Statistical method and statistical device for vehicle charging duration, equipment and medium

Abstract

The invention provides a vehicle charging duration statistical method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of charging conditions of a charging state in a statistical period for each charging state of an electric vehicle; wherein the charging state comprises fast charging and slow charging; for each charging working condition, determining target signal data of which the acquisition time is within a charging time range corresponding to the charging working condition from the to-be-screened signal data; and for each statistical time period in a statistical period, determining the charging duration of the charging working condition in the statistical time period based on the first quantity of the target signal data of which the acquisition time is within the statistical time period in the target signal data and the signal data acquisition frequency of the electric vehicle. Through the method and the device, the charging duration of the vehicle in each statistical time period can be quickly and accurately counted.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a statistical method, statistical device, equipment and medium for vehicle charging time. Background Art

[0002] With the development of science and technology and the increasing awareness of environmental protection, electric vehicles have become the choice of more and more people. Charging time is an important indicator in electric vehicle charging management, which can help optimize charging planning, analyze user charging behavior, and evaluate battery performance. Through in-depth analysis of charging time, more efficient charging management, smarter grid scheduling, and better user experience can be achieved. Therefore, how to quickly and accurately calculate the charging time of electric vehicles in each statistical period has become a technical issue that cannot be underestimated. Summary of the invention

[0003] In view of this, the purpose of the present application is to provide a statistical method, statistical device, equipment and medium for vehicle charging time. By statistically collecting the number of signal data within the statistical period and the signal data collection frequency of the vehicle, the charging time within the statistical period can be quickly calculated. Through a simpler big data statistical method, with less consumption of computing resources, the charging time of the vehicle in each statistical period can be quickly and accurately counted.

[0004] In a first aspect, an embodiment of the present application provides a method for counting vehicle charging time, the method comprising:

[0005] For each charging state of the electric vehicle, multiple charging conditions of the charging state within a statistical period are obtained; wherein the charging state includes fast charging and slow charging;

[0006] For each charging condition, target signal data whose collection time is within the charging time range corresponding to the charging condition is determined from the signal data to be screened; wherein the signal data to be screened is signal data whose collection time is within the statistical period and is determined from the signal data table of the electric vehicle;

[0007] For each statistical period, the charging duration of the charging condition in the statistical period is determined based on the first quantity of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle.

[0008] Further, the signal data to be screened is determined from the signal data table through the following steps:

[0009] For each signal type in the signal data table, determining a second quantity of signal data corresponding to the signal type whose acquisition time is within the statistical period;

[0010] The signal data corresponding to the largest second number among the plurality of second numbers is used as the signal data to be filtered.

[0011] Furthermore, determining the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened includes:

[0012] For each signal data to be screened, determining whether the collection time corresponding to the signal data to be screened is within the charging time range corresponding to the charging condition;

[0013] If so, the signal data to be screened is used as the target signal data.

[0014] Furthermore, after determining the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened, the statistical method further includes:

[0015] Determine the start time and the end time of the charging condition based on the charging time range corresponding to the charging condition, and calculate the time difference based on the start time and the end time;

[0016] The signal loss rate is calculated based on the third number of the target signal data and the time difference. When the signal loss rate is less than or equal to a preset threshold, the charging condition is considered valid; when the signal loss rate is greater than the preset threshold, the charging condition is eliminated.

[0017] Further, the determining of the charging duration of the charging condition within the statistical period based on the first quantity of the target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle includes:

[0018] The product of the first number and the signal data acquisition frequency is used as the charging duration.

[0019] Furthermore, the statistical method also includes:

[0020] For each statistical period, determining a fourth quantity of signal data to be screened whose acquisition time is within the statistical period;

[0021] Summing the first quantity corresponding to each statistical period in the total statistical period to obtain a first total quantity, and summing the fourth quantity corresponding to each statistical period in the total statistical period to obtain a second total quantity;

[0022] Determine the ratio between the first total number and the second total number as the total proportion of the charging time of the charging state in the statistical period under the total statistical cycle;

[0023] A statistical graph of the charging duration ratio of the charging state in the total statistical period is generated according to the total charging duration ratio of the charging state in each statistical period in the total statistical period.

