CN118446657B - Park user parking and charging behavior analysis method and system based on big data - Google Patents
Park user parking and charging behavior analysis method and system based on big data Download PDFInfo
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Abstract
The invention discloses a park user parking charging behavior analysis method and system based on big data, wherein the method comprises the steps of obtaining vehicle charging related information and vehicle driving related information, determining a battery remaining life estimated value of each transport vehicle, and generating a transport vehicle state list; based on the transport vehicle demand and the transport vehicle status list, a transport vehicle call is generated to drive the corresponding transport vehicle to perform a transport task. According to the invention, the battery residual life estimated value of each transport vehicle is determined, and the most appropriate residual life quantized value is selected from all transport vehicles of a transport enterprise according to the preset maintenance plan, so that the preset maintenance plan can be reasonably satisfied, and the target transport vehicle with sufficient transport resources configured by the transport enterprise at any time can be ensured to execute the received transport vehicle use request, thereby solving the technical problems of inaccurate battery residual life estimation, unreasonable transport task allocation and difficult transport vehicle maintenance planning of the transport vehicles in the conventional park.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a park user parking charging behavior analysis method and system based on big data.
Background
A campus is a particular area that is delimited by administrative means by the government of a country or region based on the inherent requirements of its own economic development. The method aims at gathering various production elements, scientifically integrating the elements in a certain space range, improving the intensive strength of industrialization, highlighting the industrial characteristics and optimizing the functional layout, so that the method becomes a modern industrial division cooperation production area suitable for market competition and industrial upgrading.
At present, considering that a production enterprise configures a transportation team by itself to have larger cost and operation pressure, a transportation mode generally adopted in an industrial park is that the production enterprise in the park distributes transportation tasks to the transportation enterprises in the park, and one transportation enterprise generally faces transportation service requirements of a plurality of production enterprises in the park. With the development of the related technology of the trolley, the electric transport truck is gradually popularized in transport enterprises by virtue of the advantages of energy conservation, environmental protection, good economy, low noise, high efficiency and convenience. However, considering that the transportation enterprises in the campus need to allocate sufficient transportation resources all the time, the electric transportation trucks need to put more strict requirements on the battery life management of the transportation trucks due to the specificity of battery energy supply, so as to avoid the situation that the transportation resources are insufficient due to the conditions of battery damage, replacement, maintenance and the like when the transportation request is received, and economic losses are brought to the production enterprises and the transportation enterprises.
Generally, the battery life management of electric transportation trucks in transportation enterprises has the following characteristics: the electric transportation truck needs to reserve charging time, the charging mode generally comprises high-power charging and low-power charging, although the high-power charging has the advantage of high charging speed, the loss of the service life of a battery is more remarkable than that of the low-power charging, but because of the property of one transportation enterprise in a park for a plurality of production enterprises, the charging cannot always be carried out in the same charging mode under the condition that the transportation enterprises are limited in configuration of transportation vehicles, even the charging is carried out in a more chaotic and profit-maximizing manner (for example, when a large number of vehicle transportation requests or emergency vehicle transportation requests are suddenly received, the idle transportation vehicles are usually required to be scheduled to execute temporary high-power charging instead of charging according to an original plan, so that the requirement of the transportation time limit is met), and the charging behavior management of the transportation vehicles of the enterprises is chaotic, and the residual service life of the battery of each transportation vehicle cannot be accurately estimated; in addition, the severe service environment (such as high-low temperature environment, high humidity environment, driving vibration environment, etc.) of the battery is one of the factors that can affect the service life of the battery, and the characteristics of the electric transportation truck just conform to the severe service environment of the battery, so that the estimation of the residual service life of the battery of the transportation vehicle is inaccurate, and the maintenance plan of the transportation vehicle cannot be managed and arranged effectively.
Therefore, how to improve the accuracy of the estimation of the remaining battery life of the transport vehicle and how to perform the transport task of the transport vehicle according to the estimated remaining battery life, and to improve the rationality of the maintenance schedule of the transport vehicle, is a technical problem that needs to be solved.
Disclosure of Invention
The invention mainly aims to provide a park user parking and charging behavior analysis method and system based on big data, and aims to solve the technical problems of inaccurate estimation of the residual life of a battery of a transport vehicle in a park, unreasonable allocation of transport tasks and difficult maintenance scheduling of the transport vehicle at present.
