peak time period, acquiring the number set of the vehicles leaving the reserved parking space positions of the staff before the off-peak time period as E' = {1,2,3., p }, wherein p refers to the number of the parking spaces before the off-peak time period; then, a functional relation is established according to the time period U ' and the parking number E ' to obtain U ' = V 2 E' + b; obtaining the number of the parking spaces in the enterprise set range in the off-peak time period as U-U';
if the number of vehicles leaving the reserved parking space position of the worker is not in the peak time period, a functional relation is established between the time period U ' and the parking space number E ' according to the number of the vehicles leaving by the gray prediction method, and U ' = V is obtained 2 E' + b; the number of parking spaces in the enterprise set range in the off-peak time period is obtained as U-U ".
In the step Z01, the longest time H1 spent by a worker for starting a vehicle to arrive at an enterprise is obtained, the late time H2 on a schedule of the worker is obtained, and the corresponding departure time period of the worker when the same late time is obtained on the schedule of the worker in historical data is obtained; when the departure time period of the staff is analyzed to be a peak time period or an off-peak time period, acquiring a time period H3 with the largest number of vacant parking spaces in a set range of the enterprise in the peak time period or the off-peak time period; obtaining 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 worker to the enterprise, H5 is the waiting time of the worker at each intersection for the red light and the average red light; h6 is the time the employee is traveling at the maximum speed allowed on the route; h4 refers to the error time.
In the step Z02, the parked vehicles in the parking lot are obtained, and the reservation platform discharges the vacant parking lots according to the sequence of the vehicles in the parking lot; obtaining the position set of the vacant parking spaces as Q = { Q = a ,q b ,...,q j Is verified to F-q a >F-q b When the parking place b is reserved by the platform to the public, the parking place a needs to be ensured to be reserved successfully; when the platform reserves the position of the parking stall C for the public, the platform needs to screen the vehicles of the reserved parking stall C according to the time limit of the reserved parking stalls of workers;
thus, the time limit for the employee to be able to reserve the next slot 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 arriving at the parking lot corresponding to the next parking space is faster than the time for other vehicles to arrive at the parking lot; y1 is the time that parking stall B was successfully parked by the vehicle.
In step Z03, acquiring enterprise position information W worked by workers, and acquiring an original search radius S of a parking space which is not successfully reserved; obtaining an optimal search radius, and if the empty parking space is reserved successfully in the optimal search radius, then: s + i is not more than beta 1 *(Z0-Z1)；
Wherein: i is a radius which is expanded outwards on the original search radius by taking an enterprise as a core; beta is a 1 Refers to the pace of the worker when walking; z0 refers to the remaining time since the employee is late; z1 is the time spent by the employee when he walks to the enterprise;
and (5) calculating i to obtain the optimal search radius.
Compared with the prior art, the invention has the following beneficial effects: forecasting the parking quantity within the set range of an enterprise according to the time information of starting a vehicle by a worker through a parking space information acquisition module; therefore, whether the current time period and the enterprise set range have the remaining parking spaces for workers to use can be analyzed; in order to ensure that the time period for starting the vehicle by the workers still has the remaining parking spaces, the departure time of the workers is optimized, and the situation that the total duty prize is lost due to the arrival delay of the workers is prevented; screening the vehicle of the next parking place in the reserved parking lot through a peak-shifting parking management module to ensure that the screened vehicle meets the condition of time limitation; the problem of traffic jam caused by the fact that a large number of vehicles simultaneously appear in the same parking lot can be effectively solved, and meanwhile, the problem that a subsequent vehicle waits for a long time due to the fact that a previous vehicle backs up and enters a parking space can be avoided; through the parking stall reservation management module, can make the worker increase the possibility of reserving the parking stall successfully in the optimal parking stall search radius, ensure that the worker who reserves the parking stall in the optimal search radius can not cause the worker to arrive late, guarantee the benefit of worker conscientiously.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the module composition of the human resource shift management system applied to big data according to the present invention;
FIG. 2 is a schematic diagram illustrating steps of a human resource shift management method applied to big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution:
the human resource scheduling management system is applied to big data and comprises a parking space information acquisition module, a peak-off parking management module and a parking space reservation management module;
the parking space information acquisition module is used for acquiring time information of starting a vehicle by a worker, predicting the number of parking spaces in an enterprise set range according to the time information, adjusting the time for starting the vehicle by the worker according to a prediction result, and optimizing the departure time of the worker;
the off-peak parking management module is used for managing the positions of the parking places within the set range of the reservation enterprise of the staff, screening the vehicles at the next parking place in the reserved parking lot and ensuring that the screened vehicles meet the time limit condition;
