Charging energy consumption statistics method, device and computer-readable storage medium

Abstract

The present invention discloses a charging energy consumption statistics method, device, and computer-readable storage medium. The charging energy consumption statistics method includes the following steps: collecting charging signal data in a current monitoring environment; when a step signal appears in the charging signal data, determining whether a charging start event has occurred; if a charging start event has occurred, recording start feature data within a preset time period; outputting a charging waveform classification result based on the start feature data; dynamically monitoring the charging signal data; when a step signal appears in the charging signal data, determining whether a charging stop event has occurred; if a charging stop event has occurred, recording stop feature data within a preset time period; and statistically analyzing charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data. By implementing the present invention, accurate statistics of the charging energy consumption of electric vehicles can be achieved when multiple electrical appliances share the same charging power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of electric bicycles, and in particular to a charging energy consumption statistics method, device and computer-readable storage medium. Background Art

[0002] Currently, electric bicycles are one of the primary means of transportation for people. Due to their convenience, economy, and environmental friendliness, their sales and usage are increasing daily. According to relevant statistics, the annual sales of electric bicycles (hereinafter referred to as "electric bicycles") in my country exceed 30 million, and the number of electric bicycles in public ownership is close to 300 million. The increase in the number of electric bicycles has also directly affected the energy consumption of electric vehicle charging and use. According to the new national standard for electric vehicles (Electric Bicycle Safety Technical Specification GB17761-2018), the motor power of electric vehicles is allowed to be no more than 400W. For example, the battery capacity of electric vehicles is generally 48V12Ah. When the battery is fully charged, it consumes 0.8 kWh of electricity to maintain the electric vehicle's range of 40 kilometers. Therefore, if all electric vehicles in society were fully charged, it would consume approximately 240 million kWh of electricity.

[0003] The best way to charge an electric vehicle is to charge it through an electric vehicle charging station or a dedicated electric vehicle charging socket. Since there are still many areas that lack these dedicated charging equipment, it is inevitable that electric vehicles need to share charging sockets with other electrical appliances.

[0004] When the above-mentioned multiple electrical appliances share the same charging power supply, it is difficult to calculate the charging energy consumption of electric vehicles. Summary of the Invention

[0005] The main purpose of the present invention is to provide a charging energy consumption statistics method, device and computer-readable storage medium, aiming to solve the technical problem of how to perform charging energy consumption statistics for electric vehicles when multiple electrical appliances share the same charging power supply.

[0006] To achieve the above object, the present invention provides a method for calculating charging energy consumption statistics, which includes the following steps:

[0007] Collecting charging signal data in the current monitoring environment, and determining whether a charging start event occurs when a step signal appears in the charging signal data;

[0008] If a charging start event occurs, the start-up characteristic data within a preset time period is recorded;

[0009] outputting a charging waveform classification result based on the startup characteristic data;

[0010] Dynamically monitoring the charging signal data, and determining whether a charging stop event occurs when a step signal appears in the charging signal data;

[0011] If a charging stop event occurs, the stop characteristic data within a preset time period is recorded;

[0012] The charging energy consumption is counted based on the charging waveform classification result, the start characteristic data and the stop characteristic data.

[0013] Optionally, the step of outputting a charging waveform classification result based on the startup feature vector data includes:

[0014] classifying the current charging waveform based on the startup characteristic data to obtain a charging waveform classification result;

[0015] Determining whether the similarity between the charging waveform classification result and a preset charging waveform template is greater than a system preset threshold;

[0016] If the similarity is greater than a system preset threshold, the classification is deemed successful and the charging waveform classification result is output.

[0017] Optionally, after the step of determining whether the similarity between the charging waveform classification result and a preset charging waveform template is greater than a system preset threshold value, the step further includes:

[0018] If the similarity is not greater than a system preset threshold, it is considered that the classification has failed and the charging waveform classification result is discarded;

[0019] Return to the step of determining whether a charging start event occurs until the step of outputting the charging waveform classification result is completed or a charging stop event occurs.

