CN116228295A - Intelligent recommendation method and system for charging pile - Google Patents
Intelligent recommendation method and system for charging pile Download PDFInfo
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Abstract
The application relates to the technical field of data processing, and provides an intelligent recommendation method and system for a charging pile. The method comprises the following steps: element extraction is carried out on the charging demand information to obtain charging demand parameter information; obtaining charging pile list information within a preset radius area based on the user position information; setting a charging pile optimizing space based on the charging pile list information; acquiring charging price strategy information of each charging pile in the charging pile list information; determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information; and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information. By adopting the method, the optimizing recommendation accuracy and recommendation efficiency of the charging pile can be improved, and the technical effect of charging experience of a user is further improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent recommendation method and system for a charging pile.
Background
The charging pile has the function similar to that of an oiling machine in a gas station, can be fixed on the ground or a wall, is installed in public buildings (public buildings, malls, public parking lots and the like) and residential community parking lots or charging stations, and can charge electric automobiles of various types according to different voltage levels. The charging pile has the advantages of centralized charging, convenient position and simple operation, and brings convenience to life of people, so that the charging pile suitable for the user selection has important application significance for optimizing user experience.
However, in the prior art, the recommendation of the charging pile is affected by various factors, and the recommendation accuracy is low, so that the technical problem of affecting the user experience is caused.
Disclosure of Invention
Based on the foregoing, it is necessary to provide an intelligent recommendation method and system for a charging pile, which can improve the accuracy and recommendation efficiency of optimizing and recommending the charging pile, and further improve the charging experience of a user.
An intelligent recommendation method of a charging pile, the method comprising: acquiring charging demand information of a target user; extracting elements from the charging demand information to obtain charging demand parameter information; acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information; setting a charging pile optimizing space based on the charging pile list information; acquiring charging price strategy information of each charging pile in the charging pile list information; determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information; and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
An intelligent recommendation system for a charging stake, the system comprising: the charging demand acquisition module is used for acquiring charging demand information of a target user; the element extraction module is used for extracting elements from the charging demand information to obtain charging demand parameter information; the charging pile list acquisition module is used for acquiring user position information and acquiring charging pile list information within a preset radius area based on the user position information; the optimizing space setting module is used for setting a charging pile optimizing space based on the charging pile list information; the charging price strategy acquisition module is used for acquiring charging price strategy information of each charging pile in the charging pile list information; the performance evaluation parameter determining module is used for determining performance evaluation parameters of the charging pile according to the charging demand parameter information and the charging price strategy information; and the optimal selection information output module is used for carrying out global optimization in the charging pile optimizing space based on the charging pile performance evaluation parameters, outputting charging pile optimal selection information and carrying out recommendation management to a target user according to the charging pile optimal selection information.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring charging demand information of a target user;
extracting elements from the charging demand information to obtain charging demand parameter information;
acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information;
setting a charging pile optimizing space based on the charging pile list information;
acquiring charging price strategy information of each charging pile in the charging pile list information;
determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information;
and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring charging demand information of a target user;
extracting elements from the charging demand information to obtain charging demand parameter information;
acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information;
setting a charging pile optimizing space based on the charging pile list information;
acquiring charging price strategy information of each charging pile in the charging pile list information;
determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information;
and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
According to the intelligent recommendation method and system for the charging pile, the technical problems that in the prior art, the charging pile recommendation is affected by various factors, the recommendation accuracy is low, and user experience is affected are solved, global optimization of the charging pile is achieved by combining multiple elements of charging requirements, the optimization recommendation accuracy and recommendation efficiency are improved, and further the technical effect of user charging experience is improved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
FIG. 1 is a flow chart of an intelligent recommendation method for a charging pile according to an embodiment;
fig. 2 is a schematic flow chart of obtaining charging demand parameter information in an intelligent recommendation method of a charging pile according to an embodiment;
FIG. 3 is a block diagram illustrating an intelligent recommendation system for a charging pile according to one embodiment;
FIG. 4 is an internal block diagram of a computer device in one embodiment;
reference numerals illustrate: the system comprises a charging demand acquisition module 11, an element extraction module 12, a charging pile list acquisition module 13, an optimizing space setting module 14, a charging price strategy acquisition module 15, a performance evaluation parameter determination module 16 and a preference information output module 17.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
As shown in fig. 1, the present application provides an intelligent recommendation method of a charging pile, where the method is applied to an online customer service identification system, and the method includes:
step S100: acquiring charging demand information of a target user;
specifically, the charging pile has the function similar to that of an oiling machine in a gas station, can be fixed on the ground or a wall, is installed in public buildings (public buildings, markets, public parking lots and the like) and residential community parking lots or charging stations, and can charge electric automobiles of various types according to different voltage levels. The charging pile has the advantages of centralized charging, convenient position and simple operation, and brings convenience to life of people, so that the charging pile suitable for the user selection has important application significance for optimizing user experience.
