CN117113033A - Charging data acquisition method and system for new energy automobile - Google Patents
Charging data acquisition method and system for new energy automobile Download PDFInfo
- Publication number
- CN117113033A CN117113033A CN202311201624.5A CN202311201624A CN117113033A CN 117113033 A CN117113033 A CN 117113033A CN 202311201624 A CN202311201624 A CN 202311201624A CN 117113033 A CN117113033 A CN 117113033A
- Authority
- CN
- China
- Prior art keywords
- charging data
- charging
- data
- user
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005065 mining Methods 0.000 claims abstract description 209
- 238000005457 optimization Methods 0.000 claims abstract description 55
- 238000012512 characterization method Methods 0.000 claims abstract description 24
- 238000013507 mapping Methods 0.000 claims abstract description 23
- 238000012790 confirmation Methods 0.000 claims abstract description 17
- 230000002159 abnormal effect Effects 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000005856 abnormality Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 abstract description 8
- 238000013480 data collection Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/29—Graphical models, e.g. Bayesian networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Economics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention provides a charging data acquisition method and system for a new energy automobile, and relates to the technical field of data processing. In the invention, the pre-built key information mining network is subjected to network optimization operation to form a target key information mining network corresponding to the key information mining network; performing key information mining operation on the received charging data of the user to be processed through a target key information mining network so as to output corresponding charging key information characteristic representation; mapping and outputting the charging key information characteristic representation through a mapping and outputting network so as to output data anomaly characterization parameters corresponding to charging data of a user to be processed; and carrying out data acquisition confirmation operation on the charging data of the user to be processed based on the data abnormal characterization parameters, and determining the charging data of the user to be processed as the acquired charging data of the target user under the condition of confirming acquisition of the charging data of the user to be processed. Based on the method, the reliability of the charging data acquisition can be improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a charging data acquisition method and system of a new energy automobile.
Background
Under the condition that the application of the new energy automobile is gradually common, the data size of the charging data of the new energy automobile is very large, so that the new energy automobile has higher application value. However, in the prior art, the collection of the charging data is generally random sampling or equidistant collection, so that there is a problem of low reliability.
Disclosure of Invention
Therefore, the invention aims to provide a charging data acquisition method and system for a new energy automobile, so as to improve the reliability of charging data acquisition.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
a charging data acquisition method of a new energy automobile comprises the following steps:
performing network optimization operation on a pre-built key information mining network to form a target key information mining network corresponding to the key information mining network;
performing key information mining operation on the received user charging data to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation;
Performing mapping output operation on the charging key information characteristic representation through a mapping output network to output data abnormality characterization parameters corresponding to the charging data of the user to be processed;
and carrying out data acquisition and confirmation operation on the user charging data to be processed based on the data anomaly characterization parameters, and determining the user charging data to be processed as acquired target user charging data under the condition of confirming acquisition of the user charging data to be processed.
In some preferred embodiments, in the method for collecting charging data of a new energy automobile, the step of performing a network optimization operation on a pre-built key information mining network to form a target key information mining network corresponding to the key information mining network includes:
determining an internal user charging data combination corresponding to first user charging data, wherein the internal user charging data combination comprises at least two internal user charging data of the first user charging data, and the at least two internal user charging data belong to charging data of the same user in different time periods;
performing key information mining operation on the user charging data in the internal user charging data combination through a key information mining network to form a corresponding charging key information cluster, wherein the charging key information cluster comprises charging key information characteristic representations corresponding to the first user charging data;
Based on the charging key information characteristic representation, analyzing internal matching parameters between internal user charging data of the first user charging data, and analyzing external matching parameters between the internal user charging data and external user charging data, wherein the external user charging data belongs to second user charging data except the first user charging data, the first user charging data and the second user charging data belong to charging data of different users, and the first user charging data and the second user charging data belong to the same time period;
analyzing a first mining error parameter corresponding to the first user charging data based on the internal matching parameter and the external matching parameter, wherein the first mining error parameter is used for reflecting the mining error degree between the internal matching parameter corresponding to the first user charging data and the external matching parameter corresponding to the first user charging data;
performing charging preference information identification operation based on the charging key information characteristic representation to output a first prediction possibility parameter of the internal user charging data in charging preference information and a second prediction possibility parameter of the internal user charging data in the charging preference information;
Analyzing a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter, wherein the second mining error parameter is used for reflecting the mining error degree between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
and carrying out network optimization operation on the key information mining network based on the first mining error parameter and the second mining error parameter so as to form a target key information mining network.
In some preferred embodiments, in the method for collecting charging data of a new energy automobile, after the step of performing a charging preference information identification operation based on the charging key information feature representation to output a first parameter of a prediction possibility that the charging data of the internal user has in charging preference information and a second parameter of a prediction possibility that the charging data of the internal user has in charging preference information, the method for collecting charging data of a new energy automobile further includes:
based on the charging key information characteristic representation, classifying the internal user charging data to form corresponding classification identification data and classification possibility degree of the internal user charging data corresponding to the classification identification data;
Determining third mining error parameters of different first user charging data in target classification identification data based on the classification possibility degree, wherein the different first user charging data refer to charging data of the same corresponding user but not belonging to the same time period;
the step of performing network optimization operation on the key information mining network based on the first mining error parameter and the second mining error parameter to form a target key information mining network includes:
and carrying out network optimization operation on the key information mining network based on the first mining error parameter, the second mining error parameter and the third mining error parameter so as to form a target key information mining network.
