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