CN115409288B - Internet of Things service management system based on regional digital economy - Google Patents

Internet of Things service management system based on regional digital economy Download PDF

Info

Publication number
CN115409288B
CN115409288B CN202211240228.9A CN202211240228A CN115409288B CN 115409288 B CN115409288 B CN 115409288B CN 202211240228 A CN202211240228 A CN 202211240228A CN 115409288 B CN115409288 B CN 115409288B
Authority
CN
China
Prior art keywords
charging
target vehicle
charging station
public
time
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.)
Active
Application number
CN202211240228.9A
Other languages
Chinese (zh)
Other versions
CN115409288A (en
Inventor
李嘉
周勃
黄瀛
刘红霖
黄武庆
黄旷
石琼玉
田子奇
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.)
Yunbao Big Data Industry Development Co ltd
Original Assignee
Yunbao Big Data Industry Development 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 Yunbao Big Data Industry Development Co ltd filed Critical Yunbao Big Data Industry Development Co ltd
Priority to CN202211240228.9A priority Critical patent/CN115409288B/en
Publication of CN115409288A publication Critical patent/CN115409288A/en
Application granted granted Critical
Publication of CN115409288B publication Critical patent/CN115409288B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0631Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313Resource planning in a project environment
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q50/00Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06Energy or water supply
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00Economic sectors
    • G16Y10/35Utilities, e.g. electricity, gas or water
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00Information sensed or collected by the things
    • G16Y20/10Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16YINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00IoT characterised by the purpose of the information processing
    • G16Y40/10Detection; Monitoring
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage systems for electromobility, e.g. batteries
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10Technologies relating to charging of electric vehicles
    • Y02T90/12Electric charging stations

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Computing Systems (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Accounting & Taxation (AREA)
  • Environmental & Geological Engineering (AREA)
  • Toxicology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Educational Administration (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses an Internet of things service management system based on regional digital economy, which belongs to the technical field of digital economy, and particularly can predict the time for a target vehicle to reach each public charging station according to the planned driving path and road conditions of the target vehicle to be charged, then arrange the target vehicle according to the change of the number of charged people in each public charging station in the period, ensure that the target vehicle can reduce queuing time as much as possible in a continuous voyage range, realize full utilization of resources and avoid the situation that part of charging stations are full of people and part of charging stations have more vacancies; according to the invention, the number of the charged people of each charging station in the past period can be statistically analyzed, so that the number of the vehicles entering the charging station for charging in the appointed time range can be predicted, the accurate arrangement of the subsequent charging guide instruction is convenient, and the situation that the target vehicle misses the optimal charging place is reduced or even avoided.

