Time-sharing power transmission management and regulation and control method based on code scanning power utilization mode

Abstract

The invention is suitable for the technical field of intelligent charging management and power dispatching control, and provides a time-sharing power transmission management and regulation and control method based on a code scanning power utilization mode, and the method comprises the steps: carrying out the code scanning of a target user when the code scanning of the target user is failed due to network abnormality, and the code scanning is successful after the network is recovered; acquiring historical operation data of the target user in the charging application; according to the method, a dynamic correction mechanism based on the operation frequency deviation degree in the code scanning stage is constructed, so that behavior sensing type regulation and control on power supply power configuration of the target user after network abnormity is recovered is realized for the first time. Different from a fixed default value or static parameter mapping mode in the prior art, the power utilization urgent behavior rule of an individual user in a specific network state is identified on the basis of historical operation data.

Description

Technical Field

[0001] This invention belongs to the field of intelligent charging management and power dispatch control technology, and particularly relates to a time-sharing power transmission management and control method based on a QR code-based power consumption mode. Background Technology

[0002] In charging scenarios, initiating the charging process by scanning a QR code has become the mainstream electricity interaction mode, especially suitable for distributed energy terminals (particularly new energy vehicles) such as shared charging piles and public charging facilities. The successful completion of this scanning process usually relies on a stable network connection. However, in cases of network anomalies or delayed recovery, users often engage in high-frequency operations such as repeated scanning, frequent clicking, and page refreshes. While these abnormal interactions do not directly trigger charging events, they indirectly reflect the urgency of users' electricity needs during specific time periods. Currently, although time-of-use power transmission strategies are widely used to regulate resource load and allocation efficiency during power supply periods, they still do not fully consider the pre-emptive impact of user behavior during the QR code scanning stage on actual power transmission control strategies.

[0003] Existing charging management systems are mostly based on power allocation strategies divided into preset time periods or fixed scheduling parameter models. When faced with the discontinuous but highly sensitive interaction process of code scanning failure—retry—recovery, they cannot identify the urgent power usage intentions of individual users within a short period of time. Furthermore, when configuring power supply after successful code scanning, the system often uses a uniform default value as the initial power supply standard, failing to differentiate based on the user's operational frequency characteristics under abnormal network conditions. In the context of increasingly refined time-sharing power transmission regulation, this approach appears slow to respond, lacks flexibility, and is prone to resource misallocation and low sensitivity of scheduling strategies. Summary of the Invention

[0004] The purpose of this invention is to provide a time-sharing power transmission management and control method based on a QR code-based electricity consumption mode, aiming to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: a time-sharing power transmission management and control method based on a QR code-based electricity consumption mode, the method comprising:

[0006] When a target user experiences a failed QR code scan due to a network anomaly, but successfully completes the scan after the network is restored, the system obtains the target user's historical operation data in the charging application.

[0007] Based on historical operation data, determine the normal operation frequency value of the target user under normal network conditions, and the abnormal operation frequency value under different abnormal network conditions, and calculate the degree of deviation of each abnormal operation frequency value from the normal operation frequency value.

[0008] The analysis examines whether the following characteristics exist: Under the same abnormal network state, the greater the deviation of the abnormal operation frequency value from the normal operation frequency value, the higher the historical electricity demand of the target user. If the proportion of abnormal network states with characteristic patterns in all abnormal network states exceeds a preset threshold, local reference data that matches the current abnormal network state and whose historical actual electricity demand is consistent with the initial power supply allocation power value set for the target user at that historical time point is extracted from the historical operation data.

[0009] The system obtains the current initial power allocation value set for the target user, and constructs a correction factor based on the deviation between the current operating frequency value and the corresponding reference operating frequency value in the local reference data. The system then corrects the current initial power allocation value according to the correction factor to achieve adaptive power control for the target user.

[0010] As a further limitation of the technical solution of the present invention, the determination of normal network status and abnormal network status is based on the communication quality parameters of the target user during the scanning process. The communication quality parameters include, but are not limited to, the average round-trip delay of the scanning request, the scanning failure rate per unit time, and the number of network connection interruptions.

[0011] A network is considered to be in normal condition if all of the following conditions are met: the average round-trip latency of the QR code scanning request is lower than the preset latency threshold, the QR code scanning failure rate per unit time is lower than the preset failure rate threshold, and the number of network connection interruptions is lower than the preset interruption threshold.

[0012] Otherwise, it is judged as an abnormal network state, and a quantitative score is made based on the ratio of the measured values ​​of the above communication quality parameters to the preset normal thresholds. The abnormal network state is divided into several levels according to the score.

[0013] As a further limitation of the technical solution of this invention embodiment, the operation frequency value refers to the average number of times the target user initiates operation behavior through the charging application during the scanning process per unit time. The operation behavior includes page refresh, scanning button click, network request retry, and interactive response after scanning failure prompt, which is used to reflect the user's activity level and power consumption intention intensity during the scanning stage.

[0014] As a further limitation of the technical solution of this invention, the step of determining the normal operation frequency value of the target user under normal network conditions and the abnormal operation frequency value under different abnormal network conditions based on historical operation data, and calculating the deviation of each abnormal operation frequency value from the normal operation frequency value includes:

[0015] Extract the target user's historical QR code charging behavior records under normal network conditions, calculate the operation frequency value corresponding to each historical QR code charging behavior, and average all operation frequency values ​​to obtain the target user's normal operation frequency value under normal network conditions.

[0016] The historical QR code charging behavior of target users under different abnormal network conditions was statistically analyzed, and the corresponding abnormal operation frequency value was calculated.

[0017] For each abnormal operation frequency value, the degree of deviation between it and the normal operation frequency value is calculated. The degree of deviation is the difference between the abnormal operation frequency value and the normal operation frequency value, or the ratio of the difference to the normal operation frequency value.

[0018] As a further limitation of the technical solution of this invention embodiment, the analysis examines whether the following characteristic pattern exists: Under the same abnormal network state, the greater the deviation of the abnormal operation frequency value from the normal operation frequency value, the higher the historical actual electricity demand corresponding to the target user. If the proportion of abnormal network states with characteristic patterns identified in all abnormal network states exceeds a preset threshold, the step of extracting local reference data from historical operation data that matches the current abnormal network state and whose historical electricity demand is consistent with the initial power supply allocation power value set for the target user at that historical time point includes:

[0019] Extract the secondary operation data of the target user under each abnormal network state level from the historical operation data, and group them according to the abnormal network state level, with each data group corresponding to the same abnormal network state level.

[0020] For each secondary operation data in each data group, calculate the deviation of its abnormal operation frequency value from the normal operation frequency value, and obtain its corresponding actual electricity demand data.

[0021] Analyze whether the following correlation characteristics exist within each data group: Under the same abnormal network state level, the secondary operation data with a greater degree of deviation corresponds to a higher actual power demand value.

[0022] If the proportion of data groups with this characteristic pattern in all data groups exceeds a preset threshold, it is determined that the target user has a historical behavioral pattern reflecting the urgency of their electricity use when the network status is abnormal.

[0023] Under the premise that the determination result is true, select local reference data from all secondary operation data that match the current abnormal network status level and whose historical actual power demand is consistent with the initial power supply allocation power value set for the target user at that historical point in time.

[0024] As a further limitation of the technical solution of this embodiment of the invention, the steps of obtaining the current initial power allocation value set for the target user, constructing a correction factor based on the deviation between the current operating frequency value and the corresponding reference operating frequency value in the local reference data, and correcting the current initial power allocation value according to the correction factor to achieve adaptive power control for the target user include:

[0025] Obtain the current initial power allocation value set for the target user, the current operation frequency value corresponding to the target user's current QR code charging behavior, and the corresponding reference operation frequency value in the local reference data;

[0026] Calculate the deviation of the current operating frequency value from the reference operating frequency value and use it as a correction factor;

[0027] The preset power value correction function is retrieved and the current initial power supply allocation power value is corrected by combining the correction factor to obtain the corrected initial power supply allocation power value.

[0028] The corrected initial power allocation value is used as the dynamic power reference value corresponding to the target user's current QR code charging behavior. It is applied to the power supply scheduling system to guide the actual power supply configuration for the target user, thereby realizing adaptive power control based on behavior perception.

[0029] As a further limitation of the technical solution of this embodiment of the invention, the power value correction function is:

[0030] ;

[0031] in, This refers to the corrected initial power allocation value. This refers to the current initial power allocation value. This refers to the current operation frequency value. This refers to the reference operation frequency value. This refers to the correction factor, which is the deviation of the current operating frequency value from the reference operating frequency value. This refers to the adjustment coefficient of the correction factor, and Greater than 0.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This invention, by constructing a dynamic correction mechanism based on the deviation of operation frequency during the QR code scanning phase, achieves for the first time behavior-aware control of power supply configuration for target users after network anomaly recovery. Unlike existing technologies that use fixed default values ​​or static parameter mapping, this invention uses historical operation data to identify the urgent power consumption patterns of individual users under specific network conditions, extracts matching historical reference data, constructs a correction factor based on the deviation of operation frequency, and combines it with a linear function to achieve real-time correction of the initial power supply, balancing control accuracy and system stability. This solution not only improves the sensitivity and rationality of resource allocation but also enhances the personalized responsiveness of charging services, demonstrating significant engineering practical value and promotional potential. Attached Figure Description

[0034] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0035] Figure 2 This is a flowchart illustrating the deviation of each abnormal operation frequency value from the normal operation frequency value in the method provided in this embodiment of the invention.

[0036] Figure 3 This is a flowchart illustrating the process of determining local reference data in the method provided in this embodiment of the invention;

[0037] Figure 4 This is a flowchart illustrating the correction of the current initial power allocation value in the method provided in this embodiment of the invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0039] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0040] Specifically, a time-sharing power transmission management and control method based on QR code-based electricity consumption mode includes the following steps:

[0041] Step S100: When the target user experiences a scanning failure due to network anomaly and successfully completes the scanning after the network is restored, obtain the target user's historical operation data in the charging application.

[0042] In this embodiment of the invention, the focus is on analyzing the target user group (especially new energy vehicle charging users) who experienced scanning failures due to network anomalies but successfully completed the scan after the network was restored. These users typically attempt to scan, refresh the page, or perform other interactive operations repeatedly during the charging process due to network issues, with a significantly higher frequency of these actions compared to a normal scanning process under stable network conditions. Therefore, under these prerequisites, the user actions recorded in the charging application will exhibit stronger expressiveness and urgency regarding electricity usage intent, thus providing a quantifiable and analyzable behavioral basis for the subsequent dynamic adjustment of power supply.

[0043] Historical operation data includes at least: timestamps for each user operation, used to construct a time series of operation behaviors; operation type identifiers to distinguish different categories such as QR code button clicks, page refreshes, retry requests, and QR code failure prompt responses; statistical counts of various operation behaviors per unit time, used to calculate operation frequency values; indicators of whether each operation was successful; network status snapshots corresponding to the operation behaviors, including network latency and connection status at the time the operation was initiated; and complete QR code scanning behavior record sequences aggregated by user identifiers, used for multi-stage behavior analysis across time periods and network states.

[0044] Furthermore, the time-sharing power transmission management and control method based on QR code-based electricity consumption also includes the following steps:

[0045] Step S200: Based on historical operation data, determine the normal operation frequency value of the target user under normal network conditions and the abnormal operation frequency value under different abnormal network conditions, and calculate the degree of deviation of each abnormal operation frequency value from the normal operation frequency value.

[0046] The determination of normal and abnormal network status is based on the communication quality parameters of the target user during the scanning process. These communication quality parameters include, but are not limited to, the average round-trip latency of the scanning request, the scanning failure rate per unit time, and the number of network connection interruptions.

[0047] A network is considered to be in normal condition if all of the following conditions are met: the average round-trip latency of the QR code scanning request is lower than the preset latency threshold, the QR code scanning failure rate per unit time is lower than the preset failure rate threshold, and the number of network connection interruptions is lower than the preset interruption threshold.

[0048] Otherwise, it is judged as an abnormal network state, and a quantitative score is made based on the ratio of the measured values ​​of the above communication quality parameters to the preset normal thresholds. The abnormal network state is divided into several levels according to the score.

[0049] The operation frequency value refers to the average number of times the target user initiates an operation through the charging application during the scanning process per unit time. The operation includes page refresh, clicking the scan button, retrying the network request, and interactive responses after the scan failure prompt. It is used to reflect the user's activity level and the intensity of their power consumption intention during the scanning stage.

[0050] In this embodiment of the invention, ...

[0051] Specifically, Figure 2 A flowchart is shown to calculate the deviation of each abnormal operation frequency value from the normal operation frequency value.

[0052] The process of determining the normal operation frequency of a target user under normal network conditions and the abnormal operation frequency under different abnormal network conditions based on historical operation data, and calculating the deviation of each abnormal operation frequency value from the normal operation frequency value, specifically includes the following steps:

[0053] Step S201: Extract the historical QR code charging behavior records of the target user under normal network conditions, calculate the operation frequency value corresponding to each historical QR code charging behavior, and average all operation frequency values ​​to obtain the normal operation frequency value of the target user under normal network conditions.

[0054] Step S202: Collect historical QR code charging behaviors of target users under different abnormal network conditions, and calculate the corresponding abnormal operation frequency value for each.

[0055] Step S203: For each abnormal operation frequency value, calculate the degree of deviation between it and the normal operation frequency value. The degree of deviation is the difference between the abnormal operation frequency value and the normal operation frequency value, or the ratio of the difference to the normal operation frequency value.

[0056] In this embodiment of the invention, the determination process for normal and abnormal network states is based on a quantitative assessment of communication quality parameters accompanying the user's QR code scanning charging behavior. These communication quality parameters include, but are not limited to, the average round-trip latency of the scanning request, the scanning failure rate per unit time, and the number of network connection interruptions. These parameters can be automatically recorded and structuredly stored through the charging application's log collection mechanism, and have mature existing implementation methods, making them widely used in mobile network communication quality assessment, terminal status monitoring, and other fields.

[0057] The various preset normal thresholds are primarily based on empirical statistics and operational standards. The average round-trip latency for QR code scanning requests is typically set within 300ms, based on the technical conditions ensuring a smooth user experience without lag or inconsistent response. The QR code scanning failure rate is set within 1%, referencing the average success rate of users operating on a large scale. The number of network connection interruptions is set to no more than one interruption per operation, based on the stability requirements of maintaining the connection during a single QR code scan. These threshold parameters can be dynamically adjusted or configured according to different scenarios based on the actual operating environment and the performance of the network module on the device side.

[0058] Based on the ratio of the measured values ​​of various communication quality parameters to their corresponding preset normal thresholds, the following scoring model can be formed: the score for each parameter is the ratio of its measured value to the normal threshold. The network anomaly score for that scanning process is obtained by taking the maximum ratio or a weighted average. This score can serve as the basis for the anomaly level of the current network status, classifying the anomaly state into multiple levels (e.g., critical edge, mild instability, moderate packet loss, severe outage, etc.). This scoring and grading scheme already has a mature application foundation in existing network health assessment and intelligent operation and maintenance systems, thus possessing feasibility and engineering implementation potential.

[0059] Extracting operation frequency values ​​also falls within the scope of existing mature data analysis techniques. Charging application front-ends typically have mechanisms for tracking or uploading operation logs, which can record user click behavior, request initiation, and response results before and after scanning the code. Based on the timestamp and operation type field, operation behaviors can be categorized and their frequency statistically analyzed. Further calculation of the average number of operations per unit time yields the operation frequency value.

[0060] During the implementation of steps S201 and S202, all the required raw data (scanning and charging behavior records, specific timestamps and categories of each operation) can be directly obtained from historical operation data without additional collection. In step S201, the average value of operation frequency under all normal network conditions is used as the normal operation frequency value for this user in order to extract stable characteristics based on individual behavior fluctuations, reduce the bias caused by occasional behavior to subsequent calculations, and improve the reliability of behavior comparison.

[0061] In step S203, the calculation of the degree of deviation includes two forms: one is a simple difference, which represents the increasing or decreasing trend of operation frequency under abnormal conditions; the other is a ratio, which reflects the relative magnitude of change and is more suitable for comparative analysis between different users. This degree of deviation serves as a quantitative indicator to measure the change in the intensity of user behavior response under abnormal network conditions, providing a data foundation for identifying potential power urgency and constructing a behavior-power correction model.

[0062] The "deviation of abnormal operation frequency value from normal operation frequency value" is used to characterize the change in the activity level of the target user's scanning behavior under abnormal network conditions compared to normal network conditions, reflecting whether the user's operation mode has changed significantly under abnormal conditions.

[0063] The degree of deviation itself does not directly represent the user's urgency of electricity use. Rather, it serves as a basis for subsequently determining whether the user exhibits the characteristic pattern of "the greater the deviation in operation frequency, the higher the actual electricity demand." In other words, this degree of deviation is only used as a neutral quantitative indicator to observe whether changes in user behavior and electricity demand show a consistent trend under different levels of abnormal network conditions, thereby supporting the construction of a behavior-oriented power supply control model. Therefore, the role of this indicator is to provide behavioral-side data support for the identification of characteristic patterns, rather than serving as the basis for judging the urgency of electricity use itself.

[0064] Furthermore, the time-sharing power transmission management and control method based on QR code-based electricity consumption also includes the following steps:

[0065] Step S300: Analyze whether the following characteristic pattern exists: Under the same abnormal network state, the greater the deviation of the abnormal operation frequency value from the normal operation frequency value, the higher the historical actual power demand of the target user. If the proportion of abnormal network states with characteristic patterns in all abnormal network states exceeds a preset threshold, then extract local reference data from the historical operation data that matches the current abnormal network state and whose historical power demand is consistent with the initial power supply allocation power value set for the target user at that historical time point.

[0066] Specifically, Figure 3 A flowchart for determining local reference data is shown.

[0067] The analysis examines whether the following patterns exist: Under the same abnormal network conditions, the greater the deviation of the abnormal operation frequency value from the normal operation frequency value, the higher the historical actual electricity demand of the target user. If the proportion of abnormal network conditions with patterns identified exceeds a preset threshold, then local reference data matching the current abnormal network condition and whose historical electricity demand is consistent with the initial power allocation value set for the target user at that historical point in time is extracted from the historical operation data. Specifically, this includes the following steps:

[0068] Step S301: Extract the secondary operation data of the target user under each abnormal network state level from the historical operation data, and group them according to the abnormal network state level, with each data group corresponding to the same abnormal network state level.

[0069] Step S302: For each secondary operation data in each data group, calculate the degree of deviation of its abnormal operation frequency value from the normal operation frequency value, and obtain its corresponding actual power demand data.

[0070] Step S303: Analyze whether the following correlation characteristics exist within each data group: Under the same abnormal network state level, the secondary operation data with a greater degree of deviation corresponds to a higher actual power demand value.

[0071] Step S304: If the proportion of data groups with this characteristic pattern in all data groups exceeds a preset threshold, it is determined that the target user has a historical behavioral pattern reflecting the urgency of electricity use by the degree of deviation of operation frequency when the network status is abnormal.

[0072] Step S305: Under the premise that the determination result is true, select local reference data from all secondary operation data that matches the current abnormal network status level and whose historical actual power demand is consistent with the initial power supply allocation power value set for the target user at that historical time point.

[0073] In this embodiment of the invention, verifying the existence of the characteristic pattern that "under the same abnormal network state level, the greater the deviation in operating frequency, the higher the actual power demand" has significant practical implications. Once this pattern holds true, it means that when a target user exhibits stronger operational activity in an abnormal network scenario, it is usually accompanied by a stronger actual power demand or urgency. Therefore, the user's behavior pattern in such scenarios possesses stable behavior-demand mapping characteristics. This verification process essentially checks the consistency of the target user's behavioral data to determine whether it is suitable for a power regulation strategy driven by operating frequency. Only when the proportion of data sets exhibiting this characteristic pattern in all abnormal network state levels exceeds a preset threshold does it indicate that the user possesses relatively stable urgency expression behavior, which can be incorporated into the "deviation-driven correction mechanism" proposed in this invention, improving the accuracy and adaptability of their individual power supply experience.

[0074] The actual electricity demand data refers to the record of the target user's actual electricity consumption behavior after the corresponding QR code scanning behavior is completed, including but not limited to indicators such as peak charging power, duration, and charging amount caused by this QR code scanning behavior. The actual electricity demand data can be automatically recorded by the charging pile monitoring system or the background metering system after successful QR code scanning and associated with this QR code scanning behavior, so it can be called as part of the historical operation data.

[0075] In step S305, the system needs to filter data from all marked secondary operation data that meets two conditions: first, the abnormal network state level corresponding to it is consistent with the current network state level; second, its historical actual power demand value is consistent with the initial power allocation value set for the user at that time. This filtering logic serves two purposes: first, by ensuring the consistency of network state levels, it ensures that the matched data have similar communication backgrounds, avoiding behavioral differences caused by differences in network environments; second, by ensuring the consistency of power values, it ensures that the selected local reference data has historical consistency among "behavior—demand—power supply configuration," facilitating the comparison of the current behavioral deviation with the behavioral characteristics under this reference state, thereby constructing an effective correction factor. In other words, the local reference data constitutes a "historically best-matching mirror image," providing a data-supported benchmark for current power supply regulation.

[0076] Furthermore, the time-sharing power transmission management and control method based on QR code-based electricity consumption also includes the following steps:

[0077] Step S400: Obtain the current initial power supply allocation value set for the target user, and construct a correction factor based on the deviation between the current operating frequency value and the corresponding reference operating frequency value in the local reference data. Correct the current initial power supply allocation value according to the correction factor to achieve adaptive control of power supply for the target user.

[0078] Specifically, Figure 4 A flowchart is shown to correct the current initial power allocation value.

[0079] The process of obtaining the current initial power allocation value set for the target user, constructing a correction factor based on the deviation between the current operating frequency value and the corresponding reference operating frequency value in the local reference data, and correcting the current initial power allocation value according to the correction factor to achieve adaptive power control for the target user specifically includes the following steps:

[0080] Step S401: Obtain the current initial power allocation value set for the target user, the current operation frequency value corresponding to the target user's current QR code charging behavior, and the corresponding reference operation frequency value in the local reference data;

[0081] Step S402: Calculate the deviation of the current operating frequency value from the reference operating frequency value and use it as a correction factor;

[0082] Step S403: Retrieve the preset power value correction function and combine it with the correction factor to correct the current initial power supply allocation power value, so as to obtain the corrected initial power supply allocation power value.

[0083] Step S404: The corrected initial power supply allocation value is used as the dynamic power supply reference value corresponding to the target user's current QR code charging behavior and applied to the power supply scheduling system to guide the actual power supply configuration for the target user, thereby realizing behavior-aware adaptive power control.

[0084] The power value correction function is:

[0085] ;

[0086] in, This refers to the corrected initial power allocation value. This refers to the current initial power allocation value. This refers to the current operation frequency value. This refers to the reference operation frequency value. This refers to the correction factor, which is the deviation of the current operating frequency value from the reference operating frequency value. This refers to the adjustment coefficient of the correction factor, and Greater than 0.

[0087] In this embodiment of the invention, the current initial power allocation value refers to the maximum power reference value initially set by the system for the target user before they successfully scan the code and are about to start charging, based on their account level, site power distribution rules, charging equipment type, and other basic conditions. This value, as the initial allocation benchmark, is usually derived from the existing power allocation strategy in the charging platform or charging pile management system, and is a common and mature configuration parameter in the prior art. However, this value does not consider the user's current behavioral characteristics and their potential emergency power needs. Therefore, this solution uses this initial value as a basis for correction, thereby better reflecting the user's real-time status.

[0088] This value is also stored in historical operation data to establish a correspondence between the current scanning behavior and past charging behavior.

[0089] The core of the correction factor lies in quantifying the deviation between the current user operation frequency and the reference operation frequency. It reflects the difference in behavioral intensity between the user's current state and historical "urgent power demand" scenarios. It's important to emphasize that this reference operation frequency value is not arbitrarily selected. Rather, it is obtained from historical data filtered through behavioral pattern verification in the aforementioned steps, specifically from data showing a positive correlation between the deviation of operation frequency and actual power demand under a certain abnormal network state level. Therefore, this reference value itself is representative of a "highly matched operation frequency and power demand," and its corresponding actual power demand is consistent with the initial power allocation value set for the user at that time.

[0090] Based on this premise, if the current operation frequency is significantly higher than the historical reference value, it indicates that the target user is exhibiting more active and urgent operational behavior, suggesting a stronger intention to consume electricity compared to similar scenarios in the past. Therefore, the corresponding current power value should be appropriately increased. If it is lower than the reference value, it should be appropriately reduced. By correcting the initial power allocation value in this way, dynamic coupling between power supply capacity and real-time user behavior can be achieved while ensuring historical adaptability. This effectively improves the sensitivity and rationality of power allocation and enhances the personalization and responsiveness of scheduling strategies.

[0091] In step S404, the corrected initial power allocation value will be transmitted to the power dispatching system as the "dynamic power supply reference value" for this QR code charging task. This value will guide the power allocation decision of the charging piles and guide the control strategy for the actual charging power of the target user. Without exceeding the grid load limit or power conflict, the dispatching system can prioritize the allocation of power resources that better match the user's urgent needs, thereby realizing flexible transmission control based on behavior perception.

[0092] The power adjustment function used in this case is a simple, computationally efficient linear function, facilitating rapid deployment and real-time response, making it particularly suitable for resource-constrained or latency-sensitive scenarios. However, it's important to note that this adjustment mechanism is essentially a mapping between the degree of behavioral deviation and the magnitude of power adjustment, exhibiting good scalability and substitutability. Other implementations can utilize nonlinear function structures, such as exponential, logarithmic, or piecewise functions, to achieve more sensitive control over extreme behaviors or noise reduction and suppression of minor fluctuations. Furthermore, multinomial models based on historical sample fitting, Bayesian regression models, and even machine learning algorithms incorporating weighted logic based on user profile features can be introduced to achieve more refined and predictive power adjustment strategies. These alternative calculation methods can be flexibly selected based on specific application scenarios and system resource availability.

[0093] Furthermore, to ensure the safety of system operation and the rationality of power regulation, the power value correction function in this case should be combined with upper and lower limit control mechanisms in practical applications. This means setting minimum and maximum threshold values ​​for the corrected power supply to ensure it remains within the system's carrying capacity. On the one hand, this prevents the risk of excessive power supply and abnormal equipment load caused by abnormally high operating frequencies; on the other hand, it avoids excessive compression of power values ​​when operating frequencies are low, affecting the user's normal charging experience. These upper and lower limits can be configured based on equipment (new energy vehicle) specifications, grid policies, and historical operating parameters, and should be uniformly judged and truncated after the correction function is executed, thus achieving both stability and flexibility in power regulation.

[0094] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A time-sharing power transmission management and control method based on QR code-based electricity consumption, characterized in that, The method includes: When a target user experiences a failed QR code scan due to a network anomaly, but successfully completes the scan after the network is restored, the system obtains the target user's historical operation data in the charging application. Based on historical operation data, determine the normal operation frequency value of the target user under normal network conditions, and the abnormal operation frequency value under different abnormal network conditions, and calculate the degree of deviation of each abnormal operation frequency value from the normal operation frequency value. The analysis examines whether the following characteristics exist: Under the same abnormal network state, the greater the deviation of the abnormal operation frequency value from the normal operation frequency value, the higher the historical electricity demand of the target user. If the proportion of abnormal network states with characteristic patterns in all abnormal network states exceeds a preset threshold, local reference data that matches the current abnormal network state and whose historical actual electricity demand is consistent with the initial power supply allocation power value set for the target user at that historical time point is extracted from the historical operation data. The system obtains the current initial power allocation value set for the target user, and constructs a correction factor based on the deviation between the current operating frequency value and the corresponding reference operating frequency value in the local reference data. The system then corrects the current initial power allocation value according to the correction factor to achieve adaptive power control for the target user.

2. The time-sharing power transmission management and control method based on QR code-based electricity consumption mode according to claim 1, characterized in that, The determination of normal and abnormal network status is based on the communication quality parameters of the target user during the scanning process. These communication quality parameters include, but are not limited to, the average round-trip latency of the scanning request, the scanning failure rate per unit time, and the number of network connection interruptions. A network is considered to be in normal condition if all of the following conditions are met: the average round-trip latency of the QR code scanning request is lower than the preset latency threshold, the QR code scanning failure rate per unit time is lower than the preset failure rate threshold, and the number of network connection interruptions is lower than the preset interruption threshold. Otherwise, it is judged as an abnormal network state, and a quantitative score is made based on the ratio of the measured values ​​of the above communication quality parameters to the preset normal thresholds. The abnormal network state is divided into several levels according to the score.

3. The time-sharing power transmission management and control method based on QR code-based electricity consumption mode according to claim 2, characterized in that, The operation frequency value refers to the average number of times the target user initiates an operation through the charging application during the scanning process per unit time. The operation includes page refresh, clicking the scan button, retrying the network request, and interactive responses after the scan failure prompt. It is used to reflect the user's activity level and the intensity of their power consumption intention during the scanning stage.

4. The time-sharing power transmission management and control method based on QR code-based electricity consumption mode according to claim 3, characterized in that, Based on historical operation data, the steps of determining the normal operation frequency of the target user under normal network conditions and the abnormal operation frequency under different abnormal network conditions, and calculating the deviation of each abnormal operation frequency value from the normal operation frequency value, include: Extract the target user's historical QR code charging behavior records under normal network conditions, calculate the operation frequency value corresponding to each historical QR code charging behavior, and average all operation frequency values ​​to obtain the target user's normal operation frequency value under normal network conditions. The historical QR code charging behavior of target users under different abnormal network conditions was statistically analyzed, and the corresponding abnormal operation frequency value was calculated. For each abnormal operation frequency value, the degree of deviation between it and the normal operation frequency value is calculated. The degree of deviation is the difference between the abnormal operation frequency value and the normal operation frequency value, or the ratio of the difference to the normal operation frequency value.

5. The time-sharing power transmission management and control method based on QR code-based electricity consumption mode according to claim 4, characterized in that, The analysis examines whether the following characteristic patterns exist: Under the same abnormal network state, the greater the deviation of the abnormal operation frequency value from the normal operation frequency value, the higher the historical actual electricity demand of the target user. If the proportion of abnormal network states with characteristic patterns identified exceeds a preset threshold among all abnormal network states, the steps for extracting local reference data from historical operation data that matches the current abnormal network state and whose historical electricity demand is consistent with the initial power supply allocation value set for the target user at that historical point in time include: Extract the secondary operation data of the target user under each abnormal network state level from the historical operation data, and group them according to the abnormal network state level, with each data group corresponding to the same abnormal network state level. For each secondary operation data in each data group, calculate the deviation of its abnormal operation frequency value from the normal operation frequency value, and obtain its corresponding actual electricity demand data. Analyze whether the following correlation characteristics exist within each data group: Under the same abnormal network state level, the secondary operation data with a greater degree of deviation corresponds to a higher actual power demand value. If the proportion of data groups with this characteristic pattern in all data groups exceeds a preset threshold, it is determined that the target user has a historical behavioral pattern reflecting the urgency of their electricity use when the network status is abnormal. Under the premise that the determination result is true, select local reference data from all secondary operation data that match the current abnormal network status level and whose historical actual power demand is consistent with the initial power supply allocation power value set for the target user at that historical point in time.

6. The time-sharing power transmission management and control method based on QR code-based electricity consumption mode according to claim 5, characterized in that, The steps of obtaining the current initial power allocation value set for the target user, constructing a correction factor based on the deviation between the current operating frequency value and the corresponding reference operating frequency value in the local reference data, and correcting the current initial power allocation value according to the correction factor to achieve adaptive power control for the target user include: Obtain the current initial power allocation value set for the target user, the current operation frequency value corresponding to the target user's current QR code charging behavior, and the corresponding reference operation frequency value in the local reference data; Calculate the deviation of the current operating frequency value from the reference operating frequency value and use it as a correction factor; The preset power value correction function is retrieved and the current initial power supply allocation power value is corrected by combining the correction factor to obtain the corrected initial power supply allocation power value. The corrected initial power allocation value is used as the dynamic power reference value corresponding to the target user's current QR code charging behavior. It is applied to the power supply scheduling system to guide the actual power supply configuration for the target user, thereby realizing adaptive power control based on behavior perception.

7. The time-sharing power transmission management and control method based on QR code-based electricity consumption mode according to claim 6, characterized in that, The power value correction function is: ； in, This refers to the corrected initial power allocation value. This refers to the current initial power allocation value. This refers to the current operation frequency value. This refers to the reference operation frequency value. This refers to the correction factor, which is the deviation of the current operating frequency value from the reference operating frequency value. This refers to the adjustment coefficient of the correction factor, and Greater than 0.

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