Control method and device for multi-power alternating current charging pile

Abstract

The invention relates to the technical field of alternating current charging, and discloses a control method and device for a multi-power alternating current charging pile, and the method comprises the steps: obtaining the instant load information of each interface and the load information of a power grid; calculating and sorting power headroom, and generating pre-adjustment power flow configuration; performing feasibility judgment and distribution optimization in combination with the power grid margin and the peak demand to obtain a final distribution proportion; the data are written into a power module after equipment state detection, and user experience and load data are synchronously collected; and generating a power fine tuning instruction according to the feedback and the power grid state, and outputting a linkage power scheduling instruction after secondary convergence and continuity curve optimization. The method can solve the problem of low utilization efficiency of power headroom in the prior art.

Description

Technical Field

[0001] This invention relates to the field of AC charging technology, and in particular to a control method and device for a multi-power AC charging pile. Background Technology

[0002] Currently, the number of new energy vehicles is increasing rapidly. As the energy interface between the power grid and vehicles, AC charging piles are tasked with allocating active power in real time among several parallel charging interfaces to match the charging needs of each vehicle with the current power supply capacity of the power grid. In order to avoid some interfaces being idle while others are queuing for a long time in scenarios where multiple charging stations are working simultaneously, charging piles need to continuously sense changes in interface current, voltage, and temperature, and recalculate the available power margin of each interface within seconds. This allows excess power to be channeled to channels with higher demand, thereby achieving a dynamic balance of power resources.

[0003] In one existing technology, the system allocates channel power in a one-time manner using a fixed threshold method. After reading the instantaneous current of each interface, it compares it with a preset current upper limit. If the limit is not exceeded, the original power output is maintained; if the limit is exceeded, the current of all interfaces is reduced proportionally, and the adjustment result is then directly sent to the power module. For example, when the current of gun 1 exceeds 32A, the system reduces the current of gun 1 to gun 4 by the same proportion until the current of gun 1 falls back below the threshold. Throughout this process, the system does not evaluate the actual margin of other interfaces, nor does it dynamically adjust the aggregation strategy based on changes in the grid load rate, resulting in excessive power adjustment or ineffective utilization of margins.

[0004] Because existing technologies employ static thresholds and one-time reduction strategies, they lack closed-loop calculations of channel power margin, grid load rate, and peak charging demand. Furthermore, there is a lack of linkage between the power aggregation path and the real-time grid capacity, making multi-gun parallel systems prone to distribution imbalances during load fluctuations or peak periods. Therefore, existing technologies suffer from low power margin utilization efficiency. Summary of the Invention This invention provides a control method and device for multi-power AC charging piles to solve the problem of low efficiency in utilizing power margin in the prior art.

[0005] Firstly, in order to solve the above-mentioned technical problems, the present invention provides a control method for a multi-power AC charging pile, comprising: Obtain real-time load data and power grid load information for each interface; Based on the real-time load data of each interface, power margin calculation and sorting operations are performed to obtain a margin distribution list. Based on the margin distribution list, an initial power aggregation result is generated to obtain a pre-adjusted power flow configuration. Based on the pre-adjusted power flow configuration and the grid load information, a power adjustment feasibility judgment is made. If power adjustment is allowed, aggregation and optimization operations are performed to obtain the final allocation ratio of each channel. Based on the final allocation ratio of each channel, the device status is detected. If the detection passes, the final allocation ratio of each channel is applied to the actual charging process, and user experience-related data and key load data are collected simultaneously to obtain synchronous feedback information. Based on the synchronous feedback information and the power grid load information, a preset standard judgment is made to obtain a power fine-tuning command; Based on the power fine-tuning command and the pre-adjusted power flow configuration, a secondary convergence path optimization is performed to obtain the final efficiency improvement configuration; Based on the final efficiency improvement configuration, the associated data of channel priority sorting and deviation statistics are extracted to obtain the resource redirection continuity parameter; Based on the resource redirection continuity parameter, the power supply capacity and resource redistribution are linked and processed to obtain the linked output.

[0006] Preferably, based on the real-time load data of each interface, power margin calculation and sorting operations are performed to obtain a margin distribution list. Based on the margin distribution list, an initial power aggregation result is generated to obtain a pre-adjusted power flow configuration, including: Based on the real-time load data of each interface, the power margin of each interface is calculated, and the power margins that exceed the preset margin threshold are sorted to obtain a margin distribution list. Based on the aforementioned surplus distribution list, extract the remaining power data for each charging channel to obtain the remaining power data; Based on the remaining power data, a proportional calculation is performed to obtain the initial power convergence result; Based on the initial power convergence results, the channel power is redistributed to obtain a pre-adjusted power flow configuration.

[0007] Preferably, based on the pre-adjusted power flow configuration and the grid load information, a power adjustment feasibility assessment is performed. If power adjustment is permitted, aggregation and optimization operations are executed to obtain the final allocation ratio for each channel, including: Obtain charging demand information during peak hours; Based on the power grid load information, a power adjustment feasibility assessment is performed. If the assessment result is permissible, the initial allocation ratio of each channel is calculated based on the pre-adjusted power flow configuration to obtain the initial channel power value. Based on the initial channel power value, the power allocation is adjusted in conjunction with the peak charging demand information to obtain the optimized channel power value. Based on the optimized channel power values, the grid load information is integrated, the power flow direction parameters of each channel are updated, and the adjusted power allocation scheme is obtained. Based on the adjusted power allocation scheme, the final allocation ratio is calculated to obtain the final allocation ratio for each channel.

[0008] Preferably, based on the final allocation ratio of each channel, device status detection is performed. If the detection passes, the final allocation ratio of each channel is applied to the actual charging process, and user experience-related data and key load data are collected simultaneously to obtain synchronous feedback information, including: Obtain device status data; Based on the device status data, a stability threshold is determined. If the determination passes, charging is performed according to the final allocation ratio of each channel to obtain the adjusted charging device operating parameters. Based on the adjusted operating parameters of the charging equipment, user experience-related data and key load data are collected to obtain synchronous feedback information.