In order to achieve the above purpose, the invention provides a park user parking charging behavior analysis method based on big data, which comprises the following steps:
acquiring vehicle charging related information and vehicle driving related information transmitted by a transportation enterprise terminal; wherein the vehicle charging-related information and the vehicle traveling-related information include a charging-related information set in which each transport vehicle performs history charging and a traveling-related information set in which history traveling is performed, respectively;
Determining a battery remaining life estimation value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generating a transport vehicle state list of a transport enterprise;
When a transport vehicle use request transmitted by a production enterprise terminal is received, extracting a transport vehicle requirement in the transport vehicle use request, and generating a transport vehicle calling instruction based on the transport vehicle requirement and a transport vehicle state list of a transport enterprise;
and sending the transport vehicle calling instruction to a transport enterprise terminal so that the transport enterprise terminal drives the corresponding transport vehicle to execute the transport task.
Optionally, the step of acquiring the vehicle charging related information and the vehicle driving related information transmitted by the transportation enterprise terminal specifically includes:
Calling a charging pile charging database; the charging pile charging database stores a plurality of pieces of charging pile use information of each charging pile in a park transmitted by a transportation enterprise terminal, wherein each piece of charging pile use information comprises a vehicle identifier and charging related information for executing the charging of the transportation vehicle;
distributing a plurality of pieces of charging pile use information to each transport vehicle based on the vehicle identification in each piece of charging pile use information, and constructing a charging association information set for each transport vehicle to execute historical charging;
Calling a vehicle running database; wherein, the vehicle running database stores running data of each transport vehicle execution history running transmitted by the transport enterprise terminal;
And extracting driving related data related to the service life of the battery in the driving data, and constructing a driving related information set of each transport vehicle for executing historical driving.
Optionally, after the step of distributing the pieces of charging pile usage information to each transport vehicle based on the vehicle identification in the pieces of charging pile usage information and constructing the charging-related information set for each transport vehicle to perform the historical charging, the method further includes:
Acquiring a non-park charging record transmitted by an intelligent terminal of a driver, and extracting a charging record keyword in the non-park charging record by using a natural language processing tool; the charging record keywords comprise vehicle identifications and charging related information of the transport vehicle for executing the charging of the transport vehicle;
and adding the charging related information in each non-campus charging record into a charging related information set constructed by the corresponding transport vehicle according to the vehicle identification in the non-campus charging record.
Optionally, the charging related information includes a charging power parameter of the transport vehicle for performing historical charging, and the driving related information includes a driving environment parameter of the transport vehicle for performing historical driving; determining a battery remaining life estimated value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generating a transport vehicle status list of a transport enterprise, specifically including:
Determining a target battery loss life estimation model for each transport vehicle in a battery loss life estimation model library based on the identification information of each transport vehicle;
Acquiring a charging association information set of each transport vehicle, extracting charging power parameters of each execution history charging in the charging association information set, acquiring a running association information set of each transport vehicle, and extracting running environment parameters of the execution history running in the running association information set;
And determining a battery remaining life estimated value of each transport vehicle according to the target battery loss life estimated model, the charging power parameter of each transport vehicle for executing the historical charging and the running environment parameter for executing the historical running, and generating a transport vehicle state list of a transport enterprise.
Optionally, before the step of determining the target battery life loss estimation model of each transport vehicle in the battery life loss estimation model library based on the identification information of each transport vehicle, the method further includes:
acquiring test service life time of a transport vehicle manufacturer for executing a plurality of transport vehicle test acquisitions;
Wherein the transport vehicle test comprises a number of test tasks performed in accordance with a combination of different charging power test parameters and different driving environment parameters, the test lifetime duration being configured as a test duration from the start of the execution of the test to the rejection of the battery;
Taking a data set formed by the charging power test parameters and the running environment parameters of each transport vehicle test as a training sample, and taking the test life time acquired by the corresponding transport vehicle test as marking information of the training sample to form a marked training sample set;
inputting the training sample set into a constructed initial neural network model for training, and taking the trained neural network model as a target battery loss life estimation model of each transport vehicle;
the identification information based on each transport vehicle is associated with a corresponding battery loss life estimation model and stored in a battery loss life estimation model library.
Optionally, determining a battery remaining life estimated value of each transport vehicle according to the target battery loss life estimated model, a charging power parameter of each transport vehicle for performing historical charging each time, and a driving environment parameter for performing historical driving, and generating a transport vehicle status list of the transport enterprise, which specifically includes:
Inputting a charging power parameter of each transport vehicle for executing historical charging and a running environment parameter for executing historical running into a corresponding target battery loss life estimation model of each transport vehicle, and generating a battery loss life estimation value of each transport vehicle;
Generating a transportation vehicle status list for the transportation enterprise based on the battery life estimate for each transportation vehicle in the transportation enterprise and the battery life limit for each transportation vehicle provided by the transportation vehicle manufacturer;
The transportation vehicle state list of the transportation enterprise comprises battery life limit values of each transportation vehicle minus battery loss life estimated values to obtain battery residual life estimated values.