[0024] Furthermore, after determining the charging duration of the charging condition in each statistical period, the statistical method further includes:

[0025] Obtain a charging time distribution result table of the charging state in the previous statistical period, and update the charging time distribution result table of the previous statistical period based on the charging time of each charging condition under the charging state in each statistical period, so as to obtain a charging time distribution result table of the charging state in the statistical period.

[0026] In a second aspect, an embodiment of the present application further provides a device for counting vehicle charging time, the device comprising:

[0027] A charging condition acquisition module is used to acquire multiple charging conditions of each charging state of the electric vehicle within a statistical period; wherein the charging state includes fast charging and slow charging;

[0028] A signal data determination module, for each charging condition, determines from the signal data to be screened target signal data whose collection time is within the charging time range corresponding to the charging condition; wherein the signal data to be screened is determined from the signal data table of the electric vehicle, and the collection time is within the statistical period;

[0029] The charging duration determination module is used to determine the charging duration of the charging condition in each statistical period based on the first number of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle.

[0030] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the vehicle charging time statistics method as described above are performed.

[0031] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for counting the vehicle charging time are executed.

[0032] The embodiments of the present application provide a method, apparatus, device and medium for counting vehicle charging time. First, for each charging state of an electric vehicle, multiple charging conditions of the charging state within a statistical period are obtained; wherein the charging state includes fast charging and slow charging; then, for each charging condition, target signal data whose collection time is within the charging time range corresponding to the charging condition is determined from the signal data to be screened; wherein the signal data to be screened is signal data whose collection time is within the statistical period, determined from a signal data table of the electric vehicle; finally, for each statistical period, the charging time of the charging condition within the statistical period is determined based on a first number of target signal data whose collection time is within the statistical period in the target signal data and a signal data collection frequency of the electric vehicle.

[0033] This application targets both fast charging and slow charging states. By counting the number of signal data collected within the statistical period and the vehicle's signal data collection frequency, the charging time within the statistical period can be quickly calculated. Through a simpler big data statistical method, with less computing resources consumed, the charging time of the vehicle in each statistical period can be quickly and accurately calculated. In this way, the charging data is displayed by the charging time of the vehicle in each statistical period, and the user's charging preferences and habits are identified, the charging service is optimized, and data support is provided for the management and optimization of charging facilities.

[0034] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flow chart of a method for calculating vehicle charging time provided in an embodiment of the present application;

[0037] Figure 2 This is one of the structural schematic diagrams of a device for counting vehicle charging time provided in an embodiment of the present application;

[0038] Figure 3 A second structural diagram of a device for counting vehicle charging time provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0041] First, the application scenarios to which the present application is applicable are introduced. The present application can be applied in the field of electric vehicle technology.

[0042] With the development of science and technology and the increasing awareness of environmental protection, electric vehicles have become the choice of more and more people. Research has found that charging time is an important indicator in electric vehicle charging management, which can help optimize charging planning, analyze user charging behavior, and evaluate battery performance. Through in-depth analysis of charging time, more efficient charging management, smarter grid scheduling, and better user experience can be achieved. Therefore, how to quickly and accurately count the charging time of electric vehicles in each statistical period has become a technical issue that cannot be underestimated.

[0043] Based on this, an embodiment of the present application provides a method for counting the charging time of a vehicle. Through a simpler big data statistical method, with less consumption of computing resources, the charging time of the vehicle in each statistical period can be quickly and accurately counted.

[0044] See also Figure 1 , Figure 1 This is a flow chart of a method for calculating the vehicle charging time provided in an embodiment of the present application. Figure 1 As shown in , the statistical method provided in the embodiment of the present application includes:

[0045] S101, for each charging state of the electric vehicle, obtaining multiple charging conditions of the charging state within a statistical period.