In some preferred embodiments, in the method for collecting charging data of a new energy automobile, the step of classifying the internal user charging data based on the charging key information feature representation to form corresponding classification identification data and a classification probability degree of the internal user charging data corresponding to the classification identification data includes:
based on the charging key information characteristic representation, classifying the internal user charging data to form corresponding classified identification data;
Performing comparison analysis operation on the classification identification data and the charging preference information to form classification possibility degree of the internal user charging data corresponding to the classification identification data;
the step of classifying the internal user charging data based on the charging key information feature representation to form corresponding classified identification data comprises the following steps:
performing identification contrast analysis operation on the charging key information characteristic representation to form a charging key information characteristic representation with identification data;
classifying the charging key information characteristic representation with the identification data to form a corresponding classification set;
and marking one identification data with the largest number in the classification set to form classification identification data corresponding to the classification set.
In some preferred embodiments, in the charging data collection method of a new energy automobile, the combination of internal user charging data includes first internal user charging data and second internal user charging data; and performing a charging preference information identification operation based on the charging key information feature representation to output a first parameter of a predicted likelihood that the internal user charging data has in charging preference information and a second parameter of a predicted likelihood that the internal user charging data has in the charging preference information, including:
Classifying the internal user charging data based on the charging key information characteristic representation to form an original classification possibility degree parameter of the internal user charging data in each piece of charging preference information;
and decomposing the original classification possibility degree parameters to form a first prediction possibility parameter corresponding to each piece of charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each piece of charging preference information of the second internal user charging data.
In some preferred embodiments, in the method for collecting charging data of a new energy automobile, the step of analyzing a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter includes:
analyzing first distinguishing data of the predicted possibility of the first user charging data in target charging preference information based on the first parameter of the predicted possibility of the target charging preference information and the second parameter of the predicted possibility of the target charging preference information;
analyzing second discrimination data of the prediction possibility between target charging preference information and other charging preference information of the first user charging data based on the first prediction possibility parameter of the target charging preference information, the second prediction possibility parameter of the target charging preference information, the first prediction possibility parameter of other charging preference information and the second prediction possibility parameter of the other charging preference information;
Analyzing a second mining error parameter to be processed of the first user charging data based on the prediction likelihood first discrimination data and the prediction likelihood second discrimination data, the second mining error parameter to be processed being used for reflecting a mining error degree between the prediction likelihood first parameter of the first user charging data and the prediction likelihood second parameter of the first user charging data, the mining error degree being the target charging preference information;
and obtaining a corresponding second mining error parameter based on the second mining error parameter to be processed.
In some preferred embodiments, in the method for collecting charging data of a new energy automobile, the step of analyzing a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter includes:
analyzing a second mining error parameter to be analyzed corresponding to the first user charging data based on a first prediction possibility parameter corresponding to each charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each charging preference information of the second internal user charging data, wherein the second mining error parameter to be analyzed is used for reflecting mining error degrees of different charging preference information between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
And obtaining a corresponding second mining error parameter based on the second mining error parameter to be analyzed.
The embodiment of the invention also provides a charging data acquisition system of the new energy automobile, which comprises the following steps:
the network optimization module is used for carrying out network optimization operation on a pre-built key information mining network so as to form a target key information mining network corresponding to the key information mining network;
the key information mining module is used for carrying out key information mining operation on the received charging data of the user to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation;
the mapping output module is used for carrying out mapping output operation on the charging key information characteristic representation through a mapping output network so as to output data abnormal characterization parameters corresponding to the charging data of the user to be processed;
and the acquisition confirmation module is used for carrying out data acquisition confirmation operation on the user charging data to be processed based on the data abnormal characterization parameters, and determining the user charging data to be processed as acquired target user charging data under the condition of confirming acquisition of the user charging data to be processed.