Optionally, when a transport vehicle use request transmitted by a production enterprise terminal is received, extracting a transport vehicle requirement in the transport vehicle use request, and generating a transport vehicle calling instruction based on the transport vehicle requirement and a transport vehicle state list of a transport enterprise, wherein the method specifically comprises the following steps:
Acquiring a transportation vehicle state list of a transportation enterprise, and rearranging battery residual life estimated values of each transportation vehicle in the transportation vehicle state list in order from low to high;
When a transport vehicle use request transmitted by a production enterprise terminal is received, extracting transport vehicle requirements in the transport vehicle use request; wherein the transport vehicle demand includes a transport vehicle demand quantity;
And selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle state list according to preset maintenance plan information and the transport vehicle demand number input by the transport enterprise terminal, and generating a transport vehicle calling instruction for sending to each target transport vehicle.
Optionally, according to preset maintenance plan information and the number of transport vehicles required input by a transport enterprise terminal, selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle status list, which specifically includes:
Acquiring preset maintenance plan information input by a transportation enterprise terminal; wherein the preset maintenance schedule information includes a transport vehicle scheduled maintenance period and a transport vehicle scheduled maintenance number each time maintenance is performed;
a target transport vehicle for executing the transport vehicle usage request is selected from the transport vehicle status list according to a current time at which the transport vehicle usage request was received, a transport vehicle scheduled maintenance period, and a transport vehicle scheduled maintenance number.
Optionally, the step of selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle status list specifically includes:
Dividing the service life value of the transport vehicle according to a preset rule to obtain a plurality of service life value intervals which are arranged from top to bottom and have service life values from small to large; wherein the preset rules are configured to ensure that at most only a number of transportation vehicles of the planned maintenance number of the transportation vehicles can be stored in each life value interval;
Sequentially placing a plurality of transport vehicles in the transport vehicle state list in a life value interval according to the sequence from low to high of the estimated values of the residual life of the batteries;
When the current time enters a planned maintenance period of a target transport vehicle, judging whether transport vehicles with the battery residual life estimated value lower than a preset life value in a minimum life value interval are less than the number of transport vehicle maintenance, if so, using transport vehicles with the battery residual life estimated value not lower than the preset life value in the minimum life value interval as target vehicles to execute the received transport vehicle use request; if not, sequentially and circularly selecting one transport vehicle in other life value intervals as a target vehicle for executing the received transport vehicle use request;
When the current time enters the planned maintenance period of the next transport vehicle, taking all transport vehicles in the minimum life value interval as the transport vehicles to be maintained, removing the life value interval, replacing the transport vehicles in each life value interval with the transport vehicles in the life value interval below the transport vehicles, and then modifying the transport vehicles in the last life value interval to be empty until the last life value interval is written after the maintenance of the transport vehicles to be maintained is completed.
In addition, in order to achieve the above object, the present invention also provides a system for analyzing parking charging behavior of a campus user based on big data, the system comprising:
The acquisition module is used for acquiring vehicle charging related information and vehicle driving related information transmitted by the transportation enterprise terminal; wherein the vehicle charging-related information and the vehicle traveling-related information include a charging-related information set in which each transport vehicle performs history charging and a traveling-related information set in which history traveling is performed, respectively;
A determining module, configured to determine a battery remaining life estimation value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generate a transport vehicle status list of a transport enterprise;
The generation module is used for extracting the transport vehicle requirements in the transport vehicle use request when the transport vehicle use request transmitted by the production enterprise terminal is received, and generating a transport vehicle calling instruction based on the transport vehicle requirements and a transport vehicle state list of a transport enterprise;
and the sending module is used for sending the transport vehicle calling instruction to the transport enterprise terminal so that the transport enterprise terminal drives the corresponding transport vehicle to execute the transport task.
The invention has the beneficial effects that: according to the method and the system for analyzing the parking and charging behaviors of the park users based on big data, the battery residual life estimated value of each transport vehicle is determined by acquiring the vehicle charging related information and the vehicle driving related information, and then the transport vehicle with the most proper residual life quantized value, which can reasonably meet the preset maintenance plan and ensure that sufficient transport resources are configured at the moment, is selected from all transport vehicles of a transport enterprise according to the preset maintenance plan to execute the received transport vehicle use request, so that the technical problems of inaccurate battery residual life estimation, unreasonable transport task allocation and difficult transport vehicle maintenance planning of the transport vehicles in the park at present are solved.
Drawings
Fig. 1 is a flow chart of a method for analyzing parking and charging behaviors of a campus user based on big data according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of a park user parking charging behavior analysis system based on big data in an embodiment of the 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
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. 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.
