Offline charging method, system, device, storage medium and program product

Abstract

The application provides an offline charging billing method, system, device, storage medium and program product. The method comprises the following steps: after detecting that charging starts, generating a charging order and recording charging data, wherein the charging data comprises vehicle information, charging process data and charging state data; after the charging ends, detecting whether the charging state data is updated, if not, determining that the charging order is an offline unsettled order; calculating an actual power curve before offline according to the charging process data of the unsettled order; generating a predicted power curve according to the vehicle information of the unsettled order; matching the position of the actual power curve before offline in the predicted power curve according to a matching method, and generating an estimated power curve after offline; calculating the charging electric energy of each time period and the estimated charging end time in the offline time according to the estimated power curve after offline and the electric energy after the charging device is online; and calculating the actual charging cost of the unsettled order according to the charging electric energy of all time periods and a billing strategy.

Description

Technical Field

[0001] This invention relates to the field of offline charging billing technology, and in particular to an offline charging billing method, system, device, storage medium and program product. Background Technology

[0002] The current offline charging billing method is as follows: Based on the energy information recorded before the charging station went offline, the average power of the charging session before that session is determined. After offline charging resumes, the offline charging duration is calculated based on the current energy value and the average power, and billing is then based on the calculated charging duration and amount of electricity charged. This scheme uses average power to divide the amount of electricity charged offline into each charging period, without considering the issue of varying charging power. This results in low accuracy in assessing offline charging duration and charging costs, and a significant discrepancy with actual charging conditions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an offline charging billing method that solves the problem of not taking into account changes in charging power in existing technologies.

[0004] To achieve the above objectives, the present invention provides an offline charging billing method, comprising the following steps:

[0005] Once charging is detected to have started, a charging order is generated and charging data is recorded, including vehicle information, charging process data, and charging status data. After charging is completed, it is checked whether the charging status data has been updated. If the charging status data has not been updated, the charging order is determined to be an offline, unsettled order.

[0006] Calculate the actual power curve before offline based on the charging process data of the unsettled orders;

[0007] A predicted power curve is generated based on the vehicle information of the unsettled orders;

[0008] The actual power curve before offline is matched to the position of the predicted power curve according to the matching method, and the estimated power curve after offline is generated.

[0009] Based on the offline estimated power curve and the online power, calculate the charging power and estimated charging end time for each period during the offline time.

[0010] The actual charging cost of the unsettled order is calculated based on the charging energy during all time periods within the estimated charging end time and the billing strategy.

[0011] The matching method includes: obtaining the charging power at the offline time point and m time points before offline on the actual power curve before offline; calculating the power change over m time periods consisting of these m+1 time points; taking the average power change over the m time periods as the first power change value, where m is a positive integer greater than or equal to 2; comparing the first power change value with the power change over each adjacent m time periods of the predicted power curve, and the power curve segment with the smallest difference is the position of the matched predicted power curve.

[0012] The offline predicted power curve includes the position of the matched predicted power curve and the subsequent predicted power curve.

[0013] The charging status data includes a charging start status and a charging end status, and detecting whether the charging status data is updated is equivalent to detecting whether the charging end status is updated.

[0014] The vehicle information includes vehicle model, charging method, ambient temperature, and vehicle battery status.

[0015] The step of generating the predicted power curve based on the vehicle information of the unsettled orders further includes the following steps: generating historical power consumption data and daily power curves for the same vehicle model as the unsettled orders based on historical orders; and generating the predicted power curve based on the historical power consumption data and the daily power curves.

[0016] On the other hand, the present invention provides an offline charging billing system, comprising: a centralized controller, charging equipment connected to the centralized controller, and a cloud platform.

[0017] The charging device is used to: record charging data and report the charging data to the central controller after detecting the start of charging. The charging data includes vehicle information, charging process data and charging status data.

[0018] The centralized controller is used to: generate charging orders and upload charging data to the cloud platform; after the charging equipment finishes charging, detect whether the charging status data has been updated. If the charging status data has not been updated, the charging order is an offline unsettled order, and the unsettled order is bound to the corresponding vehicle information and reported to the cloud platform.

[0019] The cloud platform is used to: generate a predicted power curve based on the vehicle information bound to the unsettled order, and send the predicted power curve to the centralized controller;

[0020] The centralized controller is also used to: use the matching module to match the position of the actual power curve before offline on the predicted power curve, and generate the estimated power curve after offline; calculate the charging energy and estimated charging end time for each period during the offline time based on the estimated power curve after offline and the power energy after the charging equipment comes online; and calculate the actual charging cost of the unsettled order based on the charging energy for all periods during the estimated charging end time and the billing strategy.

[0021] The matching module includes:

[0022] The first calculation unit is used to obtain the charging power of the actual power curve before offline, including the offline time point and m time points before offline, and to calculate the power change of the m+1 time points in the m time periods, where m is a positive integer greater than or equal to 2.

[0023] The second calculation unit is used to take the average power change over the m time periods as the first power change value.

[0024] The matching position confirmation unit is used to compare the first power change value with the power change of the predicted power curve for each adjacent m time periods, and confirm the power curve segment with the smallest difference as the position of the matched predicted power curve.

[0025] The charging status data includes a charging start status and a charging end status. The centralized controller is further configured to: after the charging device finishes charging, detect whether the charging end status has been updated; if the charging status data has not been updated, determine that the charging order is an unsettled order.

[0026] The cloud platform is also used to: generate historical power consumption data and daily power curves for the same vehicle model as the unsettled orders based on historical orders; and generate the predicted power curve based on the historical power consumption data and the daily power curves.

[0027] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the offline charging billing method described above.

[0028] On the other hand, the present invention also provides an electronic device including a processor for executing an offline charging billing system as described above, stored in a memory.

[0029] On the other hand, the present invention also provides a computer program product, which, when executed by a processor, implements the steps of the above-described offline charging billing method.

[0030] As can be seen from the above solutions, the advantages of the present invention are:

[0031] This invention obtains a predicted power curve from charging data in a cloud platform's big data, and calculates the actual power curve before offline by combining it with the energy information recorded before offline. It then determines the position of the charging power of the unsettled order at the offline time on the predicted power curve, allocates the increased energy value during the offline period to each billing period according to the predicted power curve, and calculates the charging end time. This results in a charging cost and charging end time that are closer to the actual charging situation, improving the accuracy of charging billing after offline. Attached Figure Description

[0032] Figure 1 This is a flowchart of the offline charging billing method of the present invention;

[0033] Figure 2 This is another flowchart of the offline charging billing method of the present invention;

[0034] Figure 3 This is a diagram showing the device relationships used in the offline charging billing method of the present invention;

[0035] Figure 4 This is a flowchart of step S40;

[0036] Figure 5 Flowchart of the matching method;

[0037] Figure 6 This is a schematic diagram of the offline charging billing system of the present invention;

[0038] Figure 7 This is a structural diagram of the matching module;

[0039] Figure 8 This is a schematic diagram of the electronic device structure of the present invention;

[0040] In the attached figures, the following labels are used:

[0041] 1-Offline charging billing method;

[0042] 2-Offline charging billing system;

[0043] 20 - Centralized controller;

[0044] 200-Matching Module;

[0045] 2000 - First Calculation Unit;

[0046] 2001 - Second Calculation Unit;

[0047] 2002 - Matching Position Confirmation Unit;

[0048] 21-Charging equipment;

[0049] 22-Cloud Platform;

[0050] 3-Electronic devices;

[0051] 30-Processor;

[0052] 31-Memory;

[0053] S10~S70,S300,S301,S500~S502 - Steps. Detailed Implementation

[0054] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments to further understand the purpose, solution and effect of the present invention, but it is not intended to limit the scope of protection of the appended claims.

[0055] References to "embodiment," "another embodiment," "this embodiment," etc., in the specification refer to embodiments that may include specific features, structures, or characteristics, but not every embodiment must include these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0056] The specification and subsequent claims use certain terms to refer to specific components or parts. Those skilled in the art will understand that users or manufacturers may use different names or terms to refer to the same component or part. This specification and claims do not distinguish components or parts by differences in name, but rather by differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "including but not limited to". Furthermore, the term "connection" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connections via other means.

[0057] Example 1:

[0058] Figure 1 and Figure 2 This is a flowchart of an offline charging billing method 1 provided in an embodiment of the present invention. Figure 3The diagram illustrates the device relationships used in this offline charging billing method 1. Data exchange between the charging devices and the central controller utilizes either CAN (Controller Area Network) or TCP (Transmission Control Protocol), while data exchange between the central controller and the cloud platform uses TCP. Specifically, the charging devices transmit real-time data such as charging status, battery information, and fault codes to the central controller via the CAN bus. The central controller processes this data, makes local control decisions, and sends any necessary data to the cloud platform via TCP. The cloud platform stores, analyzes, and processes the collected data, providing services such as remote monitoring, fault diagnosis, and user management.

[0059] In this embodiment, the offline charging billing method 1 includes the following steps:

[0060] S10: After detecting the start of charging, generate a charging order and record charging data;

[0061] S20: After charging is completed, check whether the charging status data has been updated. If the charging status data has not been updated, the charging order at this time is determined to be an offline unsettled order.

[0062] S30: Calculates the actual power curve before offline based on charging process data of unsettled orders;

[0063] S40: Generates a predicted power curve based on vehicle information for unsettled orders;

[0064] S50: Match the position of the actual power curve before offline to the predicted power curve according to the matching method, and generate the estimated power curve after offline.

[0065] S60: Calculate the charging energy and estimated charging end time for each period during the offline time based on the estimated power curve after offline and the power energy after online.

[0066] S70: Calculates the actual charging cost of unsettled orders based on the charging energy during all time periods within the estimated charging end time and the billing strategy.

[0067] In step S10, after the charging device, such as a charging pile, detects the start of charging, it periodically reports charging data to the centralized controller. The centralized controller generates a charging order after the charging device starts charging and periodically reports charging data to the cloud platform. The charging data includes vehicle information, charging process data, and charging status data. Vehicle information includes vehicle model, charging method, ambient temperature, and vehicle battery status. Charging process data includes time, charging power, and charging energy. Charging status data includes charging start status and charging end status. Specifically, charging energy includes the initial charging energy, charging process energy, and charging end energy of the charging device; the charging method is the initiation charging method, which is automatically generated by the cloud platform or centralized controller according to different charging scenarios and transmitted back to the corresponding charging device; the vehicle model information is obtained from the user's electronic device (e.g., mobile phone) by the cloud platform.

[0068] In step S20, detecting whether the charging status data has been updated is equivalent to detecting whether the charging end status has been updated. Specifically, under normal circumstances, after the charging device starts charging, the central controller records the charging start status and periodically records the charging data during the charging process until the charging ends. After the charging ends, the charging end status is updated. When charging offline, if the charging end status is not updated after the charging device comes online but the charging device has stopped charging at this time, the charging order at this time is determined to be an offline unsettled order.

[0069] In step S30, the cloud platform generates a predicted power curve for the unsettled order based on the vehicle information through big data analysis and sends it to the centralized controller.

[0070] like Figure 4 As shown, in step S50, the matching method includes the following steps:

[0071] S500: Obtain the charging power at the offline time point and m time points before offline on the actual power curve, and calculate the power change of the m+1 time points in the m time periods, where m is a positive integer greater than or equal to 2.

[0072] S501: Take the average power change over m time periods as the first power change value;

[0073] S502: Compare the first power change value with the power change of each adjacent m time interval of the predicted power curve, and the power curve segment with the smallest difference is the position of the matched predicted power curve.

[0074] The offline predicted power curve includes the position of the matched predicted power curve and the predicted power curve after that position.

[0075] Specifically, in steps S50 to S70, the charging power of the actual power curve table before offline (such as Table 1) is taken, including the offline time point and the two time points before offline. The power change (slope) of the two time periods formed by these three time points is calculated and compared with the power change of each two adjacent time periods (including three adjacent time points) of the predicted power curve (such as Table 2). The power curve segment with the smallest difference is the position of the matched predicted power curve.

[0076] Table 1: Schematic Table of Actual Power Curves (Offline)

[0077] time <![CDATA[T ac1 ]]> <![CDATA[T ac2 ]]> <![CDATA[T ac3 ]]> <![CDATA[T ac4 ]]> <![CDATA[T ac5 ]]> Offline

[0078] Table 2: Schematic Table of Predicted Power Curves

[0079] time <![CDATA[T pre1 ]]> <![CDATA[T pre2 ]]> <![CDATA[T pre3 ]]> <![CDATA[T pre4 ]]> <![CDATA[T pre5 ]]> …… <![CDATA[T pre(N) ]]>

[0080] (1) Calculate the offline time point T based on the actual power curve table before offline. ac5 and the two time points T before going offline ac4 T ac3 Power change S ac As shown in the following formula:

[0081] S ac =[(P ac5 -P ac4 ) / (T ac5 -T ac4 )+(P ac4 -P ac3 ) / (T ac4 -T ac3 )] / 2.

[0082] (2) Based on the predicted power curve table, calculate the power change at each of the three adjacent time points of the predicted power curve:

[0083] S pre1 =[(P pre3 -P pre2 ) / (T pre3 -T pre2 )+(P pre2 -P per1 ) / (T pre2 -T pre1 )] / 2

[0084] S pre2 =[(P pre4 -P pre3 ) / (T pre4 -T pre3 )+(P pre3 -P per2 ) / (T pre3-T pre2 )] / 2

[0085] ...

[0086] S pre(N-2) =[(P pre(N) -P pre(N-1) ) / (T pre(N) -T pre(N-1) )+(P pre(N-1) -P per(N-2) ) / (T pre(N-1) -T pre(N-2) )] / 2.

[0087] (3) Match the point where the offline power change is closest to the predicted power change, |(S pre(n) -S ac )| min

[0088] (4)P pre(n) P pre(n+1) P pre(n+2) The location of the power curve is the position of the matched predicted curve. At this point, the missing power curves can be filled in, as shown in Table 3. P pre(n+3) And the subsequent power composition is the estimated power after offline operation. T pre(n+3) The time following this constitutes the estimated time after offline operation.

[0089] Table 3: Schematic diagram of the power curve after completion

[0090] time <![CDATA[T ac1 ]]> <![CDATA[T ac2 ]]> <![CDATA[T ac3 ]]> <![CDATA[T ac4 ]]> <![CDATA[T ac5 ]]> <![CDATA[T pre(n+3) ]]> ……

[0091] The charging energy calculation for each time period during the offline time is as follows: The charging energy for each time period is calculated by taking the corresponding charging power and time (the actual power curve supplemented) on the predicted power curve, and the end time T can also be calculated. pre(end) (Estimated charging completion time). (Power × Time = Electrical Energy)

[0092] Offline charging energy = Offline recovery energy - Offline energy before offline charging;

[0093] Offline charging energy = [P] ac5 ×(T pre(n+3) -T ac5 )]+[P ac(n+3) ×(T pre(n+4) -T pre(n+3) )]+……+[P ac(x) ×(T pre(end) -T pre(x) )]

[0094] Among them, P ac(x) To calculate the last power point used for offline charging, T pre(x)This is the last point in time that can be used to calculate the offline charging energy, i.e., the charging end time.

[0095] On the actual power curve before offline and the estimated power curve after offline, calculate the charging energy for all time periods from the start time of the charging device to the estimated end time of charging, and calculate the actual cost of unsettled orders according to the billing strategy.

[0096] A billing strategy describes the charging rules and methods of a charging station, and may include at least one of the following: billing standards, billing time, and billing methods. In some embodiments, different charging periods may have different charging billing strategies; the charging billing strategy may also differ under different promotional activities. Upon receiving a charging order, the charging device may determine the current version of the charging billing strategy and send it to the central controller and the cloud platform. In other embodiments, the cloud platform may determine the charging billing strategy based on the billing rules configured by the user (e.g., the charging station administrator), or the cloud platform may obtain the charging billing strategy from a designated network server; this embodiment does not impose any limitations.

[0097] Example 2:

[0098] Example 2 provides another offline charging billing method. Example 2 is basically the same as the offline charging billing method 1 provided in Example 1. Only the differences are described below. In the offline charging billing method of Example 2, such as Figure 5 As shown, step S40 further includes the following steps:

[0099] S400: Generates historical power consumption data and daily power curves for the same vehicle model as unsettled orders based on historical orders;

[0100] S401: Generate the predicted power curve based on historical power consumption data and the daily power curve.

[0101] Specifically, the cloud platform can obtain historical power consumption data based on historical order data. This data typically includes daily power curves. To accurately retrieve historical power consumption data and daily power curves for the same vehicle model as the unsettled order, a predicted power curve can be obtained for billing the unsettled order.

[0102] In other embodiments, the cloud platform can query daily electricity usage, including power curves and real-time electricity prices, through the power company's customer service platform. By combining the real-time electricity price with the actual power curve before offline operation and the estimated power curve after offline operation, settlement of unsettled orders is made, ensuring that the user's settlement fee does not exceed the actual fee, thus protecting the user's rights.

[0103] The following is a system embodiment corresponding to the method embodiment one described above. This embodiment can be implemented in conjunction with the above embodiments. The relevant technical details mentioned in the above embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0104] Example 3:

[0105] like Figure 6 The diagram shown is a structural schematic of an offline charging billing system 2 according to another embodiment of the present invention, including: a central controller 20, a charging device 21 connected to the central controller 20, and a cloud platform 22.

[0106] The charging device 21 is used to: record charging data and report the charging data to the central controller 20 after detecting the start of charging. The charging data includes vehicle information, charging process data and charging status data.

[0107] The centralized controller 20 is used to: generate charging orders and upload charging data to the cloud platform 22; after the charging equipment finishes charging, check whether the charging status data is updated. If the charging status data is not updated, the charging order is determined to be an offline unsettled order; calculate the actual power curve before offline based on the charging process data of the unsettled orders recorded before offline, and bind the unsettled orders to the corresponding vehicle information and report it to the cloud platform 22.

[0108] Cloud platform 22 is used to: generate a predicted power curve based on the corresponding vehicle information bound to unsettled orders, and send the predicted power curve to the central controller 20;

[0109] The centralized controller 20 is also used to: use the matching module 200 to match the position of the actual power curve before offline to the predicted power curve, and generate the estimated power curve after offline; calculate the charging energy and estimated charging end time for each period during the offline time based on the estimated power curve after offline and the power energy after the charging equipment comes online; and calculate the actual charging cost of unsettled orders based on the charging energy for all periods during the estimated charging end time and the billing strategy.

[0110] In this embodiment, vehicle information includes vehicle model, charging method, ambient temperature, and vehicle battery status. Charging process data includes time, charging power, and charging energy. Charging status data includes charging start status and charging end status. Specifically, charging energy includes the initial charging energy, charging process energy, and charging end energy of the charging device. The charging method is the initiation charging method, which is automatically generated by the cloud platform 22 or the centralized controller 20 according to different charging scenarios and transmitted back to the corresponding charging device 21. Vehicle model information is obtained by the cloud platform 22 from information on the user's electronic device (e.g., mobile phone). The charging device 21 is, for example, a charging pile.

[0111] In this embodiment, the centralized controller 20 is further configured to: after the charging device 21 finishes charging, detect whether the charging end status has been updated; if the charging status data has not been updated, determine that the charging order is an unsettled order. Specifically: after the charging device 21 starts charging, it records charging data in real time until charging ends and updates the charging end status; after the charging device 21 recovers from offline operation, it detects the charging end status; if the charging end status has not been updated but the charging device 21 has stopped charging at this time, it confirms that the charging order at this time is an unsettled order.

[0112] In this embodiment, as Figure 7 As shown, the matching module 200 further includes:

[0113] The first calculation unit 2000 is used to obtain the charging power of the actual power curve before offline, including the offline time point and m time points before offline, and to calculate the power change of the m+1 time points in the m time periods, where m is a positive integer greater than or equal to 2.

[0114] The second calculation unit 2001 is used to take the average power change over m time periods as the first power change value.

[0115] The matching position confirmation unit 2002 is used to compare the first power change value with the power change of each adjacent m time period of the predicted power curve, and the power curve segment with the smallest difference is confirmed as the position of the matched predicted power curve.

[0116] A billing strategy describes the charging rules and methods of a charging station and may include at least one of the following: billing standards, billing time, and billing methods. In some embodiments, different charging periods may have different charging billing strategies; the charging billing strategy may also differ under different promotional activities. Upon receiving a charging order, the charging device 21 may determine the current version of the charging billing strategy and send it to the central controller 20 and the cloud platform 22. In other embodiments, the cloud platform 22 may determine the charging billing strategy based on the billing rules configured by the user (e.g., the administrator of the charging station), or the cloud platform 22 may obtain the charging billing strategy from a designated network server; this embodiment does not impose any limitations.

[0117] The following is a system embodiment corresponding to the second method embodiment described above. This embodiment can be implemented in conjunction with the above embodiments. The relevant technical details mentioned in the above embodiments remain valid in this embodiment, and will not be repeated here to reduce repetition. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0118] Example 4:

[0119] Example 4 provides an offline charging billing system. Example 4 is basically the same as the offline charging billing system 2 in Example 3. The following only describes the differences. In the offline charging billing system provided in Example 4, the cloud platform 22 is also used to: generate historical power consumption data and daily power curves of the same vehicle model as the unsettled orders based on historical orders; and generate the predicted power curve based on the historical power consumption data and daily power curves.

[0120] In other embodiments, the cloud platform can query daily electricity usage, including power curves and real-time electricity prices, through the power company's customer service platform. By combining the real-time electricity price with the actual power curve before offline operation and the estimated power curve after offline operation, settlement of unsettled orders is made, ensuring that the user's settlement fee does not exceed the actual fee, thus protecting the user's rights.

[0121] Example 5:

[0122] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the offline charging billing method described above (e.g. Figure 1 and Figure 2 The steps of the method shown.

[0123] It should be understood that the storage medium in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0124] Example 6:

[0125] like Figure 8 As shown, another embodiment of the present invention also provides an electronic device 3, including a processor 30, which is used to execute an offline charging billing system 2 stored in a memory 31.

[0126] Example 7:

[0127] Another embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the offline charging billing method described above. Figure 1 , Figure 2 , Figure 4 or Figure 5 The steps are shown below.

[0128] In summary, this invention utilizes a predicted charging power curve obtained from cloud platform big data based on information such as the current vehicle model, charging method, vehicle battery status, and ambient temperature. This curve is combined with the actual power curve calculated from the energy information recorded before offline charging to determine the position of the charging operation on the predicted power curve at the offline time. The increased energy value during the offline period is then allocated to various billing periods according to the predicted power curve, and the charging end time is calculated. This results in a charging cost calculation and charging end time that more closely approximates the actual charging situation.

[0129] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms fall within the scope of protection of the present invention.

Claims

1. An offline charging billing method, characterized in that, Includes the following steps: Once charging is detected to have started, a charging order is generated and charging data is recorded, including vehicle information, charging process data, and charging status data. After charging is completed, check whether the charging status data has been updated. If the charging status data has not been updated, the charging order is determined to be an offline unsettled order. Calculate the actual power curve before offline based on the charging process data of the unsettled orders; A predicted power curve is generated based on the vehicle information of the unsettled orders; The actual power curve before offline is matched to the position of the predicted power curve according to the matching method, and the estimated power curve after offline is generated. Based on the offline estimated power curve and the online power, calculate the charging power and estimated charging end time for each period during the offline time. The actual charging cost of the unsettled order is calculated based on the charging energy of all time periods within the estimated charging end time and the billing strategy. The matching method includes: Obtain the charging power at the offline time point and m time points before offline on the actual power curve before offline, and calculate the power change in the m time periods consisting of these m+1 time points, where m is a positive integer greater than or equal to 2. The average power change over the m time periods is taken as the first power change value; The first power change value is compared with the power change of each adjacent m time period of the predicted power curve, and the power curve segment with the smallest difference is the position of the matched predicted power curve.

2. The offline charging billing method according to claim 1, characterized in that, The offline predicted power curve includes the position of the matched predicted power curve and the subsequent predicted power curve.

3. The offline charging billing method according to claim 1, characterized in that, The charging status data includes charging start status and charging end status; The detection of whether the charging status data is updated is equivalent to detecting whether the charging end status is updated.

4. The offline charging billing method according to claim 1, characterized in that, The vehicle information includes vehicle model, charging method, ambient temperature, and vehicle battery status.

5. The offline charging billing method according to claim 4, characterized in that, The step of generating the predicted power curve based on the vehicle information of the unsettled orders further includes the following steps: Generate historical power consumption data and daily power curves for the same vehicle model as the unsettled orders based on historical orders; The predicted power curve is generated based on the historical power consumption data and the daily power curve.

6. An offline charging billing system, comprising: A centralized controller, a charging device connected to the centralized controller, and a cloud platform, characterized in that: The charging device is used to: after detecting the start of charging, record charging data and report the charging data to the central controller, wherein the charging data includes vehicle information, charging process data and charging status data; The centralized controller is used to: generate charging orders and upload the charging data to the cloud platform; after the charging device finishes charging, detect whether the charging status data has been updated; if the charging status data has not been updated, determine that the charging order is an offline unsettled order; calculate the actual power curve before going offline based on the charging process data of the unsettled order, and bind the unsettled order to the corresponding vehicle information and report it to the cloud platform. The cloud platform is used to: generate a predicted power curve based on the vehicle information corresponding to the unsettled orders, and send the predicted power curve to the centralized controller; The centralized controller is also used to: use the matching module to match the position of the actual power curve before offline on the predicted power curve, and generate the estimated power curve after offline; calculate the charging energy and estimated charging end time for each period during the offline time based on the estimated power curve after offline and the power energy after the charging equipment comes online; and calculate the actual charging cost of the unsettled order based on the charging energy for all periods during the estimated charging end time and the billing strategy. The matching module includes: The first calculation unit is used to obtain the charging power of the actual power curve before offline, including the offline time point and m time points before offline, and to calculate the power change of the m+1 time points in the m time periods, where m is a positive integer greater than or equal to 2. The second calculation unit is used to take the average power change over the m time periods as the first power change value. The matching position confirmation unit is used to compare the first power change value with the power change of the predicted power curve for each adjacent m time periods, and confirm the power curve segment with the smallest difference as the position of the matched predicted power curve.

7. The offline charging billing system according to claim 6, characterized in that, The offline predicted power curve includes the position of the matched predicted power curve and the subsequent predicted power curve.

8. The offline charging billing system according to claim 6, characterized in that, The charging status data includes charging start status and charging end status, and the centralized controller is further used for: After the charging device finishes charging, it checks whether the charging end status has been updated. If the charging end status has not been updated, the charging order is determined to be an unsettled order.

9. The offline charging billing system according to claim 6, characterized in that, The vehicle information includes vehicle model, charging method, ambient temperature, and vehicle battery status.

10. The offline charging billing system according to claim 9, characterized in that, The cloud platform is also used for: Generate historical power consumption data and daily power curves for the same vehicle model as the unsettled orders based on historical orders; The predicted power curve is generated based on the historical power consumption data and the daily power curve.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the offline charging billing method as described in any one of claims 1 to 5.

12. An electronic device, comprising a processor, characterized in that, The processor is used to execute the offline charging billing system as described in any one of claims 6 to 10, which is stored in the memory.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the offline charging billing method as described in any one of claims 1 to 5.

Images