[0009] Preferably, based on the synchronous feedback information and the grid load information, a preset standard judgment is performed to obtain a power fine-tuning command, including: Based on the synchronization feedback information, extract the timing synchronization data to obtain the timing synchronization dataset; Based on the time-series synchronization dataset and the power grid load information, a stability judgment is made to obtain the load stability status; Based on the load stability status, a judgment is made according to a preset standard. If it is determined that adjustment is needed, a power fine-tuning command is generated.

[0010] Preferably, based on the power fine-tuning command and the pre-adjusted power flow configuration, a secondary convergence path optimization is performed to obtain the final efficiency improvement configuration, including: Based on the real-time power adjustment data in the power fine-tuning instruction and the pre-adjusted power flow configuration, deviation calculation is performed to obtain a set of resource utilization deviation values; Based on the resource utilization deviation value set, the flow direction configurations with deviations exceeding a preset deviation threshold are redistributed to obtain an optimized flow direction configuration set. Based on the optimized flow configuration set, adjustments are made in real time to obtain the final efficiency improvement configuration.

[0011] Preferably, based on the final efficiency improvement configuration, correlation data extraction of channel priority sorting and deviation statistics is performed to obtain resource redirection continuity parameters, including: Based on the final efficiency improvement configuration, extract the channel priority sorting data and deviation statistics data to obtain priority-deviation data pairs; Based on the priority-deviation data pairs, correlation calculations and grouping processes are performed to obtain the resource redirection parameter set; Based on the resource redirection parameter set, continuity parameter optimization is performed to obtain resource redirection continuity parameters.

[0012] Preferably, based on the resource redirection continuity parameter, a linkage processing of power supply capacity and resource reallocation is performed to obtain a linkage output, including: Based on the resource redirection continuity parameter, real-time load data and power demand data are acquired to obtain the current load demand dataset; Based on the current load demand dataset, the fluctuation amplitude is determined, and the fluctuation features are extracted based on the determination results to obtain a fluctuation feature set; Based on the fluctuation feature set, load allocation is adjusted to obtain a resource reallocation scheme; Based on the resource reallocation scheme, continuous parameters are optimized in real time to obtain linked output.

[0013] Secondly, the present invention provides a control device for a multi-power AC charging pile, comprising: The data acquisition module is used to acquire real-time load data and power grid load information for each interface; The pre-adjustment configuration generation module is used to perform power margin calculation and sorting operations based on the real-time load data of each interface to obtain a margin distribution list, and generate an initial power aggregation result based on the margin distribution list to obtain a pre-adjustment power flow configuration. The final allocation ratio generation module is used to determine the feasibility of power adjustment based on the pre-adjusted power flow configuration and the grid load information. If power adjustment is allowed, aggregation and optimization operations are performed to obtain the final allocation ratio of each channel. The execution and feedback module is used to perform device status detection based on the final allocation ratio of each channel. If the detection passes, the final allocation ratio of each channel is applied to the actual charging process, and user experience-related data and key load data are collected simultaneously to obtain synchronous feedback information. The fine-tuning instruction generation module is used to perform preset standard judgment based on the synchronization feedback information and the power grid load information to obtain power fine-tuning instructions; The configuration optimization module is used to perform secondary convergence path optimization based on the power fine-tuning command and the pre-adjusted power flow direction configuration to obtain the final efficiency improvement configuration. The redirection parameter generation module is used to extract the associated data of channel priority sorting and deviation statistics based on the final efficiency improvement configuration to obtain resource redirection continuity parameters. The linkage output module is used to perform linkage processing of power supply capacity and resource reallocation based on the resource redirection continuity parameter, and obtain linkage output.

[0014] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a control method for a multi-power AC charging pile as described in any one of the above.

[0015] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the control method for a multi-power AC charging pile described in any one of the above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention performs real-time load data acquisition and synchronizes with grid load information for each interface of the multi-power AC charging pile. All electrical quantities are compensated for temperature drift, calibrated at two points and adaptively adjusted for sampling frequency to form an initial dataset with high consistency. Through this normalization process, the system can obtain more reliable input in subsequent margin calculation and aggregation scheme generation, reducing the judgment deviation caused by sensor zero drift or excessive sampling interval.

[0017] (2) The present invention calculates the power margin based on the standardized real-time load data, and sorts and trims it in combination with the preset margin threshold and the upper limit of the ratio to obtain the pre-adjusted power flow configuration. This process quantifies the principle of whoever has the larger margin gives priority to the transfer, so that the identification and convergence path of surplus power is interpretable at the paper stage, and reduces the risk of over-transfer of single channel brought about by the traditional fixed threshold method.

[0018] (3) The present invention integrates the charging demand information during peak hours with the channel power table in a weighted manner, and cuts it in descending order of priority when it exceeds the grid margin, thereby generating an adjusted power allocation scheme. This step ensures that the demand side expectation and the supply side capacity are continuously aligned, weakens the frequent manual intervention caused by sudden changes in demand, and retains the weight space that can be adjusted according to the operation strategy.

[0019] (4) The present invention adds a device status stability threshold judgment before hardware writing. The proportional distribution is only executed when the module temperature, voltage fluctuation and insulation impedance are all within the range given by the manufacturer, and the verification is performed after writing. This order improves the reliability of proportional distribution and reduces the probability of power drift or interruption caused by module abnormality.