The embodiment of the invention provides a park user parking and charging behavior analysis method based on big data, and referring to fig. 1, fig. 1 is a flow diagram of an embodiment of the park user parking and charging behavior analysis method based on big data.
In this embodiment, a method for analyzing parking and charging behaviors of a campus user based on big data includes the following steps:
S1: acquiring vehicle charging related information and vehicle driving related information transmitted by a transportation enterprise terminal; wherein the vehicle charging-related information and the vehicle traveling-related information include a charging-related information set in which each transport vehicle performs history charging and a traveling-related information set in which history traveling is performed, respectively;
s2: determining a battery remaining life estimation value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generating a transport vehicle state list of a transport enterprise;
S3: when a transport vehicle use request transmitted by a production enterprise terminal is received, extracting a transport vehicle requirement in the transport vehicle use request, and generating a transport vehicle calling instruction based on the transport vehicle requirement and a transport vehicle state list of a transport enterprise;
s4: and sending the transport vehicle calling instruction to a transport enterprise terminal so that the transport enterprise terminal drives the corresponding transport vehicle to execute the transport task.
It should be noted that, the battery life management of the electric transportation truck in the transportation enterprise has the following characteristics: the electric transportation truck needs to reserve charging time, the charging mode generally comprises high-power charging and low-power charging, although the high-power charging has the advantage of high charging speed, the loss of the service life of a battery is more remarkable than that of the low-power charging, but because of the property of one transportation enterprise in a park for a plurality of production enterprises, the charging cannot always be carried out in the same charging mode under the condition that the transportation enterprises are limited in configuration of transportation vehicles, even the charging is carried out in a more chaotic and profit-maximizing manner (for example, when a large number of vehicle transportation requests or emergency vehicle transportation requests are suddenly received, the idle transportation vehicles are usually required to be scheduled to execute temporary high-power charging instead of charging according to an original plan, so that the requirement of the transportation time limit is met), and the charging behavior management of the transportation vehicles of the enterprises is chaotic, and the residual service life of the battery of each transportation vehicle cannot be accurately estimated; in addition, the severe service environment (such as high-low temperature environment, high humidity environment, driving vibration environment, etc.) of the battery is one of factors influencing the service life of the battery, and the characteristics of the electric transportation truck just conform to the severe service environment of the battery, so that the estimation of the residual service life of the battery of the transportation vehicle is inaccurate, and the maintenance plan of the transportation vehicle cannot be effectively managed and arranged.
In order to solve the above problems, the present embodiment determines the estimated battery remaining life of each transport vehicle by acquiring the vehicle charging related information and the vehicle driving related information, and then selects the most appropriate remaining life quantized value from all transport vehicles of the transport enterprise according to the preset maintenance plan, so that the transport vehicles which can reasonably meet the preset maintenance plan and are guaranteed to be provided with sufficient transport resources at the moment execute the received transport vehicle use request, thereby solving the technical problems of inaccurate estimation of the remaining battery life of the transport vehicles in the current park, unreasonable allocation of transport tasks and difficult maintenance planning and arrangement of the transport vehicles.
In a preferred embodiment, the step of acquiring the vehicle charging related information and the vehicle driving related information transmitted by the transportation enterprise terminal specifically includes:
S11: calling a charging pile charging database; the charging pile charging database stores a plurality of pieces of charging pile use information of each charging pile in a park transmitted by a transportation enterprise terminal, wherein each piece of charging pile use information comprises a vehicle identifier and charging related information for executing the charging of the transportation vehicle;
S12: distributing a plurality of pieces of charging pile use information to each transport vehicle based on the vehicle identification in each piece of charging pile use information, and constructing a charging association information set for each transport vehicle to execute historical charging;
S15: calling a vehicle running database; wherein, the vehicle running database stores running data of each transport vehicle execution history running transmitted by the transport enterprise terminal;
s16: and extracting driving related data related to the service life of the battery in the driving data, and constructing a driving related information set of each transport vehicle for executing historical driving.
On the basis, based on the vehicle identification in each piece of charging pile use information, distributing a plurality of pieces of charging pile use information to each transport vehicle, and after the step of constructing a charging association information set for each transport vehicle to execute historical charging, the method further comprises the following steps:
s13: acquiring a non-park charging record transmitted by an intelligent terminal of a driver, and extracting a charging record keyword in the non-park charging record by using a natural language processing tool; the charging record keywords comprise vehicle identifications and charging related information of the transport vehicle for executing the charging of the transport vehicle;
s14: and adding the charging related information in each non-campus charging record into a charging related information set constructed by the corresponding transport vehicle according to the vehicle identification in the non-campus charging record.
In this embodiment, the vehicle charging related information is mainly obtained from charging related information transmitted by each charging pile and a driver installed by a transportation enterprise in a campus through an intelligent terminal, and the vehicle driving related information is mainly extracted from driving data acquired by a transportation vehicle, so that accurate obtaining of charging information and form information of the transportation vehicle and a battery life oil pipe is ensured.
In a preferred embodiment, the charging related information includes a charging power parameter of the transport vehicle performing history charging, and the traveling related information includes a traveling environment parameter of the transport vehicle performing history traveling; determining a battery remaining life estimated value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generating a transport vehicle status list of a transport enterprise, specifically including:
S25: determining a target battery loss life estimation model for each transport vehicle in a battery loss life estimation model library based on the identification information of each transport vehicle;
S26: acquiring a charging association information set of each transport vehicle, extracting charging power parameters of each execution history charging in the charging association information set, acquiring a running association information set of each transport vehicle, and extracting running environment parameters of the execution history running in the running association information set;
S27: and determining a battery remaining life estimated value of each transport vehicle according to the target battery loss life estimated model, the charging power parameter of each transport vehicle for executing the historical charging and the running environment parameter for executing the historical running, and generating a transport vehicle state list of a transport enterprise.
Based on the identification information of each transport vehicle, before the step of determining the target battery loss life estimation model of each transport vehicle in the battery loss life estimation model library, the method further comprises:
s21: acquiring test service life time of a transport vehicle manufacturer for executing a plurality of transport vehicle test acquisitions;
Wherein the transport vehicle test comprises a number of test tasks performed in accordance with a combination of different charging power test parameters and different driving environment parameters, the test lifetime duration being configured as a test duration from the start of the execution of the test to the rejection of the battery;
S22: taking a data set formed by the charging power test parameters and the running environment parameters of each transport vehicle test as a training sample, and taking the test life time acquired by the corresponding transport vehicle test as marking information of the training sample to form a marked training sample set;
s23: inputting the training sample set into a constructed initial neural network model for training, and taking the trained neural network model as a target battery loss life estimation model of each transport vehicle;
s24: the identification information based on each transport vehicle is associated with a corresponding battery loss life estimation model and stored in a battery loss life estimation model library.
On the basis, determining a battery remaining life estimation value of each transport vehicle according to the target battery loss life estimation model, a charging power parameter of each transport vehicle for each time performing historical charging and a driving environment parameter for performing historical driving, and generating a transport vehicle state list of a transport enterprise, wherein the method comprises the following steps:
S271: inputting a charging power parameter of each transport vehicle for executing historical charging and a running environment parameter for executing historical running into a corresponding target battery loss life estimation model of each transport vehicle, and generating a battery loss life estimation value of each transport vehicle;
S272: generating a transportation vehicle status list for the transportation enterprise based on the battery life estimate for each transportation vehicle in the transportation enterprise and the battery life limit for each transportation vehicle provided by the transportation vehicle manufacturer;
The transportation vehicle state list of the transportation enterprise comprises battery life limit values of each transportation vehicle minus battery loss life estimated values to obtain battery residual life estimated values.
In this embodiment, the estimated battery remaining life value of each transport vehicle is determined by using a battery life loss estimation model obtained by training in advance based on transport vehicle test data provided by a transport vehicle manufacturer, the model having been trained to output the estimated battery life value of the current transport vehicle when the charge-related information and the travel-related information of the transport vehicle are input, and then determining the final estimated battery remaining life value based on the battery life limit value of each transport vehicle provided by the transport vehicle manufacturer.
In a preferred embodiment, when a transport vehicle use request transmitted by a terminal of a manufacturing enterprise is received, a transport vehicle requirement in the transport vehicle use request is extracted, and a transport vehicle calling instruction step is generated based on the transport vehicle requirement and a transport vehicle state list of the manufacturing enterprise, and specifically includes:
S31: acquiring a transportation vehicle state list of a transportation enterprise, and rearranging battery residual life estimated values of each transportation vehicle in the transportation vehicle state list in order from low to high;
s32: when a transport vehicle use request transmitted by a production enterprise terminal is received, extracting transport vehicle requirements in the transport vehicle use request; wherein the transport vehicle demand includes a transport vehicle demand quantity;
S33: and selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle state list according to preset maintenance plan information and the transport vehicle demand number input by the transport enterprise terminal, and generating a transport vehicle calling instruction for sending to each target transport vehicle.