In some preferred embodiments, in the charging data acquisition system of a new energy automobile, the network optimization module includes:
the first network optimization sub-module is used for determining an internal user charging data combination corresponding to the first user charging data, wherein the internal user charging data combination comprises at least two internal user charging data of the first user charging data, and the at least two internal user charging data belong to charging data of the same user in different time periods;
the second network optimization sub-module is used for carrying out key information mining operation on the user charging data in the internal user charging data combination through a key information mining network so as to form a corresponding charging key information cluster, wherein the charging key information cluster comprises charging key information characteristic representations corresponding to the first user charging data;
a third network optimization sub-module, configured to analyze an internal matching parameter between internal user charging data of the first user charging data and an external matching parameter between the internal user charging data and external user charging data based on the charging key information feature representation, where the external user charging data belongs to second user charging data other than the first user charging data, the first user charging data and the second user charging data belong to charging data of different users, and the first user charging data and the second user charging data belong to the same time period;
A fourth network optimization sub-module, configured to analyze a first mining error parameter corresponding to the first user charging data based on the internal matching parameter and the external matching parameter, where the first mining error parameter is configured to reflect a mining error degree between the internal matching parameter corresponding to the first user charging data and the external matching parameter corresponding to the first user charging data;
a fifth network optimization sub-module, configured to perform a charging preference information identification operation based on the charging key information feature representation, so as to output a first parameter of prediction possibility that the charging data of the internal user has in the charging preference information, and a second parameter of prediction possibility that the charging data of the internal user has in the charging preference information;
a sixth network optimization sub-module, configured to analyze a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter, where the second mining error parameter is used to reflect a mining error degree between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
And a seventh network optimization sub-module, configured to perform a network optimization operation on the key information mining network based on the first mining error parameter and the second mining error parameter, so as to form a target key information mining network.
In some preferred embodiments, in the charging data acquisition system of a new energy automobile, the fifth network optimization sub-module is specifically configured to:
classifying the internal user charging data based on the charging key information characteristic representation to form an original classification possibility degree parameter of the internal user charging data in each piece of charging preference information;
and decomposing the original classification possibility degree parameters to form a first prediction possibility parameter corresponding to each piece of charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each piece of charging preference information of the second internal user charging data.
According to the charging data acquisition method and system for the new energy automobile, network optimization operation can be performed on the pre-built key information mining network to form a target key information mining network corresponding to the key information mining network; performing key information mining operation on the received charging data of the user to be processed through a target key information mining network so as to output corresponding charging key information characteristic representation; mapping and outputting the charging key information characteristic representation through a mapping and outputting network so as to output data anomaly characterization parameters corresponding to charging data of a user to be processed; and carrying out data acquisition confirmation operation on the charging data of the user to be processed based on the data abnormal characterization parameters, and determining the charging data of the user to be processed as the acquired charging data of the target user under the condition of confirming acquisition of the charging data of the user to be processed. Based on the above, the reliable basis for performing data acquisition confirmation operation can be determined by utilizing the high-precision data processing capability of the neural network, so that the reliability of charging data acquisition can be improved, and the defects in the prior art are overcome.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a charging data acquisition platform of a new energy automobile according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of steps included in the method for collecting charging data of a new energy automobile according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of each module included in the charging data acquisition system of the new energy automobile according to the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention provides a charging data acquisition platform of a new energy automobile. The charging data acquisition platform of the new energy automobile can comprise a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the method for collecting charging data of the new energy automobile according to the embodiment of the present invention (as described below).
Alternatively, in one embodiment, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
Alternatively, in one embodiment, the processor may be a general purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Alternatively, in an embodiment, the charging data collection platform of the new energy automobile may further include more or fewer components than those shown in fig. 1, or have a different configuration from that shown in fig. 1, for example, may include a communication unit for information interaction with other devices.
Alternatively, in an embodiment, the charging data collection platform of the new energy automobile may be a server with data processing capability.
With reference to fig. 2, the embodiment of the invention also provides a charging data acquisition method of a new energy automobile, which can be applied to the charging data acquisition platform of the new energy automobile. The method steps defined by the flow related to the charging data acquisition method of the new energy automobile can be realized by a charging data acquisition platform of the new energy automobile. The specific flow shown in fig. 2 will be described in detail.
And step S110, performing network optimization operation on the pre-built key information mining network to form a target key information mining network corresponding to the key information mining network.
In the embodiment of the invention, the charging data acquisition platform of the new energy automobile can perform network optimization operation on the pre-built key information mining network so as to form a target key information mining network corresponding to the key information mining network.
And step S120, performing key information mining operation on the received user charging data to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation.
In the embodiment of the invention, the charging data acquisition platform of the new energy automobile can carry out key information mining operation on the received charging data of the user to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation.
And step S130, carrying out mapping output operation on the charging key information characteristic representation through a mapping output network so as to output data abnormality characterization parameters corresponding to the charging data of the user to be processed.
In the embodiment of the invention, the charging data acquisition platform of the new energy automobile can perform mapping output operation on the charging key information characteristic representation through a mapping output network so as to output the data abnormality characterization parameters corresponding to the charging data of the user to be processed. The map output network may be formed by network optimization together with the target critical information mining network, and may include a full connection unit and an activation unit.
And step S140, carrying out data acquisition confirmation operation on the user charging data to be processed based on the data abnormal characterization parameters, and determining the user charging data to be processed as acquired target user charging data under the condition of confirming acquisition of the user charging data to be processed.