[0046] Here, the charging status of the electric vehicle includes fast charging and slow charging. The statistical period can be one day, one week, one month, etc., which is not specifically limited in this application.

[0047] With respect to the above step S101, during specific implementation, for each charging state of the electric vehicle, a plurality of charging conditions of the charging state within a statistical period are obtained.

[0048] S102 , for each charging condition, determining target signal data whose collection time is within a charging time range corresponding to the charging condition from the signal data to be filtered.

[0049] Here, the signal data to be screened is determined from the vehicle's signal data table, and the signal data collected during the statistical period. The electric vehicle's signal data table records all the signal data generated by the electric vehicle. Signal data refers to information collected from various sensors, control units, and communication systems of the vehicle, which reflect the vehicle's status, performance, and operation.

[0050] For the above step S102, in the specific implementation, first determine the signal data to be filtered whose collection time is within the statistical period from the vehicle's signal data table, and then for each charging condition, determine the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be filtered.

[0051] As an optional embodiment, with respect to the above step S102, the signal data to be screened is determined from the signal data table through the following steps:

[0052] A: For each signal type in the signal data table, determine the second quantity of signal data corresponding to the signal type whose acquisition time is within the statistical period.

[0053] B: Use the signal data corresponding to the largest second number among the plurality of second numbers as the signal data to be screened.

[0054] Here, since the vehicle will collect signal data of various signal types during operation, such as vehicle speed data, battery status data, vehicle positioning data, etc., this application does not make specific limitations on this. When determining the signal data to be screened, the signal data with a high loss rate is screened out, and the signal data with a low loss rate is required as the signal data to be screened, so as to ensure the accuracy of the subsequent determination of the target signal data from the signal data to be screened and the determination of the charging time according to the target signal data.

[0055] In the specific implementation of the above steps A to B, for each signal type in the signal data table, first determine the second quantity of signal data corresponding to the signal type whose acquisition time is within the statistical period. Then, use the signal data corresponding to the largest second quantity among the multiple second quantities as the signal data to be screened.

[0056] Specifically, with respect to the above step S102, determining the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened includes:

[0057] Step 1021 : for each signal data to be filtered, determine whether the collection time corresponding to the signal data to be filtered is within the charging time range corresponding to the charging condition.

[0058] Step 1022: If yes, the signal data to be screened is used as the target signal data.

[0059] For the above steps 1021-1022, according to the acquisition time of each signal data to be filtered, a conditional statement is used to judge the acquisition time of each signal data to be filtered, and judge whether the acquisition time is within the charging time range corresponding to the charging condition. Specifically, for each signal data to be filtered, it is judged whether the acquisition time corresponding to the signal data to be filtered is within the charging time range corresponding to the charging condition. If so, the signal data to be filtered is used as the target signal data.

[0060] As an optional embodiment, according to the statistical method provided by the present application, after determining the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened, the statistical method further includes:

[0061] I: Determine the start time and end time of the charging condition based on the charging time range corresponding to the charging condition, and calculate the time difference based on the start time and the end time.

[0062] For the above step I, in the specific implementation, first determine the start time and end time of the charging condition based on the charging time range corresponding to the charging condition. Here, the start time and end time of the charging condition are converted into the form of a timestamp according to the function UNIX_TIMESTAMP. Specifically, UNIX_TIMESTAMP(t1) represents the start time of the charging condition, and UNIX_TIMESTAMP(t2) represents the end time of the charging condition. Then, calculate the time difference based on the start time and the end time. Specifically, according to the converted timestamp, the time difference A is calculated, where the difference between the end time and the start time is taken as the time difference A, A=UNIX_TIMESTAMP(t2)-UNIX_TIMESTAMP(t1).

[0063] II: Calculate the signal loss rate based on the third number of target signal data and the time difference. When the signal loss rate is less than or equal to a preset threshold, the charging condition is considered valid. When the signal loss rate is greater than the preset threshold, the charging condition is eliminated.

