CN117172516B - Charging pile dynamic scheduling decision-making method, device, equipment and storage medium - Google Patents
Charging pile dynamic scheduling decision-making method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of charging piles and discloses a method, a device, equipment and a storage medium for dynamically scheduling and deciding the charging piles, wherein the method comprises the following steps: acquiring real-time environment data through the Internet of vehicles, wherein the real-time environment data comprises vehicle data, charging pile data and road data; carrying out data processing and data prediction on the real-time environment data to obtain an optimization target; performing multi-objective optimization on the optimization objective to generate a pareto front solution set, wherein the pareto front solution set comprises a plurality of scheduling schemes; and determining an optimal scheduling scheme of the charging pile based on the pareto front solution set. According to the invention, the optimization target is obtained by carrying out data processing and data prediction on the real-time environment data acquired by the Internet of vehicles, the pareto front solution set containing a plurality of scheduling schemes is generated based on the optimization target, and finally the optimal scheduling scheme of the charging pile is determined based on the pareto front solution set, so that the technical defects of unreasonable resource allocation and lower user charging satisfaction caused by a static scheduling method are avoided.
Description
Technical Field
The invention relates to the technical field of charging piles, in particular to a dynamic scheduling decision method, a dynamic scheduling decision device, dynamic scheduling decision equipment and a storage medium for charging piles.
Background
Today, the dispatching of electric car charging piles is commonly implemented by a static dispatching method. I.e. the usage plan of each charging peg is initially determined based on a priori information, such as the charging demand predictions of the vehicle, the user's behavior patterns and historical data, etc.
However, such static scheduling methods rely heavily on a priori information and historical data, which often cannot be fully predicted and adapted to the changing needs of the user. For example, if a new charging demand hot spot occurs in a certain area or a new charging pile is put into use, the static scheduling plan may not be able to be adjusted in time, resulting in unreasonable allocation of resources (e.g., some charging piles have a longer idle time and other charging piles have excessive demands) and difficulty in meeting the demands of users (e.g., users cannot obtain charging services meeting their changing demands in time). Therefore, there is a need in the industry for a method for dynamically scheduling charging piles according to real-time charging conditions.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a dynamic scheduling decision method, device and equipment for charging piles and a storage medium, and aims to solve the technical problem that the charging piles cannot be dynamically scheduled according to real-time charging conditions in the prior art.
In order to achieve the above purpose, the invention provides a dynamic scheduling decision method of a charging pile, which comprises the following steps:
collecting real-time environment data through the Internet of vehicles, wherein the real-time environment data comprises vehicle data, charging pile data and road data;
performing data processing and data prediction on the real-time environment data to obtain an optimization target;
performing multi-objective optimization on the optimization objective to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes;
and determining an optimal scheduling scheme of the charging pile based on the pareto front solution set.
Optionally, the step of performing data processing and data prediction on the real-time environment data to obtain an optimization target includes:
performing data processing on the real-time environment data to obtain processed data, wherein the data processing comprises data cleaning, data denoising and formatting conversion;
performing data fusion on the processed data to obtain an analysis data set, and extracting predicted target features from the analysis data set;
and constructing a prediction model based on the prediction target characteristics, and carrying out data prediction on the real-time environment data through the prediction model to obtain an optimization target.
Optionally, the step of performing multi-objective optimization on the optimization objective to generate pareto front solution set includes:
performing multi-objective optimization on the optimization targets to obtain objective functions;
generating an initial population based on a genetic algorithm, wherein the initial population comprises a plurality of individuals, and each individual represents a scheduling scheme;
generating a pareto front solution set based on the objective function and fitness values of all individuals in the initial population.
Optionally, the step of determining the optimal scheduling scheme of the charging pile based on the pareto front solution set includes:
determining the priority and the weight of the optimization target according to the charging requirement of the user;
and determining an optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight.
Optionally, the step of determining the optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight includes:
constructing a priority model based on the priority and the weight, and respectively scoring each scheduling scheme in the pareto front solution set through the priority model to obtain scheme scores;
and sequencing each scheduling scheme in the pareto front solution set based on the scheme scores, and determining an optimal scheduling scheme of the charging pile from the pareto front solution set according to the sequencing result.
Optionally, the step of collecting real-time environmental data through the internet of vehicles includes:
installing a data terminal for the vehicle and the charging pile, wherein the data terminal comprises a communication module, a positioning module and a data acquisition module;
and establishing the Internet of vehicles containing the data terminal based on a special short-range communication protocol, and acquiring real-time environment data through the Internet of vehicles.