[0020] (5) This invention calculates the power margin of the equipment and formulates a power allocation scheme based on the real-time charging demand, thereby realizing the transfer and allocation of the power margin of the equipment and effectively improving the utilization efficiency of the equipment margin. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a control method for a multi-power AC charging pile provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the control device structure of a multi-power AC charging pile provided in the second embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Reference Figure 1 The first embodiment of the present invention provides a control method for a multi-power AC charging pile, comprising the following steps: S11, obtain real-time load data and power grid load information for each interface; S12, based on the real-time load data of each interface, perform power margin calculation and sorting operations to obtain a margin distribution list, and generate an initial power aggregation result based on the margin distribution list to obtain a pre-adjusted power flow configuration; S13. Based on the pre-adjusted power flow configuration and the grid load information, determine the feasibility of power adjustment. If power adjustment is allowed, perform aggregation and optimization operations to obtain the final allocation ratio of each channel. S14. Based on the final allocation ratio of each channel, perform device status detection. If the detection passes, apply the final allocation ratio of each channel to the actual charging process, and simultaneously collect user experience-related data and key load data to obtain synchronous feedback information. S15, based on the synchronous feedback information and the power grid load information, a preset standard judgment is made to obtain a power fine-tuning command; S16, according to the power fine-tuning command and the pre-adjusted power flow direction configuration, perform secondary convergence path optimization to obtain the final efficiency improvement configuration; S17, Based on the final efficiency improvement configuration, perform correlation data extraction of channel priority sorting and deviation statistics to obtain resource redirection continuity parameters; S18, based on the resource redirection continuity parameter, perform linkage processing of power supply capacity and resource reallocation to obtain linkage output.

[0024] In step S11, real-time load data and grid load information for each interface are obtained.

[0025] In one implementation, electrical quantities are first acquired from each charging interface. The instantaneous voltage value of the interface is sampled at a frequency of 10Hz and quantized by a 12-bit ADC to obtain a digital voltage value. Simultaneously, the output of a calibrated current sensor is read, and after temperature drift compensation, a digital current value is obtained. The product of voltage and current is used as the instantaneous power, and packaged together with the Celsius temperature value output by the interface temperature sensor to form the interface-level raw electrical frame. Here, current calibration refers to storing two calibration points—zero-point current and full-scale current—in the EEPROM. After power-on, these two values ​​are read into RAM, and the original current x is corrected to a standard current y using the linear equation y = k·x + b, where k and b are calculated using a two-point formula. Temperature drift compensation refers to substituting the Celsius temperature T measured by the temperature sensor into a first-order formula: Where α is the temperature coefficient (given by the manufacturer). Using the reference temperature (room temperature), first calculate the difference term, then correct y to... This eliminates temperature-induced drift.

[0026] To eliminate sensor zero drift, two calibration coefficients stored in the EEPROM are written to RAM during the power-on initialization phase to linearly correct the current value. For example, the sensor outputs 0.05A at zero-point input and 19.8A at full-scale 20A. The two-point coefficients are obtained by least-squares fitting, and the error after correction is controlled within 0.5%. If the corrected current is higher than a preset current threshold, which is set at 22% of the charging pile's rated current of 32A to detect potential overload trends in advance, the sampling frequency is automatically increased to 25Hz to improve overload detection resolution. Specifically, the two-point coefficients obtained by least-squares fitting refer to applying two standard input currents, 0A (zero point) and 20A (full-scale), to the sensor on the calibration platform and recording the corresponding ADC output code values; obtaining two data points (x1, y1) = (standard 0A, ADC code value 1) and (x2, y2) = (standard 20A, ADC code value 2); and then using least-squares linear fitting. Where y is the ADC code value, x is the standard current; k and b are calculated directly using the two-point formula: k and b are written into the EEPROM as two-point calibration coefficients; after power-on, the real-time ADC code value is converted into standard current using the same formula to complete the linear correction.

[0027] Subsequently, a standard request frame is sent to the smart terminal at the grid edge via the local RS-485 interface to obtain grid load information, which includes at least the current grid load rate and available capacity margin. The interface electrical frame and grid status variables are aligned to UTC seconds and then merged to generate real-time load data and grid load information with timestamps, which are then written to a RAM circular buffer for the next step.

[0028] For example, at a charging station in a commercial area, the preset monitoring module measures the voltage of interface 1 as 230V, the current as 6A, and the temperature as 38°C. After calibration, the current deviation is reduced from 0.3A to 0.1A, and the instantaneous power is calculated to be 1.38kW. At the same time, the smart terminal at the edge of the power grid is queried to obtain the grid load rate as 78% and the available margin as 115kW. The above data are uniformly stamped with the same time scale and used as the output of this step for subsequent margin calculation and aggregation scheme generation.

[0029] In step S12, based on the real-time load data of each interface, power margin calculation and sorting operations are performed to obtain a margin distribution list. Based on the margin distribution list, an initial power aggregation result is generated to obtain a pre-adjusted power flow configuration, including: Based on the real-time load data of each interface, the power margin of each interface is calculated, and the power margins that exceed the preset margin threshold are sorted to obtain a margin distribution list. Based on the aforementioned surplus distribution list, extract the remaining power data for each charging channel to obtain the remaining power data; Based on the remaining power data, a proportional calculation is performed to obtain the initial power convergence result; Based on the initial power convergence results, the channel power is redistributed to obtain a pre-adjusted power flow configuration.

[0030] In this step, the real-time load data of each interface is first read from RAM, and the instantaneous power value P_intf is extracted; based on the rated channel power P_rated, the following formula is used: The power margin ΔP for each interface is calculated, where P_rated is obtained by dividing the charging pile's nameplate capacity by the number of parallel charging guns. To eliminate minor fluctuations, a preset margin threshold of 12% of P_rated is empirically set to filter out low-value margins. This value balances measurement noise and usable margin. If ΔP is less than this threshold, the interface is not included in the subsequent sorting. The remaining ΔPs are sorted in descending order to obtain a margin distribution list, where each element contains the interface number and margin value. Subsequently, the margin distribution list is traversed, and all ΔPs are summed to obtain the total remaining power P_total_rem. Then, based on the principle of prioritizing the largest margin, the power is allocated according to the formula: Calculate the transfer ratio of each channel This results in the initial power convergence. If a certain channel... If the preset ratio limit of 0.25 is exceeded, this limit is set to avoid insufficient power from excessive single-channel allocation, based on experience with a ratio of 1 / 4. Cut to 0.25 and redistribute the excess to the remaining channels proportionally to complete the channel power redistribution, resulting in a pre-adjusted power flow configuration for feasibility assessment in the next step.