[0064] For the above step II, in the specific implementation, first count the third number Z of target signal data, and then calculate the signal loss rate based on the third number Z of target signal data and the time difference calculated in the above step I. Here, the ratio between the third number Z and the time difference A is taken as the signal loss rate loss_rate, specifically, loss_rate = Z / A. The signal loss rate is then compared with the preset threshold. Here, the preset threshold can be set to 0.1, and this application does not make specific limitations on this. When the calculated signal loss rate is less than or equal to the preset threshold, the charging condition is considered valid. When the calculated signal loss rate is greater than the preset threshold, the charging condition is considered invalid and the charging condition is eliminated.

[0065] S103, for each statistical period, based on the first number of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle, determine the charging duration of the charging condition within the statistical period.

[0066] Here, the statistical period refers to the multiple periods divided in the statistical cycle. Here, as an example, when the statistical cycle is one day, each statistical period can be each hour of the day, and this application does not make specific restrictions on this. The signal data acquisition frequency refers to the frequency at which the electric vehicle collects signal data. For example, the signal data acquisition frequency can be collected once every 10 seconds, and this application does not make specific restrictions on this.

[0067] For the above step S103, in the specific implementation, for each statistical period, firstly, the first number of target signal data whose collection time is within the statistical period is counted in the target signal data, and then the charging time of the charging condition within the statistical period is determined based on the first number and the signal data collection frequency of the electric vehicle.

[0068] Specifically, with respect to the above step S103, the determining of the charging duration of the charging condition within the statistical period based on the first quantity of the target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle includes:

[0069] The product of the first number and the signal data acquisition frequency is used as the charging duration.

[0070] For the above steps, in the specific implementation, the first number is multiplied by the signal data acquisition frequency to determine the charging time. Here, as an example, when the first number of target signal data whose acquisition time is within the statistical period is 180, and the signal data acquisition frequency is once every 10 seconds, it can be determined that the charging time of the charging condition within the statistical period is 1800 seconds, that is, 30 minutes. In this way, the charging time of the charging condition in each statistical period can be quickly determined only by the number of signal data and the acquisition frequency of signal data. The statistical speed is faster and simpler, and the charging time of the vehicle in each statistical period can be accurately calculated with less consumption of computing resources.

[0071] As an optional embodiment, after calculating the charging time of each charging condition in each statistical period, the charging time of the charging state in each statistical period under the statistical cycle can also be calculated. For example, when only one charging condition is involved in a statistical period, the charging time of the charging condition in the statistical period is the charging time of the charging state in the statistical period. Since the charging state may include multiple charging conditions, each statistical period may involve multiple charging conditions. Therefore, when a statistical period involves multiple charging conditions, the charging times of multiple charging conditions in the statistical period need to be summed to obtain the total charging time of the charging state in the statistical period. When multiple charging conditions are involved in a statistical period, for example, the charging time of charging condition A from 10:00 to 11:00 is 30 minutes, and the charging time of charging condition B from 10:00 to 11:00 is 10 minutes, then the total charging time of the charging state in the statistical period is 40 minutes.

[0072] As an optional embodiment, the statistical method provided in the present application also includes:

[0073] (1) For each statistical period, a fourth number of signal data to be filtered whose acquisition time is within the statistical period is determined.

[0074] Regarding the above step (1), during the specific implementation, for each statistical period, a fourth number of signal data to be screened whose acquisition time is within the statistical period is determined.

[0075] (2): The first quantity corresponding to each statistical period in the total statistical period is summed to obtain a first total quantity, and the fourth quantity corresponding to each statistical period in the total statistical period is summed to obtain a second total quantity.

[0076] Here, the total statistical cycle includes multiple statistical cycles. As an example, when the statistical cycle is one day, the total statistical cycle can be one week, that is, the total statistical cycle includes 7 statistical cycles.