the parking space reservation management module is used for displaying the parking space state to the worker according to the driving position of the worker vehicle; expanding the parking space searching radius according to the vehicle state to obtain the optimal parking space searching radius; the possibility of successfully reserving the parking spaces can be increased for the employees;
the parking space information acquisition module is connected with the peak-shifting parking management module and the parking space reservation management module.
Furthermore, 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 shift schedule acquisition unit;
the vehicle starting triggering unit is used for acquiring time information when a worker starts a vehicle;
the parking space number prediction unit is used for predicting the information of the number of parking spaces in the set range of the enterprise according to the time for starting the vehicle by the staff;
the vehicle position positioning unit is used for acquiring the position information of the vehicle in running;
the system comprises a tabulation display unit, a data processing unit and a data processing unit, wherein the tabulation display unit is used for sending parking space quantity information corresponding to vehicles started by workers at different times to the workers for checking in a tabular mode and updating the tabular mode in real time; so that the staff can select the most suitable departure time to make advance reservation;
the scheduling time acquiring unit is used for acquiring a scheduling schedule of the staff to obtain the working time period of the staff; therefore, whether the time period of the staff on duty is the peak time period of the reserved parking space or not can be analyzed;
the optimal time starting unit is used for optimizing the starting time of workers according to the number of the parking stalls to obtain the optimal starting 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;
the parking space information acquisition module is simultaneously provided with a scheduling time acquisition unit and a vehicle starting triggering unit, and the scheduling time acquisition unit is used for acquiring the working time period of workers; the vehicle starting and departure unit obtains the starting time of the vehicle; however, in order to ensure that the employees do not arrive late, the starting time of the vehicle is often earlier than the scheduling time; therefore, in order to ensure that the parking space still exists in the starting period of the vehicle, the departure time of the employee (namely the starting time of the vehicle) needs to be optimized, and the delay of the employee is prevented.
Furthermore, the off-peak parking management module comprises a vehicle sequence reservation unit, a specified time limiting unit and a vehicle screening unit;
the vehicle sequence reservation unit is used for managing the reserved parking positions of the employees and ensuring that the reserved parking positions are sorted according to the set sequence in the parking lot;
the appointed time limiting unit is used for acquiring information of 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 parking stall reservation according to the time limited by the reservation platform, and the screening condition is determined according to the driving positions of the workers;
the output end of the vehicle sequence booking unit is connected with the input end of the appointed time limiting unit; and the output end of the appointed time limiting unit is connected with the input end of the vehicle screening unit.
Furthermore, 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 scheduling 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 which takes an enterprise as a core and is within a set radius range to a user to obtain a parking space state result within the radius range;
the parking space reservation time locking unit is used for acquiring distance information between a worker and an enterprise and displaying reserved parking space information to the worker according to the distance information;
the parking place information sending unit is used for sending the parking place information which is successfully reserved by the staff to the staff receiving port;
the reservation radius extension unit is used for checking that when the parking space is not reserved successfully by the staff in a set time period, the enterprise is used as a core to outwardly extend the parking space reservation circle and increase the radius of the reserved parking space;
the optimal radius expansion unit is used for obtaining an optimal parking space search radius from the parking space search radii expanded outwards; reserving the parking spaces 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 appointment time locking unit; the output end of the parking space appointment time locking unit is connected with the input end of the parking space information sending unit; and the reservation radius extension unit is connected with the input ends of the scheduling time acquisition unit and the optimal radius extension unit.
Further, the human resource scheduling management method applied to the big data 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 workers according to the number of vehicles; if the employee starts at the optimized departure time, acquiring the position of the vehicle of the employee when the vehicle is driven, and jumping to the step Z02; if the employee does not start at the optimized departure time, jumping to step Z03;
z02: if the distance between the vehicle of the employee and the enterprise is smaller than the preset distance, setting parking state information of the parking lot within the radius range by taking the enterprise as a core; if the parking lot in the set radius range contains vacant parking places, the parking place 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 reserving the next parking space, and screening the vehicles of the reserved parking space according to the time limit to obtain a screening result;
z03: taking an enterprise as a core, increasing the search radius of the reserved parking space to obtain the optimal parking space search radius which expands outwards; and screening the vehicles at the reserved parking places to obtain a screening result.
