An electric vehicle charging pile intelligent scheduling method based on big data analysis

Abstract

The application discloses an electric vehicle charging pile intelligent scheduling method based on big data analysis, which comprises the following steps: acquiring traffic flow, user charging behavior, power grid load and weather data, and establishing a data fusion model; using an enhanced exponential smoothing method to perform short-term charging demand prediction on the data fusion model, and generating an optimal charging pile scheduling scheme; establishing a scheduling feedback mechanism to monitor whether the time error of the user in the driving process and the idle rate of the charging pile match the optimal scheduling scheme, and according to the matching result, selecting to update or re-generate the optimal scheduling scheme; through the dynamic weight adjustment, demand prediction and scheduling feedback mechanism, the application ensures efficient scheduling of the charging pile resources in the environment with large demand fluctuation and complex load, effectively reduces the response delay and improves the stability of the scheduling system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data and charging pile scheduling, and particularly relates to an intelligent scheduling method for electric vehicle charging piles based on big data analysis. BACKGROUND

[0002] With the rapid growth of the number of electric vehicles (EVs) in possession, the demand for construction and management of charging infrastructure has surged, driving the development of intelligent charging pile scheduling technology. The initial charging pile scheduling scheme mainly adopts a static deployment mode, that is, according to the short-term average demand of the region, the charging pile distribution and resource allocation in fixed positions are set. This kind of scheme usually relies on historical data and a single data source, and sets the number and position of charging piles for a city or a specific area, which is suitable for stable demand environment, but it is easy to appear resource shortage when the demand fluctuates. With the increasing dynamicity of charging demand, prediction models based on user travel patterns, daily traffic flow and other data are gradually applied to the charging pile scheduling system, and through simple weighting or rule setting of data, the resource allocation is optimized to improve the utilization efficiency of charging piles. However, these methods are mainly based on historical average data, and the response to real-time data is insufficient, which leads to poor performance in dealing with complex urban environments or sudden charging demand. In recent years, with the development of big data and artificial intelligence technology, more and more charging pile scheduling schemes have begun to use multi-source data fusion analysis, trying to integrate dynamic factors such as traffic flow, power grid load, and weather in demand prediction, providing data support and prediction basis for dynamic scheduling of charging resources.

[0003] However, the existing charging pile scheduling technology has significant deficiencies in dealing with demand changes, sudden peak loads, and environmental changes, which are as follows: first, the real-time feedback mechanism of the scheduling system is generally not flexible enough, lacking the ability to adaptively adjust the utilization rate of charging piles and the actual driving path of users, making it difficult to quickly update the scheduling scheme according to the changes in traffic and user charging behavior, resulting in low efficiency of resource allocation between different regions; second, there is a lack of deep fusion and dynamic weighting processing of multi-source data, making the charging demand prediction lack accuracy and precise regional response; third, the path recommendation function in the current scheduling method is relatively basic and cannot provide users with optimal charging path selection considering real-time traffic conditions, thereby prolonging the waiting time of users and reducing the overall utilization rate of charging piles. SUMMARY

[0004] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification of the present application to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above existing problems, the present application is proposed. Therefore, the present application provides an electric vehicle charging pile intelligent scheduling method based on big data analysis to solve the problems proposed in the background art.

[0006] To solve the above technical problems, the present application provides the following technical solutions: an electric vehicle charging pile intelligent scheduling method based on big data analysis, comprising:

[0007] Obtain traffic flow, user charging behavior, power grid load and weather data, and establish a data fusion model;

[0008] Use an enhanced exponential smoothing method to predict short-term charging demand for the data fusion model, schedule the short-term charging demand prediction results, and generate an optimal charging pile scheduling scheme;

[0009] Establish a scheduling feedback mechanism to monitor whether the time error and charging pile idle rate of the user during the current driving process match each data in the optimal charging pile scheduling scheme;

[0010] According to the matching result, the optimal charging pile scheduling scheme is selected to be updated or regenerated, thereby forming an electric vehicle charging pile intelligent scheduling method.

[0011] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: obtaining traffic flow, user charging behavior, power grid load and weather data, and establishing a data fusion model, comprises:

[0012] Different weight coefficients are assigned to the traffic flow, user charging behavior, power grid load and weather data, and weighted summation is performed to obtain the data fusion model.