Firstly, charging demand information of a target user is acquired through a client, the target user uses a charging pile to be recommended, the charging demand information is charging self demand information of the target user, and the charging demand information comprises requirements of charging quantity, electric automobile model, charging time, residual available electric quantity and the like, and can be set by oneself, so that the charging experience effect of the user is improved, and the actual charging demand of the user is met.
Step S200: extracting elements from the charging demand information to obtain charging demand parameter information;
in one embodiment, as shown in fig. 2, the obtaining the charging demand parameter information, step S200 of the present application further includes:
step S210: extracting elements based on the charging demand information to obtain charging demand element information;
step S220: the charging demand element information comprises a charging path, available electric quantity, charging time consumption and charging power supply requirements;
step S230: performing priority evaluation on each element information in the charging requirement element information to obtain a charging element priority sequence;
step S240: and carrying out parameter arrangement on the charging demand information based on the priority order of the charging elements to obtain the charging demand parameter information.
In one embodiment, the step S230 of obtaining the priority order of the charging elements further includes:
step S231: obtaining element priority characteristic indexes, wherein the element priority characteristic indexes comprise element necessity and element preference;
step S232: constructing an element priority coordinate system according to the element priority characteristic index;
step S233: respectively evaluating the element information based on the element priority coordinate system to obtain an element feature vector set;
step S234: and sequencing the modes of the feature vectors in the element feature vector set to obtain the priority order of the charging elements.
Specifically, element extraction is performed on the charging requirement information, namely element integration and classification are performed on the charging requirement to obtain charging requirement element information, wherein the charging requirement element information mainly comprises a charging path, namely a path track reaching a charging pile; the available electric quantity is the residual available electric quantity of the electric automobile of the user; the charging time is time-consuming, i.e. the charging time is required, for example, two hours of rapid charging are required; and the power supply requirement is that the power supply of the electric automobile of the user is matched with the charging pile.
Then, priority evaluation is carried out on each element information in the charging demand element information, namely, an element priority characteristic index, namely, a priority evaluation index is obtained, wherein the element priority characteristic index comprises element necessity, namely, element condition necessity degree, for example, a charging power supply requirement is a condition which needs to be met, and the necessity is highest; and element preference, i.e. the personal element preference demand of the user, for example, the user has the highest requirement on the charging path, and the preference is correspondingly highest. And constructing an element priority coordinate system according to the element priority characteristic index, wherein the element priority coordinate system is used for evaluating element priority, and the coordinate dimension corresponds to the element priority characteristic index and comprises element necessity and element preference.
And evaluating the element information based on the element priority coordinate system, taking the index evaluation value as a coordinate value of the coordinate system, and further obtaining an element feature vector set of each element according to the coordinate value. Calculating the modulus of each element feature vector, and sorting the magnitude of the modulus of each feature vector in the element feature vector set to obtain the priority sequence of the charging elements, wherein the priority sequence of the charging elements is arranged according to the magnitude of the modulus of the vector, and the larger the modulus of the vector is, the earlier the corresponding priority sequence is. And carrying out parameter arrangement on the charging demand information based on the charging element priority order, and taking the element demand information after the sequence integration as charging demand parameter information. Through carrying out the priority order arrangement to the demand multi-element that charges, improve user demand comprehensiveness and specificity that charges, and then improve user experience effect that charges.