In step Z01, obtaining the scheduling time of the employee to obtain a time period for the employee to start the vehicle, and if the time period for the employee to start the vehicle is a peak time period, obtaining information sets of the number of parking spaces in the historical data, wherein the time period set of the employee is U = { U1, U2,.., um }, and the information set of the number of parking spaces in the time period set of the employee is K = {1,2,.., n }, and n refers to the number of parking spaces in the peak time period; establishing a functional relation according to the time period U and the parking space E; obtaining the number U of the parking spaces in the set range of the enterprise in the peak time period, wherein U = V 1 K+H；
If the time period for starting the vehicles by the workers is the low peak time period, if the number of the vehicles leaving the positions of the reserved parking places by the workers is leaving in the peak time period, acquiring the number set of the vehicles leaving the positions of the reserved parking places by the workers before the off-peak time period as E' = {1,2,3., p }, wherein p refers to the number of the parking places before the off-peak time period; then, a functional relation is established according to the time period U ' and the parking number E ' to obtain U ' = V 2 E' + b; obtaining the number of the parking spaces in the enterprise set range in the off-peak time period as U-U';
if the number of vehicles leaving the reserved parking space position of the worker is not in the peak time period, a functional relation is established between the time period U ' and the parking space number E ' according to the number of the vehicles leaving by the gray prediction method, and U ' = V is obtained 2 E' + b; obtaining the number of the parking spaces in the enterprise set range in the off-peak time period as U-U';
establishing a functional relation according to the time period U and the parking space number E and establishing a functional relation according to the time period U 'and the parking space number E', predicting the parking space number according to a least square method, and establishing a functional relation between the time period U 'and the parking space number E' through a grey prediction analysis method; because the quantity of the vehicles leaving in the off-peak time period cannot be controlled, the time and the vehicle change relation is obtained through a grey prediction analysis method; if the quantity in the enterprise setting range in the off-peak time period is to be obtained, the difference value between the vehicles entering the enterprise setting range in the peak time period and the vehicles leaving the enterprise setting range in the peak time period is obtained; if the number of the parking spaces in the off-peak time period is not obtained by a difference method, the obtained number value of the parking spaces causes errors; if errors occur, the staff will be late to work.
In the step Z01, the longest time H1 spent by a worker for starting a vehicle to arrive at an enterprise is obtained, the late time H2 on a schedule of the worker is obtained, and the corresponding departure time period of the worker when the same late time is obtained on the schedule of the worker in historical data is obtained; if the departure time period of the analysis staff is a peak time period or an off-peak time period, acquiring a time period H3 with the largest number of vacant parking spaces in the enterprise set range in the peak time period or the off-peak time period; obtaining 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 staff to the enterprise, H5 is the waiting time of the staff at each intersection with the red light; h6 is the time the employee is traveling at the maximum speed allowed on the route; h4 refers to error time;
in the above steps, for example: the tardy time of the staff is 10.00, and the time period of obtaining the most vacant parking spaces in the enterprise setting range is the first 20 minutes; the maximum time spent by the worker on the road is H1= D + H5+ H6=6 + 40s +15min= -4 min +15min= -19min; therefore, the optimal departure time of staff is set to 10.00-20-19min = 9.21; by calculating the optimized time, the success rate of the staff for reserving 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 lots according to the sequence of the vehicles in the parking lot; obtaining the position set of the vacant parking spaces as Q = { Q = a ,q b ,...,q j Is verified to F-q a >F-q b When the parking place b is reserved by the platform to the public, the parking place a needs to be ensured to be reserved successfully;when the platform reserves the position of the parking stall C for the public, the platform needs to screen the vehicles of the reserved parking stall C according to the time limit of the reserved parking stalls of workers;
thus, the time limit for the employee to be able to reserve the next slot 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 arriving at the parking lot corresponding to the next parking space is faster than the time for arriving at the parking lot by other vehicles; y1 refers to the time when the parking space B is successfully parked by the vehicle;
if the platform is 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 at random, workers need to spend a large amount of time waiting for the previous vehicle to enter the reserved parking space on the platform after completely backing up and warehousing; delaying the time of the employee.
In step Z03, acquiring enterprise position information W worked by workers, and acquiring an original search radius S of a parking space which is not reserved successfully; obtaining an optimal search radius, and if the empty parking space is reserved successfully in the optimal search radius, then: s + i is not more than beta 1 *(Z0-Z1)；
Wherein: i is a radius which is expanded outwards on the original search radius by taking an enterprise as a core; beta is a 1 Refers to the pace of the worker when walking; z0 refers to the remaining time since the employee is late; z1 is the time spent by the employee when he walks to the enterprise;
obtaining an optimal search radius by calculating i;