[0077] For the above step (1), in the specific implementation, the first quantity corresponding to each statistical cycle in the total statistical cycle is summed to obtain the first total quantity. Here, since the first quantity is for each charging condition, the number of target signal data whose collection time is within the statistical period, the first quantities corresponding to multiple statistical cycles are added together, and the first total quantity is the total number of target signal data whose collection time is within the statistical period under the total statistical cycle for the charging state. The fourth quantity corresponding to each statistical cycle in the total statistical cycle is summed to obtain the second total quantity. Here, since the fourth quantity is for each statistical cycle, the number of signal data whose collection time is within each statistical period, the fourth quantities corresponding to multiple statistical cycles are added together, and the second total quantity is the total number of signals whose collection time is within the statistical period under the total statistical cycle.

[0078] (3): Determine the ratio between the first total number and the second total number as the total proportion of the charging time of the charging state in the statistical period under the total statistical cycle.

[0079] For the above step (3), in the specific implementation, the ratio between the first total number and the second total number is determined as the total proportion of the charging time of the charging state in the statistical period, that is, the statistical result of the charging time of the charging state in all statistical periods. For example, when the total statistical period is one week and the statistical period is 10:00-11:00, the total proportion of the charging time is the proportion of the charging time of the charging state from 10:00 to 11:00 in this week.

[0080] (4): Generate a statistical graph of the proportion of charging time of the charging state in the total statistical period according to the total proportion of charging time of the charging state in each statistical period in the total statistical period.

[0081] For the above step (4), in the specific implementation, after determining the total proportion of charging time of the charging state in each statistical period under the total statistical cycle, a statistical graph of the charging time proportion of the charging state in the total statistical cycle is generated based on the total proportion of charging time of the charging state in each statistical period. Here, the statistical graph of charging time proportion can be a bar graph or a sector graph, which is not specifically limited in this application. In this way, the distribution of vehicle charging time in different time periods can be intuitively displayed through the statistical graph of charging time proportion.

[0082] As an optional embodiment, after determining the charging duration of the charging condition in each statistical period, the statistical method further includes:

[0083] Obtain a charging time distribution result table of the charging state in the previous statistical period, and update the charging time distribution result table of the previous statistical period based on the charging time of each charging condition under the charging state in each statistical period, so as to obtain a charging time distribution result table of the charging state in the statistical period.

[0084] For the above steps, in the specific implementation, it is necessary to use each statistical period as the partition key to create a corresponding charging time distribution result table. First, obtain the charging time distribution result table of the charging state in the previous statistical period. The charging time distribution result table of the previous statistical period records the charging time of the charging state in each statistical period in the previous statistical period. Then, the charging time distribution result table of the charging state in the previous statistical period and the charging time of each charging condition in the current statistical state in each statistical period are spliced ​​together through a set function (union), and then the aggregate function is used to calculate the cumulative value to update the charging time distribution result table of the previous statistical period, and obtain the charging time distribution result table of the charging state in the statistical period. In this way, the charging time distribution data in each statistical period is counted and accumulated with the data in the previous statistical period to complete the update of the charging time distribution data of the previous statistical period.

[0085] The method for counting vehicle charging time provided in an embodiment of the present application comprises: first, for each charging state of the electric vehicle, obtaining multiple charging conditions of the charging state within a statistical period; wherein the charging state includes fast charging and slow charging; then, for each charging condition, determining target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened; wherein the signal data to be screened is signal data whose collection time is within the statistical period, determined from a signal data table of the electric vehicle; finally, for each statistical period, determining the charging time of the charging condition within the statistical period based on a first number of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle.

[0086] This application targets both fast charging and slow charging states. By counting the number of signal data collected within the statistical period and the vehicle's signal data collection frequency, the charging time within the statistical period can be quickly calculated. Through a simpler big data statistical method, with less computing resources consumed, the charging time of the vehicle in each statistical period can be quickly and accurately calculated. In this way, the charging data is displayed by the charging time of the vehicle in each statistical period, and the user's charging preferences and habits are identified, the charging service is optimized, and data support is provided for the management and optimization of charging facilities.