In the embodiment of the invention, the charging data acquisition platform of the new energy automobile can perform data acquisition confirmation operation on the charging data of the user to be processed based on the data anomaly characterization parameter, and determine the charging data of the user to be processed as the acquired charging data of the target user under the condition of confirming to acquire the charging data of the user to be processed. For example, under the condition that the data anomaly characterization parameter is greater than a preset anomaly characterization parameter, it may be determined that the user charging data to be processed is not collected, so that interference caused by unreliable user charging data to subsequent analysis is avoided, or under the condition that the data anomaly characterization parameter is greater than the preset anomaly characterization parameter, it may be determined that the user charging data to be processed is collected, at this time, the collected user charging data may be used for anomaly statistical analysis, so that based on different application scenarios, the collection and confirmation manners may be different.
Based on the above, i.e. step S110 to step S140, the reliable basis for performing the data acquisition confirmation operation can be determined by using the high-precision data processing capability of the neural network, so that the reliability of the charging data acquisition can be improved, and the defects in the prior art are overcome.
Optionally, in an embodiment, the step of performing a network optimization operation on the pre-built key information mining network to form a target key information mining network corresponding to the key information mining network may further include the following description:
determining an internal user charging data combination corresponding to first user charging data, wherein the internal user charging data combination comprises at least two internal user charging data of the first user charging data, the at least two internal user charging data belong to charging data of the same user in different time periods, but the at least two internal user charging data belong to the same time period, and in addition, the definition of the time period is not limited and can be configured according to actual application requirements;
performing key information mining operation on the user charging data in the internal user charging data combination through a key information mining network to form a corresponding charging key information cluster, wherein the charging key information cluster comprises charging key information characteristic representations corresponding to the first user charging data;
Based on the characteristic representation of the charging key information, analyzing internal matching parameters, such as cosine similarity, between the internal user charging data of the first user charging data, and analyzing external matching parameters between the internal user charging data and external user charging data, wherein the external user charging data belongs to second user charging data except the first user charging data, the first user charging data and the second user charging data belong to charging data of different users, and the first user charging data and the second user charging data belong to the same time period;
analyzing a first mining error parameter corresponding to the first user charging data based on the internal matching parameter and the external matching parameter, wherein the first mining error parameter is used for reflecting the mining error degree between the internal matching parameter corresponding to the first user charging data and the external matching parameter corresponding to the first user charging data;
performing charging preference information identification operation based on the charging key information characteristic representation to output a first prediction possibility parameter of the internal user charging data in charging preference information and a second prediction possibility parameter of the internal user charging data in the charging preference information, wherein the charging preference information can be information such as charging place, time, frequency and the like;
Analyzing a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter, wherein the second mining error parameter is used for reflecting the mining error degree between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
and performing network optimization operation on the key information mining network based on the first mining error parameter and the second mining error parameter to form a target key information mining network, for example, the first mining error parameter and the second mining error parameter may be weighted and summed to obtain a target mining error parameter, and then, performing network optimization operation on the key information mining network based on the target mining error parameter to form the target key information mining network.
Optionally, in an embodiment, after the step of performing the charging preference information identifying operation based on the charging key information feature representation to output the first parameter of the prediction possibility of the charging data of the internal user in the charging preference information and the second parameter of the prediction possibility of the charging data of the internal user in the charging preference information, the charging data collection method of the new energy automobile may further include the following description:
Based on the charging key information characteristic representation, classifying the internal user charging data to form corresponding classification identification data and classification possibility degree of the internal user charging data corresponding to the classification identification data;
and determining a third mining error parameter of different first user charging data in the target classification identification data based on the classification possibility degree, wherein the different first user charging data refer to charging data which are identical to corresponding users but do not belong to the same time period, and the third mining error parameter restricts the classification possibility degree of the combination of the internal user charging data corresponding to each first user charging data on the corresponding classification identification data to be maximum.
Optionally, in an embodiment, the step of classifying the internal user charging data based on the charging key information feature representation to form corresponding classification identification data and a classification probability degree of the internal user charging data corresponding to the classification identification data may further include the following description:
based on the charging key information characteristic representation, classifying the internal user charging data to form corresponding classified identification data;
And comparing and analyzing the classification identification data and the charging preference information to form the classification possibility degree of the internal user charging data corresponding to the classification identification data.
Optionally, in an embodiment, the step of classifying the internal user charging data based on the charging key information feature representation to form corresponding classified identification data may further include the following description:
performing identification contrast analysis operation on the charging key information characteristic representation to form a charging key information characteristic representation with identification data; and classifying the charging key information characteristic representation with the identification data to form corresponding classified sets, such as a plurality of classified sets; and marking the identification data with the largest number in the classification set to form classification identification data corresponding to the classification set, wherein the same classification identification data can be allocated to the charging key information characteristic representations corresponding to the same first user charging data.
Optionally, in an embodiment, the internal user charging data combination includes first internal user charging data and second internal user charging data, based on which the step of performing a charging preference information identification operation based on the charging key information feature representation to output a first parameter of prediction likelihood that the internal user charging data has in charging preference information and a second parameter of prediction likelihood that the internal user charging data has in the charging preference information may further include the following description:
Classifying the internal user charging data based on the charging key information characteristic representation to form an original classification possibility degree parameter of the internal user charging data in each piece of charging preference information;
and decomposing the original classification possibility degree parameters to form a first prediction possibility parameter corresponding to each piece of charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each piece of charging preference information of the second internal user charging data.