[0031] For example, a commercial area charging station has four charging guns operating in parallel, each rated at 7kW. The current instantaneous load data is as follows: Gun 1 4kW, Gun 2 5kW, Gun 3 2kW, and Gun 4 6kW. The calculated margin ΔP is 3kW, 2kW, 5kW, and 1kW respectively. The preset margin threshold is 0.84kW (7kW × 12%). All channels exceed the threshold and are retained. The margin distribution list after descending sorting is [Gun 3 5kW, Gun 1 3kW, Gun 2 2kW, Gun 4 1kW]. The total remaining power is 11kW, with an initial ratio α = [0.45, 0.27, 0.18, 0.09]. Gun 3's ratio exceeds 0.25 and is pruned to 0.25. The overflow portion is distributed among the remaining channels (i.e., Gun 1, Gun 2, etc.). Gun 4) The surplus ratio (3:2:1) is allocated to ensure that the total transfer amount is consistent with the total convergence amount, and finally the pre-adjusted power flow configuration is obtained: Gun 3 transfers 1.25kW, Gun 1 transfers 0.91kW, Gun 2 transfers 0.43kW, and Gun 4 transfers 0.12kW.

[0032] In step S13, based on the pre-adjusted power flow configuration and the grid load information, a power adjustment feasibility judgment is made. If power adjustment is allowed, aggregation and optimization operations are performed to obtain the final allocation ratio for each channel, including: Obtain charging demand information during peak hours; Based on the power grid load information, a power adjustment feasibility assessment is performed. If the assessment result is permissible, the initial allocation ratio of each channel is calculated based on the pre-adjusted power flow configuration to obtain the initial channel power value. Based on the initial channel power value, the power allocation is adjusted in conjunction with the peak charging demand information to obtain the optimized channel power value. Based on the optimized channel power values, the grid load information is integrated, the power flow direction parameters of each channel are updated, and the adjusted power allocation scheme is obtained. Based on the adjusted power allocation scheme, the final allocation ratio is calculated to obtain the final allocation ratio for each channel.

[0033] In one implementation, the local clock is first compared with the regional peak segment table to identify whether it is in a peak period. If the current time falls within the definition of the regional peak segment and the load rate is higher than the benchmark value, it is marked as a peak and the corresponding peak period charging demand information is obtained. This information includes the average demand power of the peak segment and the demand growth slope. The data comes from the statistical average of the same period of the previous week and is refreshed every 1 hour. For example, the regional peak period table embedded in the EEPROM specifies that 09:00-12:00, 14:00-17:00, and 19:00-22:00 on weekdays are peak periods, and the rest are flat periods. The local clock is read at 10:30 on a weekday. After comparing with the above regional peak period table, it falls into the 10:00-11:30 peak window, and the current grid load rate is 78%, which is higher than the benchmark value of 75%, so it is marked as a peak period. The average statistical value of the same period of the previous week is retrieved through the internal EEPROM to obtain the average peak demand power of 6.2kW and the demand growth slope of 0.15kW / min. The data is refreshed at 10:00 and the refresh cycle is 1 hour. The charging demand information of this peak period is written into RAM.

[0034] Subsequently, the grid load rate and available capacity margin in the grid load information are read, and a power adjustment feasibility judgment is performed. If the grid load rate is less than 85% of the preset load limit and the available capacity margin is greater than the total pre-adjusted power aggregation, the adjustment is deemed permissible. This 85% limit is set according to the safe operation boundary given by the grid edge intelligent terminal, and is used to reserve a 15% buffer to avoid impacting the distribution transformers. After the judgment is passed, the pre-adjusted power flow configuration is read from the NVRAM, and the initial channel power value is calculated. Since 78% is less than the preset load limit of 85%, and 115kW is greater than 24.8kW, the adjustment is deemed permissible. Then, the pre-adjusted power flow configuration is read from the NVRAM, and the initial channel power values ​​are calculated by adding the aggregation transfer amount to the original gun power: Gun 1 5.64kW, Gun 2 6.76kW, Gun 3 4.75kW, and Gun 4 6.88kW, which are used for subsequent peak demand weighted fusion.

[0035] Next, the initial channel power value is weighted and fused with peak-hour charging demand information. If the target power of a channel is lower than the average peak-hour demand, the channel power is increased according to the average demand, while the amount of surplus channels being sold is reduced proportionally to ensure that the total convergence remains unchanged, resulting in the optimized channel power value. To maintain dimensional consistency, the average demand and channel power are normalized to their maximum and minimum values ​​before fusion. The normalization result ranges from 0 to 1, and the weighting coefficient is set to 0.5:0.5. This weighting setting can balance the impact of grid-side capacity and user-side demand on the final power and remain adjustable to adapt to different operational needs.

[0036] For example, the initial channel power values ​​are [Gun 1 5.64kW, Gun 2 6.76kW, Gun 3 4.75kW, Gun 4 6.88kW], and the peak demand average is 6.2kW. First, the initial channel power values ​​and the peak demand average are normalized to their maximum and minimum values. The power range is 4.75kW to 6.88kW, with a range of 2.13kW, resulting in normalized power values ​​of [0.42, 0.94, 0.00, 1.00], and the demand average is 0.68. Since the power of Gun 1 and Gun 3 is lower than the demand average, a weighted average of 0.5:0.5 is used to combine the normalized power values ​​and the demand average. For Gun 1, 0.5 × 0.42 + 0.5 × 0.68 = 0.55. After inverse normalization, 0.55 × 2.13 + 4.75 = The power of gun 1 is increased by 0.28kW (5.92 - 5.64). The power of gun 3 is increased by 0.34kW (0.5 × 0.00 + 0.5 × 0.68). After inverse normalization, the power is 5.47kW, an increase of 0.72kW. The total gap is 0.28 + 0.72 = 1.00kW. To ensure the total power is conserved, the remaining two channels are reduced. The weight of gun 2 is 0.94, and the weight of gun 4 is 1.00, with a total weight of 1.94. Gun 2 is reduced by 1.00 × 0.94 / 1.94 = 0.48kW, and gun 4 is reduced by 1.00 × 1.00 / 1.94 = 0.52kW. Finally, the optimized channel power values ​​are obtained: [gun 1 5.92kW, gun 2 6.76 - 0.48 = 6.28kW, gun 3 5.47kW, gun 4 6.88 - 0.52 = 6.36kW].