Step S300: acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information;
step S400: setting a charging pile optimizing space based on the charging pile list information;
specifically, the current position information of the user is obtained through the client terminal, and charging pile list information in a preset radius area is obtained based on the user position information, wherein the preset radius area can be set by the user, for example, charging pile information in the current position of 5km can be selected. And setting a charging pile optimizing space based on the charging pile list information, wherein the charging pile optimizing space is a global optimizing range of the charging pile.
Step S500: acquiring charging price strategy information of each charging pile in the charging pile list information;
step S600: determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information;
specifically, the charging price policy information of each charging pile in the charging pile list information is obtained through the charging pile online service system, and the charging price policy information is charging and charging conditions of each charging pile, namely the associated information of charging time length and charging price. And taking the charging demand parameter information and the charging price strategy information as charging pile performance evaluation parameters, wherein the charging pile performance evaluation parameters are performance evaluation indexes during global optimization of the charging pile.
Step S700: and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
In one embodiment, the outputting charging pile preference information, step S700 of the present application further includes:
step S710: randomly selecting a first charging pile in the charging pile optimizing space as a current optimal charging pile;
step S720: carrying out optimizing scoring on the first charging pile by adopting the charging pile performance evaluation parameters to obtain a first charging pile optimizing score;
step S730: constructing a first neighborhood of the current optimal charging pile based on a preset neighborhood mode, wherein the first neighborhood comprises a plurality of charging piles;
step S740: sequentially calculating the optimizing scores of the charging piles to obtain a plurality of optimizing scores of the charging piles;
step S750: and comparing the first charging pile optimizing score with the plurality of charging pile optimizing scores to obtain charging pile optimizing information.
In one embodiment, the comparing, based on the first charging pile optimizing score and the plurality of charging pile optimizing scores, obtains charging pile optimizing information, and step S750 of the present application further includes:
step S710: comparing the plurality of charging pile optimization scores, and screening a first optimal charging pile score of the first neighborhood;
step S720: if the score of the first optimal charging pile is larger than the optimizing score of the first charging pile, reversely selecting the charging pile with the score of the first optimal charging pile, and recording the charging pile as a second charging pile;
step S730: the second charging pile replaces the first charging pile to serve as the current optimal charging pile for iterative optimization;
step S740: and if the iteration optimization reaches the preset iteration times, outputting the charging pile preference information.
In one embodiment, the step S730 of performing iterative optimization by using the second charging pile instead of the first charging pile as the current optimal charging pile further includes:
step S731: adding the second charging pile into a tabu list for marking, and marking as a tabu mark;
step S732: calculating the tabu duration of the tabu mark to obtain the optimizing tabu duration;
step S733: when the optimizing tabu duration meets the preset tabu duration, the tabu mark is released, and the second charging pile is deleted from the tabu table;
step S734: and constructing a second neighborhood of the secondary charging pile, and continuing to perform iterative optimization.
Specifically, in order to recommend an applicable charging pile to a user, global optimization is performed in the charging pile optimizing space based on the charging pile performance evaluation parameter, and the specific optimizing process is to randomly select a first charging pile in the charging pile optimizing space to serve as a current optimal charging pile. And carrying out optimizing scoring on the first charging pile by adopting the charging pile performance evaluation parameters, namely carrying out performance evaluation on the currently selected charging pile by using the charging pile performance evaluation parameters, wherein different charging pile performance grades correspond to different optimizing scoring, and obtaining the first charging pile optimizing scoring corresponding to the charging pile. And constructing a first neighborhood of the current optimal charging pile based on a preset neighborhood mode, wherein the first neighborhood comprises a plurality of charging piles, namely, the neighborhood charging piles within a preset distance range are divided according to the current selected charging piles, and the preset range can be automatically determined according to historical experience data.
Sequentially calculating the optimizing scores of the charging piles to obtain a plurality of corresponding charging pile optimizing scores, comparing the first charging pile optimizing score with the plurality of charging pile optimizing scores, firstly comparing the plurality of charging pile optimizing scores, and screening the first optimal charging pile score of the first neighborhood, namely the highest charging pile score of the charging pile neighborhood. And if the score of the first optimal charging pile is larger than the score of the first charging pile, indicating that a new optimal charging pile score appears in the neighborhood, reversely selecting the charging pile with the score of the first optimal charging pile, marking the charging pile as a second charging pile, replacing the first charging pile with the second charging pile as a current optimal charging pile, and carrying out new round of charging pile screening comparison.