Optionally, in an embodiment, the step of analyzing the second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter may further include the following description:
analyzing first distinguishing data of the predicted possibility of the first user charging data in target charging preference information based on the first parameter of the predicted possibility of the target charging preference information and the second parameter of the predicted possibility of the target charging preference information;
analyzing second discrimination data of the prediction possibility of the first user charging data between target charging preference information and other charging preference information, which is other charging preference information than the target charging preference information, based on the first prediction possibility parameter of the target charging preference information, the second prediction possibility parameter of the target charging preference information, the first prediction possibility parameter of other charging preference information, and the second prediction possibility parameter of the other charging preference information;
Analyzing a second mining error parameter to be processed of the first user charging data based on the prediction likelihood first discrimination data and the prediction likelihood second discrimination data, the second mining error parameter to be processed being used for reflecting a mining error degree between the prediction likelihood first parameter of the first user charging data and the prediction likelihood second parameter of the first user charging data, the mining error degree being the target charging preference information;
the corresponding second mining error parameter is obtained based on the second mining error parameter to be processed, for example, the second mining error parameter to be processed may be used as the corresponding second mining error parameter, or may be used as a part of the second mining error parameter.
Optionally, in an embodiment, the step of analyzing the second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter may further include the following description:
analyzing a second mining error parameter to be analyzed corresponding to the first user charging data based on a first prediction possibility parameter corresponding to each charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each charging preference information of the second internal user charging data, wherein the second mining error parameter to be analyzed is used for reflecting mining error degrees of different charging preference information between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
And obtaining a corresponding second mining error parameter based on the second mining error parameter to be analyzed, wherein the second mining error parameter to be analyzed can be used as the corresponding second mining error parameter or can be used as a part of the second mining error parameter.
For the second mining error parameter to be analyzed, the degree of likelihood of all the internal user charging data for different charging preference information may be limited to be kept average, for example, the charging preference information includes a (a) th charging preference information and a (b) th charging preference information, and a difference between a sum value of the degree of likelihood of all the internal user charging data for the (a) th charging preference information and a sum value of the degree of likelihood of all the internal user charging data for the (b) th charging preference information may be small, such as smaller than a preset value.
With reference to fig. 3, the embodiment of the invention also provides a charging data acquisition system of the new energy automobile, which can be applied to the charging data acquisition platform of the new energy automobile. The charging data acquisition system of the new energy automobile can comprise the following software modules:
the network optimization module is used for carrying out network optimization operation on a pre-built key information mining network so as to form a target key information mining network corresponding to the key information mining network;
The key information mining module is used for carrying out key information mining operation on the received charging data of the user to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation;
the mapping output module is used for carrying out mapping output operation on the charging key information characteristic representation through a mapping output network so as to output data abnormal characterization parameters corresponding to the charging data of the user to be processed;
and the acquisition confirmation module is used for carrying out data acquisition confirmation operation on the user charging data to be processed based on the data abnormal characterization parameters, and determining the user charging data to be processed as acquired target user charging data under the condition of confirming acquisition of the user charging data to be processed.
Optionally, in one embodiment, the network optimization module includes:
the first network optimization sub-module is used for determining an internal user charging data combination corresponding to the first user charging data, wherein the internal user charging data combination comprises at least two internal user charging data of the first user charging data, and the at least two internal user charging data belong to charging data of the same user in different time periods;
The second network optimization sub-module is used for carrying out key information mining operation on the user charging data in the internal user charging data combination through a key information mining network so as to form a corresponding charging key information cluster, wherein the charging key information cluster comprises charging key information characteristic representations corresponding to the first user charging data;
a third network optimization sub-module, configured to analyze an internal matching parameter between internal user charging data of the first user charging data and an external matching parameter between the internal user charging data and external user charging data based on the charging key information feature representation, where the external user charging data belongs to second user charging data other than the first user charging data, the first user charging data and the second user charging data belong to charging data of different users, and the first user charging data and the second user charging data belong to the same time period;
a fourth network optimization sub-module, configured to analyze a first mining error parameter corresponding to the first user charging data based on the internal matching parameter and the external matching parameter, where the first mining error parameter is configured to reflect a mining error degree between the internal matching parameter corresponding to the first user charging data and the external matching parameter corresponding to the first user charging data;
A fifth network optimization sub-module, configured to perform a charging preference information identification operation based on the charging key information feature representation, so as to output a first parameter of prediction possibility that the charging data of the internal user has in the charging preference information, and a second parameter of prediction possibility that the charging data of the internal user has in the charging preference information;
a sixth network optimization sub-module, configured to analyze a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter, where the second mining error parameter is used to reflect a mining error degree between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
and a seventh network optimization sub-module, configured to perform a network optimization operation on the critical information mining network based on the first mining error parameter and the second mining error parameter, so as to form a target critical information mining network.