[0037] Next, the optimized channel power values ​​are integrated with the grid load information again, and the total channel power is checked using the available capacity margin. If the total exceeds the margin, the channels are cut in descending order of priority, with a 1% step size each time until the limit is not exceeded, thereby updating the power flow parameters of each channel and obtaining the adjusted power allocation scheme.

[0038] Finally, the power values ​​of each channel are summed and normalized to calculate the final allocation ratio for each channel, and the result is stored in RAM in per mille format.

[0039] In step S14, device status detection is performed based on the final allocation ratio of each channel. If the detection passes, the final allocation ratio of each channel is applied to the actual charging process, and user experience-related data and key load data are collected simultaneously to obtain synchronous feedback information, including: Obtain device status data; Based on the device status data, a stability threshold is determined. If the determination passes, charging is performed according to the final allocation ratio of each channel to obtain the adjusted charging device operating parameters. Based on the adjusted operating parameters of the charging equipment, user experience-related data and key load data are collected to obtain synchronous feedback information.

[0040] This step first extracts the equipment status data by polling each charging module via the CAN bus to obtain module temperature, contactor closure status, relay sticking flag, instantaneous output voltage and current values, and insulation impedance values, thus forming the equipment status data.

[0041] Subsequently, the preset stability thresholds are set according to the safe operating range provided by the module manufacturer: temperature ≤ 65°C, output voltage fluctuation ≤ 5%, and insulation resistance ≥ 1MΩ. Any indicator exceeding these limits results in a failure. This threshold combination covers common harsh operating conditions during testing. If all channel states are within the thresholds, the final allocation ratio is converted into the actual output power value and written to the charging module via the PWM register to obtain the adjusted charging equipment operating parameters. The register is immediately read back after writing; if the read value deviates from the written value by more than 1%, the writing process is repeated until they match, ensuring the command is implemented. For example, if the final allocation ratio of the four channels is [217‰, 263‰, 242‰, 278‰], polling via the CAN bus reveals that the charging module temperature is 55°C, the contactor is closing normally, the relay is not stuck, the output voltage is 229V, the current is 22.9A, and the insulation resistance is 5MΩ. All these values ​​are within the thresholds of temperature ≤ 65°C, voltage fluctuation ≤ 5%, and impedance ≥ 1MΩ, thus the test is considered successful. The target power of the gun 2 is calculated to be 1.841kW based on 7kW×263 / 1000. The corresponding PWM duty cycle is written into the module register and read back immediately. The deviation between the read-back value and the written value is 0.3% (<1%), which is confirmed as the adjusted operating parameters of the charging equipment.

[0042] Subsequently, user experience-related data and key load data are synchronously collected at a frequency of 10Hz. User experience-related data includes the actual output current ramp-up time, the percentage change rate of charging completion, and the number of interruptions. Key load data includes the real-time interface power, grid load rate, and interface temperature. All data is aligned to UTC seconds to form synchronous feedback information, which is stored in a RAM circular buffer for stability assessment in the next step. For example, the 10Hz sampling shows a current ramp-up time of 1.2s, a completion percentage increase rate of 0.8% / s, and 0 interruptions. Interface power and grid load rate are recorded in real time. After data alignment, synchronous feedback information is formed for use in the next step.

[0043] In step S15, based on the synchronization feedback information and the grid load information, a preset standard judgment is performed to obtain a power fine-tuning command, including: Based on the synchronization feedback information, extract the timing synchronization data to obtain the timing synchronization dataset; Based on the time-series synchronization dataset and the power grid load information, a stability judgment is made to obtain the load stability status; Based on the load stability status, a judgment is made according to a preset standard. If it is determined that adjustment is needed, a power fine-tuning command is generated.

[0044] First, timing synchronization data is extracted from the synchronization feedback information. Using UTC seconds as the key, the user experience-related data sampled at 10Hz, such as current ramp-up time, completion percentage growth rate, and number of interruptions, is aligned with key load data, such as interface power, grid load rate, and interface temperature, to form a timing synchronization dataset with a 1-second granularity. For example, using UTC seconds as the key, the user experience-related data sampled at 10Hz for Gun 3 is aligned with key load data: at t=10s, current ramp-up time is 1.2s, completion percentage growth rate is 0.8% / s, number of interruptions is 0, interface power is 4.84kW, grid load rate is 78%, and interface temperature is 38°C, forming a timing synchronization dataset with a 1-second granularity.

[0045] Subsequently, stability is assessed based on the time-series synchronization dataset and grid load information. The ratio of the standard deviation to the mean of the grid load rate is calculated. If the ratio is less than the preset fluctuation limit of 0.03, which is set based on the empirical value of the stable operating range given by the grid edge terminal, it is used to distinguish between normal fluctuations and potential shocks. If the number of interruptions within 60 seconds is 0, the load stability is determined to be stable; otherwise, it is marked as unstable.

[0046] When the load stability is stable, further judgment is made according to preset standards, using the deviation between the interface power and the target power as an indicator. The preset power deviation threshold is 5% of the target power, which is derived with reference to the maximum permissible error in the JJG1148-2022 Verification Procedure for AC Charging Piles of Electric Vehicles. If any channel continuously deviates by more than 5% for a duration of more than 10 seconds, it is considered that adjustment is required, and a power fine-tuning command is generated. The command content is channel ID - correction amount. The correction amount is calculated by multiplying the deviation value by 0.8 (attenuation coefficient to prevent over-adjustment). For example, in a commercial area charging station, the average grid load rate within 60 seconds is 78%, the standard deviation is 2.1%, the ratio is 0.027 < 0.03, and the number of interruptions is 0, so the load stability is judged to be stable. The time-series synchronization dataset shows that the target power of gun 2 is 5.26kW, the actual average is 4.95kW, the deviation is -0.31kW, the proportion is 5.9%, which exceeds the 5% threshold and lasts for 12 seconds, so a power fine-tuning command [gun 2 +0.25kW] is generated.