When a new round of iterative optimization is performed, the second charging pile is added into a tabu table for marking, the tabu table aims at preventing the search from circulating, ensuring that an optimal result does not fall into local optimal, taking a local optimal value as a tabu object, and marking the local optimal value as a tabu mark, namely the tabu object is not taken as a new round of optimization object. In the subsequent optimizing process, calculating the tabu duration of the tabu mark to obtain the corresponding optimizing tabu duration. Setting a preset tabu period, wherein the preset tabu period is a period in which a tabu object cannot be selected, circulation is easy to occur due to the fact that the tabu period is too short, local optimality cannot be achieved due to the fact that the tabu period is too short, and the computing time is too long due to the fact that the tabu period is too long. And when the optimizing tabu duration meets the preset tabu duration, removing the tabu mark, deleting the second charging pile from the tabu table, continuing to perform global optimizing of the charging pile, namely constructing a second neighborhood of the second charging pile, and continuing to perform iterative optimizing, wherein the second neighborhood is a plurality of charging piles within a preset distance range of the second charging pile.
And (3) repeatedly screening and iterating for a plurality of times, and outputting the charging pile optimization information if the iteration optimizing reaches the preset iteration times, wherein the preset iteration times are iteration times limit, namely screening out the charging pile with the optimal comprehensive performance score. And recommending the charging pile to the target user terminal according to the charging pile preference information, for example, planning a charging path according to the charging pile preference information, and charging the charging pile according to the charging pile planning path by a user, so as to improve the optimizing recommendation accuracy and recommendation efficiency by carrying out global optimizing of the charging pile by combining multiple factors of charging requirements, and further improve the charging experience of the user.
In one embodiment, the steps of the present application further comprise:
step S810: obtaining the use state information and the path traffic condition information of each charging pile in the charging pile list information;
step S820: taking the use state information and the path traffic condition information as decision constraint conditions;
step S830: and based on the decision constraint condition, adjusting and correcting the charging pile preference information.
Specifically, in order to ensure the actual applicability of the charging pile recommendation, the use state information and the path traffic condition information of each charging pile in the charging pile list information are acquired. The using state information of the charging pile is the occupied using state of the charging pile, comprising whether the charging pile is occupied or not and the waiting time of the completion of the use, and the path traffic condition information is the path planning traffic information of the current charging pile preference information, comprising whether traffic is jammed, the construction condition and the like. And taking the use state information and the path traffic condition information as decision constraint conditions, namely, decision additional conditions for recommending and selecting the actual charging pile. Based on the decision constraint condition, the charging pile preference information is adjusted and corrected, and the charging pile can be selected according to the charging pile list optimizing information in an exemplary case that the charging pile preference information is occupied. By considering the real-time application condition of the charging pile, the charging pile is adjusted and recommended, the optimizing recommendation accuracy and recommendation efficiency are improved, and the charging experience effect of a user is further improved.
In one embodiment, as shown in fig. 3, there is provided an intelligent recommendation system for a charging pile, including: the system comprises a charging demand acquisition module 11, an element extraction module 12, a charging pile list acquisition module 13, an optimizing space setting module 14, a charging price strategy acquisition module 15, a performance evaluation parameter determination module 16 and a preference information output module 17, wherein:
a charging demand acquisition module 11, configured to acquire charging demand information of a target user;
the element extraction module 12 is configured to perform element extraction on the charging requirement information to obtain charging requirement parameter information;
a charging pile list obtaining module 13, configured to obtain user position information, and obtain charging pile list information within a preset radius area based on the user position information;
a optimizing space setting module 14, configured to set a charging pile optimizing space based on the charging pile list information;
the charging price policy obtaining module 15 is configured to obtain charging price policy information of each charging pile in the charging pile list information;
a performance evaluation parameter determining module 16, configured to determine a performance evaluation parameter of the charging pile according to the charging demand parameter information and the charging price policy information;
and the optimization information output module 17 is used for performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
In one embodiment, the system further comprises:
the demand element information acquisition unit is used for extracting elements based on the charging demand information to acquire charging demand element information;
the demand element information determining unit is used for determining the charging demand element information, wherein the charging demand element information comprises a charging path, available electric quantity, charging time consumption and charging power supply requirements;
the element priority order obtaining unit is used for carrying out priority evaluation on each element information in the charging requirement element information to obtain a charging element priority order;
and the charging demand parameter obtaining unit is used for carrying out parameter arrangement on the charging demand information based on the priority order of the charging elements to obtain the charging demand parameter information.