[0020] Optionally, before the step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data, the step includes:

[0021] When the charging stop event occurs and the classification is successful, a classification template is generated based on the charging waveform classification result, a corresponding feature vector in the classification template is extracted, a charging start time point is obtained based on the start feature data, and a charging stop time point is obtained based on the stop feature data.

[0022] Optionally, the step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data includes:

[0023] The charging energy consumption is counted based on the corresponding feature vector in the classification template, the charging start time point and the charging stop time point.

[0024] Optionally, before the step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data, the step further includes:

[0025] When the charge stop event occurs and classification fails, the charge start feature vector is obtained based on the start feature data, and the charge stop feature vector is obtained based on the stop feature data.

[0026] Optionally, the step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data includes:

[0027] The charging energy consumption is counted based on the charging start feature vector, the charging stop feature vector, the charging start time point, and the charging stop time point.

[0028] Optionally, when a step signal appears in the charging signal data, the step of determining whether a charging start event or a charging stop event occurs includes:

[0029] When a step signal appears in the charging signal data, recording the step cache data before and after the step signal appears;

[0030] Calculate the feature vector of the current unknown event based on the step cache data;

[0031] An analysis is performed based on the feature vector to determine whether the current unknown event is a charging start event or a charging stop event.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also provides a charging energy consumption statistics device, which includes: a memory, a processor, and a charging energy consumption statistics program stored on the memory and runnable on the processor. When the charging energy consumption statistics program is executed by the processor, the steps of the charging energy consumption statistics method described above are implemented.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a charging energy consumption statistics program is stored. When the charging energy consumption statistics program is executed by a processor, the steps of the charging energy consumption statistics method described above are implemented.

[0034] The present invention proposes a charging energy consumption statistics method, device and computer-readable storage medium, which overcome the problem that it is difficult to count the charging energy consumption of electric vehicles when multiple electrical appliances share the same charging power supply. In the charging energy consumption statistics method, by collecting charging signal data in the current monitoring environment, when a step signal appears in the charging signal data, the charging start event and charging stop event of the electric vehicle are captured based on the characteristics of the step signal, and based on various characteristic data during the charging process, when multiple electrical appliances share the same charging power supply, accurate statistics of the charging energy consumption of the electric vehicle are achieved, the accuracy of the electric energy calculation is improved, and it can help users understand the energy consumption of their electric vehicles more accurately, and obtain more detailed electric vehicle charging information and the quality and life of the electric vehicle battery and charger, so that electric vehicle users can more reasonably arrange the charging time of the electric vehicle and save charging costs. It can further help electric vehicle users avoid charging peak periods and improve the safety of electric vehicle charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention;

[0036] Figure 2 This is a flow chart of the first embodiment of the charging energy consumption statistics method of the present invention;

[0037] Figure 3 This is a flow chart of a second embodiment of the charging energy consumption statistics method of the present invention;

[0038] Figure 4 This is a flow chart of a third embodiment of the charging energy consumption statistics method of the present invention;

[0039] Figure 5 It is a preset classification template diagram in each embodiment of the charging energy consumption statistics method of the present invention;

[0040] Figure 6 It is a preset classification template diagram in each embodiment of the charging energy consumption statistics method of the present invention;

[0041] Figure 7 It is a preset classification template diagram in each embodiment of the charging energy consumption statistics method of the present invention;

[0042] Figure 8 It is a preset classification template diagram in each embodiment of the charging energy consumption statistics method of the present invention;

[0043] Figure 9 It is a preset classification template diagram in each embodiment of the charging energy consumption statistics method of the present invention;

[0044] Figure 10This is a preset classification template diagram in each embodiment of the charging energy consumption statistics method of the present invention.

[0045] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] The main solution of the embodiment of the present invention is: a charging energy consumption statistics method, which includes the following steps:

[0048] Collecting charging signal data in the current monitoring environment, and determining whether a charging start event occurs when a step signal appears in the charging signal data;

[0049] If a charging start event occurs, the start-up characteristic data within a preset time period is recorded;

[0050] outputting a charging waveform classification result based on the startup characteristic data;

[0051] Dynamically monitoring the charging signal data, and determining whether a charging stop event occurs when a step signal appears in the charging signal data;

[0052] If a charging stop event occurs, the stop characteristic data within a preset time period is recorded;

[0053] The charging energy consumption is counted based on the charging waveform classification result, the start characteristic data and the stop characteristic data.

[0054] The best way to charge an electric vehicle is through an electric vehicle charging station or dedicated charging socket. However, due to the lack of dedicated charging equipment in many areas, electric vehicles inevitably need to share charging sockets with other electrical appliances. When these multiple appliances share the same charging power source, it is difficult to calculate the energy consumption of electric vehicles.

[0055] If it is possible to collect statistics on the charging energy consumption of electric vehicles, the accuracy of electric energy calculation can be improved, helping users to understand the energy consumption of their electric vehicles more accurately, obtain more detailed electric vehicle charging information and the quality and life of electric vehicle batteries and chargers, so that electric vehicle users can arrange the charging time of electric vehicles more reasonably, save charging costs, avoid charging peak periods, and improve the safety of electric vehicle charging.

[0056] The present invention provides a charging energy consumption statistics method, which overcomes the problem that it is difficult to perform statistics on the charging energy consumption of an electric vehicle when multiple electrical appliances share the same charging power supply. In the charging energy consumption statistics method, by collecting charging signal data in a current monitoring environment, when a step signal appears in the charging signal data, the charging start event and the charging stop event of the electric vehicle are captured based on the characteristics of the step signal, and based on various characteristic data during the charging process, when multiple electrical appliances share the same charging power supply, accurate statistics on the charging energy consumption of the electric vehicle are achieved, the accuracy of electric energy calculation is improved, and the user can more accurately understand the energy consumption of his electric vehicle and obtain more detailed electric vehicle charging information and the quality and life of the electric vehicle battery and charger, so that the electric vehicle user can more reasonably arrange the charging time of the electric vehicle and save charging costs. Further, the method can also help the electric vehicle user avoid charging peak periods and improve the safety of electric vehicle charging.

[0057] like Figure 1 As shown, Figure 1 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0058] The terminal in the embodiment of the present invention is a charging energy consumption statistics device, which can be a terminal device such as a PC, a smart phone, a tablet computer, a portable computer, etc.

[0059] like Figure 1 As shown, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0060] Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among them, sensors include light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal can also be configured with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be repeated here.

[0061] Those skilled in the art will understand that Figure 1 The terminal structure shown in the figure does not constitute a limitation to the terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0062] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a charging energy consumption statistics program.

[0063] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0064] Collecting charging signal data in the current monitoring environment, and determining whether a charging start event occurs when a step signal appears in the charging signal data;

[0065] If a charging start event occurs, the start-up characteristic data within a preset time period is recorded;

[0066] outputting a charging waveform classification result based on the startup characteristic data;

[0067] Dynamically monitoring the charging signal data, and determining whether a charging stop event occurs when a step signal appears in the charging signal data;

[0068] If a charging stop event occurs, the stop characteristic data within a preset time period is recorded;

[0069] The charging energy consumption is counted based on the charging waveform classification result, the start characteristic data and the stop characteristic data.

[0070] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0071] The step of outputting the charging waveform classification result based on the startup feature vector data includes:

[0072] classifying the current charging waveform based on the startup characteristic data to obtain a charging waveform classification result;

[0073] Determining whether the similarity between the charging waveform classification result and a preset charging waveform template is greater than a system preset threshold;

[0074] If the similarity is greater than a system preset threshold, the classification is deemed successful and the charging waveform classification result is output.

[0075] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0076] After the step of determining whether the similarity between the charging waveform classification result and the preset charging waveform template is greater than a system preset threshold value, the method further includes:

[0077] If the similarity is not greater than a system preset threshold, it is considered that the classification has failed and the charging waveform classification result is discarded;

[0078] Return to the step of determining whether a charging start event occurs until the step of outputting the charging waveform classification result is completed or a charging stop event occurs.

[0079] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0080] The step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data and the stop feature data includes:

[0081] When the charging stop event occurs and the classification is successful, a classification template is generated based on the charging waveform classification result, a corresponding feature vector in the classification template is extracted, a charging start time point is obtained based on the start feature data, and a charging stop time point is obtained based on the stop feature data.

[0082] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0083] The step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data includes:

[0084] The charging energy consumption is counted based on the corresponding feature vector in the classification template, the charging start time point and the charging stop time point.

[0085] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0086] Before the step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data and the stop feature data, the following step is further included:

[0087] When the charge stop event occurs and classification fails, the charge start feature vector is obtained based on the start feature data, and the charge stop feature vector is obtained based on the stop feature data.

[0088] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0089] The step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data and the stop feature data includes:

[0090] The charging energy consumption is counted based on the charging start feature vector, the charging stop feature vector, the charging start time point, and the charging stop time point.

[0091] Furthermore, the processor 1001 may call a charging energy consumption statistics program stored in the memory 1005 and perform the following operations:

[0092] When a step signal appears in the charging signal data, the step of determining whether a charging start event or a charging stop event occurs includes:

[0093] When a step signal appears in the charging signal data, recording the step cache data before and after the step signal appears;

[0094] Calculate the feature vector of the current unknown event based on the step cache data;

[0095] An analysis is performed based on the feature vector to determine whether the current unknown event is a charging start event or a charging stop event.

[0096] Reference Figure 2 A first embodiment of the present invention provides a method for counting charging energy consumption, the method comprising:

[0097] Step S10, collecting charging signal data in the current monitoring environment, and when a step signal appears in the charging signal data, determining whether a charging start event occurs;

[0098] It should be noted that the execution subject in this embodiment is a charging energy consumption statistics device, which can be a terminal device such as a PC, smartphone, tablet computer, or portable computer. The charging energy consumption statistics device can monitor the charging signal of a single charging power supply shared by multiple electrical appliances, with the primary charging energy consumption statistics object being an electric bicycle. The charging signal data can be voltage and current data. The specific monitoring object for the occurrence of a step signal is active power. That is, when a step signal occurs in active power, a charge start event is determined. The charge start event is the charge start event of the electric bicycle.

[0099] Step S20, if a charging start event occurs, recording start-up characteristic data within a preset time period;

[0100] It should be noted that the preset time period can be one time window, two time windows, or three time windows, and this embodiment does not impose any restrictions on this. The startup characteristic data is the changing trend and situation of the relevant characteristic vectors in a certain time window. The characteristic vector is defined as follows: EIG = [PFC, THD, Pmax, Pmin, Pave, Ps, Time], where EIG represents the current window feature set, PFC is the average power factor of each cycle point in the current window and the power factor before the event occurs, THD is the average value of the total current harmonic difference between each cycle point in the current window and the power factor before the event occurs, Pmax and Pmin are the maximum and minimum active power difference values ​​between each cycle point in the current window and the power factor before the event occurs, Pave represents the average active power in the current window, Ps represents the slope of the active power change in the current window, and Time represents the cycle interval length from the first cycle in the current window to the electric vehicle startup event.

[0101] It should be noted that, in this embodiment, another judgment branch is included in parallel with step S20:

[0102] Step s21: If no charging start event occurs, continue to collect charging signal data in the current monitoring environment and dynamically monitor the charging signal data.

[0103] It is understandable that if the event that currently causes the step signal to appear is not an electric vehicle charging event, there is no need to record it, and the monitoring of the charging signal data in the current monitoring environment can be continued.

[0104] Step S30, outputting a charging waveform classification result based on the startup characteristic data;

[0105] In this embodiment, step S30 includes:

[0106] Step s31, classifying the current charging waveform based on the startup characteristic data to obtain a charging waveform classification result;

[0107] Step s32, determining whether the similarity between the charging waveform classification result and a preset charging waveform template is greater than a system preset threshold;

[0108] Step s33: If the similarity is greater than a system preset threshold, the classification is deemed successful, and the charging waveform classification result is output.

[0109] It should be noted that step s32 also includes another judgment branch parallel to step s33:

[0110] Step s34: If the similarity is not greater than a system preset threshold, it is considered that the classification has failed, and the charging waveform classification result is discarded;

[0111] Step s35 , returning to the step of determining whether a charging start event occurs, until the step of outputting the charging waveform classification result is completed or a charging stop event occurs.

[0112] It should be noted that this embodiment includes a number of preset charging waveform templates. Figures 5 to 10 , Figures 5 to 10 The figure shows a template diagram of the electric vehicle charging waveform, where the vertical axis is active power in W and the horizontal axis is time in power frequency cycles.

[0113] It is understood that the electric vehicle charging waveform is classified using the cached data of each feature vector obtained in step 20 above, and the similarity between the cached data and each electric vehicle charging waveform template is calculated. The result with the highest similarity is compared with the system preset threshold value. If the current similarity exceeds the threshold value, the electric bicycle charging waveform classification result is output and the current classification result is cached. Otherwise, no response is output (i.e., the charging waveform classification result is discarded), and the process returns to step S20 to update the cached data and start the next classification until the classification is successful or the electric vehicle charging stop event occurs.

[0114] In a specific implementation, the category obtained in step s33 with the highest similarity to the classification template library and with a similarity greater than a threshold is used as the category of the current electric vehicle charging waveform. The similarity calculation formula is: Among them, SIM represents the similarity between the feature vector in the current window and the template, TLB represents the feature vector in the template library, k represents the index of the corresponding module in the template library, j represents the index of the specified feature in the feature vector, m represents the total number of features in the feature vector, and W jIndicates the calculated weight of the specified feature in the corresponding template.

[0115] Step S40, dynamically monitoring the charging signal data, and when a step signal appears in the charging signal data, determining whether a charging stop event occurs;

[0116] It should be noted that in this embodiment, the monitoring of the charging signal data is real-time. If an electric vehicle start-up event occurs, it is necessary to judge the event when the step signal appears. However, before the current charging event ends, it will only be judged whether the current event is an electric vehicle charging stop event. The stop feature data is similar to the start feature data, and is also the change trend and situation of the relevant feature vector in a certain time window, which will not be repeated here.

[0117] Step S50, if a charging stop event occurs, recording stop characteristic data within a preset time period;

[0118] It can be understood that if, after the electric vehicle start-up event occurs, the analysis result of the event when the step signal appears is that the current event is an electric vehicle charging stop event, then the change trend and situation of the relevant characteristic vectors in a certain time window when the current event occurs are calculated, analyzed and cached, and the energy consumption of the electric vehicle is calculated.

[0119] Step S60 , collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data.

[0120] It is understandable that when both the electric vehicle charging start event and the electric vehicle charging stop event have been monitored, it indicates that the current electric vehicle has completed the charging behavior, and its charging energy consumption can be counted at this time.

[0121] In this embodiment, a charging energy consumption statistics method is provided, which overcomes the problem that it is difficult to count the charging energy consumption of an electric vehicle when multiple electrical appliances share the same charging power supply. In the charging energy consumption statistics method, by collecting charging signal data in the current monitoring environment, when a step signal appears in the charging signal data, the charging start event and the charging stop event of the electric vehicle are captured based on the characteristics of the step signal, and based on various characteristic data during the charging process, when multiple electrical appliances share the same charging power supply, accurate statistics of the charging energy consumption of the electric vehicle are achieved, the accuracy of the electric energy calculation is improved, and it can help users understand the energy consumption of their electric vehicles more accurately, and obtain more detailed electric vehicle charging information and the quality and life of the electric vehicle battery and charger, so that electric vehicle users can more reasonably arrange the charging time of the electric vehicle and save charging costs. It can further help electric vehicle users avoid charging peak periods and improve the safety of electric vehicle charging.

[0122] Further, refer to Figure 3 , proposes the second embodiment of the charging energy consumption statistics method of the present invention, based on the above Figure 2 In the embodiment shown, the step S60 includes:

[0123] Step A10: When the charging stop event occurs and the classification is successful, a classification template is generated based on the charging waveform classification result, a corresponding feature vector in the classification template is extracted, a charging start time point is obtained based on the start feature data, and a charging stop time point is obtained based on the stop feature data.

[0124] Based on step A10, one implementation of step S60 is:

[0125] Step A20 , collecting statistics on charging energy consumption based on the corresponding feature vectors in the classification template, the charging start time point, and the charging stop time point.

[0126] It should be noted that when the charging classification waveform is successfully output, a classification template for the energy consumption statistics of electric bicycles can be obtained. At this time, the parameters for statistical charging energy consumption are: the corresponding feature vector in the classification template, the electric vehicle charging start time point obtained when a charging start event occurs, and the electric vehicle charging stop time point obtained when a charging stop event occurs. The energy consumption calculation formula when the classification result is successfully obtained is as follows:

[0127] Energy=P s *PER*(TIME s -TIME e );

[0128]

[0129]

[0130] Among them, Ps represents the power difference before and after the electric vehicle start event, that is, the electric vehicle starting power, PER represents the energy consumption ratio obtained by matching the template library, TIMEs represents the cycle time index of the electric vehicle start event, and TIMEe represents the cycle time point index of the electric vehicle stop event. tlb Indicates the total energy consumption of the template during the electric vehicle operation time, Indicates the power difference before and after the electric vehicle start event in the template, i represents the index of each cycle time point during the electric vehicle operation time, Indicates the active power value at each cycle time point in the template library.

[0131] In this embodiment, before step S60, the following steps are further included:

[0132] Step B10: When the charging stop event occurs and classification fails, the charging start feature vector is obtained based on the start feature data, the charging stop feature vector is obtained based on the stop feature data, the charging start time point is obtained based on the start feature data, and the charging stop time point is obtained based on the stop feature data.

[0133] Based on step B10, another implementation of step S60 is:

[0134] Step B20: collecting statistics on the charging energy consumption based on the charging start feature vector, the charging stop feature vector, the charging start time point, and the charging stop time point.

[0135] It should be noted that if the charging waveform classification result is still not output after the charging stop event occurs, the energy consumption calculation is not performed using the classification template. At this time, the parameters for statistical charging energy consumption are: the corresponding feature vector of the electric vehicle charging start event, the corresponding feature quantity of the electric vehicle charging end event, the electric vehicle charging start time point obtained when the charging start event occurs, and the electric vehicle charging stop time point obtained when the charging stop event occurs. The energy consumption calculation formula for failure to obtain a classification result is as follows:

[0136]

[0137] Among them, Ps represents the power difference before and after the electric vehicle starts, that is, the electric vehicle starting power, Pe represents the power difference before and after the electric vehicle stops, TIMEs represents the cycle time index of the electric vehicle starts, TIMEe represents the cycle time point index of the electric vehicle ends, and i represents the cycle time point index during the running time of the electric vehicle.

[0138] In this embodiment, a charging energy consumption statistics method is provided, which specifically involves providing different charging energy consumption calculation methods based on whether there is a charging waveform classification result after the electric vehicle stops charging. Based on the above content, accurate statistics of the charging energy consumption of the electric vehicle are achieved, and the accuracy of the electric energy calculation is improved. It can help users understand the energy consumption of their electric vehicles more accurately, and obtain more detailed electric vehicle charging information and the quality and life of the electric vehicle battery and charger, so that electric vehicle users can arrange the charging time of the electric vehicle more reasonably, save charging costs, and further help electric vehicle users avoid charging peak periods and improve the safety of electric vehicle charging.

[0139] Further, refer to Figure 4 , proposes the third embodiment of the charging energy consumption statistics method of the present invention, based on the above Figure 2 and Figure 3In the embodiment shown, in step S10 and step S40, when a step signal appears in the charging signal data, the step of determining whether a charging start event or a charging stop event occurs includes:

[0140] Step C10: When a step signal appears in the charging signal data, record the step cache data before and after the step signal appears;

[0141] Step C20, calculating a feature vector of the current unknown event based on the step cache data;

[0142] Step C30 : analyzing the feature vector to determine whether the current unknown event is a charging start event or a charging stop event.

[0143] It should be noted that, in this embodiment, the voltage and current data at the entrance of the monitored environment are collected in real time through charging energy consumption statistics. When the active power generates a step signal, the step cache data before and after the step moment is recorded, and the step cache data includes voltage, current sampling data and active power values; the related feature vector of the current unknown event is calculated based on the recorded voltage, current sampling data and active power values, and finally the calculated feature vector is analyzed to determine whether the current event is an electric vehicle charging start event or an electric vehicle charging stop event.

[0144] In the specific implementation, it is based on Pmax and Pmin (the maximum and minimum difference between each cycle point in the current window and the active power before the event occurs), and the formula:

[0145] and Perform step signal judgment, where Pave represents the average value of the active power in the current window, Ps represents the slope of change of the active power in the current window, i represents the frequency point index in the current window, △P represents the difference between the active power of each frequency point in the current window and the active power before the event occurs, and n represents the total number of cycles in the window.

[0146] In addition, the calculation formula for the unknown eigenvector shown is as follows:

[0147] Where THDi represents the total harmonic content of the electric vehicle at the i-th cycle index point within the window. M represents the highest harmonic statistical order, i represents the cycle index within the current window, j represents the currently selected harmonic order, REj represents the real part of each current harmonic, IMj represents the imaginary part of each current harmonic, and THDe represents the average total harmonic content in the most recent window before the event.

[0148] This embodiment provides a charging energy consumption statistics method, which specifically involves the analysis of the current unknown event when the step signal appears, as well as the corresponding judgment method and calculation analysis method. Based on the above analysis, the electric vehicle charging event in the charging event can be captured more accurately, and the influence of other electrical appliances can be filtered out, so that the final charging energy consumption statistics of the electric bicycle are more accurate and effective.

[0149] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a charging energy consumption statistics program is stored. When the charging energy consumption statistics program is executed by a processor, the following operations are implemented:

[0150] Collecting charging signal data in the current monitoring environment, and determining whether a charging start event occurs when a step signal appears in the charging signal data;

[0151] If a charging start event occurs, the start-up characteristic data within a preset time period is recorded;

[0152] outputting a charging waveform classification result based on the startup characteristic data;

[0153] Dynamically monitoring the charging signal data, and determining whether a charging stop event occurs when a step signal appears in the charging signal data;

[0154] If a charging stop event occurs, the stop characteristic data within a preset time period is recorded;

[0155] The charging energy consumption is counted based on the charging waveform classification result, the start characteristic data and the stop characteristic data.

[0156] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0157] The step of outputting the charging waveform classification result based on the startup feature vector data includes:

[0158] classifying the current charging waveform based on the startup characteristic data to obtain a charging waveform classification result;

[0159] Determining whether the similarity between the charging waveform classification result and a preset charging waveform template is greater than a system preset threshold;

[0160] If the similarity is greater than a system preset threshold, the classification is deemed successful and the charging waveform classification result is output.

[0161] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0162] After the step of determining whether the similarity between the charging waveform classification result and the preset charging waveform template is greater than a system preset threshold value, the method further includes:

[0163] If the similarity is not greater than a system preset threshold, it is considered that the classification has failed and the charging waveform classification result is discarded;

[0164] Return to the step of determining whether a charging start event occurs until the step of outputting the charging waveform classification result is completed or a charging stop event occurs.

[0165] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0166] The step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data and the stop feature data includes:

[0167] When the charging stop event occurs and the classification is successful, a classification template is generated based on the charging waveform classification result, a corresponding feature vector in the classification template is extracted, a charging start time point is obtained based on the start feature data, and a charging stop time point is obtained based on the stop feature data.

[0168] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0169] The step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data, and the stop feature data includes:

[0170] The charging energy consumption is counted based on the corresponding feature vector in the classification template, the charging start time point and the charging stop time point.

[0171] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0172] Before the step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data and the stop feature data, the following step is further included:

[0173] When the charge stop event occurs and classification fails, the charge start feature vector is obtained based on the start feature data, and the charge stop feature vector is obtained based on the stop feature data.