Optionally, in an embodiment, the fifth network optimization sub-module is specifically configured to:
classifying the internal user charging data based on the charging key information characteristic representation to form an original classification possibility degree parameter of the internal user charging data in each piece of charging preference information;
And decomposing the original classification possibility degree parameters to form a first prediction possibility parameter corresponding to each piece of charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each piece of charging preference information of the second internal user charging data.
In summary, according to the method and the system for acquiring the charging data of the new energy automobile, the pre-built key information mining network can be subjected to network optimization operation to form the target key information mining network corresponding to the key information mining network; performing key information mining operation on the received charging data of the user to be processed through a target key information mining network so as to output corresponding charging key information characteristic representation; mapping and outputting the charging key information characteristic representation through a mapping and outputting network so as to output data anomaly characterization parameters corresponding to charging data of a user to be processed; and carrying out data acquisition confirmation operation on the charging data of the user to be processed based on the data abnormal characterization parameters, and determining the charging data of the user to be processed as the acquired charging data of the target user under the condition of confirming acquisition of the charging data of the user to be processed. Based on the above, the reliable basis for performing data acquisition confirmation operation can be determined by utilizing the high-precision data processing capability of the neural network, so that the reliability of charging data acquisition can be improved, and the defects in the prior art are overcome.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The charging data acquisition method of the new energy automobile is characterized by comprising the following steps of:
performing network optimization operation on a pre-built key information mining network to form a target key information mining network corresponding to the key information mining network;
performing key information mining operation on the received user charging data to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation;
performing mapping output operation on the charging key information characteristic representation through a mapping output network to output data abnormality characterization parameters corresponding to the charging data of the user to be processed;
and carrying out data acquisition and confirmation operation on the user charging data to be processed based on the data anomaly characterization parameters, and determining the user charging data to be processed as acquired target user charging data under the condition of confirming acquisition of the user charging data to be processed.
2. The method for collecting charging data of a new energy automobile according to claim 1, wherein the step of performing network optimization operation on a pre-built key information mining network to form a target key information mining network corresponding to the key information mining network comprises the steps of:
determining an internal user charging data combination corresponding to first user charging data, wherein the internal user charging data combination comprises at least two internal user charging data of the first user charging data, and the at least two internal user charging data belong to charging data of the same user in different time periods;
performing key information mining operation on the user charging data in the internal user charging data combination through a key information mining network to form a corresponding charging key information cluster, wherein the charging key information cluster comprises charging key information characteristic representations corresponding to the first user charging data;
based on the charging key information characteristic representation, analyzing internal matching parameters between internal user charging data of the first user charging data, and analyzing external matching parameters between the internal user charging data and external user charging data, wherein the external user charging data belongs to second user charging data except the first user charging data, the first user charging data and the second user charging data belong to charging data of different users, and the first user charging data and the second user charging data belong to the same time period;
Analyzing a first mining error parameter corresponding to the first user charging data based on the internal matching parameter and the external matching parameter, wherein the first mining error parameter is used for reflecting the mining error degree between the internal matching parameter corresponding to the first user charging data and the external matching parameter corresponding to the first user charging data;
performing charging preference information identification operation based on the charging key information characteristic representation to output a first prediction possibility parameter of the internal user charging data in charging preference information and a second prediction possibility parameter of the internal user charging data in the charging preference information;
analyzing a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter, wherein the second mining error parameter is used for reflecting the mining error degree between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
and carrying out network optimization operation on the key information mining network based on the first mining error parameter and the second mining error parameter so as to form a target key information mining network.
3. The method for collecting charging data of a new energy vehicle according to claim 2, wherein after the step of performing a charging preference information identification operation based on the charging key information feature representation to output a first parameter of a predicted likelihood that the charging data of the interior user has in charging preference information and a second parameter of a predicted likelihood that the charging data of the interior user has in charging preference information, the method for collecting charging data of a new energy vehicle further comprises:
based on the charging key information characteristic representation, classifying the internal user charging data to form corresponding classification identification data and classification possibility degree of the internal user charging data corresponding to the classification identification data;
determining third mining error parameters of different first user charging data in target classification identification data based on the classification possibility degree, wherein the different first user charging data refer to charging data of the same corresponding user but not belonging to the same time period;
the step of performing network optimization operation on the key information mining network based on the first mining error parameter and the second mining error parameter to form a target key information mining network includes:
And carrying out network optimization operation on the key information mining network based on the first mining error parameter, the second mining error parameter and the third mining error parameter so as to form a target key information mining network.
4. The method for collecting charging data of a new energy automobile according to claim 3, wherein the step of classifying the internal user charging data based on the charging key information feature representation to form corresponding classification identification data and a classification probability degree of the internal user charging data corresponding to the classification identification data comprises the steps of:
based on the charging key information characteristic representation, classifying the internal user charging data to form corresponding classified identification data;
performing comparison analysis operation on the classification identification data and the charging preference information to form classification possibility degree of the internal user charging data corresponding to the classification identification data;
the step of classifying the internal user charging data based on the charging key information feature representation to form corresponding classified identification data comprises the following steps:
Performing identification contrast analysis operation on the charging key information characteristic representation to form a charging key information characteristic representation with identification data;
classifying the charging key information characteristic representation with the identification data to form a corresponding classification set;
and marking one identification data with the largest number in the classification set to form classification identification data corresponding to the classification set.
