CN115438909B - Big data-based electric vehicle charging pile power resource distribution method and system - Google Patents
Big data-based electric vehicle charging pile power resource distribution method and system Download PDFInfo
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Abstract
The invention discloses a big data-based electric power resource distribution method and a big data-based electric power resource distribution system for an electric vehicle charging pile, wherein the method comprises the following steps: acquiring charging distribution service of each charging pile in a charging station, determining charging parameters of a vehicle to be charged corresponding to the charging pile according to the charging distribution service of each charging pile, analyzing charging index weights of the charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on big data analysis rules, and distributing electric power resources in corresponding proportion for each charging pile according to the charging index weights of each charging pile. The charging index weight of each charging pile is analyzed according to the charging distribution service of each charging pile, so that the power resources to be distributed are determined, the electric energy can be reasonably distributed by each charging pile according to the charging service condition of each charging pile, the electric energy supply problem that each charging pile charges different types of vehicles can be solved, and the practicability and the overall charging efficiency are improved.
Description
Technical Field
The invention relates to the technical field of resource allocation, in particular to a method and a system for allocating electric power resources of an electric vehicle charging pile based on big data.
Background
Along with the enhancement of environmental awareness and the importance of the country to environmental protection, new energy electric vehicles are generated, however, because the battery capacity of the new energy electric vehicles is limited and needs to be charged at any time, the electric vehicles can be guaranteed to be charged at any time by the arrangement of the electric vehicle charging piles, and then public charging piles are arranged in areas with higher use frequency of each electric vehicle, and the electric power resource distribution system of the existing public charging piles distributes electric energy to each charging pile for the system average, so that each charging pile keeps the same electric energy supply, but the method has the following problems: because the types of the vehicles butted by the charging piles are different, the electric energy required by the charging piles is also different, so that the situation that the electric energy is insufficient in the charging process of some charging piles can cause that the charging work of the vehicles can not be completed on time, and the charging efficiency is greatly reduced.
Disclosure of Invention
Aiming at the problems shown in the prior art, the invention provides a large data-based electric power resource distribution method and a large data-based electric power resource distribution system for electric automobile charging piles, which are used for solving the problems that in the background art, because the types of vehicles which are in butt joint with each charging pile are different, the electric energy required by the charging piles is different, so that the charging work of the vehicles cannot be completed on time due to the fact that the electric energy of some charging piles is insufficient in the charging process, and the charging efficiency is greatly reduced.
The electric power resource distribution method of the charging pile of the electric vehicle based on big data comprises the following steps:
acquiring charging distribution service of each charging pile in a charging station;
determining charging parameters of the vehicle to be charged corresponding to each charging pile according to the charging distribution service of the charging pile;
analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to the charging pile based on the big data analysis rule;
and distributing electric power resources in corresponding proportion to each charging pile according to the charging index weight of the charging pile.
Preferably, the obtaining the charging distribution service of each charging pile in the charging station includes:
acquiring a reserved charging request instruction sent by each vehicle to be charged;
determining the vehicle type of each vehicle to be charged according to a reserved charging request instruction sent by the vehicle to be charged;
detecting the current service state of each charging pile;
and distributing reasonable charging piles for each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging distribution task of each charging pile according to the distribution result.
Preferably, the determining, according to the charging distribution service of each charging pile, the charging parameters of the vehicle to be charged corresponding to the charging pile includes:
determining the charging demand of the vehicle to be charged corresponding to each charging pile based on the charging distribution service of the charging pile;
acquiring charging configuration data of each vehicle to be charged corresponding to each charging pile;
determining target charging duration, charging power, charging voltage and charging current of each vehicle to be charged according to charging configuration data of each vehicle to be charged corresponding to each charging pile and charging demand of the vehicle to be charged;
and integrating the target charging duration, the charging power, the charging voltage and the charging current of each vehicle to be charged to obtain the charging parameters of the vehicle to be charged.