[0013] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: different weight coefficients are assigned to the traffic flow, user charging behavior, power grid load and weather data, comprising:

[0014] The historical traffic flow, user charging behavior, power grid load and weather data are calculated with the real-time traffic flow, user charging behavior, power grid load and weather data to obtain an influence coefficient;

[0015] The real-time data is represented as a time window, and the change rate of each real-time data is defined as the difference ratio between adjacent two time windows;

[0016] According to the influence coefficient and the change rate of each real-time data, the comprehensive score of historical data and real-time data is obtained, and the comprehensive score is sorted in descending order, and the first two data sources after sorting are assigned to high priority, and the remaining data sources are assigned to low priority.

[0017] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: the short-term charging demand prediction of the data fusion model is carried out by using an enhanced exponential smoothing method, comprising:

[0018] The predicted value of the data fusion model at the current time is The short-term charging demand value at the current time is F(t), and the short-term charging demand value at the last time is F(t-1);

[0019] By considering the influence of the idle rate of the charging pile, the traffic convenience in and F(t), the exponential smoothing formula is obtained;

[0020] According to the short-term charging demand value F(t) at the current time and the short-term charging demand value F(t-1) at the last time, the demand fluctuation rate ΔF is calculated;

[0021] The demand fluctuation rate ΔF is used as a smoothing coefficient to participate in the operation of the exponential smoothing formula, and an enhanced exponential smoothing formula is obtained.

[0022] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: the short-term charging demand prediction result is scheduled to generate an optimal charging pile scheduling scheme, comprising:

[0023] An optimization objective function in the enhanced exponential smoothing formula is established to generate an optimal charging pile scheduling scheme.

[0024] The optimization objective function is obtained by minimizing the idle rate of the charging pile and maximizing the traffic convenience.

[0025] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: further comprising:

[0026] Every Δt, the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions, are obtained from various data platforms, and the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions, are input into the optimization objective function.

[0027] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: a scheduling feedback mechanism is established, comprising:

[0028] Based on the obtained optimal charging pile scheduling scheme, available charging pile positions are recommended to users through a mobile application or a vehicle-mounted system, and when the user selects a charging pile position, path navigation is provided according to the optimization objective function to guide the user to reach the most suitable charging pile.

[0029] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: monitoring whether the time error of the user in the current driving process and the idle rate of the charging pile match each data in the optimal charging pile scheduling scheme, comprising:

[0030] The monitoring data includes the actual time difference of the user arriving at the charging pile and the real-time utilization rate change of the charging pile;

[0031] If the actual arrival time of the user does not match the time in the optimal charging pile scheduling scheme, the traffic convenience in the optimal charging pile scheduling scheme is incremented, and the weight of the traffic convenience in the scheduling scheme is increased;

[0032] If the real-time utilization rate of the charging pile is higher than the idle rate of the charging pile in the optimal charging pile scheduling scheme, the idle rate of the charging pile in the optimal charging pile scheduling scheme is reduced.

[0033] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: according to the matching result, the optimal charging pile scheduling scheme is selected to be updated or regenerated, comprising:

[0034] If the weight of the improved traffic convenience is greater than 1, the optimal charging pile scheduling scheme is regenerated, otherwise the charging pile scheduling scheme is updated.

[0035] As a preferred scheme of the electric vehicle charging pile intelligent scheduling method based on big data analysis, wherein: further comprising:

[0036] Before reducing the idle rate of the charging pile, the real-time utilization rate is judged by using a time window, and if the real-time utilization rate continues to occur in multiple time windows, the idle rate of the charging pile is reduced, the charging pile scheduling scheme is updated, otherwise the optimal charging pile scheduling scheme is regenerated.