In one embodiment, the system further comprises:
an element feature determination unit configured to obtain an element priority feature index including an element necessity and an element preference;
the element priority coordinate system construction unit is used for constructing an element priority coordinate system according to the element priority characteristic index;
an element evaluation unit, configured to evaluate each element information based on the element priority coordinate system, to obtain an element feature vector set;
and the vector ordering unit is used for ordering the modes of the feature vectors in the element feature vector set to obtain the priority order of the charging elements.
In one embodiment, the system further comprises:
the random selection unit is used for randomly selecting a first charging pile in the charging pile optimizing space to serve as a current optimal charging pile;
the first optimizing score obtaining unit is used for optimizing the first charging pile by adopting the charging pile performance evaluation parameters to obtain a first charging pile optimizing score;
the first neighborhood construction unit is used for constructing a first neighborhood of the current optimal charging pile based on a preset neighborhood mode, wherein the first neighborhood comprises a plurality of charging piles;
the N-th optimizing score obtaining unit is used for sequentially calculating optimizing scores of the plurality of charging piles to obtain a plurality of charging pile optimizing scores;
and the optimizing score comparison unit is used for comparing the optimizing scores of the first charging piles with the optimizing scores of the plurality of charging piles to obtain the optimizing information of the charging piles.
In one embodiment, the system further comprises:
the scoring and screening unit is used for comparing the optimizing scores of the charging piles and screening the first optimal charging pile score of the first neighborhood;
the reverse selection unit is used for reversely selecting the charging pile with the first optimal charging pile score and recording the charging pile as a second charging pile if the first optimal charging pile score is larger than the first charging pile optimizing score;
the iterative optimization unit is used for iteratively optimizing the second charging pile serving as the current optimal charging pile instead of the first charging pile;
and the charging pile optimization information output unit is used for outputting the charging pile optimization information if the iteration optimization reaches the preset iteration times.
In one embodiment, the system further comprises:
a tabu marking unit for adding the second charging pile into a tabu table for marking, and marking as a tabu mark;
the tabu duration obtaining unit is used for calculating the tabu duration of the tabu mark and obtaining the optimized tabu duration;
a tabu mark releasing unit, configured to release the tabu mark and delete the second charging pile from the tabu table when the optimized tabu duration satisfies a preset tabu duration;
and the second neighborhood construction unit is used for constructing a second neighborhood of the secondary charging pile and continuing iterative optimization.
In one embodiment, the system further comprises:
the real-time information acquisition unit is used for acquiring the use state information and the path traffic condition information of each charging pile in the charging pile list information;
the decision constraint condition determining unit is used for taking the use state information and the path traffic condition information as decision constraint conditions;
and the adjustment and correction unit is used for adjusting and correcting the charging pile preference information based on the decision constraint condition.
For a specific embodiment of the intelligent recommendation system for a charging pile, reference may be made to the above embodiment of the intelligent recommendation method for a charging pile, which is not described herein. The modules in the intelligent recommendation device of the charging pile can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing news data, time attenuation factors and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements an intelligent recommendation method for a charging pile.
Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: acquiring charging demand information of a target user; extracting elements from the charging demand information to obtain charging demand parameter information; acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information; setting a charging pile optimizing space based on the charging pile list information; acquiring charging price strategy information of each charging pile in the charging pile list information; determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information; and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring charging demand information of a target user; extracting elements from the charging demand information to obtain charging demand parameter information; acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information; setting a charging pile optimizing space based on the charging pile list information; acquiring charging price strategy information of each charging pile in the charging pile list information; determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information; and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information. The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. An intelligent recommendation method for a charging pile is characterized by comprising the following steps:
acquiring charging demand information of a target user;
extracting elements from the charging demand information to obtain charging demand parameter information;
acquiring user position information, and acquiring charging pile list information within a preset radius area based on the user position information;
setting a charging pile optimizing space based on the charging pile list information;
acquiring charging price strategy information of each charging pile in the charging pile list information;
determining charging pile performance evaluation parameters according to the charging demand parameter information and the charging price strategy information;
and performing global optimization in the charging pile optimization space based on the charging pile performance evaluation parameters, outputting charging pile optimization information, and performing recommendation management to a target user according to the charging pile optimization information.