Preferably, the analyzing, based on the big data analysis rule, the charging index weight of each charging pile according to the charging parameter of the vehicle to be charged corresponding to the charging pile includes:
acquiring a plurality of analysis indexes of each vehicle to be charged according to the charging parameters of each vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing an incidence matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charge quantity;
calculating random response of each analysis index according to the preset electric quantity data and the incidence matrix, and determining influence weights of the analysis index and the vehicle charge quantity according to the random response of each analysis index;
determining comprehensive distribution charging indexes of the charging piles according to the analysis index parameter values of each vehicle to be charged corresponding to each charging pile;
and determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services.
Preferably, the allocating power resources of corresponding proportion to each charging pile according to the charging index weight of the charging pile includes:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring response parameters corresponding to the charging dynamic triggering operation of each charging pile, and determining the power loss of the charging pile based on the response parameters;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining the power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding power resources for each charging pile according to the power distribution proportion.
Preferably, the detecting the current service state of each charging pile includes:
detecting the current output power of each charging pile;
obtaining a corresponding state identifier according to the current output power, confirming membership degree of the state identifier, and judging the current state of each charging pile according to the membership degree, wherein the current state comprises the following steps: a working state and an idle state;
when the current state of the charging pile is an idle state, confirming that the current service state of the charging pile is no service, and when the current state of the charging pile is a working state, confirming that the current service state of the charging pile is in service;
and detecting the charging progress of each first target charging pile which is in service in the current service state, and determining the service progress of each first target charging pile according to the charging progress.
Preferably, the allocating reasonable charging piles according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging allocation task of each charging pile according to the allocation result, includes:
according to the vehicle type of each vehicle to be charged, carrying out charging characteristic analysis on the vehicle to be charged, and obtaining an analysis result;
determining the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged according to the analysis result of the vehicle to be charged;
determining the charging service efficiency requirement of each vehicle to be charged according to the maximum system electric quantity loss and the charging power requirement of the vehicle to be charged;
and distributing reasonable charging piles for each vehicle to be charged according to the charging service efficiency requirement of each vehicle to be charged and the current service state of each charging pile.
Preferably, the method further comprises:
acquiring a charging decision variable of each distribution vehicle of each charging pile;
constructing a charging business constraint condition of the charging pile according to the charging decision variable of each distribution vehicle;
evaluating the utilization rate of each charging pile based on the charging service constraint condition of the charging pile;
confirming whether the utilization rate of each charging pile is greater than or equal to a preset threshold value, if so, not carrying out subsequent operation, and if not, acquiring a condition parameter corresponding to a charging service constraint condition of a second target charging pile with the utilization rate smaller than the preset threshold value;
replacing the first condition parameters of each second target charging pile with the second condition parameters of the remaining second target charging piles until the estimated utilization rate of the modified charging service constraint condition of each charging pile is greater than or equal to the preset threshold value;
and carrying out cooperative scheduling on all vehicles to be charged according to the modified charging service preset conditions of each charging pile.
Preferably, before acquiring the charging distribution service of each charging pile in the charging station, the method further comprises:
collecting physiological data of a driver corresponding to each vehicle to be charged when the vehicle is driven;
determining a driving characteristic of each driver based on the driving physiological data of the driver;
estimating single longest electric quantity supporting driving mileage data of each vehicle to be charged based on driving characteristics of each driver;
collecting a plurality of characteristic parameters of a built-in battery unit of each vehicle to be charged;
inputting each characteristic parameter of each vehicle to be charged into a preset initial characteristic model to obtain an initial characteristic value corresponding to each characteristic parameter;
constructing an attenuation function of each characteristic parameter according to the attenuation characteristic of the characteristic value of the characteristic parameter;
determining a decay time sequence of each characteristic parameter through a decay function of the characteristic parameter;
correlating the sequence factors in the decay time sequence of each characteristic parameter with the stage mileage in the single longest electric quantity supporting mileage data of each vehicle to be charged to obtain a correlation result;
determining a battery state index value of each vehicle to be charged in each stage mileage according to the association result;
carrying out power shortage judgment on the built-in battery pack unit of each vehicle to be charged based on the battery state index value of each vehicle to be charged in each stage mileage and the mileage number of the last period of the vehicle to be charged, and obtaining a judgment result;
setting the charging service type of each vehicle to be charged according to the judging result of the vehicle to be charged;
and distributing an adaptive charging pile for each vehicle to be charged according to the charging service type of the vehicle to be charged.