[0047] In step S16, based on the power fine-tuning command and the pre-adjusted power flow configuration, a secondary convergence path optimization is performed to obtain the final efficiency improvement configuration, including: Based on the real-time power adjustment data in the power fine-tuning instruction and the pre-adjusted power flow configuration, deviation calculation is performed to obtain a set of resource utilization deviation values; Based on the resource utilization deviation value set, the flow direction configurations with deviations exceeding a preset deviation threshold are redistributed to obtain an optimized flow direction configuration set. Based on the optimized flow configuration set, adjustments are made in real time to obtain the final efficiency improvement configuration.

[0048] This step first parses the real-time power adjustment data in the power fine-tuning command to obtain the correction amount ΔP_adj that needs to be increased or decreased for each channel; it then reads the pre-adjusted power flow configuration from NVRAM to obtain the original power relinquishment table P_pre; (Note:) The deviation between the two is calculated to form a set of resource utilization deviation values, where positive values ​​indicate that additional output is required and negative values ​​indicate recoverable power.

[0049] It should be noted that the preset deviation threshold is 3% of the target power. This value is set according to half of the maximum permissible error in JJG1148-2022. This can capture significant deviations while avoiding frequent adjustments. If |ΔP_dev| is less than 3% of the target power, the redistribution is skipped and the original configuration is used directly. If it exceeds 3%, redistribution is initiated: the surplus power is distributed to the gap channels according to the weight ratio of ΔP_dev, based on the principle of prioritizing high deviation channels and distributing equally among channels with the same deviation, to obtain the optimized flow direction configuration set.

[0050] The optimized flow configuration set is then written into the power module's PWM register, and the actual output value is read back. If the readback deviation is greater than 1%, iterative correction is performed once to obtain a consistent final efficiency improvement configuration, which is then stored in NVRAM for the next step to extract priority and deviation statistics.

[0051] For example, the fine-tuning instruction for gun 2 is ΔP_adj = +0.25kW, the original P_pre = -0.25kW (yielding), ΔP_dev = +0.50kW, the target proportion is 9.5% > 3%, triggering redistribution; in the total surplus power of 11kW, gun 3 has a deviation of +0.30kW and gun 1 has a deviation of +0.20kW. After weighting, gun 3 reduces yielding by 0.30kW, gun 1 reduces yielding by 0.20kW, and gun 2 gains +0.50kW, forming an optimized flow configuration set; the total recovery amount of 0.50kW is equal to ΔP_dev, the total yielding amount remains unchanged at 8.8kW, and after consistent readback, the final efficiency improvement configuration is obtained [gun 1 5.14kW, gun 2 5.76kW, gun 3 4.54kW, gun 4 6.88kW].

[0052] In step S17, based on the final efficiency improvement configuration, the correlation data of channel priority sorting and deviation statistics is extracted to obtain resource redirection continuity parameters, including: Based on the final efficiency improvement configuration, extract the channel priority sorting data and deviation statistics data to obtain priority-deviation data pairs; Based on the priority-deviation data pairs, correlation calculations and grouping processes are performed to obtain the resource redirection parameter set; Based on the resource redirection parameter set, continuity parameter optimization is performed to obtain resource redirection continuity parameters.

[0053] First, extract the channel priority sorting data and deviation statistics data, read the target power value in the final efficiency improvement configuration, compare it with the rated single-gun power, and sort them in descending order according to the principle that the higher the target power, the higher the priority, to obtain the channel priority sorting data; at the same time, calculate the difference between the actual output of each channel and the target power as the deviation statistics data, forming a priority-deviation data pair.

[0054] Then, correlation calculations are performed, using channel priority values ​​as variable X and channel deviation as variable Y. Pearson's r is calculated for all (X, Y) samples from all channels. |r|>0.7 is considered a strong correlation, resulting in a correlated dataset. If r is positive, it indicates that high-priority channels are likely to be abundant; if it is negative, it indicates that there is likely to be a gap. Subsequently, grouping is performed, using the 20th percentile of the priority sequence as the boundary to divide the data into high-priority and low-priority groups. Each group is further subdivided according to the positive or negative deviation, forming four subsets. After merging, the resource redirection parameter set is obtained.

[0055] For example, the parameter set is optimized for continuity by using linear interpolation. Five equally spaced transition points are inserted between adjacent deviation intervals to form a smooth curve, which yields the resource redirection continuity parameter. The curve output value range is 0-1, corresponding to a transfer ratio of 0-100%, and is stored in EEPROM for real-time table lookup in the next step.

[0056] For example, the target power of four guns is [5.14, 5.76, 4.54, 6.88] kW. After normalization to the maximum and minimum values, it is mapped to the interval [60, 80], resulting in the priority value Pri = [65, 70, 60, 80]. The actual power is [5.10, 5.80, 4.50, 6.90] kW, and the calculated deviation is [-0.04, +0.04, -0.04, +0.02] kW. Pearson r ≈ +0.74, indicating that the high-priority channel is prone to redundancy. Using the 20th percentile of the priority value sequence as the boundary, the high-priority group is gun 4; the low-priority group includes guns 1, 2, and 3. Gun 4 in the high-priority group is marked as yielding with a deviation of +0.02 kW, and gun 2 in the low-priority group has a deviation of -0.04 kW. The received data is marked to form a resource redirection parameter set; five equally spaced transition points are inserted between +0.02kW and −0.04kW to generate a slope continuous curve, thus obtaining the resource redirection continuity.

[0057] In step S18, based on the resource redirection continuity parameter, a linkage process is performed between power supply capacity and resource reallocation to obtain a linkage output, including: Based on the resource redirection continuity parameter, real-time load data and power demand data are acquired to obtain the current load demand dataset; Based on the current load demand dataset, the fluctuation amplitude is determined, and the fluctuation features are extracted based on the determination results to obtain a fluctuation feature set; Based on the fluctuation feature set, load allocation is adjusted to obtain a resource reallocation scheme; Based on the resource reallocation scheme, continuous parameters are optimized in real time to obtain linked output.

[0058] First, periodic queries are initiated to the smart terminal at the edge of the power grid via the RS-485 interface to obtain real-time load data and power demand data. The data items include at least the grid load rate, available capacity margin, and predicted demand increment. The query cycle is maintained at 1Hz to form the current load demand dataset.

[0059] Subsequently, the fluctuation amplitude of the current load demand dataset is assessed. The standard deviation σ of the grid load rate over the past 60 seconds is calculated. If σ is greater than a preset fluctuation threshold of 0.02 (this threshold is set based on the experience value of light / heavy load switching provided by the grid edge terminal and is used to distinguish between normal random fluctuations and trend-based shocks), it is determined to be a significant fluctuation. If it is determined to be a significant fluctuation, the peak-to-valley difference, rate of change, and duration are extracted to form a fluctuation feature set. For example, by querying the grid edge smart terminal at a 1Hz cycle through the RS-485 interface, the current load demand dataset is obtained. The instantaneous grid load rate is 81%, the available capacity margin is 110kW, and the predicted demand increment is 5kW. The standard deviation σ of the data over the past 60 seconds is calculated to be 0.025, which is greater than the preset fluctuation threshold of 0.02, and is determined to be a significant fluctuation. The extracted fluctuation feature set is: peak-to-valley difference 3%, rate of change 0.05% / s, and duration 90s.

[0060] Subsequently, the fluctuation feature set is input into the resource redirection continuity parameter curve. The yield / receive ratio of each channel is obtained by table lookup and linear interpolation. Then, the load allocation is adjusted in the order of yielding high-value channels first and filling gaps with low-value channels later, generating a resource redistribution scheme. The scheme includes channel ID, target power and deployment timing.

[0061] Finally, the resource reallocation scheme set is continuously optimized in real time using a first-order inertial filter with a filter coefficient of 0.8 (time constant 5s). The absolute change in a single step is limited to ≤0.2kW. If the calculated change is >0.2kW, it is executed in steps of 0.2kW each until completion. The smoothed power scheduling command is output as the linkage output. This output is directly written to the PWM register of the power module and synchronously read back for verification to ensure that the grid and the charging side respond synchronously.

[0062] For example, the current load demand dataset shows that the grid load rate jumps from 78% to 81%, σ=0.025>0.02, triggering the fluctuation feature set {peak-valley difference 3%, change rate 0.05% / s}. Looking up the continuity parameter curve, we find that gun 4 relinquishes 15% and gun 2 receives 12%, forming a resource redistribution scheme set: the target power of gun 4 decreases from 6.88kW to 5.17kW (change -1.71kW), and gun 2 increases from 5.26kW to 5.71kW (change +0.45kW). Continuity optimization breaks down the -1.71kW of gun 4 into 9 steps (each step -0.2kW, final step -0.11kW), with a step interval of 1s, completed within 9s; the +0.45kW of gun 2 is broken down into 3 steps (final step +0.05kW), completed within 3s.

[0063] In summary, this invention discloses a control method for multi-power AC charging piles. It acquires real-time load information from each interface and the power grid load; calculates and sorts the power margin to generate a pre-adjusted power flow configuration; combines grid margin and peak demand to perform feasibility assessment and allocation optimization to obtain the final allocation ratio; writes the data to the power module after equipment status detection, and simultaneously collects user experience and load data; generates power fine-tuning commands based on feedback and grid status; and outputs linked power scheduling commands after secondary convergence and continuity curve optimization. This solves the problem of low power margin utilization efficiency in existing technologies.

[0064] Reference Figure 2 The second embodiment of the present invention provides a control device for a multi-power AC charging pile, comprising: The data acquisition module is used to acquire real-time load data and power grid load information for each interface; The pre-adjustment configuration generation module is used to perform power margin calculation and sorting operations based on the real-time load data of each interface to obtain a margin distribution list, and generate an initial power aggregation result based on the margin distribution list to obtain a pre-adjustment power flow configuration. The final allocation ratio generation module is used to determine the feasibility of power adjustment based on the pre-adjusted power flow configuration and the grid load information. If power adjustment is allowed, aggregation and optimization operations are performed to obtain the final allocation ratio of each channel. The execution and feedback module is used to perform device status detection based on the final allocation ratio of each channel. If the detection passes, the final allocation ratio of each channel is applied to the actual charging process, and user experience-related data and key load data are collected simultaneously to obtain synchronous feedback information. The fine-tuning instruction generation module is used to perform preset standard judgment based on the synchronization feedback information and the power grid load information to obtain power fine-tuning instructions; The configuration optimization module is used to perform secondary convergence path optimization based on the power fine-tuning command and the pre-adjusted power flow direction configuration to obtain the final efficiency improvement configuration. The redirection parameter generation module is used to extract the associated data of channel priority sorting and deviation statistics based on the final efficiency improvement configuration to obtain resource redirection continuity parameters. The linkage output module is used to perform linkage processing of power supply capacity and resource reallocation based on the resource redirection continuity parameter, and obtain linkage output.

[0065] It should be noted that the control device for a multi-power AC charging pile provided in this embodiment of the invention is used to execute all the process steps of the control method for a multi-power AC charging pile in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0066] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a control program for a multi-power AC charging pile. When the processor executes the computer program, it implements the steps in the control method embodiments of the various multi-power AC charging piles described above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the data acquisition module.

[0067] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0068] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0069] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0070] The memory can be used to store the computer program or module. The processor implements various functions of the electronic device by running or executing the computer program or module stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0071] If the modules integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0072] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0073] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A control method of a multi-power alternating current charging pile, characterized in that, The method comprises the following steps: acquiring real-time load data of each interface and power grid load information; performing power margin calculation and sorting operation according to the real-time load data of each interface to obtain a margin distribution list, and generating an initial power convergence result according to the margin distribution list to obtain a pre-adjusted power flow configuration; performing power adjustment feasibility judgment according to the pre-adjusted power flow configuration and the power grid load information, and if power adjustment is allowed, performing convergence and optimization operation to obtain a final allocation ratio of each channel; performing device state detection according to the final allocation ratio of each channel, and if the detection is passed, applying the final allocation ratio of each channel to an actual charging process, and synchronously collecting user experience related data and key load data to obtain synchronous feedback information; performing preset standard judgment according to the synchronous feedback information and the power grid load information to obtain a power fine-tuning instruction; performing secondary convergence path optimization according to the power fine-tuning instruction and the pre-adjusted power flow configuration to obtain a final efficiency improvement configuration; performing association data extraction of channel priority sorting and deviation statistics according to the final efficiency improvement configuration to obtain a resource redirection continuity parameter; performing linkage processing of power supply capacity and resource reallocation according to the resource redirection continuity parameter to obtain linkage output. 2.The method of claim 1, wherein, The method of performing power margin calculation and sorting operation according to the real-time load data of each interface to obtain a margin distribution list, and generating an initial power convergence result according to the margin distribution list to obtain a pre-adjusted power flow configuration comprises: calculating the power margin of each interface according to the real-time load data of each interface, and performing sorting operation on the power margin exceeding a preset margin threshold to obtain a margin distribution list; extracting residual power data of each charging channel according to the margin distribution list to obtain residual power data; performing ratio calculation according to the residual power data to obtain an initial power convergence result; redistributing channel power according to the initial power convergence result to obtain a pre-adjusted power flow configuration. 3.The method of claim 1, wherein, The method of performing power adjustment feasibility judgment according to the pre-adjusted power flow configuration and the power grid load information, and if power adjustment is allowed, performing convergence and optimization operation to obtain a final allocation ratio of each channel comprises: acquiring peak period charging demand information; performing power adjustment feasibility judgment according to the power grid load information, and if the judgment result is allowed, calculating an initial allocation ratio of each channel according to the pre-adjusted power flow configuration to obtain an initial channel power value; performing power distribution adjustment according to the initial channel power value in combination with the peak period charging demand information to obtain an optimized channel power value; integrating the power grid load information according to the optimized channel power value to update power flow parameters of each channel to obtain an adjusted power distribution scheme; performing final allocation ratio calculation according to the adjusted power distribution scheme to obtain a final allocation ratio of each channel. 4.The method of claim 1, wherein, The device state detection is performed according to the final distribution ratio of each channel, if the detection passes, the final distribution ratio of each channel is applied to the actual charging process, and user experience related data and key load data are synchronously collected to obtain synchronous feedback information, including: Obtain device state data; According to the device state data, stability threshold judgment is performed, if the judgment passes, charging is performed according to the final distribution ratio of each channel to obtain adjusted charging device operation parameters; According to the adjusted charging device operation parameters, user experience related data and key load data are collected to obtain synchronous feedback information. 5.The method of claim 1, wherein, The preset standard is judged according to the synchronous feedback information and the power grid load information to obtain a power fine tuning instruction, including: According to the synchronous feedback information, time sequence synchronization data is extracted to obtain a time sequence synchronization data set; According to the time sequence synchronization data set and the power grid load information, stability judgment is performed to obtain a load stability state; According to the load stability state, the preset standard is judged, if it is judged that adjustment is needed, a power fine tuning instruction is generated. 6.The method of claim 1, wherein, The secondary convergence path optimization is performed according to the power fine tuning instruction and the pre-adjusted power flow direction configuration to obtain the final efficiency improvement configuration, including: According to the real-time power adjustment data in the power fine tuning instruction and the pre-adjusted power flow direction configuration, deviation calculation is performed to obtain a resource utilization deviation value set; According to the resource utilization deviation value set, the flow direction configuration whose deviation exceeds the preset deviation threshold is re-distributed to obtain an optimized flow direction configuration set; According to the optimized flow direction configuration set, real-time adjustment is performed to obtain the final efficiency improvement configuration.

7. The control method of a multi-power AC charging pile according to claim 1, wherein, According to the final efficiency improvement configuration, the associated data extraction of channel priority sorting and deviation statistics is performed to obtain a resource redirection continuity parameter, including: According to the final efficiency improvement configuration, channel priority sorting data and deviation statistics data are extracted to obtain a priority-deviation data pair; According to the priority-deviation data pair, correlation calculation and grouping processing are performed to obtain a resource redirection parameter set; According to the resource redirection parameter set, continuity parameter optimization is performed to obtain a resource redirection continuity parameter. 8.The method of claim 1, wherein, According to the resource redirection continuity parameter, power supply capacity and resource reallocation linkage processing are performed to obtain a linkage output, including: According to the resource redirection continuity parameter, real-time load data and power supply demand data are obtained to obtain a current load demand data set; According to the current load demand data set, fluctuation amplitude judgment is performed, and fluctuation characteristics are extracted according to the judgment result to obtain a fluctuation characteristic set; According to the fluctuation characteristic set, load distribution adjustment is performed to obtain a resource reallocation scheme; According to the resource reallocation scheme, continuity parameter real-time optimization is performed to obtain a linkage output.

9. A control device of a multi-power alternating current charging pile, characterized in that, Including: A data acquisition module for acquiring real-time load data and power grid load information of each interface; The pre-adjustment configuration generation module is configured to perform power margin calculation and sorting operation according to the instant load data of each interface to obtain a margin distribution list, and generate an initial power aggregation result according to the margin distribution list to obtain a pre-adjustment power flow configuration; The final allocation ratio generation module is configured to perform power adjustment feasibility judgment according to the pre-adjustment power flow configuration and the grid load information, and perform aggregation and optimization operation to obtain a final allocation ratio of each channel if power adjustment is allowed; The execution and feedback module is configured to perform device state detection according to the final allocation ratio of each channel, and apply the final allocation ratio of each channel to an actual charging process if the detection is passed, and synchronously collect user experience related data and key load data to obtain synchronous feedback information; The fine-tuning instruction generation module is configured to perform preset standard judgment according to the synchronous feedback information and the grid load information to obtain a power fine-tuning instruction; The configuration optimization module is configured to perform secondary aggregation path optimization according to the power fine-tuning instruction and the pre-adjustment power flow configuration to obtain a final efficiency improvement configuration; The redirection parameter generation module is configured to perform channel priority sorting and deviation statistical associated data extraction according to the final efficiency improvement configuration to obtain a resource redirection continuity parameter; The linkage output module is configured to perform linkage processing of power supply capacity and resource reallocation according to the resource redirection continuity parameter to obtain a linkage output.

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