2. The method of claim 1, wherein the obtaining the charging demand parameter information comprises:
extracting elements based on the charging demand information to obtain charging demand element information;
the charging demand element information comprises a charging path, available electric quantity, charging time consumption and charging power supply requirements;
performing priority evaluation on each element information in the charging requirement element information to obtain a charging element priority sequence;
and carrying out parameter arrangement on the charging demand information based on the priority order of the charging elements to obtain the charging demand parameter information.
3. The method of claim 2, wherein the obtaining the charging element priority order comprises:
obtaining element priority characteristic indexes, wherein the element priority characteristic indexes comprise element necessity and element preference;
constructing an element priority coordinate system according to the element priority characteristic index;
respectively evaluating the element information based on the element priority coordinate system to obtain an element feature vector set;
and sequencing the modes of the feature vectors in the element feature vector set to obtain the priority order of the charging elements.
4. The method of claim 1, wherein outputting charging pile preference information comprises:
randomly selecting a first charging pile in the charging pile optimizing space as a current optimal charging pile;
carrying out optimizing scoring on the first charging pile by adopting the charging pile performance evaluation parameters to obtain a first charging pile optimizing score;
constructing a first neighborhood of the current optimal charging pile based on a preset neighborhood mode, wherein the first neighborhood comprises a plurality of charging piles;
sequentially calculating the optimizing scores of the charging piles to obtain a plurality of optimizing scores of the charging piles;
and comparing the first charging pile optimizing score with the plurality of charging pile optimizing scores to obtain charging pile optimizing information.
5. The method of claim 4, wherein the comparing based on the first charging stake optimizing score and the plurality of charging stake optimizing scores to obtain charging stake preference information comprises:
comparing the plurality of charging pile optimization scores, and screening a first optimal charging pile score of the first neighborhood;
if the score of the first optimal charging pile is larger than the optimizing score of the first charging pile, reversely selecting the charging pile with the score of the first optimal charging pile, and recording the charging pile as a second charging pile;
the second charging pile replaces the first charging pile to serve as the current optimal charging pile for iterative optimization;
and if the iteration optimization reaches the preset iteration times, outputting the charging pile preference information.
6. The method of claim 5, wherein iteratively optimizing the second charging pile as the current optimal charging pile instead of the first charging pile comprises:
adding the second charging pile into a tabu list for marking, and marking as a tabu mark;
calculating the tabu duration of the tabu mark to obtain the optimizing tabu duration;
when the optimizing tabu duration meets the preset tabu duration, the tabu mark is released, and the second charging pile is deleted from the tabu table;
and constructing a second neighborhood of the secondary charging pile, and continuing to perform iterative optimization.
7. The method of claim 1, wherein the method comprises:
obtaining the use state information and the path traffic condition information of each charging pile in the charging pile list information;
taking the use state information and the path traffic condition information as decision constraint conditions;
and based on the decision constraint condition, adjusting and correcting the charging pile preference information.
8. An intelligent recommendation system for a charging stake, the system comprising:
the charging demand acquisition module is used for acquiring charging demand information of a target user;
the element extraction module is used for extracting elements from the charging demand information to obtain charging demand parameter information;
the charging pile list acquisition module is used for acquiring user position information and acquiring charging pile list information within a preset radius area based on the user position information;
the optimizing space setting module is used for setting a charging pile optimizing space based on the charging pile list information;
the charging price strategy acquisition module is used for acquiring charging price strategy information of each charging pile in the charging pile list information;
the performance evaluation parameter determining module is used for determining performance evaluation parameters of the charging pile according to the charging demand parameter information and the charging price strategy information;
and the optimal selection information output module is used for carrying out global optimization in the charging pile optimizing space based on the charging pile performance evaluation parameters, outputting charging pile optimal selection information and carrying out recommendation management to a target user according to the charging pile optimal selection information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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