KR102091799B1 - Method and apparatus for analyzing transaction related to electric vehicle charging - Google Patents

Abstract

Disclosed by the present invention are a method and a device for analyzing transactions regarding the charging of an electric vehicle. The method includes steps of: acquiring transaction data including transactions including a charging start time and a charging completion time, respectively; setting sample lengths of a search section covering the charging start time and charge completion time of at least one of the transactions included in the transaction data; calculating inclusion ratios of the sample lengths based on each of the maximum number of transactions capable of being covered by each search section of the sample lengths, the number of the total transactions according to transition data, and sample lengths; selecting reference lengths among the sample lengths based on a change pattern according to sample lengths of inclusion ratios with respect to the sample lengths; and determining the optimum lengths of search sections corresponding to a maximum inclusion rate based on inclusion ratios of reference lengths. The present invention is able to solve problems caused by the constraints of battery capacity.

Description

METHOD AND DEVICE TO ANALYSIS TRANSACTIONS REGARDING THE CHARGING OF ELECTRIC VEHICLES

The embodiments below relate to a method and apparatus for analyzing a transaction related to charging of an electric vehicle.

Electric vehicles are expected to replace gasoline vehicles for a variety of advantages, such as being environmentally friendly and not dependent on fossil fuels. Electric vehicles include batteries that store power and provide the energy needed for driving. Electric vehicles may have restrictions according to battery capacity. Despite the growth of battery technology, there is a limit to the distance an electric vehicle can travel without additional charging of the battery, and it may take considerable time to charge the battery.

According to an embodiment, a method of analyzing a transaction related to charging of an electric vehicle may include: obtaining transaction data including transactions each including a charging start time and a charging completion time related to charging of the electric vehicle; Setting sample lengths of a search period covering a charge start time and a charge completion time of at least one of the transactions included in the transaction data; Calculating inclusion ratios for the sample lengths based on the maximum number of transactions that can be covered by the search interval of each of the sample lengths, the total number of transactions according to the transaction data, and each of the sample lengths; Selecting reference lengths from among the sample lengths based on a change pattern according to the sample lengths of the inclusion ratios with respect to the sample lengths; And determining an optimal length of the search interval corresponding to the maximum inclusion ratio based on the inclusion ratios with respect to the reference lengths.

The step of selecting reference lengths may include selecting, among the sample lengths, at least a portion of the sample lengths in which the inclusion ratios related to the sample lengths increase as the sample lengths increase as the reference lengths. have. Determining the optimal length may include determining the optimal length using binary search based on the reference lengths. The reference lengths may include a first length, a second length having a value greater than the first length, and a third length having a value greater than the second length, and determining the optimal length includes: And determining the optimal length using a binary search in a section between a length and the second length.

Determining the optimal length may include calculating an inclusion ratio with respect to an intermediate length between the first length and the second length, wherein an inclusion ratio with respect to the intermediate length is included with respect to the first length If it is greater than the ratio, may include determining the optimal length in the section between the intermediate length and the second length, and if the inclusion ratio for the intermediate length is less than the inclusion ratio for the first length, the And determining the optimal length in a section between the first length and the intermediate length. The intermediate length may correspond to an average of the first length and the second length.

The sample lengths may include a first length, and calculating the inclusion ratios related to the sample lengths may include a maximum transaction that can be covered by the search duration of the first length while moving the search duration corresponding to the first length. It may include the step of determining the number of.

The sample lengths may include a first length, and the step of calculating the inclusion ratios for the sample lengths is a charge start time while scanning transactions belonging to a first data set given priority based on a charge completion time. Storing in a second data set that is given priority based on; The second data set is maintained or the second data set is compared by comparing a difference value obtained by subtracting the first length from the charging completion times of a scan target transaction and a charging start time of each transaction stored in the second data set. Removing at least one transaction including a charging start time smaller than the difference value from the; And determining the maximum number of transactions that can be covered by the first length search period based on the number of transactions stored in the second data set.