Description

Internet of things service management system based on regional digital economy
Technical Field
The invention belongs to the technical field of digital economy, and particularly relates to an Internet of things service management system based on regional digital economy.
Background
The digital economy is a main economic form following the agricultural economy and the industrial economy, is a new economic form which takes data resources as key elements, takes a modern information network as a main carrier, takes information communication technology fusion application and full-element digital transformation as important driving forces, and promotes fairness and efficiency to be more unified. The digital economy has the advantages of high development speed, wide radiation range and deep influence degree; in the technical level, the method comprises emerging technologies such as big data, cloud computing, internet of things, blockchain, artificial intelligence, 5G communication and the like. At the application level, "new retail", "new manufacturing" and the like are all typical representatives thereof.
The use of charging piles in the prior art has the following drawbacks: on the expressway, due to the influences of factors such as different construction scales of charging stations in each service area, different passenger flows and different residence time of each service area caused by self positions and construction conditions, the problems of large passenger flows, crowded people, long queuing time, rare passenger flows of partial charging stations and more vacant charging stations can be caused, the use experience of users is reduced, and the waste of charging pile resources is caused.
Disclosure of Invention
The invention aims to provide an Internet of things service management system based on regional digital economy, which solves the problems that in the prior art, vehicles are randomly charged at a high speed, partial charging stations are easy to cause large passenger flow, many people are crowded, queuing time is long, partial charging stations are sparse in passenger flow, and charging station vacancies are more.
The aim of the invention can be achieved by the following technical scheme:
an internet of things service management system based on regional digital economy, comprising:
the positioning unit is used for positioning the target vehicle and transmitting the target vehicle to the control unit;
the navigation unit is used for identifying the driving path of the target vehicle and the road condition on the driving path of the target vehicle;
the terminal unit is used for establishing connection with the target vehicle and the control unit and displaying information that the control unit guides the target vehicle to enter the public charging pile for charging;
the working method of the service management system of the Internet of things based on regional digital economy comprises the following steps:
s1, when a user needs to charge a target vehicle in a high-speed running process, sending out charging request information, and when the control unit receives the charging request information, acquiring the number m of remaining chargeable vacancies in a first public charging station, and acquiring the predicted time t1 from the target vehicle to arrive at the first public charging station after sending out the charging request information;
s2, acquiring charging request information sent from a target vehicle until the charging request information reaches a predicted time t1 of a corresponding public charging station, wherein the number of newly increased charging people m1 is predicted by a first public charging station;
when m+mi1 is more than m1, the control unit guides the target vehicle to enter the first public charging pile for charging, wherein mi1 is the number of people who finish charging from the time when the target vehicle sends charging request information to the time when the target vehicle leaves the charging position within the prediction time t1 when the target vehicle reaches the first public charging station;
s3, when m+mi1 is less than or equal to m1, calculating according to a formula D1= [ m1- (m+mi1) ]/mz1 to obtain a queuing coefficient D1 of the target vehicle at the first public charging station, wherein mz1 is the number of charging piles which can be normally used in the first public charging station;
if the target vehicle has a second public charging station on the driving path, acquiring the distance L between the second public charging station on the driving path of the target vehicle and the target vehicle, and if L is more than or equal to L1+Ly1, guiding the target vehicle to enter a first public charging pile for charging by a control unit;
wherein L1 is the remaining endurance distance of the target vehicle, and Ly1 is a preset value;
if L is less than L1+Ly1, the control unit acquires the number m2 of remaining chargeable vacancies in the second public charging station, the predicted time t2 from the target vehicle to the arrival of the charging request information at the second public charging station and the predicted newly increased number m3 of charging persons of the second public charging station within the time t2, if m2+mi2 is more than m3, the control unit guides the target vehicle to enter the second public charging pile for charging, and if m2+mi2 is less than or equal to m3, the queuing coefficient D2 of the target vehicle at the second public charging station is obtained according to the formula D2 = [ m3- (m2+mi2) ]/mz2, wherein mz2 is the number of charging piles which can be normally used of the second public charging station;