Optionally, the charging pile dynamic scheduling decision method further includes:
acquiring monitoring data of the charging pile in real time through a sensor, and judging whether the charging pile has a fault or not through the monitoring data;
if so, carrying out resource scheduling on the charging pile based on the charging pile fault and the occupied condition of the charging pile in the current area.
In addition, in order to achieve the above purpose, the present invention further provides a dynamic scheduling decision device for a charging pile, where the dynamic scheduling decision device for a charging pile includes:
the data acquisition module is used for acquiring real-time environment data through the Internet of vehicles, wherein the real-time environment data comprises vehicle data, charging pile data and road data;
the data prediction module is used for carrying out data processing and data prediction on the real-time environment data to obtain an optimization target;
the data optimization module is used for performing multi-objective optimization on the optimization targets to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes;
and the data decision module is used for determining an optimal scheduling scheme of the charging pile based on the pareto front solution set.
In addition, in order to achieve the above purpose, the present invention also provides a charging pile dynamic scheduling decision device, which includes: the system comprises a memory, a processor and a charging pile dynamic scheduling decision program stored on the memory and capable of running on the processor, wherein the charging pile dynamic scheduling decision program is configured to realize the steps of the charging pile dynamic scheduling decision method.
In addition, in order to achieve the above objective, the present invention further provides a storage medium, on which a dynamic scheduling decision program of a charging pile is stored, which when executed by a processor, implements the steps of the dynamic scheduling decision method of a charging pile as described above.
The method comprises the steps of collecting real-time environment data through the Internet of vehicles, wherein the real-time environment data comprises vehicle data, charging pile data and road data; performing data processing and data prediction on the real-time environment data to obtain an optimization target; performing multi-objective optimization on the optimization objective to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes; and determining an optimal scheduling scheme of the charging pile based on the pareto front solution set. Compared with the prior art, the method for scheduling the charging pile by the static scheduling method has the advantages that the optimization target is obtained by carrying out data processing and data prediction on real-time environment data acquired by the Internet of vehicles, the pareto front edge solution set containing a plurality of scheduling schemes is generated based on the optimization target, and finally the optimal scheduling scheme of the charging pile is determined based on the pareto front edge solution set, so that the technical defects of unreasonable resource allocation and low user charging satisfaction caused by the conventional static scheduling method are avoided, and the charging pile can be dynamically scheduled according to the real-time charging condition.
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Fig. 1 is a schematic structural diagram of a charging pile dynamic scheduling decision-making device in a hardware operation environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of the dynamic scheduling decision method of the charging pile according to the present invention;
FIG. 3 is a flow chart of a second embodiment of the dynamic scheduling decision method of the charging pile according to the present invention;
FIG. 4 is a flowchart illustrating a third embodiment of a dynamic scheduling decision method for a charging pile according to the present invention;
fig. 5 is a block diagram of a first embodiment of a dynamic scheduling decision device for a charging pile according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a charging pile dynamic scheduling decision-making device in a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 1, the charging pile dynamic scheduling decision device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 1 does not constitute a limitation of the charging pile dynamic scheduling decision device, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a charging pile dynamic scheduling decision program may be included in the memory 1005 as one storage medium.
In the dynamic scheduling decision device of the charging pile shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the charging pile dynamic scheduling decision device can be arranged in the charging pile dynamic scheduling decision device, and the charging pile dynamic scheduling decision device calls a charging pile dynamic scheduling decision program stored in the memory 1005 through the processor 1001 and executes the charging pile dynamic scheduling decision method provided by the embodiment of the invention.
The embodiment of the invention provides a dynamic scheduling decision method for a charging pile, and referring to fig. 2, fig. 2 is a flow chart of a first embodiment of the dynamic scheduling decision method for the charging pile.
In this embodiment, the method for dynamically scheduling and deciding the charging pile includes the following steps:
step S10: real-time environment data is acquired through the Internet of vehicles, and the real-time environment data comprises vehicle data, charging pile data and road data.
It should be noted that, the execution body of the method of the embodiment may be a terminal device with functions of data transmission, data processing and program running, for example, a smart phone, a smart watch, etc., or may be an electronic device with the same or similar functions, for example, the above-mentioned charging pile dynamic scheduling decision device. The present embodiment and the following embodiments will be described below by taking a dynamic scheduling decision device (hereinafter referred to as decision device) for a charging pile as an example.
It is understood that the above-described internet of vehicles may refer to technologies and systems that connect and interact vehicles with the internet, communication networks. The intelligent vehicle-mounted sensor, the communication equipment and the cloud computing platform can be integrated to realize information exchange and intelligent service among vehicles, between vehicles and infrastructure and between vehicles and users.
It should be understood that the above-described real-time environment data may include vehicle data, charging pile data, and road data. The vehicle data may include vehicle position data, vehicle electric quantity data, and the like, the charging pile data may include charging pile idle time data, charging pile fault data, and the like, and the road data may include road congestion data, road planning data, and the like.
Step S20: and carrying out data processing and data prediction on the real-time environment data to obtain an optimization target.
It should be noted that the above data processing may include data cleansing (checking and processing abnormal values such as error values, missing values, repeated values, etc. in data), data integration (integrating data of a plurality of data sources into a unified data set), data transformation (processing such as normalization, standardization, discretization, normalization, etc. on data), feature selection (selecting features with most predictive capability from original data to reduce feature dimensions and redundant information such as correlation analysis, chi-square inspection, information gain, etc.), feature construction (creating new features by combining, converting, deriving, etc. according to domain knowledge and understanding), data dimension reduction (performing dimension reduction processing on high-dimensional data to reduce feature numbers and preserve most important information such as principal component analysis, linear discriminant analysis, etc.), data division (dividing data set into training set, validation set and test set to evaluate performance and generalization capability of model in modeling process), data standardization (normalizing or normalizing data to ensure data to have similar dimensions and scope, and avoid oversized influence of some features on model).
It should be appreciated that the above-described real-time environmental data may be data predicted by a predictive model. For example, an LSTM (Long Short-Term Memory) neural network may be used to predict traffic flow, a GBT (Gradient Boosting Tree, gradient-lifted tree) model may be used to predict peak of charge demand, etc., and the manner of data prediction is not limited in this embodiment.
Step S30: and performing multi-objective optimization on the optimization objective to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes.
It should be noted that Pareto Front solution set (Pareto Front) is an important concept in multi-objective optimization.
It should be appreciated that the pareto front solution set may contain multiple scheduling schemes. The pareto front solution set represents the set of optimal solutions that can be achieved under multiple objective functions without sacrificing other objectives by improving one objective.
Step S40: and determining an optimal scheduling scheme of the charging pile based on the pareto front solution set.
In a specific implementation, the above-mentioned determination of the optimal scheduling scheme of the charging pile can be regarded as a multi-objective optimization problem, in which there are multiple conflicting objective functions. These objective functions are often contradictory, and improving one of the objectives may lead to deterioration of the other. Pareto front solution set means that in this case, one solution cannot be found anymore to optimize the solution set for all targets at the same time. Based on this, the optimal scheduling scheme of the charging pile can be determined through pareto front solution set.
The method comprises the steps that real-time environment data are collected through the Internet of vehicles, wherein the real-time environment data comprise vehicle data, charging pile data and road data; performing data processing and data prediction on the real-time environment data to obtain an optimization target; performing multi-objective optimization on the optimization objective to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes; and determining an optimal scheduling scheme of the charging pile based on the pareto front solution set. Compared with the prior art, the method for scheduling the charging pile through the static scheduling method, the method for dynamically scheduling the charging pile according to the real-time charging condition can avoid the technical defects of unreasonable resource allocation and low user charging satisfaction caused by the existing static scheduling method by performing data processing and data prediction on real-time environment data acquired by the Internet of vehicles to obtain an optimization target, generating a pareto front solution set containing a plurality of scheduling schemes based on the optimization target, and finally determining the optimal scheduling scheme of the charging pile based on the pareto front solution set.
Referring to fig. 3, fig. 3 is a flowchart illustrating a second embodiment of a dynamic scheduling decision method for a charging pile according to the present invention.
Based on the first embodiment, in this embodiment, in order to improve accuracy of data prediction, the step S20 may include:
step S201: and carrying out data processing on the real-time environment data to obtain processed data, wherein the data processing comprises data cleaning, data denoising and formatting conversion.
In a specific implementation, the data cleaning may be implemented by removing an abnormal value such as an error value, a missing value, or a repeated value in the real-time environment data, the data denoising may be implemented by a data denoising method such as a median filtering method, a moving average method, or a wavelet transform method, and the real-time environment data may be encoded by a uniform encoding format (for example, ASCII, ANSI, UTF-8, etc.), so that the format conversion of the real-time environment data may be implemented.
Step S202: and carrying out data fusion on the processed data to obtain an analysis data set, and extracting predicted target features from the analysis data set.
The prediction target feature may be a battery charge rate, a vehicle travel speed feature, or the like.
In a specific implementation, the weighted average method can be used for carrying out weighted average operation on the processed data, and the weight can be set according to the quality, reliability or other indexes of the data, so that data fusion is realized, and an analysis data set is obtained. Of course, the data fusion of the processed data may also be implemented by a feature level fusion method and a decision level fusion method, which is not limited in this embodiment.
Step S203: and constructing a prediction model based on the prediction target characteristics, and carrying out data prediction on the real-time environment data through the prediction model to obtain an optimization target.
Further, in this embodiment, in order to generate the pareto front solution set more accurately, so as to improve the rationality of the dynamic scheduling decision of the charging pile, the step S30 may include:
step S301: and performing multi-objective optimization on the optimization targets to obtain objective functions.
It should be appreciated that the objective function described above may be derived from determining multiple objective problems based on optimization objectives, such as maximizing resource utilization, minimizing user latency, etc.
Step S302: an initial population is generated based on a genetic algorithm, the initial population comprising a plurality of individuals, and each individual exhibits a scheduling scheme.
It should be noted that the genetic algorithm (Genetic Algorithm, GA) is an optimization algorithm simulating the natural selection and evolution process. The heuristic search algorithm searches the optimal solution by simulating mechanisms such as inheritance, variation, fitness selection and the like in the biological evolution. Genetic algorithms are based on the theory of Darwin's evolution, and find the optimal solution of the problem by simulating the process of biological evolution.
Step S303: generating a pareto front solution set based on the objective function and fitness values of all individuals in the initial population.
In a specific implementation, the pareto front solution set may be generated by the following steps. First, determining an objective function: the objective function of the multi-objective problem is determined based on the optimization objective, such as maximizing resource utilization, minimizing user latency, etc. Secondly, generating an initial population: an initial population meeting the constraint is randomly generated, and each individual represents a viable scheduling scheme. Third, individual coding: each individual, i.e., a feasible solution, is represented using a binary, integer, etc. coding. Fourth, calculate the fitness: and calculating the fitness value of each individual according to the objective function, and evaluating the advantages and disadvantages of each individual. Fifth step, selecting crossover: and selecting individuals from the population according to the fitness to perform cross operation to generate new individuals. Sixth step, gene mutation: gene mutation operations are performed on new individuals to increase population diversity. Seventh, generating a new population: generating a new generation population by using the parent individuals and the cross mutant individuals. Eighth step, iteration solution: repeating the third step to the seventh step until the termination condition is met, and obtaining the optimal solution set. A ninth step of outputting pareto front: and extracting the pareto front solution set from the optimal solution set, thereby obtaining a plurality of optimization schemes.
Based on the above-mentioned first embodiment, in this embodiment, in order to improve the accuracy of the optimal scheduling scheme of the charging pile, the step S40 may include:
step S401: and determining the priority and the weight of the optimization target according to the charging requirement of the user.
In a specific implementation, the priority and the weight corresponding to each optimization target can be determined based on the service requirement of the user in the actual charging process. For example, if a user is more concerned with the power utilization of the charging process, then the priority of the optimization objective may be set to resource utilization > charge latency > charge load balancing. Accordingly, the weight of the optimization target may be set as: resource utilization weight 0.6, charge latency weight 0.3, charge load balancing weight 0.1. Of course, the above-mentioned contents about priority and weight are only for illustration, and specifically, the priority and weight may be flexibly set according to the actual service requirement of the user, which is not limited in this embodiment.
Step S402: and determining an optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight.
The embodiment obtains processed data by carrying out data processing on the real-time environment data, wherein the data processing comprises data cleaning, data denoising and formatting conversion; performing data fusion on the processed data to obtain an analysis data set, and extracting predicted target features from the analysis data set; constructing a prediction model based on the prediction target characteristics, and carrying out data prediction on the real-time environment data through the prediction model to obtain an optimization target; performing multi-objective optimization on the optimization targets to obtain objective functions; generating an initial population based on a genetic algorithm, wherein the initial population comprises a plurality of individuals, and each individual represents a scheduling scheme; generating a pareto front solution set based on the objective function and the fitness values of all individuals in the initial population; determining the priority and the weight of the optimization target according to the charging requirement of the user; and determining an optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight. Compared with the prior art that the charging piles are scheduled through a static scheduling method, the method disclosed by the embodiment performs data fusion on the processed data obtained through data cleaning, data denoising and formatting conversion to obtain an analysis data set, and builds a prediction model based on the prediction target characteristics in the analysis data set, so that the prediction model is used for predicting the data of the real-time environment, and the accuracy of prediction is improved; meanwhile, the optimization targets are optimized in a multi-target mode based on a genetic algorithm, and a pareto front solution set is generated, so that a scheduling decision can be performed based on the latest information, charging requirements and traffic changes can be responded quickly, and the real-time performance and response capability of scheduling are improved.
Referring to fig. 4, fig. 4 is a flowchart illustrating a third embodiment of a dynamic scheduling decision method for a charging pile according to the present invention.
Based on the foregoing embodiments, in this embodiment, in order to further improve the rationality of the optimal scheduling scheme of the charging pile, the step S402 may include:
step S4021: and constructing a priority model based on the priority and the weight, and respectively scoring each scheduling scheme in the pareto front solution set through the priority model to obtain scheme scores.
Step S4022: and sequencing each scheduling scheme in the pareto front solution set based on the scheme scores, and determining an optimal scheduling scheme of the charging pile from the pareto front solution set according to the sequencing result.
In a specific implementation, the optimal scheduling scheme of the charging pile can be determined through the following steps. First, according to a priority model, a comprehensive score is calculated for each solution in the pareto front set, and a higher score indicates that the solution meets the priority requirement. And secondly, sorting the solution sets according to the scores, and forming a candidate set by the top N solutions with the highest scores. Third, each solution in the execution candidate set is simulated using a simulation platform, and its effect in actual situations is evaluated. And fourthly, comparing the performances of the candidate solution sets in the simulation, and selecting a solution which can obtain the best comprehensive effect (namely the optimal scheduling solution of the charging pile). And fifthly, deploying the selected optimal scheduling scheme of the charging pile into an actual system for dynamic scheduling.
Based on the above embodiments, in this embodiment, in order to improve the collection efficiency of the real-time environmental data, the step S10 may include:
step S101: and installing a data terminal for the vehicle and the charging pile, wherein the data terminal comprises a communication module, a positioning module and a data acquisition module.
Step S102: and establishing the Internet of vehicles containing the data terminal based on a special short-range communication protocol, and acquiring real-time environment data through the Internet of vehicles.
In a specific implementation, the real-time environmental data may be collected by the following steps. The method comprises the steps of firstly, installing a vehicle-mounted data terminal on an electric automobile, wherein the terminal comprises a communication module, a positioning module and a data acquisition module. And secondly, installing a charging data terminal on the charging pile, wherein the charging data terminal also comprises a communication module, a positioning module and a collecting module. And thirdly, the vehicle-mounted terminal obtains the real-time position of the vehicle through a GPS or Beidou satellite positioning system. And fourthly, the vehicle-mounted terminal acquires real-time state data of the vehicle, such as battery capacity, expected arrival time, charging requirements and the like, through the CAN bus. And fifthly, the charging pile terminal acquires data such as real-time occupation state, configuration parameters and the like through a local sensor. And sixthly, the vehicle-mounted terminal and the charging pile terminal realize data interaction between the vehicle piles through DSRC special short-distance communication or an LTE cellular network. And seventhly, the vehicle-mounted terminal and the charging pile terminal are connected through the IP of the Internet, and data are sent to a dispatch service platform and a database at the rear end. Eighth, the back-end platform collects, stores and analyzes real-time data from the vehicle and the pile head.
Based on the foregoing embodiments, in this embodiment, in order to avoid that the charging pile with a fault affects the normal charging requirement of the user, the charging pile dynamic scheduling decision method may further include:
step S50: and acquiring monitoring data of the charging pile in real time through a sensor, and judging whether the charging pile has a fault or not through the monitoring data.
Step S60: if so, carrying out resource scheduling on the charging pile based on the charging pile fault and the occupied condition of the charging pile in the current area.
In a specific implementation, the charging pile can be subjected to resource scheduling through the following steps. Firstly, installing a sensor on the charging pile, and monitoring parameters such as current, voltage, temperature and the like in real time. And secondly, extracting the characteristics of the monitoring data through edge calculation, and judging whether the charging pile is abnormal or not. And thirdly, when the abnormality is detected, performing software-level fault diagnosis, and if the abnormality cannot be solved, dispatching maintenance personnel to perform hardware inspection and maintenance. And fourthly, feeding all fault information back to the system, and establishing a knowledge base for future analysis. And fifthly, calculating the resource supply indexes of different areas according to the fault and occupation condition data of the charging pile. Sixth, when the resource supply of a certain area is insufficient, the system starts the resource scheduling program. And seventhly, calculating the condition of the adjacent area resources by the algorithm, and optimally adjusting a resource allocation scheme. And eighth step, a scheduling instruction is issued by the new scheme, so that the cooperation and resource scheduling among the charging piles are realized.
In addition, the embodiment can also optimize the operation of the charging pile through the following steps. In the first step, various monitoring data and user feedback scores during the charging process are collected. And secondly, analyzing pain points and improvement spaces in the charging process according to data statistics. And thirdly, determining key factors affecting core indexes, such as long waiting time, too slow charging and the like. And step four, gradually optimizing by adjusting parameters of a dispatching module, power of the charging pile and the like. And fifthly, comparing the change of the indexes before and after optimization to determine the optimal parameter combination. And sixthly, circulating the optimization process until the charging service reaches the assessment standard.
In this embodiment, a priority model is built based on the priority and the weight, and each scheduling scheme in the pareto front solution set is scored through the priority model, so as to obtain a scheme score; sorting each scheduling scheme in the pareto front solution set based on the scheme scores, and determining an optimal scheduling scheme of the charging pile from the pareto front solution set according to the sorting result; installing a data terminal for the vehicle and the charging pile, wherein the data terminal comprises a communication module, a positioning module and a data acquisition module; establishing a vehicle networking comprising the data terminal based on a special short-range communication protocol, and acquiring real-time environment data through the vehicle networking; acquiring monitoring data of the charging pile in real time through a sensor, and judging whether the charging pile has a fault or not through the monitoring data; if so, carrying out resource scheduling on the charging pile based on the charging pile fault and the occupied condition of the charging pile in the current area. Compared with the prior art that the charging pile is scheduled through a static scheduling method, the method can make scheduling decisions based on accurate data support, multiple targets are comprehensively considered through an intelligent algorithm and a decision model, and fault conditions of the charging pile can be timely found and processed through a fault early warning and remote monitoring system, so that the downtime is reduced, and the reliability and stability of the charging pile are improved.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a charging pile dynamic scheduling decision program, and the charging pile dynamic scheduling decision program realizes the steps of the charging pile dynamic scheduling decision method when being executed by a processor.
Referring to fig. 5, fig. 5 is a block diagram illustrating a first embodiment of a dynamic scheduling decision device for a charging pile according to the present invention.
As shown in fig. 5, the charging pile dynamic scheduling decision device provided by the embodiment of the invention includes:
the data acquisition module 501 is configured to acquire real-time environmental data through the internet of vehicles, where the real-time environmental data includes vehicle data, charging pile data, and road data;
the data prediction module 502 is configured to perform data processing and data prediction on the real-time environmental data to obtain an optimization target;
a data optimization module 503, configured to perform multi-objective optimization on the optimization objective, generate a pareto front solution set, where the pareto front solution set includes a plurality of scheduling schemes;
and the data decision module 504 is configured to determine an optimal scheduling scheme of the charging pile based on the pareto front solution set.
The method comprises the steps that real-time environment data are collected through the Internet of vehicles, wherein the real-time environment data comprise vehicle data, charging pile data and road data; performing data processing and data prediction on the real-time environment data to obtain an optimization target; performing multi-objective optimization on the optimization objective to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes; and determining an optimal scheduling scheme of the charging pile based on the pareto front solution set. Compared with the prior art, the method for scheduling the charging pile through the static scheduling method, the method for dynamically scheduling the charging pile according to the real-time charging condition can avoid the technical defects of unreasonable resource allocation and low user charging satisfaction caused by the existing static scheduling method by performing data processing and data prediction on real-time environment data acquired by the Internet of vehicles to obtain an optimization target, generating a pareto front solution set containing a plurality of scheduling schemes based on the optimization target, and finally determining the optimal scheduling scheme of the charging pile based on the pareto front solution set.
Based on the first embodiment of the dynamic scheduling decision device for the charging pile, a second embodiment of the dynamic scheduling decision device for the charging pile is provided.
In this embodiment, the data acquisition module 501 is further configured to install a data terminal for the vehicle and the charging pile, where the data terminal includes a communication module, a positioning module, and a data acquisition module; and establishing the Internet of vehicles containing the data terminal based on a special short-range communication protocol, and acquiring real-time environment data through the Internet of vehicles.
Further, the data prediction module 502 is further configured to perform data processing on the real-time environmental data to obtain processed data, where the data processing includes data cleaning, data denoising and format conversion; performing data fusion on the processed data to obtain an analysis data set, and extracting predicted target features from the analysis data set; and constructing a prediction model based on the prediction target characteristics, and carrying out data prediction on the real-time environment data through the prediction model to obtain an optimization target.
Further, the data optimization module 503 is further configured to perform multi-objective optimization on the optimization objective to obtain an objective function; generating an initial population based on a genetic algorithm, wherein the initial population comprises a plurality of individuals, and each individual represents a scheduling scheme; generating a pareto front solution set based on the objective function and fitness values of all individuals in the initial population.
Further, the data decision module 504 is further configured to determine a priority and a weight of the optimization objective according to a charging requirement of the user; and determining an optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight.
Further, the data decision module 504 is further configured to construct a priority model based on the priority and the weight, and score each scheduling scheme in the pareto front solution set through the priority model, so as to obtain a scheme score; and sequencing each scheduling scheme in the pareto front solution set based on the scheme scores, and determining an optimal scheduling scheme of the charging pile from the pareto front solution set according to the sequencing result.
Further, the data decision module 504 is further configured to obtain monitoring data of the charging pile in real time through a sensor, and determine whether the charging pile has a fault according to the monitoring data; if so, carrying out resource scheduling on the charging pile based on the charging pile fault and the occupied condition of the charging pile in the current area.
Other embodiments or specific implementation manners of the dynamic scheduling decision device for charging piles can refer to the above method embodiments, and are not described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of embodiments, it will be clear to a person skilled in the art that the above embodiment method may be implemented by means of software plus a necessary general hardware platform, but may of course also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (6)
1. The dynamic scheduling decision method for the charging pile is characterized by comprising the following steps of:
collecting real-time environment data through the Internet of vehicles, wherein the real-time environment data comprises vehicle data, charging pile data and road data;
performing data processing and data prediction on the real-time environment data to obtain an optimization target;
performing multi-objective optimization on the optimization objective to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes;
determining an optimal scheduling scheme of the charging pile based on the pareto front solution set;
the step of generating the pareto front solution set includes:
performing multi-objective optimization on the optimization targets to obtain objective functions;
generating an initial population based on a genetic algorithm, wherein the initial population comprises a plurality of individuals, and each individual represents a scheduling scheme;
generating a pareto front solution set based on the objective function and the fitness values of all individuals in the initial population;
the step of determining the optimal scheduling scheme of the charging pile based on the pareto front solution set comprises the following steps:
determining the priority and the weight of the optimization target according to the charging requirement of a user, wherein the optimization target comprises resource utilization rate, charging waiting time and charging load balance;
determining an optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight;
the step of determining the priority and the weight of the optimization target according to the charging requirement of the user comprises the following steps:
if the charging demand of the user is the focused electric energy utilization rate, determining the priority of the optimization target as resource utilization rate > charging waiting time > charging load balance, wherein the weight of the resource utilization rate is 0.6, the weight of the charging waiting time is 0.3, and the weight of the charging load balance is 0.1;
the step of performing data processing and data prediction on the real-time environment data to obtain an optimization target comprises the following steps:
performing data processing on the real-time environment data to obtain processed data, wherein the data processing comprises data cleaning, data denoising and formatting conversion, the data cleaning represents removing error values, missing values and repeated values in the real-time environment data, the data denoising represents removing noise in the real-time environment data through a median filtering method, a moving average method or a wavelet transformation method, and the formatting conversion represents performing ASCII (integrated circuit code for information interchange) coding on the real-time environment data;
performing data fusion on the processed data to obtain an analysis data set, and extracting predicted target features from the analysis data set;
constructing a prediction model based on the prediction target characteristics, and carrying out data prediction on the real-time environment data through the prediction model to obtain an optimization target;
the step of collecting real-time environment data through the Internet of vehicles comprises the following steps:
installing a data terminal for the vehicle and the charging pile, wherein the data terminal comprises a communication module, a positioning module and a data acquisition module;
and establishing the Internet of vehicles containing the data terminal based on a special short-range communication protocol, and acquiring real-time environment data through the Internet of vehicles.
2. The method of claim 1, wherein the step of determining an optimal scheduling scheme for the charging pile from the pareto front solution set based on the priority and the weight comprises:
constructing a priority model based on the priority and the weight, and respectively scoring each scheduling scheme in the pareto front solution set through the priority model to obtain scheme scores;
and sequencing each scheduling scheme in the pareto front solution set based on the scheme scores, and determining an optimal scheduling scheme of the charging pile from the pareto front solution set according to the sequencing result.
3. The charging pile dynamic scheduling decision method of claim 1, further comprising:
acquiring monitoring data of the charging pile in real time through a sensor, and judging whether the charging pile has a fault or not through the monitoring data;
if so, carrying out resource scheduling on the charging pile based on the charging pile fault and the occupied condition of the charging pile in the current area.
4. The utility model provides a fill electric pile dynamic scheduling decision device which characterized in that, fill electric pile dynamic scheduling decision device includes:
the data acquisition module is used for acquiring real-time environment data through the Internet of vehicles, wherein the real-time environment data comprises vehicle data, charging pile data and road data;
the data prediction module is used for carrying out data processing and data prediction on the real-time environment data to obtain an optimization target;
the data optimization module is used for performing multi-objective optimization on the optimization targets to generate a pareto front edge solution set, wherein the pareto front edge solution set comprises a plurality of scheduling schemes;
the data decision module is used for determining an optimal scheduling scheme of the charging pile based on the pareto front solution set;
the step of generating the pareto front solution set includes:
performing multi-objective optimization on the optimization targets to obtain objective functions;
generating an initial population based on a genetic algorithm, wherein the initial population comprises a plurality of individuals, and each individual represents a scheduling scheme;
generating a pareto front solution set based on the objective function and the fitness values of all individuals in the initial population;
the step of determining the optimal scheduling scheme of the charging pile based on the pareto front solution set comprises the following steps:
determining the priority and the weight of the optimization target according to the charging requirement of a user, wherein the optimization target comprises resource utilization rate, charging waiting time and charging load balance;
determining an optimal scheduling scheme of the charging pile from the pareto front solution set based on the priority and the weight;
the step of determining the priority and the weight of the optimization target according to the charging requirement of the user comprises the following steps:
if the charging demand of the user is the focused electric energy utilization rate, determining the priority of the optimization target as resource utilization rate > charging waiting time > charging load balance, wherein the weight of the resource utilization rate is 0.6, the weight of the charging waiting time is 0.3, and the weight of the charging load balance is 0.1;
the step of performing data processing and data prediction on the real-time environment data to obtain an optimization target comprises the following steps:
performing data processing on the real-time environment data to obtain processed data, wherein the data processing comprises data cleaning, data denoising and formatting conversion, the data cleaning represents removing error values, missing values and repeated values in the real-time environment data, the data denoising represents removing noise in the real-time environment data through a median filtering method, a moving average method or a wavelet transformation method, and the formatting conversion represents performing ASCII (integrated circuit code for information interchange) coding on the real-time environment data;
performing data fusion on the processed data to obtain an analysis data set, and extracting predicted target features from the analysis data set;
constructing a prediction model based on the prediction target characteristics, and carrying out data prediction on the real-time environment data through the prediction model to obtain an optimization target;
the step of collecting real-time environment data through the Internet of vehicles comprises the following steps:
installing a data terminal for the vehicle and the charging pile, wherein the data terminal comprises a communication module, a positioning module and a data acquisition module;
and establishing the Internet of vehicles containing the data terminal based on a special short-range communication protocol, and acquiring real-time environment data through the Internet of vehicles.
5. A charging pile dynamic scheduling decision device, characterized in that the device comprises: a memory, a processor and a charging pile dynamic scheduling decision program stored on the memory and executable on the processor, the charging pile dynamic scheduling decision program being configured to implement the steps of the charging pile dynamic scheduling decision method of any one of claims 1 to 3.
6. A storage medium, wherein a dynamic scheduling decision program for a charging pile is stored on the storage medium, and the dynamic scheduling decision program for a charging pile, when executed by a processor, implements the steps of the dynamic scheduling decision method for a charging pile according to any one of claims 1 to 3.
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