[0037] Compared with the prior art, the application has the following advantages:

[0038] 1、The application adopts a dynamic weighting adjustment mechanism, adjusts the weight of different data sources according to the real-time change rate and influence coefficient of the data source, and thus has high adaptability, can not only respond to changes in charging demand in time, but also can avoid the hysteresis of resource allocation;

[0039] 2. By considering the idle rate of charging piles and traffic convenience in the scheduling optimization target, and generating an optimal charging path based thereon, more efficient path recommendations are provided for users, effectively shortening charging waiting time and driving time; in addition, by combining path recommendations with real-time traffic conditions, users can be dynamically guided to the most suitable charging pile, improving the overall charging experience of users;

[0040] 3. A scheduling feedback mechanism is constructed to monitor the actual arrival time of users and the real-time utilization rate of charging piles, and by judging the utilization of charging piles and the deviation of user paths, the scheduling scheme is selected to be updated or regenerated to optimize resource utilization efficiency; effectively solving the problem of rigid scheduling scheme in the prior art that cannot flexibly respond to environmental changes. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0042] Figure 1 The overall flowchart of the intelligent scheduling method of electric vehicle charging piles based on big data analysis according to an embodiment of the present application;

[0043] Figure 2 The influence diagram of dynamic weight adjustment of the intelligent scheduling method of electric vehicle charging piles based on big data analysis according to an embodiment of the present application on charging pile utilization rate;

[0044] Figure 3 The scheduling system response delay diagram of the intelligent scheduling method of electric vehicle charging piles based on big data analysis according to an embodiment of the present application under multivariate dynamic prediction and feedback control. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0046] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0047] It should also be noted that, as used in the specification and the appended claims, the articles "a," "an," or "the" can be used to describe one or more than one, unless otherwise specified. In addition, the term "or" is used in the context of features that are combined to form a single feature. The term "and / or" is used in the context of features that are combined to form a single feature or features. The term "based on" is used to describe one or more feature that affect the outcome of a given process, step, action, or function and can be based on any of the features described in the detailed description or claims. The term "determining" is used to describe one or more features that affect the outcome of a given process, step, action, or function and can be based on any of the features described in the detailed description or claims. The term "in response to" is used to describe one or more features that affect the outcome of a given process, step, action, or function and can be based on any of the features described in the detailed description or claims.

[0048] The present application is described in detail below with reference to the attached drawing figures, wherein the implementations of the present application are shown and described in conjunction with exemplary embodiments. The present application can be understood from the description set forth below in conjunction with the drawings. It should be noted that the drawings are not drawn to scale. In the drawings, like reference numerals refer to like elements through the several embodiments. The use of "proximal" and "distal" herein refer to the clinician's perspective in using a device. Thus, endocardial surface refers to the surface of the heart that is closest to the clinician, while epicardial surface refers to the surface of the heart that is farthest from the clinician. It will be apparent to those skilled in the art that substantial variations can be made in detail of implementation without departing from the spirit of the present application. Accordingly, the present application is not limited to the implementations described below, but include all implementations falling within the scope of the appended claims.

[0049] In the description of the present application, it should be noted that the terms "upper and lower, inner and outer" and the like indicate the positional or relative relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0050] Unless otherwise defined, the terms "mounting, connecting, and connecting" in the present application should be understood broadly, for example: it can be fixedly connected, detachably connected or integrally connected; it can also be mechanically connected, electrically connected or directly connected; it can also be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0051] Embodiment 1

[0052] Reference Figure 1 For the first embodiment of the present application, the embodiment provides an electric vehicle charging pile intelligent scheduling method based on big data analysis, comprising:

[0053] S1, obtain traffic flow, user charging behavior, power grid load and weather data, and establish a data fusion model;

[0054] Specifically, the traffic flow includes real-time traffic conditions, traffic speed and congestion index; the user charging behavior includes charging time, frequency and charging amount of user charging behavior; the power grid load includes real-time load conditions of each region; the weather data includes temperature, humidity and weather conditions (such as rain, snow, sunny, etc.);

[0055] It should be explained that, in order to ensure the effective use of data in the model, an adaptive weighting method is needed to assign different weight coefficients to different data sources;

[0056] Further, different weight coefficients are assigned to traffic flow, user charging behavior, power grid load and weather data, and weighted summation is performed to obtain a data fusion model;

[0057] Specifically, the data fusion model D(t) is represented as:

[0058] D(t) = α × T(t) + β × U(t) + γ × W(t) + δ × L(t)

[0059] Wherein, T(t) represents traffic flow, U(t) represents user charging behavior, W(t) represents power grid load, and L(t) represents weather data; α, β, γ, δ represent weight coefficients of traffic flow, user charging behavior, power grid load and weather data, respectively;

