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

Abstract

The invention provides an off-line charging billing method, system and device, a storage medium and a program product, and the method comprises the steps: generating a charging order and recording charging data after the start of charging is detected, and the charging data comprises vehicle information, charging process data and charging state data; after charging is finished, whether the charging state data is updated or not is detected, and if the charging state data is not updated, it is judged that the charging order is an offline non-settlement 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; according to the estimated power curve after offline and the electric energy after the charging equipment is online, calculating the charging electric energy and the estimated charging end time of each time period within the offline time; and calculating the actual charging cost of the unsettled order according to the charging electric energy of all the time periods and the charging strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of off-line charging billing, and in particular to an off-line charging billing method, system, equipment, storage medium and program product. Background Art

[0002] The current offline charging billing method is: determine the average power of the target charging pile before it goes offline through the power information recorded before the charging pile goes offline; calculate the offline charging duration based on the current power value and average power after offline recovery, and bill based on the calculated charging duration and power. This solution uses average power to divide the offline charging amount into each charging period, without considering the problem of charging power changes, resulting in low accuracy in evaluating offline charging duration and charging costs, which is quite different from the actual charging situation. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides an offline charging billing method, which solves the problem that the prior art does not take into account the change in charging power.

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

[0005] After detecting that charging has started, a charging order is generated and charging data is recorded, wherein the charging data includes vehicle information, charging process data and charging status data; after charging is completed, whether the charging status data is updated is detected, and if the charging status data is not updated, the charging order is determined to be an offline unsettled order;

[0006] Calculate the actual power curve before offline according to the charging process data of the unsettled order;

[0007] Generate a predicted power curve according to the vehicle information of the unsettled order;

[0008] Matching the position of the actual power curve before offline to the predicted power curve according to a matching method to generate a post-offline estimated power curve;

[0009] Calculate the charging power and estimated charging end time for each period during the offline time according to the estimated power curve after offline and the power after online;

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

[0011] The matching method includes: obtaining the charging power including the offline time point and m time points before offline on the actual power curve before offline, and calculating the power changes of m time periods composed of these m+1 time points; taking the average value of the power changes of 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 changes of 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 post-offline estimated 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 the detecting whether the charging status data is updated is 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 method of generating a predicted power curve based on the vehicle information of the unsettled order also includes the following steps: generating historical power consumption data and a daily power curve of the same vehicle model as the unsettled order based on historical orders; and generating the predicted power curve based on the historical power consumption data and the daily power curve.

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

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

[0018] The centralized controller is used to: generate a charging order and upload the charging data to the cloud platform; after the charging device is finished charging, detect whether the charging status data is updated. If the charging status data is not 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 according to 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 a matching module to match the position of the actual power curve before offline to the predicted power curve to generate an estimated power curve after offline; calculate the charging energy for each period during the offline time and the estimated charging end time according to the estimated power curve after offline and the electric energy of the charging equipment after going online; calculate the actual charging cost of the unsettled order according to the charging energy for all periods during the estimated charging end time and the billing strategy.

[0021] The matching module comprises:

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

[0023] A second calculation unit, configured to take an average value of the power changes in the m time periods as a first power change value;

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

[0025] The charging status data includes a charging start status and a charging end status. The centralized controller is further used to: after the charging device finishes charging, detect whether the charging end status is updated. If the charging status data is not 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 order based on historical orders; and generate the predicted power curve based on the historical power consumption data and the daily power curve.

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

[0028] On the other hand, the present invention further provides an electronic device, comprising a processor, wherein the processor is used to execute the above-mentioned offline charging billing system stored in a memory.

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

[0030] It can be seen from the above scheme that the advantages of the present invention are:

[0031] The present invention obtains a predicted power curve by using charging data in the big data of the cloud platform, and calculates the actual power curve before offline by combining it with the electric energy information recorded before offline, determines the position of the predicted power curve of the charging power of the unsettled order at the time of offline, allocates the increased electric energy value during the offline period to each billing period according to the predicted power curve, and calculates the charging end time, so as to obtain the charging fee and charging end time that are closer to the actual charging situation, and improves the accuracy of charging billing after offline. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0033] Figure 2 Another flow chart of the offline charging billing method of the present invention;

[0034] Figure 3 A diagram showing the relationship between devices used in the offline charging billing method of the present invention;

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

[0036] Figure 5 is a flow chart of the matching method;

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

[0038] Figure 7 It is a schematic diagram of the structure of the matching module;

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

[0040] Wherein, the reference numerals are:

[0041] 1- Offline charging billing method;

[0042] 2-Offline charging billing system;

[0043] 20-Centralized controller;

[0044] 200-matching module;

[0045] 2000-first computing unit;

[0046] 2001- Second computing unit;

[0047] 2002-matching position confirmation unit;

[0048] 21- Charging equipment;

[0049] 22- Cloud platform;

[0050] 3- Electronic equipment;

[0051] 30-processor;

[0052] 31- memory;

[0053] S10~S70, S300, S301, S500~S502-steps. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described in detail below in conjunction with 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 claims attached to the present invention.

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

[0056] Certain words are used in the specification and subsequent claims to refer to specific components or parts. People of ordinary skill in the art should understand that technical users or manufacturers can refer to the same component or part with different nouns or terms. This specification and claims do not use differences in names as a way to distinguish components or parts, but use differences in the functions of components or parts as the criteria for distinction. "Including" and "including" mentioned throughout the specification and claims are open-ended terms and should be interpreted as "including but not limited to". In addition, the word "connected" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connection through other devices.

[0057] Embodiment 1:

[0058] Figure 1 and Figure 2 This is a flow chart of an offline charging billing method 1 provided in an embodiment of the present invention. Figure 3This is the device relationship diagram used in the offline charging billing method 1. Among them, the data exchange between the charging device and the centralized controller adopts CAN (Controller Area Network) or TCP (Transmission Control Protocol), and the data exchange between the centralized controller and the cloud platform adopts TCP. Specifically, the charging device sends real-time data such as charging status, battery information, fault code, etc. to the centralized controller through the CAN bus. The centralized controller processes this data, makes local control decisions, and sends the data that needs to be uploaded to the cloud platform through the TCP protocol. The cloud platform stores, analyzes and processes the collected data, and provides remote monitoring, fault diagnosis, user management and other services.

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

[0060] S10: after detecting that charging has started, generating a charging order and recording charging data;

[0061] S20: After charging is completed, detecting whether the charging status data is updated. If the charging status data is not updated, determining that the charging order at this time is an offline unsettled order;

[0062] S30: Calculating the actual power curve before offline according to the charging process data of the unsettled order;

[0063] S40: generating a predicted power curve according to vehicle information of unsettled orders;

[0064] S50: matching the position of the actual power curve before offline with the predicted power curve according to the matching method to generate the estimated power curve after offline;

[0065] S60: Calculate the charging power and estimated charging end time for each period during the offline time according to the estimated power curve after offline and the power after online;

[0066] S70: Calculate the actual charging cost of the unsettled order based on the charging power of 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 that charging has started, it periodically reports charging data to the centralized controller. After the charging device starts charging, the centralized controller generates a charging order and periodically reports charging data to the cloud platform. The charging data includes vehicle information, charging process data, and charging status data, wherein the vehicle information includes vehicle model, charging method, ambient temperature, and vehicle battery status, etc. The charging process data includes time, charging power, charging energy, etc. The charging status data includes charging start status and charging end status. Specifically, the charging energy includes the starting energy of the charging device, the charging process energy, and the charging end energy, etc.; the charging method is the start charging method, which is automatically generated by the cloud platform or the centralized controller according to different charging scenarios and transmitted back to the corresponding charging device; the vehicle model is supplemented by the cloud platform obtaining information from the user on the electronic device (such as a mobile phone).

[0068] In step S20, detecting whether the charging status data is updated is to detect whether the charging end status is updated. Specifically, under normal circumstances, the centralized controller records the charging start status after the charging device starts charging, and periodically records the charging data during the charging process until charging is completed. After charging is completed, the charging end status is updated. During offline charging, after the charging device is online, if the charging end status is not updated but the charging device has stopped charging at this time, it is determined that the charging order at this time is an offline unsettled order.

[0069] In step S30, the cloud platform generates a predicted power curve of 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: obtaining charging power at m time points including the offline time point and before the offline time point on the actual power curve before offline, and calculating power changes in m time periods consisting of the m+1 time points, where m is a positive integer greater than or equal to 2;

[0072] S501: taking an average value of power changes in m time periods as a first power change value;

[0073] S502: Compare the first power change value with the power changes in each of m adjacent 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.

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

[0075] Specifically, in step S50 to step S70, the charging power including the offline time point and the two time points before offline is taken from the actual power curve table before offline (such as Table 1), and the power change (slope) of the two time periods formed by these three time points is calculated. It is compared with the power change of each adjacent two 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 matching predicted power curve.

[0076] Table 1: Schematic diagram of actual power curve (offline)

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

[0078] Table 2: Schematic diagram of predicted power curve

[0079] power <![CDATA[P pre1 ]]> <![CDATA[P pre2 ]]> <![CDATA[P pre3 ]]> <![CDATA[P pre4 ]]> <![CDATA[P pre5 ]]> …… <![CDATA[P pre(N) ]]> 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 according to the actual power curve table before offline ac5 and two time points T before offline ac4 , T ac3 Power change S ac , as follows:

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

[0082] (2) According to the predicted power curve table, calculate the power change of each 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 power curve where the power curve is located is the matching prediction curve position, and the power curve missing offline can be supplemented at this time, as shown in Table 3. pre(n+3) The power after that constitutes the estimated power after offline. pre(n+3) The time after that constitutes the estimated time after offline

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

[0090] power <![CDATA[P ac1 ]]> <![CDATA[P ac2 ]]> <![CDATA[P ac3 ]]> <![CDATA[P ac4 ]]> <![CDATA[P ac5 ]]> <![CDATA[P pre(n+3) ]]> …… 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 in each period during the offline time is calculated as follows: Take the corresponding charging power and time on the predicted power curve (the actual power curve fills in the part) to calculate the charged energy in each period, and the end time T can be calculated pre(end) (Estimated charging end time). (Power × time = electric energy)

[0092] Energy charged offline = energy after offline recovery - energy before offline recovery;

[0093] Offline charged 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 that can be used for offline charging, T pre(x)To calculate the last time point that can be used for offline charging, that is, the charging end time point.

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

[0096] The billing strategy is used to describe the charging rules and charging methods of the charging station, and may include: at least one of the billing standard, billing time, and billing method. In some embodiments, different charging time periods may have different charging billing strategies; different promotional activities may also have different charging billing strategies. When receiving the charging instruction of the charging order, the charging device may determine the current version of the charging billing policy and send it to the centralized 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 (such as the administrator of the charging station), or the cloud platform may obtain the charging billing strategy from a designated network server, which is not limited in this embodiment.

[0097] Embodiment 2:

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

[0099] S400: Generate 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 according to historical power consumption data and the daily power curve.

[0101] Specifically, the cloud platform can obtain historical power consumption data based on historical orders. These data usually include daily power curves. In order to accurately retrieve the historical power consumption data and daily power curves of the same model as the unsettled order, the predicted power curve can be obtained for billing the unsettled order.

[0102] In other embodiments, the cloud platform can query the daily power usage, including the power curve and the real-time power price, through the customer service platform of the power company. The unsettled orders are settled by combining the real-time power price and the actual power curve before offline and the estimated power curve after offline, so as to ensure that the user's settlement fee does not exceed the actual fee and protect the user's rights and interests.

[0103] The following is a system embodiment corresponding to the above method embodiment 1. This embodiment can be implemented in conjunction with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment. In order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0104] Embodiment three:

[0105] like Figure 6 FIG. 2 is a schematic diagram of the structure of an offline charging billing system 2 provided by another embodiment of the present invention, comprising: a centralized controller 20, a charging device 21 connected to the centralized controller 20, and a cloud platform 22.

[0106] The charging device 21 is used to: after detecting the start of charging, record the charging data and report the charging data to the centralized controller 20, the charging data including 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 is finished charging, detect whether the charging status data is updated, if the charging status data is not updated, determine that the charging order is an offline unsettled order; calculate the actual power curve before offline based on the charging process data of the unsettled order recorded before offline, and report the corresponding vehicle information bound to the unsettled order to the cloud platform 22;

[0108] The cloud platform 22 is used to: generate a predicted power curve according to the vehicle information corresponding to the unsettled order binding, and send the predicted power curve to the centralized 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 with the predicted power curve to generate an estimated power curve after offline; calculate the charging energy for each period during the offline time and the estimated charging end time based on the estimated power curve after offline and the electric energy after the charging equipment is online; calculate the actual charging cost of unsettled orders based on the charging energy for all periods within the estimated charging end time and the billing strategy.

[0110] In this embodiment, the vehicle information includes the vehicle model, charging method, ambient temperature, and vehicle battery status, etc. The charging process data includes time, charging power, charging energy, etc. The charging status data includes charging start status and charging end status. Specifically, the charging energy includes the starting energy of the charging device, the charging process energy, and the charging end energy, etc.; the charging method is the start 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; the vehicle model information is supplemented by the cloud platform 22 by obtaining the user's information on the electronic device (such as a mobile phone). The charging device 21 is, for example, a charging pile.

[0111] In this embodiment, the centralized controller 20 is also used to: after the charging device 21 finishes charging, detect whether the charging end status is updated, and if the charging status data is not updated, determine that the charging order is an unsettled order. Specifically: after the charging device 21 starts charging, the charging data is recorded in real time until the charging is completed, and the charging end status is updated; after the charging device 21 is offline and restored, the charging end status is detected. If the charging end status is not updated but the charging device 21 has stopped charging at this time, it is confirmed that the charging order at this time is an unsettled order.

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

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

[0114] The second calculation unit 2001 is used to take the average value of the power changes in the 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 changes in each of m adjacent time periods of the predicted power curve, and confirm the power curve segment with the smallest difference as the position of the matched predicted power curve.

[0116] The billing strategy is used to describe the charging rules and charging methods of the charging station, and may include: at least one of the billing standard, billing time, and billing method. In some embodiments, different charging time periods may have different charging billing strategies; different promotional activities may also have different charging billing strategies. Upon receiving the charging instruction of the charging order, the charging device 21 may determine the current version of the charging billing strategy and send it to the centralized 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 (such as the administrator of the charging station), or the cloud platform 22 may obtain the charging billing strategy from a designated network server, which is not limited in this embodiment.

[0117] The following is a system embodiment corresponding to the above method embodiment 2. This embodiment can be implemented in conjunction with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment. In order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0118] Embodiment 4:

[0119] Embodiment 4 provides an offline charging billing system. Embodiment 4 is basically the same as the offline charging billing system 2 in embodiment 3. Only the differences are described below. In the offline charging billing system provided by Embodiment 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; generate the predicted power curve based on the historical power consumption data and the daily power curve.

[0120] In other embodiments, the cloud platform can query the daily power usage, including the power curve and the real-time power price, through the customer service platform of the power company. The unsettled orders are settled by combining the real-time power price and the actual power curve before offline and the estimated power curve after offline, so as to ensure that the user's settlement fee does not exceed the actual fee and protect the user's rights and interests.

[0121] Embodiment five:

[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 above-mentioned offline charging billing method (such as Figure 1 and Figure 2 The steps of the method shown).

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

[0124] Embodiment six:

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

[0126] Embodiment seven:

[0127] Another embodiment of the present invention further provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above-mentioned offline charging billing method ( Figure 1 , Figure 2 , Figure 4 or Figure 5 steps as shown).

[0128] In summary, the present invention uses the predicted charging power curve obtained based on the current vehicle model, charging method, vehicle battery status, ambient temperature and other information provided by the cloud platform big data, combined with the actual power curve calculated by the electric energy information recorded before offline, to determine the point of the predicted power curve of the charging at the time of offline. The electric energy value increased during the offline period is allocated to each billing period according to the predicted power curve and the end charging time is calculated, so as to obtain a charging cost calculation and charging end time that are closer to the actual charging situation.

[0129] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which belong to the protection scope of the present invention.

Claims

1. An offline charging billing method, characterized in that: The following steps are involved: After detecting that charging has started, a charging order is generated and charging data is recorded, wherein the charging data includes vehicle information, charging process data and charging status data; After charging is completed, detecting whether the charging status data is updated, and if the charging status data is not updated, determining that the charging order is an offline unsettled order; Calculate the 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 to the predicted power curve according to a matching method to generate a post-offline estimated power curve; Calculate the charging power and estimated charging end time for each period during the offline time according to the estimated power curve after offline and the power after online; The actual charging cost of the unsettled order is calculated based on the charging power of all time periods within the estimated charging end time and the billing strategy.

2. The offline charging billing method according to claim 1, characterized in that: The matching method comprises: Obtain the charging power at m time points including the offline time point and before the offline time point on the actual power curve before offline, and calculate the power change of m time periods composed of these m+1 time points, where m is a positive integer greater than or equal to 2; Taking the average value of the power changes in the m time periods as the first power change value; The first power change value is compared with the power change of 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.

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

4. The offline charging billing method according to claim 1, characterized in that: The charging status data includes a charging start status and a charging end status; The detecting whether the charging status data is updated is detecting whether the charging end status is updated.

5. 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.

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

7. 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 a centralized controller, wherein the charging data includes vehicle information, charging process data and charging status data; The centralized controller is used to: generate a charging order and upload the charging data to the cloud platform; after the charging device is finished charging, detect whether the charging status data is updated, and if the charging status data is not updated, determine that the charging order is an offline unsettled order; calculate the actual power curve before offline according to the charging process data of the unsettled order, and report the vehicle information corresponding to the unsettled order to the cloud platform; The cloud platform is used to: generate a predicted power curve according to the vehicle information corresponding to the unsettled order binding, and send the predicted power curve to the centralized controller; The centralized controller is also used to: use a matching module to match the position of the actual power curve before offline to the predicted power curve to generate an estimated power curve after offline; calculate the charging energy for each period during the offline time and the estimated charging end time according to the estimated power curve after offline and the electric energy of the charging equipment after going online; calculate the actual charging cost of the unsettled order according to the charging energy for all periods during the estimated charging end time and the billing strategy.

8. The off-line charging billing system according to claim 7, characterized in that: The matching module comprises: A first calculation unit is used to obtain the charging power of m time points including the offline time point and before the offline time point on the actual power curve before offline, and calculate the power change of m time periods composed of these m+1 time points, where m is a positive integer greater than or equal to 2; A second calculation unit, configured to take an average value of the power changes in the m time periods as a first power change value; A matching position confirmation unit is used to compare the first power change value with the power change of each adjacent m time periods of the predicted power curve, and confirm the power curve segment with the smallest difference as the matching position of the predicted power curve.

9. The offline charging billing system according to claim 8, characterized in that: The post-offline estimated power curve includes the position of the matched predicted power curve and the subsequent predicted power curve.

10. The off-line charging billing system according to claim 7, characterized in that: The charging status data includes a charging start status and a charging end status, and the centralized controller is further used for: After the charging device finishes charging, it is detected whether the charging completion status is updated. If the charging completion status is not updated, it is determined that the charging order is the unsettled order.

11. The off-line charging billing system according to claim 7, characterized in that: The vehicle information includes vehicle model, charging method, ambient temperature and vehicle battery status.

12. The off-line charging billing system according to claim 11, characterized in that: The cloud platform is also used for: Generate historical power consumption data and daily power curves of the same vehicle model as the unsettled order based on the historical orders; The predicted power curve is generated according to the historical power consumption data and the daily power curve.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the offline charging billing method according to any one of claims 1 to 6 are implemented.

14. An electronic device, comprising a processor, characterized in that: The processor is used to execute the offline charging billing system according to any one of claims 7 to 12 stored in the memory.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the offline charging billing method according to any one of claims 1 to 6 are implemented.

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