On the basis, according to preset maintenance plan information and the number of transport vehicle demands input by a transport enterprise terminal, selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle state list, wherein the method specifically comprises the following steps:
S331: acquiring preset maintenance plan information input by a transportation enterprise terminal; wherein the preset maintenance schedule information includes a transport vehicle scheduled maintenance period and a transport vehicle scheduled maintenance number each time maintenance is performed;
S332: a target transport vehicle for executing the transport vehicle usage request is selected from the transport vehicle status list according to a current time at which the transport vehicle usage request was received, a transport vehicle scheduled maintenance period, and a transport vehicle scheduled maintenance number.
On the basis, selecting a target transport vehicle from the transport vehicle state list, wherein the target transport vehicle is used for executing the transport vehicle use request, and the method specifically comprises the following steps of:
S3321: dividing the service life value of the transport vehicle according to a preset rule to obtain a plurality of service life value intervals which are arranged from top to bottom and have service life values from small to large; wherein the preset rules are configured to ensure that at most only a number of transportation vehicles of the planned maintenance number of the transportation vehicles can be stored in each life value interval;
s3322: sequentially placing a plurality of transport vehicles in the transport vehicle state list in a life value interval according to the sequence from low to high of the estimated values of the residual life of the batteries;
S3323: when the current time enters a planned maintenance period of a target transport vehicle, judging whether transport vehicles with the battery residual life estimated value lower than a preset life value in a minimum life value interval are less than the number of transport vehicle maintenance, if so, using transport vehicles with the battery residual life estimated value not lower than the preset life value in the minimum life value interval as target vehicles to execute the received transport vehicle use request; if not, sequentially and circularly selecting one transport vehicle in other life value intervals as a target vehicle for executing the received transport vehicle use request;
S3324: when the current time enters the planned maintenance period of the next transport vehicle, taking all transport vehicles in the minimum life value interval as the transport vehicles to be maintained, removing the life value interval, replacing the transport vehicles in each life value interval with the transport vehicles in the life value interval below the transport vehicles, and then modifying the transport vehicles in the last life value interval to be empty until the last life value interval is written after the maintenance of the transport vehicles to be maintained is completed.
After having the ability to learn the battery remaining life estimate in real time for each transport vehicle, sufficient transport resources are allocated for achieving reasonable allocation of transport tasks, normal execution of transport vehicle maintenance schedules, and guaranteed time-to-time configuration (i.e., there will be no large batch of battery remaining life estimates for transport vehicles while values that will need maintenance replacement, thereby affecting the situation of transport task execution). In this embodiment, it is adopted that in each transportation vehicle planned maintenance period, the received transportation vehicle use request is first allocated preferentially to a lifetime section (the lifetime section is configured with only the number of transportation vehicles to be maintained in the remaining battery lifetime estimated value range of the transportation vehicle to be maintained) within the range of the remaining battery lifetime estimated values to be maintained, after all the remaining battery lifetime estimated values of the transportation vehicles in the lifetime section enter the region to be maintained, the transportation vehicle in the region is regarded as a vehicle waiting maintenance to be maintained, and thereafter the received transportation vehicle use request is allocated to other lifetime sections (i.e., the received first transportation vehicle use request is allocated to the second lifetime section, and the received second transportation vehicle use request is allocated to the third lifetime section … to be cycled) in turn until the next transportation vehicle planned maintenance period comes. In this way, it can be ensured that only the battery life of a number of transportation vehicles (for example, five transportation vehicles) scheduled for maintenance of each transportation vehicle in a planned maintenance period (for example, one week) enters a region requiring maintenance, and the battery lives of other transportation vehicles are still in positions capable of executing transportation tasks, thereby solving the technical problems of inaccurate estimation of the remaining battery life of the transportation vehicles in the current campus, unreasonable allocation of transportation tasks and difficult maintenance scheduling of the transportation vehicles.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of a system for analyzing parking and charging behavior of a campus user based on big data according to the present invention.
As shown in fig. 2, the system for analyzing parking charging behavior of a campus user based on big data according to the embodiment of the present invention includes:
An acquiring module 10, configured to acquire vehicle charging related information and vehicle driving related information transmitted by a transportation enterprise terminal; wherein the vehicle charging-related information and the vehicle traveling-related information include a charging-related information set in which each transport vehicle performs history charging and a traveling-related information set in which history traveling is performed, respectively;
A determining module 20, configured to determine a battery remaining life estimated value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generate a transport vehicle status list of a transport enterprise;
the generating module 30 is configured to, when receiving a transport vehicle use request transmitted by a terminal of a manufacturing enterprise, extract a transport vehicle requirement in the transport vehicle use request, and generate a transport vehicle calling instruction based on the transport vehicle requirement and a transport vehicle state list of the transportation enterprise;
And the sending module 40 is configured to send the transport vehicle calling instruction to a transport enterprise terminal, so that the transport enterprise terminal drives the corresponding transport vehicle to execute the transport task.
Other embodiments or specific implementation manners of the park user parking and charging behavior analysis system based on big data can refer to the above method embodiments, and are not repeated here.
It is appreciated that in the description herein, reference to the terms "one embodiment," "another embodiment," "other embodiments," or "first through nth embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
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 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 (2)
1. A method for analyzing park user parking and charging behaviors based on big data, which is characterized by comprising the following steps:
acquiring vehicle charging related information and vehicle driving related information transmitted by a transportation enterprise terminal; wherein the vehicle charging-related information and the vehicle traveling-related information include a charging-related information set in which each transport vehicle performs history charging and a traveling-related information set in which history traveling is performed, respectively;
Determining a battery remaining life estimation value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generating a transport vehicle state list of a transport enterprise;
When a transport vehicle use request transmitted by a production enterprise terminal is received, extracting a transport vehicle requirement in the transport vehicle use request, and generating a transport vehicle calling instruction based on the transport vehicle requirement and a transport vehicle state list of a transport enterprise;
the transportation vehicle calling instruction is sent to a transportation enterprise terminal, so that the transportation enterprise terminal drives the corresponding transportation vehicle to execute a transportation task;
The method comprises the steps of acquiring vehicle charging related information and vehicle driving related information transmitted by a transportation enterprise terminal, and specifically comprises the following steps:
Calling a charging pile charging database; the charging pile charging database stores a plurality of pieces of charging pile use information of each charging pile in a park transmitted by a transportation enterprise terminal, wherein each piece of charging pile use information comprises a vehicle identifier and charging related information for executing the charging of the transportation vehicle;
distributing a plurality of pieces of charging pile use information to each transport vehicle based on the vehicle identification in each piece of charging pile use information, and constructing a charging association information set for each transport vehicle to execute historical charging;
Calling a vehicle running database; wherein, the vehicle running database stores running data of each transport vehicle execution history running transmitted by the transport enterprise terminal;
extracting driving related data related to the service life of a battery in the driving data, and constructing a driving related information set of each transport vehicle for executing historical driving;
wherein, based on the vehicle identification in each piece of charging pile use information, distribute a plurality of pieces of charging pile use information to each transport vehicle, after the step of constructing the charging association information set that each transport vehicle carries out historical charging, the method further includes:
Acquiring a non-park charging record transmitted by an intelligent terminal of a driver, and extracting a charging record keyword in the non-park charging record by using a natural language processing tool; the charging record keywords comprise vehicle identifications and charging related information of the transport vehicle for executing the charging of the transport vehicle;
According to the vehicle identification in the non-park charging records, charging related information in each non-park charging record is added into a charging related information set constructed by a corresponding transport vehicle;
wherein the charging related information comprises a charging power parameter of the transport vehicle for performing historical charging, and the driving related information comprises a driving environment parameter of the transport vehicle for performing historical driving; determining a battery remaining life estimated value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generating a transport vehicle status list of a transport enterprise, specifically including:
Determining a target battery loss life estimation model for each transport vehicle in a battery loss life estimation model library based on the identification information of each transport vehicle;
Acquiring a charging association information set of each transport vehicle, extracting charging power parameters of each execution history charging in the charging association information set, acquiring a running association information set of each transport vehicle, and extracting running environment parameters of the execution history running in the running association information set;
Determining a battery remaining life estimated value of each transport vehicle according to the target battery loss life estimated model, the charging power parameter of each transport vehicle for executing historical charging and the running environment parameter for executing historical running, and generating a transport vehicle state list of a transport enterprise;
Wherein, based on the identification information of each transport vehicle, before the step of determining the target battery life estimation model of each transport vehicle in the battery life estimation model library, further comprises:
acquiring test service life time of a transport vehicle manufacturer for executing a plurality of transport vehicle test acquisitions;
Wherein the transport vehicle test comprises a number of test tasks performed in accordance with a combination of different charging power test parameters and different driving environment parameters, the test lifetime duration being configured as a test duration from the start of the execution of the test to the rejection of the battery;
Taking a data set formed by the charging power test parameters and the running environment parameters of each transport vehicle test as a training sample, and taking the test life time acquired by the corresponding transport vehicle test as marking information of the training sample to form a marked training sample set;
inputting the training sample set into a constructed initial neural network model for training, and taking the trained neural network model as a target battery loss life estimation model of each transport vehicle;
associating and storing the identification information of each transport vehicle with a corresponding battery loss life estimation model into a battery loss life estimation model library;
the method specifically includes the steps of determining a battery remaining life estimated value of each transport vehicle according to a target battery loss life estimated model, a charging power parameter of each transport vehicle for performing historical charging each time, and a running environment parameter for performing historical running, and generating a transport vehicle state list of a transport enterprise:
Inputting a charging power parameter of each transport vehicle for executing historical charging and a running environment parameter for executing historical running into a corresponding target battery loss life estimation model of each transport vehicle, and generating a battery loss life estimation value of each transport vehicle;
Generating a transportation vehicle status list for the transportation enterprise based on the battery life estimate for each transportation vehicle in the transportation enterprise and the battery life limit for each transportation vehicle provided by the transportation vehicle manufacturer;
The transportation vehicle state list of the transportation enterprise comprises battery life limit values of each transportation vehicle minus battery loss life estimated values to obtain battery residual life estimated values;
When a transport vehicle use request transmitted by a production enterprise terminal is received, extracting a transport vehicle demand in the transport vehicle use request, and generating a transport vehicle calling instruction based on the transport vehicle demand and a transport vehicle state list of a transport enterprise, wherein the transport vehicle calling instruction comprises the following steps:
Acquiring a transportation vehicle state list of a transportation enterprise, and rearranging battery residual life estimated values of each transportation vehicle in the transportation vehicle state list in order from low to high;
When a transport vehicle use request transmitted by a production enterprise terminal is received, extracting transport vehicle requirements in the transport vehicle use request; wherein the transport vehicle demand includes a transport vehicle demand quantity;
Selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle state list according to preset maintenance plan information and the transport vehicle demand number input by a transport enterprise terminal, and generating a transport vehicle calling instruction for sending to each target transport vehicle;
The step of selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle state list according to preset maintenance plan information and the transport vehicle demand number input by a transport enterprise terminal specifically includes:
Acquiring preset maintenance plan information input by a transportation enterprise terminal; wherein the preset maintenance schedule information includes a transport vehicle scheduled maintenance period and a transport vehicle scheduled maintenance number each time maintenance is performed;
Selecting a target transport vehicle for executing a transport vehicle usage request from the transport vehicle status list according to a current time at which the transport vehicle usage request was received, a transport vehicle scheduled maintenance period, and a transport vehicle scheduled maintenance number;
Wherein the step of selecting a target transport vehicle for executing the transport vehicle use request from the transport vehicle status list specifically includes:
Dividing the service life value of the transport vehicle according to a preset rule to obtain a plurality of service life value intervals which are arranged from top to bottom and have service life values from small to large; wherein the preset rules are configured to ensure that at most only a number of transportation vehicles of the planned maintenance number of the transportation vehicles can be stored in each life value interval;
Sequentially placing a plurality of transport vehicles in the transport vehicle state list in a life value interval according to the sequence from low to high of the estimated values of the residual life of the batteries;
When the current time enters a planned maintenance period of a target transport vehicle, judging whether transport vehicles with the battery residual life estimated value lower than a preset life value in a minimum life value interval are less than the number of transport vehicle maintenance, if so, using transport vehicles with the battery residual life estimated value not lower than the preset life value in the minimum life value interval as target vehicles to execute the received transport vehicle use request; if not, sequentially and circularly selecting one transport vehicle in other life value intervals as a target vehicle for executing the received transport vehicle use request;
When the current time enters the planned maintenance period of the next transport vehicle, taking all transport vehicles in the minimum life value interval as the transport vehicles to be maintained, removing the life value interval, replacing the transport vehicles in each life value interval with the transport vehicles in the life value interval below the transport vehicles, and then modifying the transport vehicles in the last life value interval to be empty until the last life value interval is written after the maintenance of the transport vehicles to be maintained is completed.
2. A big data based park user parking charging behavior analysis system for use in a big data based park user parking charging behavior analysis method as claimed in claim 1, the system comprising:
The acquisition module is used for acquiring vehicle charging related information and vehicle driving related information transmitted by the transportation enterprise terminal; wherein the vehicle charging-related information and the vehicle traveling-related information include a charging-related information set in which each transport vehicle performs history charging and a traveling-related information set in which history traveling is performed, respectively;
A determining module, configured to determine a battery remaining life estimation value of each transport vehicle based on the vehicle charging related information and the vehicle driving related information, and generate a transport vehicle status list of a transport enterprise;
The generation module is used for extracting the transport vehicle requirements in the transport vehicle use request when the transport vehicle use request transmitted by the production enterprise terminal is received, and generating a transport vehicle calling instruction based on the transport vehicle requirements and a transport vehicle state list of a transport enterprise;
and the sending module is used for sending the transport vehicle calling instruction to the transport enterprise terminal so that the transport enterprise terminal drives the corresponding transport vehicle to execute the transport task.
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