Big data-based electric vehicle charging pile power resource distribution system, which comprises:
the acquisition module is used for acquiring the charging distribution service of each charging pile in the charging station;
the determining module is used for determining the charging parameters of the vehicle to be charged corresponding to each charging pile according to the charging distribution service of the charging pile;
the analysis module is used for analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
and the distribution module is used for distributing the electric power resources with corresponding proportion for each charging pile according to the charging index weight of each charging pile.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a working flow chart of an electric vehicle charging pile power resource allocation method based on big data;
fig. 2 is another workflow diagram of a method for distributing electric power resources of an electric vehicle charging pile based on big data provided by the invention;
fig. 3 is a further workflow diagram of a method for distributing electric power resources of an electric vehicle charging pile based on big data according to the present invention;
fig. 4 is a schematic structural diagram of an electric power resource distribution system of an electric vehicle charging pile based on big data.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Along with the enhancement of environmental awareness and the importance of the country to environmental protection, new energy electric vehicles are generated, however, because the battery capacity of the new energy electric vehicles is limited and needs to be charged at any time, the electric vehicles can be guaranteed to be charged at any time by the arrangement of the electric vehicle charging piles, and then public charging piles are arranged in areas with higher use frequency of each electric vehicle, and the electric power resource distribution system of the existing public charging piles distributes electric energy to each charging pile for the system average, so that each charging pile keeps the same electric energy supply, but the method has the following problems: because the types of the vehicles butted by the charging piles are different, the electric energy required by the charging piles is also different, so that the situation that the electric energy is insufficient in the charging process of some charging piles can cause that the charging work of the vehicles can not be completed on time, and the charging efficiency is greatly reduced. In order to solve the problems, the embodiment discloses an electric vehicle charging pile power resource distribution method based on big data.
The electric power resource distribution method of the charging pile of the electric vehicle based on big data, as shown in fig. 1, comprises the following steps:
step S101, acquiring charging distribution service of each charging pile in a charging station;
step S102, determining charging parameters of a vehicle to be charged corresponding to each charging pile according to the charging distribution service of the charging pile;
step S103, analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to the charging pile based on the big data analysis rule;
and step S104, distributing power resources with corresponding proportion to each charging pile according to the charging index weight of the charging pile.
The working principle of the technical scheme is as follows: acquiring charging distribution service of each charging pile in a charging station, determining charging parameters of a vehicle to be charged corresponding to the charging pile according to the charging distribution service of each charging pile, analyzing charging index weights of the charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on big data analysis rules, and distributing electric power resources in corresponding proportion for each charging pile according to the charging index weights of each charging pile.
The beneficial effects of the technical scheme are as follows: the charging index weight of each charging pile is analyzed according to the charging distribution service of each charging pile, so that the electric power resource to be distributed is determined, each charging pile can reasonably distribute electric energy according to the charging service condition of the charging pile, thus the electric energy supply problem that each charging pile charges different types of vehicles can be solved, the practicability and the overall charging efficiency are improved, the problem that the electric energy needed by each charging pile is different because the types of the vehicles which are in butt joint are different in the prior art, the electric energy shortage condition of certain charging piles in the charging process can cause that the vehicle charging work cannot be completed on time is solved, and the charging efficiency is greatly reduced.
In one embodiment, as shown in fig. 2, the obtaining the charging distribution service of each charging post in the charging station includes:
step S201, acquiring a reserved charging request instruction sent by each vehicle to be charged;
step S202, determining the vehicle type of each vehicle to be charged according to a reserved charging request instruction sent by the vehicle to be charged;
step S203, detecting the current service state of each charging pile;
and step S204, distributing reasonable charging piles for each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging distribution task of each charging pile according to the distribution result.