s4, judging the subsequent public charging stations in sequence according to the method in the step S3;
s5, when the queuing coefficients of a plurality of public charging stations on the driving path of the target vehicle are sequentially obtained and the control unit does not guide the target vehicle into any public charging station for charging, the queuing coefficients of the public charging stations are compared, and the charging unit selects the public charging station with the smallest queuing coefficient and guides the target vehicle into the public charging station for charging operation.
As a further scheme of the invention, the calculation method for predicting the number of newly added charging people m1 comprises the following steps:
dividing the time of day equal time difference into k prediction sections, respectively obtaining the number of newly-increased charging people in the k prediction sections, and marking the number of newly-increased charging people in the k prediction sections as Y1, Y2, … … and Yk in sequence;
the Yj value in the continuous R days is obtained and is marked as Yj1, yj2, … … and YjR in sequence, wherein j is more than or equal to 1 and less than or equal to k;
according to the formulaCalculating to obtain dispersion values F of R data of Yj1, yj2, … … and YjR, and taking Yjp as an average newly increased charging number mp of a corresponding Yj prediction section if F is less than or equal to F1, wherein Yjp = (Yj1+Yj2+, … …, + YjR)/R;
if F > F1 is satisfied, deleting Yjr values in sequence from | Yjr-Yjp | to the small until F is satisfied and is less than or equal to F1, and calculating the average value of the remaining undeleted Yjr values at the moment as the average newly increased charging population mp of the corresponding Yj predicted section;
wherein R is more than or equal to 1 and less than or equal to R, and F1 is a preset value;
acquiring a time period from when charging request information is sent from a target vehicle to when a predicted time t1 of a corresponding public charging station is reached, and when t1 is within one predicted time period, obtaining the charging request information according to the following stepsCalculating to obtain m1;
when t1 is within h predicted segments, thenCalculating to obtain m1, wherein h is more than or equal to 2, tyr is the time occupied by t1 in the corresponding prediction section, and x is more than or equal to 1 and less than or equal to h;
where ty is the duration of one predicted segment.
As a further scheme of the invention, the calculation method for predicting the number of charged people mi1 comprises the following steps:
when t1 is less than or equal to ty1, acquiring the residual charging time ts of the vehicle being charged in the corresponding charging station, and when ts+tz is less than or equal to t1, considering that the corresponding vehicle can withdraw from the corresponding charging station in the time t1, counting the number of vehicles which currently meet ts+tz is less than or equal to t1 in the corresponding charging station, and taking the number of vehicles as mi1;
and tz is the average residence time of the corresponding charging station vehicle after charging is completed.
ty1 is a preset value, and ty1 represents an average time taken for the vehicle to start charging to finish charging in each charging station.
As a further scheme of the invention, tz is the average value of the residual residence time calculated after deleting the data with larger deviation value by collecting the residence time of the vehicle in the corresponding charging station after the charging is completed within a period of time.
As a further scheme of the invention, the calculation method for predicting the number of charged people mi1 comprises the following steps: when t1 is more than ty1, the predicted charge number mi1 is calculated according to the method for calculating and predicting the newly increased charge number m 1.
The invention has the beneficial effects that:
(1) According to the method, the time for the target vehicle to reach each public charging station can be predicted according to the planned driving path and road conditions of the target vehicle to be charged, then the target vehicle is arranged according to the change of the number of charged people in each public charging station in the period, the queuing time of the target vehicle can be reduced as far as possible in a continuous range, the full utilization of resources can be realized, and the situation that part of charging stations are full of people and part of charging stations have more vacancies is avoided;
(2) According to the invention, when the queuing coefficient is calculated, the influence of the number of charging piles which can be normally used in each charging station is considered, namely, the more the number of the charging piles is, the faster the rotation frequency is, so that the influence caused by the calculation errors of the number of predicted charging people and the number of the predicted charging people can be obviously reduced;
(3) According to the invention, the number of the charged people of each charging station in the past period can be statistically analyzed, so that the number of the vehicles entering the charging station for charging in the appointed time range can be predicted, the accurate arrangement of the subsequent charging guide instruction is convenient, and the situation that the target vehicle misses the optimal charging place is reduced or even avoided.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a framework structure of an internet of things service management system based on regional digital economy.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The service management system of the internet of things based on regional digital economy, as shown in fig. 1, comprises:
the positioning unit is used for positioning the target vehicle and transmitting the target vehicle to the control unit, and the control unit acquires the distance between the vehicle and the corresponding charging pile according to the position of the vehicle and the position of the charging pile;
the navigation unit is used for identifying the driving path of the target vehicle and the road condition on the driving path of the target vehicle;
the terminal unit is used for establishing connection with the target vehicle and the control unit and displaying information that the control unit guides the target vehicle to enter the public charging pile for charging;
the working method of the service management system of the Internet of things based on regional digital economy comprises the following steps:
s1, when a user needs to charge a target vehicle in a high-speed running process, sending charging request information to a control unit through a terminal unit, when the control unit receives the charging request information, acquiring the number m of remaining chargeable vacancies in a first public charging station, acquiring position information of the target vehicle and road condition information between the target vehicle and the first public charging station, which are acquired by a navigation unit, and acquiring predicted time t1 from sending the charging request information to reaching the first public charging station according to real-time road condition information and the distance between the target vehicle and the first public charging station by the control unit;
the chargeable vacancy refers to a charging pile which is reserved or occupied by an unmanned aerial vehicle and can be normally used;
when the public charging station has no vacant space and someone is queuing, the value of m is a corresponding negative number;
s2, acquiring charging request information sent from a target vehicle until the charging request information reaches a predicted time t1 of a corresponding public charging station, wherein the number of newly increased charging people m1 is predicted by a first public charging station;
when m+mi1 is more than m1, the control unit guides the target vehicle to enter the first public charging pile for charging, wherein mi1 is the number of people who finish charging from the time when the target vehicle sends charging request information to the time when the target vehicle leaves the charging position within the prediction time t1 when the target vehicle reaches the first public charging station;
s3, when m+mi1 is less than or equal to m1, calculating according to a formula D1= [ m1- (m+mi1) ]/mz1 to obtain a queuing coefficient D1 of the target vehicle at the first public charging station, wherein mz1 is the number of charging piles which can be normally used in the first public charging station;
if the target vehicle has a second public charging station on the driving path, acquiring the distance L between the second public charging station on the driving path of the target vehicle and the target vehicle, and if L is more than or equal to L1+Ly1, guiding the target vehicle to enter a first public charging pile for charging by a control unit;
wherein L1 is the remaining cruising distance of the target vehicle, ly1 is a preset value, and the risk caused by cruising calculation errors is reduced, and in one embodiment of the invention, the Ly1 is 20km;
if L is less than L1+Ly1, the control unit acquires the number m2 of remaining chargeable vacancies in the second public charging station, the predicted time t2 from the target vehicle to the arrival of the charging request information at the second public charging station and the predicted newly increased number m3 of charging persons of the second public charging station within the time t2, if m2+mi2 is more than m3, the control unit guides the target vehicle to enter the second public charging pile for charging, and if m2+mi2 is less than or equal to m3, the queuing coefficient D2 of the target vehicle at the second public charging station is obtained according to the formula D2 = [ m3- (m2+mi2) ]/mz2, wherein mz2 is the number of charging piles which can be normally used of the second public charging station;
s4, judging the subsequent public charging stations in sequence according to the method in the step S3;
s5, when the queuing coefficients of a plurality of public charging stations on the driving path of the target vehicle are sequentially obtained and the control unit does not guide the target vehicle into any public charging station for charging, the queuing coefficients of the public charging stations are compared, and the charging unit selects the public charging station with the smallest queuing coefficient and guides the target vehicle into the public charging station for charging operation;
the first public charging station is a first public charging station which can enter a target vehicle in the subsequent driving process on a target vehicle driving path, and the second public charging station is a second public charging station which can enter the target vehicle in the subsequent driving process on the target vehicle driving path;
according to the method, the time for the target vehicle to reach each public charging station can be predicted according to the planned driving path and road conditions of the target vehicle to be charged, then the target vehicle is arranged according to the change of the number of charged people in each public charging station in the period, the queuing time of the target vehicle can be reduced as far as possible in a continuous range, the full utilization of resources can be realized, and the situation that part of charging stations are full of people and part of charging stations have more vacancies is avoided;
in addition, when the queuing coefficient is calculated, the influence of the number of the charging piles which can be normally used in each charging station is considered, namely, the more the number of the charging piles is, the faster the rotation frequency is, so that the influence caused by the calculation errors of the number of the predicted charging persons and the number of the predicted charging persons can be obviously reduced;
the calculation method for predicting the number of newly-increased charging people m1 comprises the following steps:
dividing the time of day equal time difference into k prediction sections, respectively obtaining the number of newly-increased charging people in the k prediction sections, and marking the number of newly-increased charging people in the k prediction sections as Y1, Y2, … … and Yk in sequence;
taking Yj as an example, the Yj value in the continuous R days is obtained and is marked as Yj1, yj2, … … and YjR in sequence, wherein j is more than or equal to 1 and k is more than or equal to k;
according to the formulaCalculating to obtain dispersion values F of R data of Yj1, yj2, … … and YjR, and taking Yjp as an average newly increased charging number mp of a corresponding Yj prediction section if F is less than or equal to F1, wherein Yjp = (Yj1+Yj2+, … …, + YjR)/R;
if F > F1 is satisfied, deleting Yjr values in sequence from | Yjr-Yjp | to the small until F is satisfied and is less than or equal to F1, and calculating the average value of the remaining undeleted Yjr values at the moment as the average newly increased charging population mp of the corresponding Yj predicted section;
wherein R is more than or equal to 1 and less than or equal to R, and F1 is a preset value;
acquiring a time period from when charging request information is sent from a target vehicle to when a predicted time t1 of a corresponding public charging station is reached, and when t1 is within one predicted time periodThen according toCalculating to obtain m1;
when t1 is within h predicted segments, thenCalculating to obtain m1, wherein h is more than or equal to 2, tyr is the time occupied by t1 in the corresponding prediction section, and x is more than or equal to 1 and less than or equal to h;
wherein ty is the duration of one predicted segment;
because the traffic flow of the expressway is stable, the number of vehicles entering the charging stations for charging every day is also stable, the invention predicts the number of vehicles entering the charging stations for charging in a specified time range by counting the number of vehicles entering each charging station for charging operation in a past period of time, thereby facilitating the accurate arrangement of subsequent charging guide instructions;
the calculation method for predicting the number of charged people mi1 comprises the following steps:
when t1 is less than or equal to ty1, acquiring the residual charging time ts of the vehicle being charged in the corresponding charging station, and when ts+tz is less than or equal to t1, considering that the corresponding vehicle can withdraw from the corresponding charging station in the time t1, counting the number of vehicles which currently meet ts+tz is less than or equal to t1 in the corresponding charging station, and taking the number of vehicles as mi1;
in one embodiment of the present invention, the tz is calculated by collecting the retention time of the vehicle in the corresponding charging station after the charging is completed within a period of time, and calculating the average value of the remaining retention time as the average retention time tz after deleting the data with larger deviation value;
when t1 is more than ty1, calculating according to a method for calculating and predicting the newly increased charging people number m1 to obtain the predicted charging people number mi1;
ty1 is a preset value, and ty1 represents an average time taken for the vehicle to start charging to finish charging in each charging station;
according to the invention, by processing the two cases of t1 > ty1 and t1 less than or equal to ty1 separately, the accuracy of the prediction result can be improved for the case of t1 less than or equal to ty 1.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.

Claims (5)

1. Regional digital economy-based internet of things service management system, which is characterized by comprising:
the positioning unit is used for positioning the target vehicle and transmitting the target vehicle to the control unit;
the navigation unit is used for identifying the driving path of the target vehicle and the road condition on the driving path of the target vehicle;
the terminal unit is used for establishing connection with the target vehicle and the control unit and displaying information that the control unit guides the target vehicle to enter the public charging pile for charging;
the working method of the service management system of the Internet of things based on regional digital economy comprises the following steps:
s1, when a user needs to charge a target vehicle in a high-speed running process, sending out charging request information, and when the control unit receives the charging request information, acquiring the number m of remaining chargeable vacancies in a first public charging station, and acquiring the predicted time t1 from the target vehicle to arrive at the first public charging station after sending out the charging request information;
s2, acquiring charging request information sent from a target vehicle until the charging request information reaches a predicted time t1 of a corresponding public charging station, wherein the number of newly increased charging people m1 is predicted by a first public charging station;
when m+mi1 is more than m1, the control unit guides the target vehicle to enter the first public charging pile for charging, wherein mi1 is the number of people who finish charging from the time when the target vehicle sends charging request information to the time when the target vehicle leaves the charging position within the prediction time t1 when the target vehicle reaches the first public charging station;
s3, when m+mi1 is less than or equal to m1, calculating according to a formula D1= [ m1- (m+mi1) ]/mz1 to obtain a queuing coefficient D1 of the target vehicle at the first public charging station, wherein mz1 is the number of charging piles which can be normally used in the first public charging station;
if the target vehicle has a second public charging station on the driving path, acquiring the distance L between the second public charging station on the driving path of the target vehicle and the target vehicle, and if L is more than or equal to L1+Ly1, guiding the target vehicle to enter a first public charging pile for charging by a control unit;
wherein L1 is the remaining endurance distance of the target vehicle, and Ly1 is a preset value;
if L is less than L1+Ly1, the control unit acquires the number m2 of remaining chargeable vacancies in the second public charging station, the predicted time t2 from the target vehicle to the arrival of the charging request information at the second public charging station and the predicted newly increased number m3 of charging persons of the second public charging station within the time t2, if m2+mi2 is more than m3, the control unit guides the target vehicle to enter the second public charging pile for charging, and if m2+mi2 is less than or equal to m3, the queuing coefficient D2 of the target vehicle at the second public charging station is obtained according to the formula D2 = [ m3- (m2+mi2) ]/mz2, wherein mz2 is the number of charging piles which can be normally used of the second public charging station;
s4, judging the subsequent public charging stations in sequence according to the method in the step S3;
s5, when the queuing coefficients of a plurality of public charging stations on the driving path of the target vehicle are sequentially obtained and the control unit does not guide the target vehicle into any public charging station for charging, the queuing coefficients of the public charging stations are compared, and the charging unit selects the public charging station with the smallest queuing coefficient and guides the target vehicle into the public charging station for charging operation.
2. The service management system of the internet of things based on regional digital economy according to claim 1, wherein the calculation method for predicting the number of newly added charging people m1 is as follows:
dividing the time of day equal time difference into k prediction sections, respectively obtaining the number of newly-increased charging people in the k prediction sections, and marking the number of newly-increased charging people in the k prediction sections as Y1, Y2, … … and Yk in sequence;
the Yj value in the continuous R days is obtained and is marked as Yj1, yj2, … … and YjR in sequence, wherein j is more than or equal to 1 and less than or equal to k;
according to the formulaCalculating to obtain dispersion values F of R data of Yj1, yj2, … … and YjR, and taking Yjp as an average newly increased charging number mp of a corresponding Yj prediction section if F is less than or equal to F1, wherein Yjp = (Yj1+Yj2+, … …, + YjR)/R;
if F > F1 is satisfied, deleting Yjr values in sequence from | Yjr-Yjp | to the small until F is satisfied and is less than or equal to F1, and calculating the average value of the remaining undeleted Yjr values at the moment as the average newly increased charging population mp of the corresponding Yj predicted section;
wherein R is more than or equal to 1 and less than or equal to R, and F1 is a preset value;
acquiring a time period from when charging request information is sent from a target vehicle to when a predicted time t1 of a corresponding public charging station is reached, and when t1 is within one predicted time period, obtaining the charging request information according to the following stepsCalculating to obtain m1;
when t1 is within h predicted segments, thenCalculating to obtain m1, wherein h is more than or equal to 2, tyr is the time occupied by t1 in the corresponding prediction section, and x is more than or equal to 1 and less than or equal to h;
where ty is the duration of one predicted segment.
3. The internet of things service management system based on regional digital economy according to claim 1, wherein the calculation method for predicting the number of charged people mi1 is as follows:
when t1 is less than or equal to tyy, acquiring the residual charging time ts of the vehicle being charged in the corresponding charging station, and when ts+tz is less than or equal to t1, considering that the corresponding vehicle can withdraw from the corresponding charging station in the time t1, counting the number of vehicles which currently meet ts+tz is less than or equal to t1 in the corresponding charging station, and taking the number of vehicles as mi1;
the tz is the average residence time of the corresponding charging station vehicle after charging is completed;
tyy is a preset value, tyy represents the average time taken for the vehicle to start charging to finish charging in each charging station.
4. The internet of things service management system based on regional digital economy according to claim 3, wherein tz is an average value of remaining residence time calculated by collecting residence time of vehicles in corresponding charging stations after charging is completed within a period of time, and deleting data in which a deviation value is large.
5. The internet of things service management system based on regional digital economy according to claim 3, wherein the calculation method for predicting the number of charged people mi1 is as follows: when t1 is more than tyy, the number of charged people mi1 is calculated and predicted according to the method for calculating and predicting the number of newly added charged people m 1.
CN202211240228.9A 2022-10-11 2022-10-11 Internet of Things service management system based on regional digital economy Active CN115409288B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211240228.9A CN115409288B (en) 2022-10-11 2022-10-11 Internet of Things service management system based on regional digital economy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211240228.9A CN115409288B (en) 2022-10-11 2022-10-11 Internet of Things service management system based on regional digital economy

Publications (2)

Publication Number Publication Date
CN115409288A CN115409288A (en) 2022-11-29
CN115409288B true CN115409288B (en) 2023-10-20

Family

ID=84168986

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211240228.9A Active CN115409288B (en) 2022-10-11 2022-10-11 Internet of Things service management system based on regional digital economy

Country Status (1)

Country Link
CN (1) CN115409288B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014225167A (en) * 2013-05-16 2014-12-04 昭和シェル石油株式会社 Electric vehicle charging station guide system
CN107392336A (en) * 2017-07-17 2017-11-24 哈尔滨工程大学 Distributed electric automobile charging dispatching method based on reservation in intelligent transportation
CN108199100A (en) * 2018-01-08 2018-06-22 哈尔滨工程大学 The long-distance operation charging planing method of electric vehicle in intelligent transportation
CN110428165A (en) * 2019-07-31 2019-11-08 电子科技大学 The electric car charging schedule method of reservation and queuing is taken into account in a kind of charging station
CN114491298A (en) * 2021-12-09 2022-05-13 华人运通(上海)云计算科技有限公司 Recommendation method and recommendation service providing platform for highway charging station
TW202223816A (en) * 2020-11-30 2022-06-16 拓連科技股份有限公司 Charging queue management systems and methods of electric vehicle charging stations
CN114971135A (en) * 2022-02-18 2022-08-30 科润智能控制股份有限公司 Intelligent charging station power equipment management system based on 5G Internet of things and scheduling method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4031405A4 (en) * 2019-09-20 2023-10-11 Amply Power, Inc. Real-time electric vehicle fleet management

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014225167A (en) * 2013-05-16 2014-12-04 昭和シェル石油株式会社 Electric vehicle charging station guide system
CN107392336A (en) * 2017-07-17 2017-11-24 哈尔滨工程大学 Distributed electric automobile charging dispatching method based on reservation in intelligent transportation
CN108199100A (en) * 2018-01-08 2018-06-22 哈尔滨工程大学 The long-distance operation charging planing method of electric vehicle in intelligent transportation
CN110428165A (en) * 2019-07-31 2019-11-08 电子科技大学 The electric car charging schedule method of reservation and queuing is taken into account in a kind of charging station
TW202223816A (en) * 2020-11-30 2022-06-16 拓連科技股份有限公司 Charging queue management systems and methods of electric vehicle charging stations
CN114491298A (en) * 2021-12-09 2022-05-13 华人运通(上海)云计算科技有限公司 Recommendation method and recommendation service providing platform for highway charging station
CN114971135A (en) * 2022-02-18 2022-08-30 科润智能控制股份有限公司 Intelligent charging station power equipment management system based on 5G Internet of things and scheduling method

Also Published As

Publication number Publication date
CN115409288A (en) 2022-11-29

Similar Documents

Publication Publication Date Title
CN108773279B (en) Method and device for planning charging path of electric vehicle
Amirgholy et al. Optimal design of sustainable transit systems in congested urban networks: A macroscopic approach
CN109795371A (en) Electric vehicle charging based reminding method and device based on mobile terminal route planning
CN101127158B (en) Predictive traffic information creating method, predictive traffic information creating apparatus, and traffic information display terminal
CN107274665A (en) Bus transport capacity resource method and system for planning
CN115547052B (en) Dynamic demand response electric bus scheduling method for improving self-adaptive large neighborhood algorithm
CN106296355A (en) System and method of hiring a car preengage by a kind of WEB door based on electricity coupling
CN109741626A (en) Parking situation prediction technique, dispatching method and system
Zhang et al. Online cruising mile reduction in large-scale taxicab networks
CN110570656B (en) Method and device for customizing public transport line
CN114743401B (en) Data visualization bus dispatching management platform based on bus digital transformation
CN117371596A (en) Public transport comprehensive regulation and control system for smart city based on multi-source data
CN116109081A (en) Dynamic bus scheduling optimization method and device based on Internet of things
CN116805193A (en) Scheduling method and system of networking electric automobile, electronic equipment and storage medium
CN113393109B (en) Electric vehicle charging load calculation method
CN113642796B (en) Dynamic sharing electric automatic driving vehicle path planning method based on historical data
Wang et al. ForETaxi: data-driven fleet-oriented charging resource allocation in large-scale electric taxi networks
CN113140108B (en) Cloud traffic situation prediction method in internet-connected intelligent traffic system
CN110490365A (en) A method of based on the pre- survey grid of multisource data fusion about vehicle order volume
CN109977527A (en) A kind of charging pile configuration method of city public charging station network
CN111723871B (en) Estimation method for real-time carriage full load rate of bus
CN115409288B (en) Internet of Things service management system based on regional digital economy
CN113269339B (en) Method and system for automatically creating and distributing network appointment tasks
CN112669603B (en) Urban traffic cooperation method and device based on big data
US20220270488A1 (en) Systems and methods for order dispatching and vehicle repositioning

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
GR01 Patent grant
GR01 Patent grant