[0087] See also Figure 2 , Figure 3 , Figure 2 This is one of the structural schematic diagrams of a device for counting vehicle charging time provided in an embodiment of the present application. Figure 3 This is a second structural diagram of a device for counting vehicle charging time provided in an embodiment of the present application. Figure 2 As shown in , the statistical device 200 includes:

[0088] The charging condition acquisition module 201 is used to acquire multiple charging conditions of each charging state of the electric vehicle within a statistical period; wherein the charging state includes fast charging and slow charging;

[0089] The signal data determination module 202 is used to determine, for each charging condition, target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened; wherein the signal data to be screened is signal data whose collection time is within the statistical period and is determined from the signal data table of the electric vehicle;

[0090] The charging duration determination module 203 is used to determine the charging duration of the charging condition in each statistical period based on the first number of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle.

[0091] Furthermore, the signal data determination module 202 is further configured to determine the signal data to be screened from the signal data table through the following steps:

[0092] For each signal type in the signal data table, determining a second quantity of signal data corresponding to the signal type whose acquisition time is within the statistical period;

[0093] The signal data corresponding to the largest second number among the plurality of second numbers is used as the signal data to be filtered.

[0094] Furthermore, when the signal data determination module 202 is used to determine the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be filtered, the signal data determination module 202 is also used to:

[0095] For each signal data to be screened, determining whether the collection time corresponding to the signal data to be screened is within the charging time range corresponding to the charging condition;

[0096] If so, the signal data to be screened is used as the target signal data.

[0097] For further information, see Figure 3 The statistical device 200 further includes a working condition detection module 204. After determining the target signal data whose acquisition time is within the charging time range corresponding to the charging working condition from the signal data to be screened, the working condition detection module 204 is used to:

[0098] Determine the start time and the end time of the charging condition based on the charging time range corresponding to the charging condition, and calculate the time difference based on the start time and the end time;

[0099] The signal loss rate is calculated based on the third number of the target signal data and the time difference. When the signal loss rate is less than or equal to a preset threshold, the charging condition is considered valid; when the signal loss rate is greater than the preset threshold, the charging condition is eliminated.

[0100] Further, when the charging duration determination module 203 is used to determine the charging duration of the charging condition within the statistical period based on the first number of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle, the charging duration determination module 203 is also used to:

[0101] The product of the first number and the signal data acquisition frequency is used as the charging duration.

[0102] For further information, see Figure 3 The statistical device 200 further includes a statistical graph generating module 205, and the statistical graph generating module 205 is used to:

[0103] For each statistical period, determining a fourth quantity of signal data to be screened whose acquisition time is within the statistical period;

[0104] Summing the first quantity corresponding to each statistical period in the total statistical period to obtain a first total quantity, and summing the fourth quantity corresponding to each statistical period in the total statistical period to obtain a second total quantity;

[0105] Determine the ratio between the first total number and the second total number as the total proportion of the charging time of the charging state in the statistical period under the total statistical cycle;

[0106] A statistical graph of the charging duration ratio of the charging state in the total statistical period is generated according to the total charging duration ratio of the charging state in each statistical period in the total statistical period.

[0107] For further information, see Figure 3 The statistical device 200 further includes a data updating module 206. After determining the charging duration of the charging condition in each statistical period, the data updating module 206 is used to:

[0108] Obtain a charging time distribution result table of the charging state in the previous statistical period, and update the charging time distribution result table of the previous statistical period based on the charging time of each charging condition under the charging state in each statistical period, so as to obtain a charging time distribution result table of the charging state in the statistical period.

[0109] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown in , the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0110] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 The steps of the method for counting the vehicle charging time in the method embodiment shown, and the specific implementation method thereof can be referred to the method embodiment, which will not be described in detail here.