S+i≤β 1 * (Z0-Z1) the calculation mode is that when the worker does not successfully reserve the parking space on the platform, the search radius needs to be expanded; the success probability of the workers searching the parking places is increased through the extended radius, and meanwhile, the positions of the reserved parking places need to be ensured, so that the workers can arrive at the enterprise before arriving; in the process of calculating i, an optimization algorithm is used to obtain an optimized value i, wherein the optimization algorithm is specifically a gradient descent method, and the gradient descent method is the content of the prior art and is not excessively described; and obtaining a global optimal solution by a gradient descent method.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. Be applied to manpower resources management system of scheduling of big data, its characterized in that: 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 a vehicle by a worker, predicting the number of parking spaces in an enterprise set range according to the time information, adjusting the time for starting the vehicle by the worker according to a prediction result, and optimizing the departure time of the worker;
the off-peak parking management module is used for managing the parking position within the set range of the employee reservation enterprise, screening the vehicle of the next parking position in the reserved parking lot and ensuring that the screened vehicle meets the condition of time limitation;
the parking space reservation management module is used for displaying the parking space state to the worker according to the driving position of the worker vehicle; expanding the parking space searching radius according to the vehicle state to obtain the 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 human resources shift management system applied to big data as claimed in 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 and a scheduling time acquisition unit;
the vehicle starting triggering unit is used for acquiring time information when a worker starts a vehicle;
the parking space number prediction unit is used for predicting the information of the number of parking spaces in the set range of the enterprise according to the time for starting the vehicle by the staff;
the vehicle position positioning unit is used for acquiring the position information of the vehicle in running;
the system comprises a tabulation display unit, a data processing unit and a data processing unit, wherein the tabulation display unit is used for sending parking space quantity information corresponding to vehicles started by workers at different times to the workers for checking in a tabular mode and updating the tabular mode in real time; so that the staff can select the most suitable departure time to make advance reservation;
the scheduling time acquiring unit is used for acquiring a scheduling schedule of the staff to obtain the working time period of the staff;
the optimal time starting unit is used for optimizing the starting time of workers according to the number of the parking stalls to obtain the optimal starting 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; and the output end of the parking space number prediction unit is connected with the input end of the scheduling time acquisition unit.
3. The human resources shift management system applied to big data as claimed in claim 1, wherein: the off-peak parking management module comprises a vehicle sequence reservation unit, a specified time limiting unit and a vehicle screening unit;
the vehicle sequence reservation unit is used for managing the positions of the reserved parking spaces of the workers and ensuring that the reserved parking spaces are sequenced according to the set sequence in the parking lot;
the appointed time limiting unit is used for acquiring information of 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 parking stall reservation according to the time limited by the reservation platform, and the screening condition is determined according to the driving positions of the workers;
the output end of the vehicle sequence booking unit is connected with the input end of the appointed time limiting unit; and the output end of the appointed time limiting unit is connected with the input end of the vehicle screening unit.
4. The human resources shift management system applied to big data as claimed in 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 expansion unit and an optimal radius expansion unit;
the parking space state display unit is used for displaying the parking space state which takes an enterprise as a core and is within a set radius range to a user to obtain a parking space state result within the radius range;
the parking space reservation time locking unit is used for acquiring distance information between a worker and an enterprise and displaying reserved parking space information to the worker according to the distance information;
the parking place information sending unit is used for sending the parking place information which is successfully reserved by the staff to the staff receiving port;
the reservation radius extension unit is used for checking that when the parking space is not reserved successfully by the staff in a set time period, the enterprise is used as a core to outwardly extend the parking space reservation circle and increase the radius of the reserved parking space;
the optimal radius expansion unit is used for obtaining an optimal parking space search radius from the parking space search radii expanded outwards; reserving the parking spaces 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 appointment time locking unit is connected with the input end of the parking space information sending unit; and the input end of the reservation radius extension unit is connected with the input end of the optimal radius extension unit.
5. The human resource scheduling management method applied to the big data is characterized by comprising the following steps: 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 workers according to the number of vehicles; if the employee starts at the optimized departure time, acquiring the position of the vehicle of the employee when the vehicle is driven, and jumping to the step Z02; if the employee does not start at the optimized departure time, jumping to step Z03;