According to an embodiment, an apparatus for analyzing a transaction related to charging of an electric vehicle includes a processor; And a memory for storing instructions executed in the processor, and when the instructions are executed in the processor, the processor includes transaction data including transactions including a charging start time and a charging completion time for charging the electric vehicle, respectively. To obtain, set the sample lengths of the search interval covering the charge start time and the charge completion time of at least one of the transactions included in the transaction data, and the maximum coverable by each search interval of the sample lengths Based on the number of transactions, the total number of transactions according to the transaction data, and each of the sample lengths, calculate inclusion ratios regarding the sample lengths, and the sample lengths of the inclusion ratios relative to the sample lengths Based on the change pattern according to Reference lengths are selected from among sample lengths, and based on the inclusion ratios for the reference lengths, an optimal length of the search interval corresponding to the maximum inclusion ratio is determined.

1 is a view showing a change in the number of simultaneous charges according to an embodiment.
2 is a diagram illustrating transactions covered by certain search intervals according to an embodiment.
3 is a diagram illustrating a configuration for obtaining the maximum number of transactions according to an embodiment.
4 is a diagram showing the maximum number of transactions according to a location of a search section according to an embodiment.
5 to 7 are views showing a process for obtaining the maximum number of transactions according to an embodiment.
8 is a diagram illustrating a relationship between a length of a search section and the maximum number of transactions according to an embodiment.
9 is a diagram illustrating transactions according to an embodiment.
10 is a graph showing the maximum number of transactions according to the length of a search section according to an embodiment.
11 is a graph showing a ratio between a length of a search section and a maximum number of transactions according to a length of a search section according to an embodiment.
12 is an operation flowchart showing a transaction analysis method according to an embodiment
13 is a diagram illustrating an apparatus for analyzing a transaction according to an embodiment.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed in this specification are exemplified only for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiments according to the concept of the present invention These can be implemented in various forms and are not limited to the embodiments described herein.

Embodiments according to the concept of the present invention can be applied to various changes and can have various forms, so that the embodiments will be illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosure forms, and includes modifications, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various components, but the components should not be limited by the terms. The above terms are only for the purpose of distinguishing one component from other components, for example, without departing from the scope of rights according to the concept of the present invention, the first component may be referred to as the second component, Similarly, the second component may also be referred to as the first component.

When an element is said to be “connected” or “connected” to another component, it is understood that other components may be directly connected to or connected to other components, but other components may exist in the middle. It should be. On the other hand, when a component is said to be “directly connected” or “directly connected” to another component, it should be understood that no other component exists in the middle. Expressions that describe the relationship between the elements, for example, “between” and “immediately between” or “directly neighboring to” should also be interpreted.

The terminology used herein is only used to describe specific embodiments and is not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, the terms “include” or “have” are intended to designate the presence of a feature, number, step, action, component, part, or combination thereof as described, one or more other features or numbers, It should be understood that the existence or addition possibilities of steps, actions, components, parts or combinations thereof are not excluded in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person skilled in the art to which the present invention pertains. Terms such as those defined in a commonly used dictionary should be interpreted as having meanings consistent with meanings in the context of related technologies, and should not be interpreted as ideal or excessively formal meanings unless explicitly defined herein. Does not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. The same reference numerals in each drawing denote the same members.

1 is a view showing a change trend of the number of simultaneous charges according to an embodiment.

The operation data of the electric vehicle charger may include charging start time and charging completion time for each transaction for each charger. Data including charging start time and charging end time for charging an electric vehicle, respectively, may be referred to as transactions, and operational data including such transactions may be referred to as transaction data.

Operational data is steadily accumulating, and the amount of data is continuously increasing, and analysis of these data is expected to take a lot of time in proportion to the amount of data. Therefore, it is necessary to introduce and apply an intelligent algorithm optimized for the subject to be analyzed. An apparatus for analyzing a transaction related to charging of an electric vehicle (hereinafter, referred to as an analysis apparatus) may analyze transactions included in a transaction set using an optimization algorithm according to an embodiment.

About 250 rapid chargers are being monitored in Jeju Island. After monitoring for 8 months, about 180,000 charging transactions were detected. If you want to measure the case where the most charges are performed simultaneously using these, sort the start time and end time of each transaction according to the time, scan one by one, count the start event as +1, count the end event as -1, and count Find the largest case. The results are shown in FIG. 1. 1 is not related to the entire time, but shows the change in the number of simultaneous charges during a partial time period.

2 is a diagram illustrating transactions covered by certain search intervals according to an embodiment.

Analyzing the number of transactions processed within a given length of time is more useful in measuring the service rate of the charger than simply analyzing whether the charger is used or not. have. In the following, this time period may be referred to as a search period. The search period may cover charging start time and charging completion time of at least one transaction. That is, the start time of the search period may be earlier than the charging start time, and the end time of the search period may be later than the charging completion time.

FIG. 2 shows an example of a search section, and it is possible to identify a section that has serviced the most electric vehicles through a search section of a predetermined length, estimate demand for the section, and design additional services related to advertisements. Referring to FIG. 2, two hypothetical time periods (ie, search periods) A and B having a given length L are shown.

Four electric vehicles have completed use of the service during the search period A, and no electric vehicles have completed use of the service during the search period B. 2 shows the service flow of the electric vehicle chargers T1, T2, T3, and T4, and the charger T1 has performed one transaction, and the charger T4 has performed three transactions. The charging time of each charger may vary depending on the amount of power depending on the type of charger, the remaining battery power of the electric vehicle to be charged, and the like. Various solution methods can be used to calculate the section of size (or length) L that provides the most services and to find the section.

3 is a diagram illustrating a configuration for obtaining the maximum number of transactions according to an embodiment.

When the length of the search section is fixed, the analysis apparatus may determine the maximum number of transactions that can be covered by the search section while moving the search section. For example, while moving the search section, the analysis device may determine the number of transactions covered by the search section corresponding to each position of the search section, and the maximum transaction corresponding to the position of the search section covering the most transactions You can decide the number of. When the length of the search section is variable, this process may be repeatedly performed for each length.

The analysis device can obtain the maximum number of transactions based on a given data set. For example, the data set may correspond to the entire operational data, or may correspond to a portion of the overall operational data. Referring to FIG. 3, a first data set 310 including transactions 315 and a second data set 320 including transactions 325 are shown.

The analysis device scans the transactions 315 belonging to the first data set 310 one by one and stores the transactions 315 in the second data set 320. Transactions 325 represent the state in which transactions 315 are stored in second data set 320 and may be part of transactions 315.

The analysis device may sort the transactions 315 according to the charging completion time of the transactions 315 and scan the transactions 315 one by one. This process can be performed with O (n log n) complexity. In addition, through the sorting process, priorities may be given to the transactions 315 in the first data set 310 based on the charging completion time. The analysis device may increment the count by 1 each time a transaction is scanned and store the transaction in the second data set 320. For example, the second data set 320 may correspond to a priority queue.

The analysis device can sort the transactions 325 according to the charging start time. Through this sorting process, priorities may be given to transactions 325 in the second data set 320 based on a charging start time. Accordingly, when at least some of the transactions 325 are fetched (or removed) from the second data set 320, the transactions 325 may be fetched (or removed) in the order in which charging start time is fast. .

The analysis device scans the transactions 315 one by one, and among all the transactions 325 stored in the second data set 320, the charging start time is L before the charging end time of the current transaction (ie, the transaction being scanned). Can be removed. For example, the analysis device compares a difference value obtained by subtracting the L value from the charging completion times of the scan target transaction, and the charging start time of each of the transactions 325 stored in the second data set 320, thereby comparing the second data set. It is possible to maintain the 320 or remove at least one transaction including the charging start time less than the difference value from the second data set 320. The analysis device may track the number of transactions 325 in the second data set 320 and determine when the number is the largest as the maximum service interval. The number of transactions 325 stored in the second data set 320 in the maximum service interval may correspond to the maximum number of transactions.

4 is a diagram illustrating the maximum number of transactions according to a location of a search section according to an embodiment. 4, transactions related to charging of the chargers T1 to T4 are illustrated. A fixed size search period may be used to detect the maximum number of transactions. The search periods L1 to L4 may be understood as a fixed size search period moving along a time axis. The search period L2 and the search period L4 include two completed transactions, and this value is maximized. As a result, when there are n transactions in total in order to find out the maximum number of transactions included in a specific L period, operation of time complexity of O (n log n) may be involved.

5 to 7 are diagrams illustrating a process for obtaining the maximum number of transactions according to an embodiment.

5 to 7, first data sets 510, 610, and 710 and second data sets 520, 620, and 720 are shown.

The first data sets 510, 610, and 710 and the second data sets 520, 620, and 720 may correspond to the first data set 310 and the second data set 320 of FIG. 3, respectively. The first data sets 510, 610, and 710 are illustrated as including eight transactions as one example, 'idx' indicates an index for classifying transactions, 'start' indicates a charging start time, and ' end 'represents the charging end time. The first data sets 510, 610, and 710 may be sorted based on the charging end time, and the second data sets 520, 620, and 720 may be sorted based on the charging start time. The analysis device may process transactions by giving priority to the first data set 510, 610, 710 and the second data set 520, 620, 720 in sorted order. It is assumed that the length of the search section is '30'.

The analysis device can scan the transactions (0 to 7) in the first data set (510, 610, 710) and store them in the second data set (520, 620, 720), the second data set (520, 620) , 720, all of the transactions having a charging start time before the charging end time of the current transaction (ie, the transaction being scanned) may be removed. 5 to 7 show a process related to transactions 3 to 5 among them.

Referring to FIG. 5, the analysis device may scan the transaction 3 in the first data set 510 and store the transaction 3 in the second data set 520. Transactions 0 to 3 are stored in the second data set 520. Among them, the transactions 0 and 1 are stored in the process of processing the transactions 0 and 1 in the first data set 510. The charge start time '25' of the transaction 3 is greater than the value '5' minus the length '30' of the search period from the charge end time '35' of the transaction 3 currently being scanned. Accordingly, the analysis device can maintain the second data set 520. In the current state, the number of transactions stored in the second data set 520 and the maximum number of transactions are both '4'.

Referring to FIG. 6, the analysis device may scan the transaction 4 in the first data set 610 and store the transaction 4 in the second data set 620. Transactions 0 to 4 are stored in the second data set 620. The charging start time '5' of the transaction 4 is smaller than the value '10' minus the length '30' of the search section from the charging end time '40' of the transaction 4 currently being scanned. The analysis device can remove the transaction 4 from the second data set 620. In the current state, transactions 0 to 3 are stored in the second data set 620, and the number of transactions and the maximum number of transactions stored in the second data set 620 are both '4'.

Referring to FIG. 7, the analysis device may scan the transaction 5 in the first data set 710 and store the transaction 5 in the second data set 720. Transactions 0 to 3 and 5 are stored in the second data set 720. In the second data set 720, the charging start time '10' of the transactions (0, 1) is a value '20 minus the length of the search section '30' from the charging end time '50' of the transaction (5) being scanned. Less than The analysis device may remove the transactions (0, 1) from the second data set 720. In the current state, the number of transactions stored in the second data set 720 is '3', and the maximum number of transactions is '4'. If this process is performed for the remaining transactions 6 and 7, the maximum number of transactions is determined as '4'.

8 is a diagram illustrating a relationship between a length of a search section and a maximum number of transactions according to an embodiment.

When a specific search interval is used, the number of processing transactions according to the time as shown in FIG. 1 is shown. If you find the section with the fastest change in throughput along the time axis and the change is maintained, you can predict the change in this time zone to reserve power. Prediction of the maximum demand simply by the number of simultaneous charges can be used to calculate the real-time power generation of the power, while the identification of the maximum throughput and the maximum throughput section can play an important role in determining the buffer of the power, that is, the capacity of the battery.

In order to grasp this section, it is necessary to obtain the length of the section in which the maximum throughput is maintained higher than the other sections, that is, the search section. The larger the length L, the larger the maximum throughput included in L, and if L is 0, the number of transactions included in L is 0. If the size of L is found, it is possible to detect the number of transactions included in L according to the process for obtaining the maximum number of transactions described above, and to detect the start and end points of L when the number becomes the maximum.

As shown in FIG. 8, the size of L to be searched in the embodiments corresponds to a time period having the most rapid throughput. In FIG. 8, both L1 and L2 include four transactions, but L1 is shorter than L2. Therefore, it can be determined that L1 is a section having a higher throughput than other sections.

9 is a diagram illustrating transactions according to an embodiment, FIG. 10 is a graph showing the maximum number of transactions according to the length of a search section according to an embodiment, and FIG. 11 is according to a length of a search section according to an embodiment This graph shows the ratio between the length of the search section and the maximum number of transactions.

In the example of FIG. 9, the problem to be sought can be grasped. If L is 12 when the entire search period is 12, all 7 transactions are included in L. On the other hand, if L is very small, no transaction is included in L.

In order to find an appropriate size L, the number of transactions in a section must be calculated according to a process for obtaining the maximum number of transactions for the possible L values, and based on this, an L value including a peak must be determined. FIG. 10 shows the number of transactions included for each L for the transaction set given in the example of FIG. 9. In FIG. 10, the period where the coverage changes rapidly can be seen as L being '1' and '4'.

In order to find the rapidly changing criteria, it is possible to consider the length of the interval and the ratio of transactions involved in each L. As shown in FIG. 11, it can be seen that the ratios of '2' and '1.25' in the section of '1' and '4' are greater than other L values. Of course, the smaller the length of the section, the higher the ratio with the number of transactions involved.

Ultimately, the problem to be solved is to determine the size of this L. If the number of transactions is large and the size of L increases, calculating the total number of transactions contained in L for all possible L takes a lot of time. It is important to reduce.

First, it is assumed that the total number of transactions and the length Lm of the entire search interval are given. Lm is the maximum value that L that you are looking for can have. The L to be searched can be referred to as the optimal length.

In calculating the inclusion ratio for a specific L value, it may be calculated as the ratio of the total transaction to the inclusive transaction rather than a simple number in order to calculate the fair ratio for each L size. For example, the inclusion ratio may be calculated according to Equation 1 below.

In Equation 1, 'F (L)' denotes an inclusion ratio, 'T _L ' denotes the maximum number of transactions that can be covered by a search interval of length L, and 'T _A ' denotes the total number of transactions according to transaction data. And 'L' represents the L value, that is, the length of the search section. For example, if the total number of transactions is 200, the length of the search section is 2, and the maximum number of transactions that can be covered by the search section is 20, the inclusion ratio may be calculated as (20/200) / 2 = 0.05. In order to calculate the inclusion ratio for a particular L, the execution time of O (n log n) is required. In order to reduce the calculation regarding the inclusion ratio, the inclusion ratio according to the minimum value L1 given first may be calculated.

12 is an operation flowchart illustrating a transaction analysis method according to an embodiment. The optimal length of the search section may be determined through transaction analysis.

Referring to FIG. 12, in step 1210, the analysis device sets the sample length (hereinafter, referred to as L _S ) of the search interval, and includes ratios related to the sample lengths (hereinafter, referred to as R _S ) Can be calculated). For example, L _S values may be 2, 4, 6, 8, 16, ..., etc. R _S value may be calculated based on Equation 1 mentioned above. For example, R _S values may be calculated based on the maximum number of transactions that can be covered by a search interval of each of the sample lengths, the total number of transactions according to transaction data, and each of the sample lengths.

In step 1220, analysis device is selected to refer to the length (which may be referred to below, L _R) in the L values _S based on the change pattern of the R _S L _S values according to the values. Here, the change pattern is R _S value in accordance with an increase of R monotone increasing the _S values increased pattern monotone decreasing that R _S values decreased with the increase of the L _S value pattern, and L _S value in accordance with an increase of the L _S value They may include an increasing and decreasing refractive pattern. For example, among the L _S values, the analysis apparatus may select at least a part of the L _S value in which the R _S values increase as the L _S values increase, as L _R values. Analysis device can select a value of _S L _{F (L S)> F (} 2 * L S)> F (4 * L S) to _R L. Such a change pattern may correspond to an interval in which the inclusion ratio changes according to the L value, but the increase in the L value no longer significantly affects the inclusion ratio.

In step 1230, the analysis device determines the optimal length of the search section corresponding to the maximum inclusion ratio R _M (hereinafter, L) based on the inclusion ratios for the reference lengths (hereinafter, may be referred to as R _R ). _O may be referred to). The analysis device may determine the L _O value using a binary search based on the L _R values. For example, the analysis apparatus may determine the value of L _O by finding L having an increased inclusion ratio by binary search between L _R and 2 * L _R.

The analysis device sets the lower limit value L (L) = L _R , the upper limit value H (L) = 2 * L _R, and sets the intermediate value (or intermediate length) L 'between the upper limit value and the lower limit value, L You can calculate the inclusion rate for '. When the inclusion ratio for L 'is greater than the inclusion ratio for L _R , the analysis device may determine the L _O value in the interval between L' and 2 * L _R. When the inclusion ratio for L 'is smaller than the inclusion ratio for L _R , the analysis device may determine the L _O value in the interval between L _R and L'. For example, the median length can correspond to the average of the upper and lower limits. That is, L '= (L (L) + H (L)) / 2 can be set. If L '<L, the analyzer can determine L as L _O.

If the initial value of L = 1 is accepted, L = 1 may be the answer, and a value with a higher inclusion ratio may be selected by comparison with L obtained in a subsequent process. L can be assumed to start at a sufficiently small value,

Hereinafter, one example of determining the optimal length will be described.

The analysis device may set L _S values to 2, 4, 8, 16, ..., and obtain F (2), F (4), F (8), F (16), .... Table 1 shows data corresponding to the corresponding L _S values. As another example, L _S values may be set to include 1.

L ... 2 4 6 8 16 ... F (L) ... 0.01 0.013 0.014 0.01 0.008 ...

Referring to Table 1, since F (4) <F (8) <F (16), L _R values are 4, 8, and 16, and L (L) = 4, H (L) = 8. Accordingly, L '= 6 and F (4) <F (6), so that in the next step, L (L) = 6, H (L) = 8, and L' = 7.

L ... 2 4 6 7 8 16 ... F (L) ... 0.01 0.013 0.014 0.012 0.01 0.008 ...

Here, F (6)> F (7), and 6 is selected as L _O corresponding to the maximum inclusion ratio. If F (7) is larger, L (L) = 7, H (L) = 8, and L 'becomes 7, which is the same as the threshold, and ends. FIG. 13 is a device for analyzing a transaction according to an embodiment It is a figure showing.

Referring to FIG. 13, the device 1300 includes a processor 1310 and a memory 1320. The memory 1320 may be connected to the processor 1310 and store instructions executable by the processor 1310, data to be processed by the processor 1310, or data processed by the processor 1310. The memory 1320 includes non-transitory computer-readable media, such as high-speed random access memory and / or non-volatile computer-readable storage media (eg, one or more disk storage devices, flash memory devices, or other non-volatile solid state memory devices). It can contain.

The processor 1310 may execute instructions for performing one or more operations described with reference to FIGS. 1 to 12. For example, the processor 1310 acquires transaction data including transactions each including a charging start time and a charging completion time for charging of the electric vehicle, and charging of at least one of the transactions included in the transaction data Set the sample lengths of the search period covering the start time and the charge completion time, and based on each of the sample lengths, the maximum number of transactions that can be covered by the search period, the total number of transactions according to the transaction data, and each of the sample lengths. , Calculates inclusion ratios for sample lengths, selects reference lengths among sample lengths based on a change pattern according to sample lengths of inclusion ratios for sample lengths, and based on inclusion ratios for reference lengths Thus, an optimal length of the search section corresponding to the maximum inclusion ratio can be determined.

The device described above may be implemented with hardware components, software components, and / or combinations of hardware components and software components. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an Arithmetic Logic Unit (ALU), a digital signal processor (micro signal processor), a microcomputer, and a Field Programmable Gate Array (FPGA). , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose computers or special purpose computers. The processing device may run an operating system (OS) and one or more software applications running on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For convenience of understanding, a processing device may be described as one being used, but a person having ordinary skill in the art, the processing device may include a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that may include. For example, the processing device may include a plurality of processors or a processor and a controller. In addition, other processing configurations, such as parallel processors, are possible.

The software may include a computer program, code, instruction, or a combination of one or more of these, and configure the processing device to operate as desired, or process independently or collectively You can command the device. Software and / or data may be interpreted by a processing device, or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodied in the transmitted signal wave. The software may be distributed on networked computer systems, and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer-readable medium may include program instructions, data files, data structures, or the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiments or may be known and usable by those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs, DVDs, and magnetic media such as floptical disks. -Hardware devices specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language code that can be executed by a computer using an interpreter, etc., as well as machine language codes produced by a compiler. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described by a limited embodiment and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques are performed in a different order than the described method, and / or the components of the described system, structure, device, circuit, etc. are combined or combined in a different form from the described method, or other components Alternatively, even if replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (10)

In the method of analyzing the transaction related to the charging of the electric vehicle,
Acquiring transaction data including transactions each including a charging start time and a charging completion time for charging the electric vehicle;
Setting sample lengths of a search period covering a charge start time and a charge completion time of at least one of the transactions included in the transaction data;
Calculating inclusion ratios for the sample lengths based on the maximum number of transactions that can be covered by the search interval of each of the sample lengths, the total number of transactions according to the transaction data, and each of the sample lengths;
Selecting reference lengths from among the sample lengths based on a change pattern according to the sample lengths of the inclusion ratios with respect to the sample lengths; And
Determining an optimal length of the search interval corresponding to the maximum inclusion ratio based on the inclusion ratios with respect to the reference lengths
How to include. According to claim 1,
The step of selecting the reference lengths
And selecting, among the sample lengths, as the reference lengths, at least a portion of the sample lengths in which the inclusion ratios with respect to the sample lengths increase as the sample lengths increase. According to claim 1,
Determining the optimal length is
And determining the optimal length using binary search based on the reference lengths. According to claim 1,
The reference lengths include a first length, a second length having a value greater than the first length, and a third length having a value greater than the second length,
Determining the optimal length includes determining the optimal length using a binary search in a section between the first length and the second length. According to claim 4,
Determining the optimal length is
Calculating an inclusion ratio with respect to an intermediate length between the first length and the second length;
Determining an optimal length in a section between the intermediate length and the second length when the inclusion ratio with respect to the intermediate length is greater than the inclusion ratio with respect to the first length; And
Determining an optimal length in a section between the first length and the intermediate length when the inclusion ratio with respect to the intermediate length is smaller than the inclusion ratio with respect to the first length
How to include. The method of claim 5,
Wherein the intermediate length corresponds to the average of the first length and the second length. According to claim 1,
The sample lengths include a first length,
Calculating the inclusion ratios for the sample lengths is
And determining a maximum number of transactions that can be covered by the first length of the search period while moving the search period corresponding to the first length. According to claim 1,
The sample lengths include a first length,
Calculating the inclusion ratios for the sample lengths is
Scanning the transactions belonging to the first data set given priority based on the charging completion time and storing in the second data set given priority based on the charging start time;
Maintain the second data set, or compare the difference value obtained by subtracting the first length from the charge completion times of the scan target transaction and the start time of each transaction stored in the second data set, or maintain the second data set, or Removing at least one transaction including a charging start time smaller than the difference value from the; And
Determining a maximum number of transactions that can be covered by a search interval of the first length based on the number of transactions stored in the second data set
How to include. A computer-readable storage medium storing one or more programs comprising instructions for performing the method of claim 1. In the device for analyzing the transaction related to the charging of the electric vehicle,
Processor; And
Memory for storing instructions executed in the processor
Including,
When the instructions are executed on the processor, the processor
Acquire transaction data including transactions each including a charging start time and a charging completion time for charging the electric vehicle,
Set sample lengths of a search period covering a charge start time and a charge completion time of at least one of the transactions included in the transaction data,
Calculate inclusion ratios for the sample lengths based on the maximum number of transactions that can be covered by the search interval of each of the sample lengths, the total number of transactions according to the transaction data, and each of the sample lengths,
Reference lengths are selected from among the sample lengths based on a change pattern according to the sample lengths of the inclusion ratios with respect to the sample lengths,
Determining an optimal length of the search interval corresponding to the maximum inclusion ratio based on the inclusion ratios with respect to the reference lengths,
Device comprising a.

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