[0111] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of the method for counting the vehicle charging time in the method embodiment shown, and the specific implementation method thereof can be referred to the method embodiment, which will not be described in detail here.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0116] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0117] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A statistical method for vehicle charging time, characterized in that: The statistical methods include: For each charging state of the electric vehicle, multiple charging conditions of the charging state within a statistical period are obtained; wherein the charging state includes fast charging and slow charging; For each charging condition, target signal data whose collection time is within the charging time range corresponding to the charging condition is determined from the signal data to be screened; wherein the signal data to be screened is signal data whose collection time is within the statistical period and is determined from the signal data table of the electric vehicle; For each statistical period, the charging duration of the charging condition in the statistical period is determined based on the first quantity of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle.

2. The statistical method according to claim 1, characterized in that: The signal data to be screened is determined from the signal data table by the following steps: For each signal type in the signal data table, determining a second quantity of signal data corresponding to the signal type whose acquisition time is within the statistical period; The signal data corresponding to the largest second number among the plurality of second numbers is used as the signal data to be filtered.

3. The statistical method according to claim 1, characterized in that: The step of determining, from the signal data to be screened, target signal data whose acquisition time is within the charging time range corresponding to the charging condition, comprises: For each signal data to be screened, determining whether the collection time corresponding to the signal data to be screened is within the charging time range corresponding to the charging condition; If so, the signal data to be screened is used as the target signal data.

4. The statistical method according to claim 1, characterized in that: After determining the target signal data whose collection time is within the charging time range corresponding to the charging condition from the signal data to be screened, the statistical method further includes: Determine the start time and the end time of the charging condition based on the charging time range corresponding to the charging condition, and calculate the time difference based on the start time and the end time; The signal loss rate is calculated based on the third number of the target signal data and the time difference. When the signal loss rate is less than or equal to a preset threshold, the charging condition is considered valid; when the signal loss rate is greater than the preset threshold, the charging condition is eliminated.

5. The statistical method according to claim 1, characterized in that: The determining of the charging duration of the charging condition within the statistical period based on the first quantity of the target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle comprises: The product of the first number and the signal data acquisition frequency is used as the charging duration.

6. The statistical method according to claim 1, characterized in that: The statistical method also includes: For each statistical period, determining a fourth quantity of signal data to be screened whose acquisition time is within the statistical period; Summing the first quantity corresponding to each statistical period in the total statistical period to obtain a first total quantity, and summing the fourth quantity corresponding to each statistical period in the total statistical period to obtain a second total quantity; Determine the ratio between the first total number and the second total number as the total proportion of the charging time of the charging state in the statistical period under the total statistical cycle; A statistical graph of the charging duration ratio of the charging state in the total statistical period is generated according to the total charging duration ratio of the charging state in each statistical period in the total statistical period.

7. The statistical method according to claim 1, characterized in that: After determining the charging time of the charging condition in each statistical period, the statistical method further includes: Obtain a charging time distribution result table of the charging state in the previous statistical period, and update the charging time distribution result table of the previous statistical period based on the charging time of each charging condition under the charging state in each statistical period, so as to obtain a charging time distribution result table of the charging state in the statistical period.

8. A device for counting vehicle charging time, characterized in that: The statistical device comprises: A charging condition acquisition module is used to acquire multiple charging conditions of each charging state of the electric vehicle within a statistical period; wherein the charging state includes fast charging and slow charging; A signal data determination module, for each charging condition, determines from the signal data to be screened target signal data whose collection time is within the charging time range corresponding to the charging condition; wherein the signal data to be screened is determined from the signal data table of the electric vehicle, and the collection time is within the statistical period; The charging duration determination module is used to determine the charging duration of the charging condition in each statistical period based on the first number of target signal data whose collection time is within the statistical period in the target signal data and the signal data collection frequency of the electric vehicle.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the vehicle charging time statistics method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for counting the vehicle charging time as claimed in any one of claims 1 to 7 are executed.

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