[0174] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0175] The step of collecting statistics on charging energy consumption based on the charging waveform classification result, the start feature data and the stop feature data includes:

[0176] The charging energy consumption is counted based on the charging start feature vector, the charging stop feature vector, the charging start time point, and the charging stop time point.

[0177] Furthermore, when the charging energy consumption statistics program is executed by the processor, the following operations are also implemented:

[0178] When a step signal appears in the charging signal data, the step of determining whether a charging start event or a charging stop event occurs includes:

[0179] When a step signal appears in the charging signal data, recording the step cache data before and after the step signal appears;

[0180] Calculate the feature vector of the current unknown event based on the step cache data;

[0181] An analysis is performed based on the feature vector to determine whether the current unknown event is a charging start event or a charging stop event.

[0182] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0183] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0184] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0185] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A charging energy consumption statistics method, characterized in that: The charging energy consumption statistics method comprises the following steps: Collect charging signal data in the current monitoring environment, and when a step signal appears in the charging signal data, record step buffer data before and after the step signal appears; calculate a feature vector of the current unknown event based on the step buffer data; and analyze the feature vector to determine whether the current unknown event is a charging start event or a charging stop event; If a charging start event occurs, the start-up characteristic data within a preset time period is recorded; classifying the current charging waveform based on the startup characteristic data to obtain a charging waveform classification result; determining whether a similarity between the charging waveform classification result and a preset charging waveform template is greater than a system preset threshold value; if the similarity is greater than the system preset threshold value, the classification is deemed successful, and the charging waveform classification result is output; If a charging stop event occurs, the stop characteristic data within a preset time period is recorded; When the charging stop event occurs and the classification is successful, a classification template is generated based on the charging waveform classification result, the corresponding feature vector in the classification template is extracted, the charging start time point is obtained based on the start feature data, and the charging stop time point is obtained based on the stop feature data. The charging energy consumption is statistically analyzed based on the corresponding feature vector in the classification template, the charging start time point, and the charging stop time point. The energy consumption calculation formula when the classification result is successfully obtained is as follows: ； ； ； Among them, Ps represents the power difference before and after the electric vehicle start event, that is, the electric vehicle starting power, PER represents the energy consumption ratio obtained by matching the template library, TIMEs represents the cycle time index of the electric vehicle start event, and TIMEe represents the cycle time point index of the electric vehicle stop event. Indicates the total energy consumption of the template during the electric vehicle operation time, Indicates the power difference before and after the electric vehicle start event in the template, i represents the index of each cycle time point during the electric vehicle operation time, Indicates the active power value at each cycle time point in the template library; When the charging stop event occurs and classification fails, the charging start feature vector is obtained based on the start feature data, and the charging stop feature vector is obtained based on the stop feature data. Charging energy consumption is statistically analyzed based on the charging start feature vector, the charging stop feature vector, the charging start time point, and the charging stop time point. The energy consumption calculation formula when no classification result is obtained is as follows: ； Among them, Ps represents the power difference before and after the electric vehicle starts, that is, the electric vehicle starting power, Pe represents the power difference before and after the electric vehicle stops, TIMEs represents the cycle time index of the electric vehicle starts, TIMEe represents the cycle time point index of the electric vehicle ends, and i represents the cycle time point index during the running time of the electric vehicle.

2. The charging energy consumption statistics method according to claim 1, characterized in that: After the step of determining whether the similarity between the charging waveform classification result and the preset charging waveform template is greater than a system preset threshold value, the method further includes: If the similarity is not greater than a system preset threshold, it is considered that the classification has failed and the charging waveform classification result is discarded; Return to the step of determining whether a charging start event occurs until the step of outputting the charging waveform classification result is completed or a charging stop event occurs.

3. A charging energy consumption statistics device, characterized in that: The charging energy consumption statistics device includes: a memory, a processor, and a charging energy consumption statistics program stored in the memory and executable on the processor. When the charging energy consumption statistics program is executed by the processor, the steps of the charging energy consumption statistics method according to any one of claims 1 to 2 are implemented.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a charging energy consumption statistics program, which, when executed by a processor, implements the steps of the charging energy consumption statistics method according to any one of claims 1 to 2.

Images