z02: if the distance between the vehicle of the employee and the enterprise is smaller than the preset distance, setting parking state information of the parking lot within the radius range by taking the enterprise as a core; if the parking lot within the set radius range contains vacant parking places, the parking place 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 reserving the next parking space, and screening the vehicles of the reserved parking space according to the time limit to obtain a screening result;
z03: taking an enterprise as a core, increasing the search radius of the reserved parking space to obtain the optimal parking space search radius which expands outwards; and screening the vehicles at the reserved parking places to obtain a screening result.
6. The human resource scheduling management method applied to big data according to claim 5, wherein: in step Z01, the scheduling time of the employees is obtained, and the time period for the employees to start the vehicles is obtained, if so, the employees start the vehiclesIf the time period when the vehicle is started by the employee is the peak time period, acquiring historical data, wherein the information set of the number of the parking places of the employee in the time period set of U = { U1, U2,. Multidot.um } is K = {1,2,. Multidot.n }, and n is the number of the parking places in the peak time period; establishing a functional relation according to the time period U and the parking space E; obtaining the number U of the parking spaces in the set range of the enterprise in the peak time period, wherein U = V 1 K+H；
If the time period for starting the vehicles by the workers is the low-peak time period, if the number of the vehicles leaving the positions of the reserved parking places by the workers is the number of the vehicles leaving the positions of the reserved parking places by the workers in the peak time period, the number of the vehicles leaving the positions of the reserved parking places by the workers before the off-peak time period is obtained as E Γ = {1,2,3,. Once, p }, and p refers to the number of the parking places before the off-peak time period; then, a functional relation is established according to the time period U ' and the parking number E ' to obtain U ' = V 2 E' + b; obtaining the number of the parking spaces in the enterprise set range in the off-peak time period as U-U';
if the number of vehicles leaving the reserved parking space position of the worker is not in the peak time period, a functional relation is established between the time period U ' and the parking space number E ' according to the number of the vehicles leaving by the gray prediction method, and U ' = V is obtained 2 E' + b; the number of parking spaces in the enterprise set range in the off-peak time period is obtained as U-U ".
7. The human resource scheduling management method applied to big data according to claim 6, wherein: in the step Z01, the longest time H1 spent by starting a vehicle to reach an enterprise by a worker is obtained, the late arrival time H2 on a schedule of the worker is obtained, and the corresponding departure time period of the worker when the same late arrival time is obtained on the schedule of the worker in historical data is obtained; if the departure time period of the analysis staff is a peak time period or an off-peak time period, acquiring a time period H3 with the largest number of vacant parking spaces in the enterprise set range in the peak time period or the off-peak time period; obtaining 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 staff to the enterprise, H5 is the waiting time of the staff at each intersection with the red light; h6 is the time the employee is traveling at the maximum speed allowed on the route; h4 refers to the error time.
8. The human resource scheduling management method applied to big data according to claim 5, wherein: in the step Z02, the parked vehicles in the parking lot are obtained, and the reservation platform discharges the vacant parking lots according to the sequence of the vehicles in the parking lot; obtaining the position set of the vacant parking spaces as Q = { Q = a ,q b ,...,q j Is verified to F-q a >F-q b When the parking place b is reserved by the platform to the public, the parking place a needs to be ensured to be reserved successfully; when the platform reserves the position of the parking stall C for the public, the platform needs to screen the vehicles of the reserved parking stall C according to the time limit of the reserved parking stalls of workers;
thus, the time limit for the employee to be able to reserve the next slot is Y = Y2-Y1;
wherein: y2 is a vehicle reserving a next parking space, and the conditions of the vehicle are: the time for arriving at the parking lot corresponding to the next parking space is faster than the time for arriving at the parking lot by other vehicles; y1 is the time that parking stall B was successfully parked by the vehicle.
9. The human resource scheduling management method applied to big data according to claim 5, wherein: in step Z03, acquiring enterprise position information W worked by workers, and acquiring an original search radius S of a parking space which is not successfully reserved; obtaining an optimal search radius, and if the empty parking space is reserved successfully in the optimal search radius, then:
S+i≤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 is a 1 Refers to the pace of the worker when walking; z0 refers to the remaining time since the employee is late; z1 is the time spent by the employee when he walks to the enterprise;
and (5) calculating i to obtain the optimal search radius.
CN202210914142.3A 2022-08-01 2022-08-01 Human resource scheduling management system and method applied to big data Pending CN115511251A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210914142.3A CN115511251A (en) 2022-08-01 2022-08-01 Human resource scheduling management system and method applied to big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210914142.3A CN115511251A (en) 2022-08-01 2022-08-01 Human resource scheduling management system and method applied to big data

Publications (1)

Publication Number Publication Date
CN115511251A true CN115511251A (en) 2022-12-23

Family

ID=84501090

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210914142.3A Pending CN115511251A (en) 2022-08-01 2022-08-01 Human resource scheduling management system and method applied to big data

Country Status (1)

Country Link
CN (1) CN115511251A (en)

Citations (4)

* 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
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 (4)

* 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
CN114218483A (en) * 2021-12-16 2022-03-22 城云科技（中国）有限公司 Parking recommendation method and application thereof

Similar Documents

Publication Publication Date Title
Bischoff et al. City-wide shared taxis: A simulation study in Berlin
CN111009151B (en) Parking space recommendation method, storage medium and terminal device
CA2768578C (en) Method and system for adjusting a demand-response transit schedule
US10692375B1 (en) Lotless storage of vehicle inventory
US8504295B2 (en) Preserving assigned carpools after a cancellation
US10719785B2 (en) System for improved vehicle late return prediction and detection
CN104240403B (en) A kind of high in the clouds parking service method and device
JP6891263B2 (en) Shuttle bus route processing methods, equipment, equipment and computer storage media
CN110363319B (en) Resource allocation method, server, resource claim method and client
CN111868654A (en) Method and apparatus for route planning
CN109657820B (en) Taxi matching method capable of making reservation
Nugraha et al. Off-street parking space allocation and reservation system using event-driven algorithm
CN112906980A (en) Order processing method, device and system and readable storage medium
CN106991484B (en) Secondary resource reservation method based on intelligent repeated reservation
Andrade-Michel et al. Vehicle and reliable driver scheduling for public bus transportation systems
CN116013059A (en) Scheduling method and device for operating vehicle, electronic equipment and storage medium
He et al. A real-time reservation service for smart parking system
CN115511251A (en) Human resource scheduling management system and method applied to big data
CN113642761A (en) Robotaxi automatic driving shared network car booking resource allocation method
CN112288112A (en) Method and device for reserving vehicle and computer storage medium
Zhao et al. Urban parking system based on dynamic resource allocation in an era of connected and automated vehicles
Ceder A step function for improving transit operations planning using fixed and variable scheduling
CN115204433A (en) Shared parking place reservation matching method for automatic driving automobile
CN114881692A (en) Network appointment scheduling method and device, electronic equipment and storage medium
CN114492951A (en) Resource allocation method, device and equipment based on passenger flow prediction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination