CN117689185B - Equipment data scheduling optimization method based on Internet of things - Google Patents
Equipment data scheduling optimization method based on Internet of things Download PDFInfo
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Abstract
The invention discloses a device data scheduling optimization method based on the Internet of things, which belongs to the technical field of data processing and comprises the following steps: s1, acquiring an electronic map of a park and park information from an internet of things, and determining an optimized area to be scheduled in the park; s2, constructing an area operation model for the to-be-scheduled optimization area; s3, determining the actual dispatching quantity of the vehicle equipment in the optimized area to be dispatched according to the area operation model of the optimized area to be dispatched. The invention discloses a device data scheduling optimization method based on the Internet of things, which is used for analyzing the driving condition and the traffic condition of vehicles in a park, generating an optimized region to be scheduled, which needs to be allocated with vehicle devices, and constructing a region operation model for the optimized region to be scheduled; and determining the actual dispatching quantity of the vehicle equipment meeting the maximum bearing capacity of the park according to the regional operation model, ensuring the normal operation of the park traffic, avoiding traffic jam and meeting the traffic demand of park personnel.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a device data scheduling optimization method based on the Internet of things.
Background
Along with the popularization of networks, the intelligent degree of various aspects of life is improved, and various intelligent products are distinguished from providing high-efficiency services for life. The intelligent process of the park is promoted by the application of the internet of things to the intelligent park, and efficient service is provided for the development of the park industry. The intelligent park is a standard building or building group which is built by companies in places to supply water, power, air, communication, roads, storage and other matched facilities, is reasonable in layout and can meet the production of logistics industry, the economic industry structure can be adjusted, and the industrial advantages are gathered. Some parks dispatch vehicle equipment in the parks, bear the traffic task of personnel in the parks, however there is a big problem in traditional parks and is the traffic jam problem in the parks, especially in the peak period in the morning and evening, and vehicle equipment can't carry out reasonable dispatch in the parks.
Disclosure of Invention
The invention provides a device data scheduling optimization method based on the Internet of things in order to solve the problems.
The technical scheme of the invention is as follows: the equipment data scheduling optimization method based on the Internet of things comprises the following steps:
S1, acquiring an electronic map of a park and park information from an internet of things, and determining an optimized area to be scheduled in the park;
s2, constructing an area operation model for the to-be-scheduled optimization area;
S3, determining the actual dispatching quantity of the vehicle equipment in the optimized area to be dispatched according to the area operation model of the optimized area to be dispatched.
Further, the campus information includes the maximum load capacity of the campus, the real-time total traffic of the campus, and the real-time traffic of each road in the campus.
Further, S1 comprises the following sub-steps:
S11, judging whether the real-time total people flow of the park is greater than or equal to the maximum bearing capacity of the park, if so, entering S12, otherwise, entering S13;
S12, taking the whole park area as an optimized area to be scheduled;
s13, determining a scheduling flow index of each road according to the real-time flow of people on each road in the park;
s14, constructing a first scheduling flow evaluation condition and a second scheduling flow evaluation condition, and taking the road position which does not meet the first scheduling flow evaluation condition and the second scheduling flow evaluation condition at the same time as an optimized area to be scheduled.
The beneficial effects of the above-mentioned further scheme are: in the invention, the maximum bearing capacity refers to the maximum number of people which can be accommodated in the whole park in a certain time, and when the real-time people flow of the park is larger than the maximum bearing capacity, the whole park is in a crowded state, so that the whole park area is used as an optimization area to be scheduled. When the real-time traffic of people in the park is smaller than the maximum bearing capacity, the whole park is not in a saturated state, and only the area needing to be subjected to vehicle dispatching optimization is determined in the park. According to the invention, the dispatching flow index of each road is calculated according to the number of vehicles in each road, the running speed of the vehicles and the traffic flow, and the traffic flow and traffic flow abnormal roads are screened according to the constructed first dispatching flow evaluation condition and the second dispatching flow evaluation condition.
Further, in S13, the calculation formula of the traffic flow index Φ of the road is: ; where X 0 represents the position of the start point of the road on the electronic map, X 1 represents the position of the end point of the road on the electronic map, N represents the number of vehicle devices in the road, v n represents the running speed of the nth vehicle device, X n represents the position of the nth vehicle device on the electronic map, and p represents the real-time traffic volume of the road.
Further, in S14, the expression of the first scheduled flow evaluation condition is: ; where, phi m denotes a scheduled flow index of an mth road, e denotes an index, K denotes the number of roads adjacent to the mth road, and phi m_k denotes a scheduled flow index of a kth road adjacent to the mth road.
Further, in S14, the expression of the second scheduled flow evaluation condition is: ; where p m represents the real-time traffic of the mth road and M represents the number of roads in the campus.
Further, in S2, the expression of the region operation model G is: ; wherein P 0 represents the real-time total traffic of the park, P represents the maximum bearing capacity of the park, phi h represents the scheduling flow index of the H road in the optimized area to be scheduled, P h represents the real-time traffic of the H road in the optimized area to be scheduled, and H represents the number of roads in the optimized area to be scheduled.
Further, S3 comprises the following sub-steps:
S31, setting the initial scheduling number of the vehicle equipment to be 1;
s32, calculating the maximum bearable capacity of the optimized area to be scheduled according to the initial scheduling quantity of the vehicle equipment and the area operation model of the optimized area to be scheduled;
And S33, judging whether the sum of the maximum bearable amount of the to-be-scheduled optimizing area and the real-time total people flow of the park residual area is larger than the park maximum bearable amount, if yes, ending the scheduling optimization, taking the other initial scheduling amount of the vehicle equipment as the actual scheduling amount, otherwise, increasing the initial scheduling amount of the vehicle equipment one by one until the sum of the maximum bearable amount of the to-be-scheduled optimizing area and the real-time total people flow of the park residual area is larger than the park maximum bearable amount, and determining the actual scheduling amount of the vehicle equipment.
The beneficial effects of the above-mentioned further scheme are: in the invention, after determining an optimized area to be scheduled of vehicle equipment to be dispatched in step S1, step S2 builds an area operation model reflecting the area traffic condition for the optimized area to be scheduled. In step S3, the initial scheduling number of the vehicle devices is set to 1, and whether the maximum bearing capacity of the park is met is judged, if yes, it is indicated that one vehicle device is added, normal riding of the park personnel can be guaranteed, no waiting for too long time is needed, otherwise, the initial scheduling number of the vehicle devices is increased by one at each time until the maximum bearing capacity of the park is met, normal riding of the park personnel can be guaranteed by adopting the vehicle devices with the number, and vehicle resource waste is avoided.
Further, in S12, the calculation formula of the maximum bearable amount Z of the to-be-scheduled optimization area is: ; wherein H represents the number of roads in the optimized area to be scheduled, p h represents the real-time traffic of the H road in the optimized area to be scheduled,/> The number of nuclear carriers of the vehicle equipment is represented, phi h represents the scheduling flow index of the h road in the optimal area to be scheduled, max (DEG) represents the maximum value operation, and log (DEG) represents the logarithmic operation.
The beneficial effects of the invention are as follows: the invention discloses a device data scheduling optimization method based on the Internet of things, which is used for analyzing the driving condition and the traffic condition of vehicles in a park, generating an optimized region to be scheduled, which needs to be allocated with vehicle devices, and constructing a region operation model for the optimized region to be scheduled; and determining the actual dispatching quantity of the vehicle equipment meeting the maximum bearing capacity of the park according to the regional operation model, ensuring the normal operation of the park traffic, avoiding traffic jam and meeting the traffic demand of park personnel.
Drawings
Fig. 1 is a flowchart of a device data scheduling optimization method based on the internet of things.
Description of the embodiments
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a device data scheduling optimization method based on the internet of things, which comprises the following steps:
S1, acquiring an electronic map of a park and park information from an internet of things, and determining an optimized area to be scheduled in the park;
s2, constructing an area operation model for the to-be-scheduled optimization area;
S3, determining the actual dispatching quantity of the vehicle equipment in the optimized area to be dispatched according to the area operation model of the optimized area to be dispatched.
In the embodiment of the invention, the park information comprises the maximum bearing capacity of the park, the real-time total traffic of the park and the real-time traffic of each road in the park.
The sum of the real-time traffic of all roads in the campus is equal to the real-time total traffic of the campus.
In an embodiment of the present invention, S1 comprises the following sub-steps:
S11, judging whether the real-time total people flow of the park is greater than or equal to the maximum bearing capacity of the park, if so, entering S12, otherwise, entering S13;
S12, taking the whole park area as an optimized area to be scheduled;
s13, determining a scheduling flow index of each road according to the real-time flow of people on each road in the park;
s14, constructing a first scheduling flow evaluation condition and a second scheduling flow evaluation condition, and taking the road position which does not meet the first scheduling flow evaluation condition and the second scheduling flow evaluation condition at the same time as an optimized area to be scheduled.
In the invention, the maximum bearing capacity refers to the maximum number of people which can be accommodated in the whole park in a certain time, and when the real-time people flow of the park is larger than the maximum bearing capacity, the whole park is in a crowded state, so that the whole park area is used as an optimization area to be scheduled. When the real-time traffic of people in the park is smaller than the maximum bearing capacity, the whole park is not in a saturated state, and only the area needing to be subjected to vehicle dispatching optimization is determined in the park. According to the invention, the dispatching flow index of each road is calculated according to the number of vehicles in each road, the running speed of the vehicles and the traffic flow, and the traffic flow and traffic flow abnormal roads are screened according to the constructed first dispatching flow evaluation condition and the second dispatching flow evaluation condition.
In the embodiment of the present invention, in S13, the calculation formula of the traffic flow index Φ of the road is: ; where X 0 represents the position of the start point of the road on the electronic map, X 1 represents the position of the end point of the road on the electronic map, N represents the number of vehicle devices in the road, v n represents the running speed of the nth vehicle device, X n represents the position of the nth vehicle device on the electronic map, and p represents the real-time traffic volume of the road.
In the embodiment of the present invention, in S14, the expression of the first scheduled flow evaluation condition is: ; where, phi m denotes a scheduled flow index of an mth road, e denotes an index, K denotes the number of roads adjacent to the mth road, and phi m_k denotes a scheduled flow index of a kth road adjacent to the mth road.
Taking the mth road as an example, vehicles in a plurality of roads adjacent to the mth road (here, adjacent means a road crossing any position of the mth road) are likely to drive into the mth road, so the present invention constructs a first scheduling flow evaluation condition taking this as a consideration.
In the embodiment of the present invention, in S14, the expression of the second scheduled flow evaluation condition is: ; where p m represents the real-time traffic of the mth road and M represents the number of roads in the campus.
In the embodiment of the present invention, in S2, the expression of the region operation model G is: ; wherein P 0 represents the real-time total traffic of the park, P represents the maximum bearing capacity of the park, phi h represents the scheduling flow index of the H road in the optimized area to be scheduled, P h represents the real-time traffic of the H road in the optimized area to be scheduled, and H represents the number of roads in the optimized area to be scheduled.
In an embodiment of the present invention, S3 comprises the following sub-steps:
S31, setting the initial scheduling number of the vehicle equipment to be 1;
s32, calculating the maximum bearable capacity of the optimized area to be scheduled according to the initial scheduling quantity of the vehicle equipment and the area operation model of the optimized area to be scheduled;
And S33, judging whether the sum of the maximum bearable amount of the to-be-scheduled optimizing area and the real-time total people flow of the park residual area is larger than the park maximum bearable amount, if yes, ending the scheduling optimization, taking the other initial scheduling amount of the vehicle equipment as the actual scheduling amount, otherwise, increasing the initial scheduling amount of the vehicle equipment one by one until the sum of the maximum bearable amount of the to-be-scheduled optimizing area and the real-time total people flow of the park residual area is larger than the park maximum bearable amount, and determining the actual scheduling amount of the vehicle equipment.
In the invention, after determining an optimized area to be scheduled of vehicle equipment to be dispatched in step S1, step S2 builds an area operation model reflecting the area traffic condition for the optimized area to be scheduled. In step S3, the initial scheduling number of the vehicle devices is set to 1, and whether the maximum bearing capacity of the park is met is judged, if yes, it is indicated that one vehicle device is added, normal riding of the park personnel can be guaranteed, no waiting for too long time is needed, otherwise, the initial scheduling number of the vehicle devices is increased by one at each time until the maximum bearing capacity of the park is met, normal riding of the park personnel can be guaranteed by adopting the vehicle devices with the number, and vehicle resource waste is avoided.
In the embodiment of the present invention, in S12, the calculation formula of the maximum bearable amount Z of the to-be-scheduled optimization area is: ; wherein H represents the number of roads in the optimized area to be scheduled, p h represents the real-time traffic of the H road in the optimized area to be scheduled,/> The number of nuclear carriers of the vehicle equipment is represented, phi h represents the scheduling flow index of the h road in the optimal area to be scheduled, max (DEG) represents the maximum value operation, and log (DEG) represents the logarithmic operation.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (5)
1. The equipment data scheduling optimization method based on the Internet of things is characterized by comprising the following steps of:
S1, acquiring an electronic map of a park and park information from an internet of things, and determining an optimized area to be scheduled in the park;
s2, constructing an area operation model for the to-be-scheduled optimization area;
S3, determining the actual dispatching quantity of the vehicle equipment in the optimized area to be dispatched according to the area operation model of the optimized area to be dispatched;
The step S1 comprises the following substeps:
S11, judging whether the real-time total people flow of the park is greater than or equal to the maximum bearing capacity of the park, if so, entering S12, otherwise, entering S13;
S12, taking the whole park area as an optimized area to be scheduled;
s13, determining a scheduling flow index of each road according to the real-time flow of people on each road in the park;
s14, constructing a first scheduling flow evaluation condition and a second scheduling flow evaluation condition, and taking the road position which does not meet the first scheduling flow evaluation condition and the second scheduling flow evaluation condition at the same time as an optimized area to be scheduled;
in S13, the traffic flow index of the road The calculation formula of (2) is as follows: Wherein, X 0 represents the position of the starting point of the road on the electronic map, X 1 represents the position of the ending point of the road on the electronic map, N represents the number of vehicle devices in the road, v n represents the running speed of the nth vehicle device, X n represents the position of the nth vehicle device on the electronic map, and p represents the real-time traffic volume of the road;
in S14, the expression of the first scheduled flow evaluation condition is: in the/> A scheduled flow index indicating an mth road, e indicating an index, K indicating the number of roads adjacent to the mth road,/>A scheduled flow index indicating a kth link adjacent to the mth link;
in S14, the expression of the second scheduled flow evaluation condition is: where p m represents the real-time traffic of the mth road and M represents the number of roads in the campus.
2. The internet of things-based device data scheduling optimization method of claim 1, wherein the campus information includes a maximum campus bearing capacity, a real-time total campus traffic, and real-time traffic for each road in the campus.
3. The method for optimizing device data scheduling based on the internet of things according to claim 1, wherein in S2, the expression of the region operation model G is: Wherein P 0 represents real-time total traffic of the campus, P represents maximum bearing capacity of the campus,/> And (3) representing a scheduling flow index of the H road in the optimized area to be scheduled, wherein p h represents the real-time traffic of the H road in the optimized area to be scheduled, and H represents the number of the roads in the optimized area to be scheduled.
4. The method for optimizing device data scheduling based on the internet of things according to claim 1, wherein the step S3 comprises the following sub-steps:
S31, setting the initial scheduling number of the vehicle equipment to be 1;
s32, calculating the maximum bearable capacity of the optimized area to be scheduled according to the initial scheduling quantity of the vehicle equipment and the area operation model of the optimized area to be scheduled;
And S33, judging whether the sum of the maximum bearable amount of the to-be-scheduled optimizing area and the real-time total people flow of the park residual area is larger than the park maximum bearable amount, if yes, ending the scheduling optimization, taking the other initial scheduling amount of the vehicle equipment as the actual scheduling amount, otherwise, increasing the initial scheduling amount of the vehicle equipment one by one until the sum of the maximum bearable amount of the to-be-scheduled optimizing area and the real-time total people flow of the park residual area is larger than the park maximum bearable amount, and determining the actual scheduling amount of the vehicle equipment.
5. The method for optimizing device data scheduling based on the internet of things according to claim 4, wherein in S32, a calculation formula of a maximum bearable amount Z of the optimized area to be scheduled is: Wherein H represents the number of roads in the optimized area to be scheduled, p h represents the real-time traffic flow of the H road in the optimized area to be scheduled, p' represents the number of nuclear carriers of the vehicle equipment,/> The scheduling flow index of the h-th road in the to-be-scheduled optimization area is represented, max (·) represents maximum value operation, and log (·) represents logarithmic operation.
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