[0060] Further, the historical traffic flow, user charging behavior, power grid load and weather data are calculated with real-time traffic flow, user charging behavior, power grid load and weather data to obtain an influence coefficient;

[0061] Specifically, the influence coefficient is represented as:

[0062]

[0063] Wherein, H i is the historical mean of each data source; R i is the real-time value of each data source at the current time; σ i,r represents the historical standard deviation of each data source; σ i,h represents the real-time standard deviation of each data source; i represents the number of data sources;

[0064] It should be noted that I i represents the relative influence of each data source at the current time compared with the historical data, and the larger the value of I i , the greater the influence of the data source on the current demand;

[0065] Further, the real-time data is represented as a time window, and the change rate of each real-time data is defined as the difference ratio between adjacent two time windows;

[0066] Specifically, the change rate AR of each real-time data i (t) is represented as:

[0067]

[0068] wherein, R i (t) represents the real-time value of each data source in the current window, R i (t-1) represents the real-time value of each data source in the next window;

[0069] Specifically, by monitoring the change rate of each data source in real time, the severe fluctuation of demand can be obtained, such as user travel tide or weather change, etc.

[0070] It should be noted that the influence coefficient captures the difference between the short-term stability and the short-term volatility of the data source through the comparison of the historical data and the real-time data, while the real-time change rate quantifies the volatility of each data source at the current time; for example, when the demand fluctuates severely, the real-time change rate amplifies the weight of the demand response, thereby accelerating the scheduling response, while when the demand is stable, the scheduling frequency is kept at a low level, avoiding frequent resource reallocation;

[0071] Further, according to the influence coefficient and the change rate of each real-time data, the comprehensive score of the historical data and the real-time data is obtained, and the comprehensive score is arranged in descending order, and the first two data sources after descending are assigned to high priority, and the remaining data sources are assigned to low priority;

[0072] Specifically, the first two data sources after descending are assigned to high priority, and the remaining data sources are assigned to low priority, which is equivalent to taking the first two highest priority data from each data source and eliminating the remaining data, wherein if the two data are not the same, the second priority data is used as the backup data of the first priority data;

[0073] Specifically, the comprehensive score of the historical data and the real-time data is represented as:

[0074] S i = I i × (1 + AR i (t))

[0075] wherein, S i represents the comprehensive score of each data source;

[0076] It should be noted that by calculating the comprehensive score and arranging it in descending order, high priority and low priority can be divided for different data sources, ensuring that data sources with large influence are given priority in scheduling, in addition, the priority division can reduce the dependence on low priority data sources and avoid the influence of noise data;

[0077] S2, using an enhanced exponential smoothing method to predict the short-term charging demand of the data fusion model, scheduling the short-term charging demand prediction result, and generating an optimal charging pile scheduling scheme;

[0078] It should be explained that for electric vehicle charging pile scheduling, both past demand trends and current demand fluctuations must be considered; the traditional exponential smoothing method cannot effectively balance these two in the smoothing process, often lagging behind real-time demand or being overly sensitive to short-term fluctuations, so the traditional exponential smoothing method needs to be enhanced;

[0079] Specifically, the enhancement steps are as follows:

[0080] Further, let the prediction value of the data fusion model at the current time be The short-term charging demand value at the current time is F(t), and the short-term charging demand value at the previous time is F(t-1);

[0081] Further, by considering the influence of the idle rate of the charging pile and the traffic convenience on and F(t), the exponential smoothing formula is obtained;

[0082] Specifically, the exponential smoothing formula is expressed as:

[0083]

[0084] wherein ε is a smoothing coefficient with a value of 0.1-0.9, R(t) is the comprehensive influence value of the idle rate of the charging pile and the traffic convenience, is the output value of the exponential smoothing formula;

[0085] Further, R(t) is expressed as:

[0086] R(t)=L(t)×γ+(1-C(t))×α

[0087] wherein C(t) is the traffic convenience, reflecting the influence of traffic flow α, the higher the value of C(t), the worse the traffic convenience; L(t) represents the idle rate of the charging pile, the higher the value of L(t), the more idle the charging pile;

[0088] Further, according to the short-term charging demand value F(t) at the current time and the short-term charging demand value F(t-1) at the previous time, the demand fluctuation rate ΔF is calculated;

[0089] It should be noted that the degree of change of short-term charging demand needs to be judged by the fluctuation rate of short-term charging demand;

[0090] Specifically, the demand fluctuation rate ΔF is expressed as:

[0091]

[0092] Further, the demand fluctuation rate ΔF is involved in the operation of the exponential smoothing formula as a smoothing coefficient to obtain an enhanced exponential smoothing formula;

[0093] Specifically, the enhanced exponential smoothing formula is expressed as:

[0094]

[0095] It should be noted that by operating and F(t), the exponential smoothing formula can correct the accuracy of the predicted value in real time, so that the formula can adjust the prediction weight according to the actual change of demand, instead of only relying on historical trends;

[0096] S3, a scheduling feedback mechanism is established to monitor whether the time error of the user in the current driving process and the idle rate of the charging pile match each data in the optimal charging pile scheduling scheme;

[0097] Further, an optimal objective function in the enhanced exponential smoothing formula is established to generate an optimal charging pile scheduling scheme;

[0098] Specifically, the optimal objective function is obtained by minimizing the idle rate of the charging pile and maximizing the traffic convenience;

[0099] Specifically, the optimal objective function J is expressed as:

[0100]

[0101] Where N represents the total number of charging piles, L i (t) represents the idle rate of the i-th charging pile at the current time t, C i (t) represents the traffic convenience of the i-th charging pile at the current time t; ∈ is a very small positive number to ensure that the denominator is not zero; ω L and ω C respectively represent the weights of the charging pile idle rate and the traffic convenience;

[0102] It should be noted that by operating in the operation, it is inclined to select the charging pile with a lower idle rate, because the smaller the idle rate is, the larger the denominator value is, so that the value of this term becomes smaller, achieving the minimization of the idle rate; as mentioned earlier, the higher the value of C(t) is, the worse the traffic convenience is, so the lower the value of C(t) is, the better the traffic convenience is, so it is only necessary to minimize C i (t) to achieve the maximization of traffic convenience;

[0103] Further, every time interval Δt, the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions, are obtained from various data platforms, and the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions, are input into the optimization objective function;

[0104] It should be noted that by obtaining the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions in real time, the mismatch of resources caused by data lag is avoided, and the charging pile allocation scheme can be adjusted in time according to the changes in demand, thereby realizing efficient and reasonable utilization of charging resources;

[0105] Further, based on the obtained optimal charging pile scheduling scheme, the available charging pile positions are recommended to the user through a mobile application or a vehicle-mounted system, and when the user selects a charging pile position, path navigation is provided according to the optimization objective function to guide the user to the most suitable charging pile;

[0106] S4, according to the matching result, selecting to update or re-generate the optimal charging pile scheduling scheme, thereby forming an intelligent charging pile scheduling method for electric vehicles;

[0107] Further, the monitoring data includes the actual arrival time difference of the user to the charging pile and the real-time utilization rate change of the charging pile;

[0108] Further, if the actual arrival time of the user does not match the time in the optimal charging pile scheduling scheme, the traffic convenience in the optimal charging pile scheduling scheme is incremented, and the weight of traffic convenience in the scheduling scheme is increased;

[0109] It should be noted that when it is found that the actual arrival time does not match the estimated arrival time at the charging pile, it means that the current traffic conditions have changed, affecting the charging experience of the user; by increasing the weight of traffic convenience, the importance of traffic factors can be improved, so that the scheduling scheme pays more attention to traffic convenience, thereby giving priority to positions with better traffic conditions when selecting charging piles;

[0110] Further, if the increased weight of traffic convenience exceeds 1, the optimal charging pile scheduling scheme is re-generated, otherwise the charging pile scheduling scheme is updated;

[0111] Further, if the real-time utilization rate of the charging pile is higher than the idle rate of the charging pile in the optimal charging pile scheduling scheme, the idle rate of the charging pile in the optimal charging pile scheduling scheme is reduced;

[0112] Further, before reducing the idle rate of the charging pile, the real-time utilization is judged by using a time window, if the real-time utilization continues to occur in multiple time windows, the idle rate of the charging pile is reduced, the charging pile scheduling scheme is updated, otherwise the optimal charging pile scheduling scheme is regenerated;

[0113] It should be noted that if the idle rate is directly reduced for short-term fluctuations in demand, it may lead to over-response; through the judgment of the time window, the "temporary fluctuations" (time window intermittently) and "persistent growth" (time window continues to occur) of the charging pile utilization rate can be clearly distinguished; for temporary fluctuations, the scheme is regenerated without adjusting the idle rate of the charging pile, ensuring that the response to the charging pile demand is appropriate without affecting long-term resource allocation.

[0114] Embodiment 2

[0115] Referring to Figure 2 and Figure 3 , the second embodiment of the present application provides an electric vehicle charging pile intelligent scheduling method based on big data analysis, comprising: in order to verify the application effect of the intelligent scheduling system of the present application in the dynamic environment, comparing the response performance of the traditional scheduling method and the scheduling method of the present application under different traffic flow, power grid load, user demand and weather conditions;

[0116] Experimental site: select a large city area with dense charging piles, there are multiple charging sites in the area, each charging site is equipped with 5-10 charging piles; the data acquisition frequency is every 5 minutes, the real-time data of traffic, weather and power grid load are obtained in real time through the platform, and the charging demand in different time periods is simulated;

[0117] Through Figure 2 It can be seen that the charging pile utilization rate of the traditional scheduling system presents a certain volatility, and the utilization rate is relatively high during high load (in 20-40 time steps), but then the charging pile utilization rate decreases rapidly (in 50-80 time steps) as the demand decreases, and the efficient use of resources is not stable; while the charging pile utilization rate of the scheduling system of the present application shows a more stable trend, even in the stage of demand reduction (in 50-80 time steps) still maintains a high resource utilization rate, in the period of resource demand increase, the system responds quickly, and can maintain the efficient use of charging piles in a relatively stable range; secondly, referring to Figure 3The response delay of the traditional scheduling system fluctuates greatly with time, especially during the high load period (at 20-40 time steps), the response delay reaches more than 80 minutes, indicating that the system has obvious delay under high demand; while the response delay of the scheduling system of the application is significantly lower than that of the traditional system, and is more stable at different time steps, even under high load (20-40 time steps), the response delay remains about 60 minutes, which is much lower than that of the traditional system;

[0118] Therefore, it can be shown that the scheduling system of the application significantly reduces the response delay under different load conditions, especially under high demand load, which shows the advantages of stability and fast response, and can realize the scheduling and management of charging piles under short-term demand.

[0119] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting language JavaScript.

[0120] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that implement the functions specified in one or more flows and / or blocks.

[0121] These computer program instructions can also be stored in a computer-readable storage medium that can guide the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable storage medium produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that implement the functions specified in one or more flows and / or blocks.

[0122] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0123] Although preferred embodiments of the application have been described herein, it will be apparent to those skilled in the art that various modifications and changes can be made to the embodiments without departing from the spirit and scope of the application. Accordingly, it is intended that all claims be interpreted to include all such modifications and changes as fall within the true spirit and scope of the application.

[0124] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for intelligent scheduling of electric vehicle charging piles based on big data analysis, characterized in that, The application relates to an electric vehicle charging pile intelligent scheduling method. The application comprises the following steps: Obtaining traffic flow, user charging behavior, power grid load and weather data, and establishing a data fusion model; Using an enhanced exponential smoothing method to perform short-term charging demand prediction on the data fusion model, scheduling the short-term charging demand prediction result, and generating an optimal charging pile scheduling scheme; Let the prediction value of the data fusion model at the current time be The short-term charging demand value at the current time is F(t), and the short-term charging demand value at the previous time is F(t-1); By considering the idle rate of charging piles, traffic convenience, and the influence of F(t), the exponential smoothing formula is obtained. and F(t) of influence, get exponential smoothing formula; The method comprises the following steps: According to the short-term charging demand value F(t) at the current time and the short-term charging demand value F(t-1) at the last time, a demand fluctuation rate AF is calculated; The demand fluctuation rate AF is used as a smoothing coefficient to participate in the operation of an exponential smoothing formula, and an enhanced exponential smoothing formula is obtained; The method comprises the following steps: An optimal objective function in the enhanced exponential smoothing formula is established to minimize the idle rate of the charging pile and maximize the traffic convenience, and an optimal charging pile scheduling scheme is generated; The optimal objective function is obtained by minimizing the idle rate of the charging pile and maximizing the traffic convenience; A scheduling feedback mechanism is established to monitor whether the time error of the user in the current driving process and the idle rate of the charging pile match each data in the optimal charging pile scheduling scheme; 2.The method of claim 1, wherein, According to the matching result, the optimal charging pile scheduling scheme is updated or regenerated, thereby forming the electric vehicle charging pile intelligent scheduling method. The method comprises the following steps: 3.The method of claim 2, wherein, Different weight coefficients are assigned to the traffic flow, user charging behavior, power grid load and weather data, and weighted summation is performed to obtain the data fusion model. The method comprises the following steps: In particular, the influence coefficient I i is expressed as: where H i is the historical mean value for each data source; R i is the real-time value at the current time for each data source; σ i,r denotes the historical standard deviation for each data source; σ i,h denotes the real-time standard deviation for each data source; i denotes the number of data sources; The historical traffic flow, user charging behavior, power grid load and weather data are calculated with the real-time traffic flow, user charging behavior, power grid load and weather data to obtain an influence coefficient; In particular, the rate of change AR of each real-time data i (t) is represented as: wherein R i (t) represents the real-time value of each data source at the current window, R i (t-1) represents the real-time value of each data source at the next window; The real-time data are represented as a time window, and the change rate of each real-time data is defined as the difference ratio between adjacent two time windows; According to the influence coefficient and the change rate of each real-time data, a comprehensive score of the historical data and the real-time data is obtained, and the comprehensive score is sorted in descending order, and the first two data sources after the sorting are assigned to a high priority, and the rest of the data sources are assigned to a low priority; Specifically, the first two data sources with the highest priority are taken out from each data source, and the rest of the data is removed, and if the two data sources are different, the data source with the second priority is used as the backup data of the data source with the first priority. S i = I i × (1 + ΔR i (t)) where S i is expressed as a composite score for each data source. 4.The method of claim 1, wherein, Specifically, the comprehensive score of the historical data and the real-time data is represented as: The method further comprises the following steps:

5. The big data analysis based electric vehicle charging pile intelligent scheduling method of claim 1, wherein, Every time interval At, the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions are obtained from various data platforms, and the current idle rate and power load data of each charging pile, as well as the traffic flow and road traffic conditions are input into the optimal objective function. The scheduling feedback mechanism is established, and the method comprises the following steps: Based on the obtained optimal charging pile scheduling scheme, the available charging pile positions are recommended to the user through a mobile application or a vehicle-mounted system. When the user selects a charging pile position, path navigation is provided according to the optimization objective function to guide the user to the most suitable charging pile.

6. The big data analysis based electric vehicle charging pile intelligent scheduling method of claim 5, wherein, The time error of the user in the current driving process and the idle rate of the charging pile are monitored to match each data in the optimal charging pile scheduling scheme, including: The monitoring data includes the time difference of the user actually arriving at the charging pile and the real-time utilization rate change of the charging pile; If the actual arrival time of the user does not match the time in the optimal charging pile scheduling scheme, the traffic convenience in the optimal charging pile scheduling scheme is incremented, and the weight of the traffic convenience in the scheduling scheme is increased; If the real-time utilization rate of the charging pile is higher than the idle rate of the charging pile in the optimal charging pile scheduling scheme, the idle rate of the charging pile in the optimal charging pile scheduling scheme is reduced.

7. The big data analysis based electric vehicle charging pile intelligent scheduling method of claim 6, wherein, According to the matching result, the optimal charging pile scheduling scheme is selected to be updated or regenerated, including: If the weight of the improved traffic convenience exceeds 1, the optimal charging pile scheduling scheme is regenerated, otherwise the charging pile scheduling scheme is updated. 8.The method of claim 6, wherein, Further comprising: Before reducing the idle rate of the charging pile, the real-time utilization rate is judged by using a time window. If the real-time utilization rate continuously occurs in multiple time windows, the idle rate of the charging pile is reduced, the charging pile scheduling scheme is updated, otherwise the optimal charging pile scheduling scheme is regenerated.

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