The beneficial effects of the technical scheme are as follows: the idle charging piles can be reasonably distributed according to the vehicle type of each vehicle to be charged, so that the charging efficiency is further improved, and further, the charging piles can be enabled to stably use the same charging mode to charge the vehicle without switching the charging mode back and forth according to the vehicle type by distributing the charging piles to the vehicles of the same type, and the practicability and the charging efficiency are further improved.
In one embodiment, as shown in fig. 3, the determining, according to the charging distribution service of each charging pile, the charging parameters of the vehicle to be charged corresponding to the charging pile includes:
step S301, determining the charging demand of the vehicle to be charged corresponding to each charging pile based on the charging distribution service of the charging pile;
step S302, charging configuration data of each vehicle to be charged corresponding to each charging pile is obtained;
step S303, determining target charging duration, charging power, charging voltage and charging current of each vehicle to be charged according to charging configuration data of each vehicle to be charged corresponding to each charging pile and the charging demand of the vehicle to be charged;
step S304, integrating the target charging duration, the charging power, the charging voltage and the charging current of each vehicle to be charged to obtain the charging parameters of the vehicle to be charged.
The beneficial effects of the technical scheme are as follows: the comprehensive charging parameters of each vehicle to be charged can be obtained, so that conditions are laid for the subsequent distribution of the charging piles, and the practicability is further improved.
In one embodiment, the analyzing, based on the big data analysis rule, the charging index weight of each charging pile according to the charging parameter of the vehicle to be charged corresponding to the charging pile includes:
acquiring a plurality of analysis indexes of each vehicle to be charged according to the charging parameters of each vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing an incidence matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charge quantity;
calculating random response of each analysis index according to the preset electric quantity data and the incidence matrix, and determining influence weights of the analysis index and the vehicle charge quantity according to the random response of each analysis index;
determining comprehensive distribution charging indexes of the charging piles according to the analysis index parameter values of each vehicle to be charged corresponding to each charging pile;
and determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services.
The beneficial effects of the technical scheme are as follows: the comprehensive charging index of each charging pile can be comprehensively determined, and then the charging index weight of each charging pile is determined according to the index corresponding proportion, so that the rationality and objectivity of the charging index weight distribution of each charging pile are improved.
In one embodiment, the allocating the power resource of the corresponding proportion according to the charging index weight of each charging pile includes:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring response parameters corresponding to the charging dynamic triggering operation of each charging pile, and determining the power loss of the charging pile based on the response parameters;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining the power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding power resources for each charging pile according to the power distribution proportion.
The beneficial effects of the technical scheme are as follows: the electric quantity increment value of each charging pile is determined by calculating the electric power loss of each charging pile, so that the optimal distribution electric power can be reasonably determined according to the charging loss of each charging pile, the influence caused by the working parameters of the charging pile is avoided, and the charging efficiency and the charging stability are improved.
In one embodiment, the detecting the current service status of each charging stake includes:
detecting the current output power of each charging pile;
obtaining a corresponding state identifier according to the current output power, confirming membership degree of the state identifier, and judging the current state of each charging pile according to the membership degree, wherein the current state comprises the following steps: a working state and an idle state;
when the current state of the charging pile is an idle state, confirming that the current service state of the charging pile is no service, and when the current state of the charging pile is a working state, confirming that the current service state of the charging pile is in service;
and detecting the charging progress of each first target charging pile which is in service in the current service state, and determining the service progress of each first target charging pile according to the charging progress.
The beneficial effects of the technical scheme are as follows: the service state and the service progress of each charging pile are respectively determined, so that a worker can quickly and accurately know the running state of each charging pile, a reference basis is provided for the subsequent arrangement of charging vehicles, further, whether each charging pile is running or not can be intuitively and quickly determined by determining the service state of each charging pile according to the current output power, and the judging efficiency and the judging accuracy are improved.
In one embodiment, the allocating reasonable charging piles according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging allocation task of each charging pile according to the allocation result, includes:
according to the vehicle type of each vehicle to be charged, carrying out charging characteristic analysis on the vehicle to be charged, and obtaining an analysis result;
determining the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged according to the analysis result of the vehicle to be charged;
determining the charging service efficiency requirement of each vehicle to be charged according to the maximum system electric quantity loss and the charging power requirement of the vehicle to be charged;
and distributing reasonable charging piles for each vehicle to be charged according to the charging service efficiency requirement of each vehicle to be charged and the current service state of each charging pile.
The beneficial effects of the technical scheme are as follows: the charging mode corresponding to each vehicle to be charged can be accurately determined by acquiring the charging service efficiency requirement of each vehicle to be charged, so that the charging piles in the idle high charging mode are rapidly distributed to the vehicles, the distribution rationality of the charging piles is improved, and meanwhile, the charging efficiency is also improved.
In one embodiment, the method further comprises:
acquiring a charging decision variable of each distribution vehicle of each charging pile;
constructing a charging business constraint condition of the charging pile according to the charging decision variable of each distribution vehicle;
evaluating the utilization rate of each charging pile based on the charging service constraint condition of the charging pile;
confirming whether the utilization rate of each charging pile is greater than or equal to a preset threshold value, if so, not carrying out subsequent operation, and if not, acquiring a condition parameter corresponding to a charging service constraint condition of a second target charging pile with the utilization rate smaller than the preset threshold value;
replacing the first condition parameters of each second target charging pile with the second condition parameters of the remaining second target charging piles until the estimated utilization rate of the modified charging service constraint condition of each charging pile is greater than or equal to the preset threshold value;
and carrying out cooperative scheduling on all vehicles to be charged according to the modified charging service preset conditions of each charging pile.
The beneficial effects of the technical scheme are as follows: the charging service preset condition of each charging pile is balanced, so that the distributed power resource of each charging pile can be digested and utilized to the maximum extent, the electric energy utilization rate is improved, the charging stability of each charging pile is also ensured, and the working efficiency is further improved.
In one embodiment, before acquiring the charge distribution service of each charging peg in the charging station, the method further comprises:
collecting physiological data of a driver corresponding to each vehicle to be charged when the vehicle is driven;
determining a driving characteristic of each driver based on the driving physiological data of the driver;
estimating single longest electric quantity supporting driving mileage data of each vehicle to be charged based on driving characteristics of each driver;
collecting a plurality of characteristic parameters of a built-in battery unit of each vehicle to be charged;
inputting each characteristic parameter of each vehicle to be charged into a preset initial characteristic model to obtain an initial characteristic value corresponding to each characteristic parameter;
constructing an attenuation function of each characteristic parameter according to the attenuation characteristic of the characteristic value of the characteristic parameter;
determining a decay time sequence of each characteristic parameter through a decay function of the characteristic parameter;
correlating the sequence factors in the decay time sequence of each characteristic parameter with the stage mileage in the single longest electric quantity supporting mileage data of each vehicle to be charged to obtain a correlation result;
determining a battery state index value of each vehicle to be charged in each stage mileage according to the association result;
carrying out power shortage judgment on the built-in battery pack unit of each vehicle to be charged based on the battery state index value of each vehicle to be charged in each stage mileage and the mileage number of the last period of the vehicle to be charged, and obtaining a judgment result;
setting the charging service type of each vehicle to be charged according to the judging result of the vehicle to be charged;
and distributing an adaptive charging pile for each vehicle to be charged according to the charging service type of the vehicle to be charged.
The beneficial effects of the technical scheme are as follows: the power shortage state of each vehicle to be charged is evaluated in real time according to the driving parameters of the driver of each vehicle to be charged and the working characteristic parameters of the built-in battery pack unit, so that the required charging type of each vehicle to be charged can be intuitively determined, the charging piles can be rapidly distributed to the vehicles to be charged, the stable performance of the charging work of each subsequent vehicle to be charged is ensured, and the stability and the practicability are improved.
The embodiment also discloses an electric automobile fills electric pile power resource distribution system based on big data, as shown in fig. 4, and this system includes:
the acquiring module 401 is configured to acquire a charging distribution service of each charging pile in the charging station;
a determining module 402, configured to determine a charging parameter of a vehicle to be charged corresponding to each charging pile according to a charging distribution service of the charging pile;
the analysis module 403 is configured to analyze the charging index weight of each charging pile according to the charging parameter of the vehicle to be charged corresponding to the charging pile based on the big data analysis rule;
and the allocation module 404 is configured to allocate a corresponding proportion of power resources to each charging pile according to the charging index weight of the charging pile.
The working principle and the beneficial effects of the above technical solution are described in the method claims, and are not repeated here.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. The electric power resource distribution method for the charging pile of the electric vehicle based on the big data is characterized by comprising the following steps of:
acquiring charging distribution service of each charging pile in a charging station;
determining charging parameters of the vehicle to be charged corresponding to each charging pile according to the charging distribution service of the charging pile;
analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to the charging pile based on the big data analysis rule;
distributing electric power resources in corresponding proportion to each charging pile according to the charging index weight of the charging pile;
the method for analyzing the charging index weight of the charging pile based on the big data analysis rule according to the charging parameters of the vehicle to be charged corresponding to each charging pile comprises the following steps:
acquiring a plurality of analysis indexes of each vehicle to be charged according to the charging parameters of each vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing an incidence matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charge quantity;
calculating random response of each analysis index according to the preset electric quantity data and the incidence matrix, and determining influence weights of the analysis index and the vehicle charge quantity according to the random response of each analysis index;
determining comprehensive distribution charging indexes of the charging piles according to the analysis index parameter values of each vehicle to be charged corresponding to each charging pile;
determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services;
the allocating the electric power resources with corresponding proportion for each charging pile according to the charging index weight of the charging pile comprises the following steps:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring response parameters corresponding to the charging dynamic triggering operation of each charging pile, and determining the power loss of the charging pile based on the response parameters;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining the power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding power resources for each charging pile according to the power distribution proportion.
2. The method for distributing electric power resources of charging piles of electric vehicles based on big data according to claim 1, wherein the obtaining the charging distribution service of each charging pile in the charging station comprises:
acquiring a reserved charging request instruction sent by each vehicle to be charged;
determining the vehicle type of each vehicle to be charged according to a reserved charging request instruction sent by the vehicle to be charged;
detecting the current service state of each charging pile;
and distributing reasonable charging piles for each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging distribution task of each charging pile according to the distribution result.
3. The method for distributing electric power resources of charging piles of electric vehicles based on big data according to claim 1, wherein the determining the charging parameters of the vehicles to be charged corresponding to each charging pile according to the charging distribution service of the charging pile comprises:
determining the charging demand of the vehicle to be charged corresponding to each charging pile based on the charging distribution service of the charging pile;
acquiring charging configuration data of each vehicle to be charged corresponding to each charging pile;
determining target charging duration, charging power, charging voltage and charging current of each vehicle to be charged according to charging configuration data of each vehicle to be charged corresponding to each charging pile and charging demand of the vehicle to be charged;
and integrating the target charging duration, the charging power, the charging voltage and the charging current of each vehicle to be charged to obtain the charging parameters of the vehicle to be charged.
4. The method for distributing power resources of charging piles for electric vehicles based on big data according to claim 2, wherein the detecting the current service state of each charging pile comprises:
detecting the current output power of each charging pile;
obtaining a corresponding state identifier according to the current output power, confirming membership degree of the state identifier, and judging the current state of each charging pile according to the membership degree, wherein the current state comprises the following steps: a working state and an idle state;
when the current state of the charging pile is an idle state, confirming that the current service state of the charging pile is no service, and when the current state of the charging pile is a working state, confirming that the current service state of the charging pile is in service;
and detecting the charging progress of each first target charging pile which is in service in the current service state, and determining the service progress of each first target charging pile according to the charging progress.
5. The method for distributing electric power resources of charging piles of electric vehicles based on big data according to claim 2, wherein the distributing reasonable charging piles for each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging distribution task of each charging pile according to the distribution result, comprises the following steps:
according to the vehicle type of each vehicle to be charged, carrying out charging characteristic analysis on the vehicle to be charged, and obtaining an analysis result;
determining the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged according to the analysis result of the vehicle to be charged;
determining the charging service efficiency requirement of each vehicle to be charged according to the maximum system electric quantity loss and the charging power requirement of the vehicle to be charged;
and distributing reasonable charging piles for each vehicle to be charged according to the charging service efficiency requirement of each vehicle to be charged and the current service state of each charging pile.
6. The big data-based electric vehicle charging pile power resource allocation method according to claim 1, characterized in that the method further comprises:
acquiring a charging decision variable of each distribution vehicle of each charging pile;
constructing a charging business constraint condition of the charging pile according to the charging decision variable of each distribution vehicle;
evaluating the utilization rate of each charging pile based on the charging service constraint condition of the charging pile;
confirming whether the utilization rate of each charging pile is greater than or equal to a preset threshold value, if so, not carrying out subsequent operation, and if not, acquiring a condition parameter corresponding to a charging service constraint condition of a second target charging pile with the utilization rate smaller than the preset threshold value;
replacing the first condition parameters of each second target charging pile with the second condition parameters of the remaining second target charging piles until the estimated utilization rate of the modified charging service constraint condition of each charging pile is greater than or equal to the preset threshold value;
and carrying out cooperative scheduling on all vehicles to be charged according to the modified charging service preset conditions of each charging pile.
7. The big data based electric vehicle charging pile power resource allocation method according to claim 1, wherein before acquiring the charging allocation service of each charging pile in the charging station, the method further comprises:
collecting physiological data of a driver corresponding to each vehicle to be charged when the vehicle is driven;
determining a driving characteristic of each driver based on the driving physiological data of the driver;
estimating single longest electric quantity supporting driving mileage data of each vehicle to be charged based on driving characteristics of each driver;
collecting a plurality of characteristic parameters of a built-in battery unit of each vehicle to be charged;
inputting each characteristic parameter of each vehicle to be charged into a preset initial characteristic model to obtain an initial characteristic value corresponding to each characteristic parameter;
constructing an attenuation function of each characteristic parameter according to the attenuation characteristic of the characteristic value of the characteristic parameter;
determining a decay time sequence of each characteristic parameter through a decay function of the characteristic parameter;
correlating the sequence factors in the decay time sequence of each characteristic parameter with the stage mileage in the single longest electric quantity supporting mileage data of each vehicle to be charged to obtain a correlation result;
determining a battery state index value of each vehicle to be charged in each stage mileage according to the association result;
carrying out power shortage judgment on the built-in battery pack unit of each vehicle to be charged based on the battery state index value of each vehicle to be charged in each stage mileage and the mileage number of the last period of the vehicle to be charged, and obtaining a judgment result;
setting the charging service type of each vehicle to be charged according to the judging result of the vehicle to be charged;
and distributing an adaptive charging pile for each vehicle to be charged according to the charging service type of the vehicle to be charged.
8. Big data-based electric vehicle charging pile power resource distribution system is characterized in that the system comprises:
the acquisition module is used for acquiring the charging distribution service of each charging pile in the charging station;
the determining module is used for determining the charging parameters of the vehicle to be charged corresponding to each charging pile according to the charging distribution service of the charging pile;
the analysis module is used for analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
the distribution module is used for distributing electric power resources with corresponding proportion for each charging pile according to the charging index weight of the charging pile;
in the analysis module, a method for analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to the charging pile based on the big data analysis rule comprises the following steps:
acquiring a plurality of analysis indexes of each vehicle to be charged according to the charging parameters of each vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing an incidence matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charge quantity;
calculating random response of each analysis index according to the preset electric quantity data and the incidence matrix, and determining influence weights of the analysis index and the vehicle charge quantity according to the random response of each analysis index;
determining comprehensive distribution charging indexes of the charging piles according to the analysis index parameter values of each vehicle to be charged corresponding to each charging pile;
determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services;
in the allocation module, a method for allocating power resources with corresponding proportion to each charging pile according to the charging index weight of the charging pile comprises the following steps:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring response parameters corresponding to the charging dynamic triggering operation of each charging pile, and determining the power loss of the charging pile based on the response parameters;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining the power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding power resources for each charging pile according to the power distribution proportion.
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