5. The method for collecting charging data of a new energy vehicle according to claim 2, wherein the combination of internal user charging data includes first internal user charging data and second internal user charging data; and performing a charging preference information identification operation based on the charging key information feature representation to output a first parameter of a predicted likelihood that the internal user charging data has in charging preference information and a second parameter of a predicted likelihood that the internal user charging data has in the charging preference information, including:
classifying the internal user charging data based on the charging key information characteristic representation to form an original classification possibility degree parameter of the internal user charging data in each piece of charging preference information;
And decomposing the original classification possibility degree parameters to form a first prediction possibility parameter corresponding to each piece of charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each piece of charging preference information of the second internal user charging data.
6. The method for collecting charging data of a new energy automobile according to claim 5, wherein the step of analyzing the second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter comprises:
analyzing first distinguishing data of the predicted possibility of the first user charging data in target charging preference information based on the first parameter of the predicted possibility of the target charging preference information and the second parameter of the predicted possibility of the target charging preference information;
analyzing second discrimination data of the prediction possibility between target charging preference information and other charging preference information of the first user charging data based on the first prediction possibility parameter of the target charging preference information, the second prediction possibility parameter of the target charging preference information, the first prediction possibility parameter of other charging preference information and the second prediction possibility parameter of the other charging preference information;
Analyzing a second mining error parameter to be processed of the first user charging data based on the prediction likelihood first discrimination data and the prediction likelihood second discrimination data, the second mining error parameter to be processed being used for reflecting a mining error degree between the prediction likelihood first parameter of the first user charging data and the prediction likelihood second parameter of the first user charging data, the mining error degree being the target charging preference information;
and obtaining a corresponding second mining error parameter based on the second mining error parameter to be processed.
7. The method for collecting charging data of a new energy automobile according to claim 5, wherein the step of analyzing the second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter comprises:
analyzing a second mining error parameter to be analyzed corresponding to the first user charging data based on a first prediction possibility parameter corresponding to each charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each charging preference information of the second internal user charging data, wherein the second mining error parameter to be analyzed is used for reflecting mining error degrees of different charging preference information between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
And obtaining a corresponding second mining error parameter based on the second mining error parameter to be analyzed.
8. The utility model provides a new energy automobile's data acquisition system that charges which characterized in that includes:
the network optimization module is used for carrying out network optimization operation on a pre-built key information mining network so as to form a target key information mining network corresponding to the key information mining network;
the key information mining module is used for carrying out key information mining operation on the received charging data of the user to be processed through the target key information mining network so as to output corresponding charging key information characteristic representation;
the mapping output module is used for carrying out mapping output operation on the charging key information characteristic representation through a mapping output network so as to output data abnormal characterization parameters corresponding to the charging data of the user to be processed;
and the acquisition confirmation module is used for carrying out data acquisition confirmation operation on the user charging data to be processed based on the data abnormal characterization parameters, and determining the user charging data to be processed as acquired target user charging data under the condition of confirming acquisition of the user charging data to be processed.
9. The charging data acquisition system of the new energy automobile of claim 8, wherein the network optimization module comprises:
the first network optimization sub-module is used for determining an internal user charging data combination corresponding to the first user charging data, wherein the internal user charging data combination comprises at least two internal user charging data of the first user charging data, and the at least two internal user charging data belong to charging data of the same user in different time periods;
the second network optimization sub-module is used for carrying out key information mining operation on the user charging data in the internal user charging data combination through a key information mining network so as to form a corresponding charging key information cluster, wherein the charging key information cluster comprises charging key information characteristic representations corresponding to the first user charging data;
a third network optimization sub-module, configured to analyze an internal matching parameter between internal user charging data of the first user charging data and an external matching parameter between the internal user charging data and external user charging data based on the charging key information feature representation, where the external user charging data belongs to second user charging data other than the first user charging data, the first user charging data and the second user charging data belong to charging data of different users, and the first user charging data and the second user charging data belong to the same time period;
A fourth network optimization sub-module, configured to analyze a first mining error parameter corresponding to the first user charging data based on the internal matching parameter and the external matching parameter, where the first mining error parameter is configured to reflect a mining error degree between the internal matching parameter corresponding to the first user charging data and the external matching parameter corresponding to the first user charging data;
a fifth network optimization sub-module, configured to perform a charging preference information identification operation based on the charging key information feature representation, so as to output a first parameter of prediction possibility that the charging data of the internal user has in the charging preference information, and a second parameter of prediction possibility that the charging data of the internal user has in the charging preference information;
a sixth network optimization sub-module, configured to analyze a second mining error parameter corresponding to the first user charging data based on the first prediction possibility parameter and the second prediction possibility parameter, where the second mining error parameter is used to reflect a mining error degree between the first prediction possibility parameter of the first user charging data and the second prediction possibility parameter of the first user charging data;
And a seventh network optimization sub-module, configured to perform a network optimization operation on the critical information mining network based on the first mining error parameter and the second mining error parameter, so as to form a target critical information mining network.
10. The charging data acquisition system of the new energy automobile according to claim 9, wherein the fifth network optimization sub-module is specifically configured to:
classifying the internal user charging data based on the charging key information characteristic representation to form an original classification possibility degree parameter of the internal user charging data in each piece of charging preference information;
and decomposing the original classification possibility degree parameters to form a first prediction possibility parameter corresponding to each piece of charging preference information of the first internal user charging data and a second prediction possibility parameter corresponding to each piece of charging preference information of the second internal user charging data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311201624.5A CN117113033A (en) | 2023-09-18 | 2023-09-18 | Charging data acquisition method and system for new energy automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311201624.5A CN117113033A (en) | 2023-09-18 | 2023-09-18 | Charging data acquisition method and system for new energy automobile |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117113033A true CN117113033A (en) | 2023-11-24 |
Family
ID=88805567
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311201624.5A Pending CN117113033A (en) | 2023-09-18 | 2023-09-18 | Charging data acquisition method and system for new energy automobile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117113033A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220091818A1 (en) * | 2020-09-22 | 2022-03-24 | Shanghai Icekredit, Inc. | Data feature processing method and data feature processing apparatus |
| CN114565008A (en) * | 2022-01-12 | 2022-05-31 | 西安理工大学 | Lithium battery abnormity detection method based on long-time and short-time memory self-encoder |
| CN114638440A (en) * | 2022-05-17 | 2022-06-17 | 国网湖北省电力有限公司经济技术研究院 | Charging load ultra-short-term prediction method based on charging pile utilization degree |
| CN115792637A (en) * | 2022-12-09 | 2023-03-14 | 杭州电子科技大学 | Lithium battery health state estimation method |
| CN115840922A (en) * | 2022-09-15 | 2023-03-24 | 杭州齐智科技有限公司 | Charging abnormal behavior analysis method based on deep learning algorithm |
-
2023
- 2023-09-18 CN CN202311201624.5A patent/CN117113033A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220091818A1 (en) * | 2020-09-22 | 2022-03-24 | Shanghai Icekredit, Inc. | Data feature processing method and data feature processing apparatus |
| CN114565008A (en) * | 2022-01-12 | 2022-05-31 | 西安理工大学 | Lithium battery abnormity detection method based on long-time and short-time memory self-encoder |
| CN114638440A (en) * | 2022-05-17 | 2022-06-17 | 国网湖北省电力有限公司经济技术研究院 | Charging load ultra-short-term prediction method based on charging pile utilization degree |
| CN115840922A (en) * | 2022-09-15 | 2023-03-24 | 杭州齐智科技有限公司 | Charging abnormal behavior analysis method based on deep learning algorithm |
| CN115792637A (en) * | 2022-12-09 | 2023-03-14 | 杭州电子科技大学 | Lithium battery health state estimation method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110658807A (en) | Vehicle fault diagnosis method, device and system | |
| CN116126947B (en) | Big data analysis method and system applied to enterprise management system | |
| CN116821777B (en) | Novel basic mapping data integration method and system | |
| CN116757870A (en) | Intelligent energy monitoring data processing method and system for energy Internet of things | |
| CN116070149A (en) | Data analysis method and system based on artificial intelligence and cloud platform | |
| CN117113033A (en) | Charging data acquisition method and system for new energy automobile | |
| CN116566766A (en) | Intelligent power gateway management and control method and system | |
| CN115484044A (en) | Data state monitoring method and system | |
| CN113538735B (en) | Vehicle use identification method and device | |
| CN113673430A (en) | User behavior analysis method based on Internet of things | |
| CN117141290A (en) | Charging management and control method and system for new energy automobile | |
| CN117150065B (en) | Image information acquisition method and system | |
| CN114880298A (en) | Smart city data sharing method and system | |
| CN116996403B (en) | Network traffic diagnosis method and system applying AI model | |
| CN116994609B (en) | Data analysis method and system applied to intelligent production line | |
| CN117113507A (en) | Data acquisition method and system based on building informatization | |
| CN115687792B (en) | Big data acquisition method and system for online internet service | |
| CN117251790A (en) | Intelligent logistics data acquisition method and system | |
| CN114978040B (en) | Attenuation condition analysis method and system based on solar cell data | |
| CN115980279B (en) | Stability optimization method and system for neon purity detection system | |
| CN114880714A (en) | Data security verification method and system based on smart city data sharing | |
| CN116958838B (en) | Forest resource monitoring method and system based on unmanned aerial vehicle aerial survey technology | |
| CN117668962A (en) | Monitoring method and system based on building informatization | |
| CN116662415B (en) | Intelligent matching method and system based on data mining | |
| CN116910729B (en) | Nuclear body processing method and system applied to multi-organization architecture |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |