A method and device for remote control of smart charging piles

Abstract

This invention relates to the field of new energy vehicle technology and proposes a remote control method for intelligent charging piles. The method includes: deploying an edge intelligent acquisition terminal between the charging pile and the charging gun port to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile; using the high-frequency electrical timing signals and environmental physical quantity signals, generating an edge event trigger vector for the charging pile to set the adaptive control action corresponding to the charging pile at the current moment; when the adaptive control action is a preset high-priority control action, the cloud control platform determines the load redistribution coefficient of each charging pile within the charging group, and based on the load redistribution coefficient, generates differentiated power adjustment commands for each charging pile within the charging group to execute remote control of the charging pile. This invention enables real-time matching between remote control commands for charging piles and dynamic operating conditions at the charging site.

Description

Technical Field

[0001] This invention relates to a remote control method for intelligent charging piles, belonging to the field of new energy vehicle technology. Background Technology

[0002] Smart charging piles are electric vehicle energy replenishment terminals that integrate power control, metering and billing, and network communication. Their remote control capabilities directly determine the operational efficiency of the charging network and the user experience.

[0003] However, existing methods mostly use static threshold allocation or simple polling strategies for remote control of charging piles. While this can achieve basic remote on / off and power limiting functions of charging piles, it lacks the ability to dynamically perceive real-time load fluctuations of the power grid, the actual state of charge of the vehicle battery, and on-site environmental parameters, resulting in the inability of remote control commands to match the dynamic operating conditions of the charging site in real time.

[0004] Therefore, existing technologies lack a solution that can achieve real-time matching between remote control commands for charging piles and dynamic operating conditions at the charging site. Summary of the Invention

[0005] This invention provides a remote control method for intelligent charging piles, the main purpose of which is to achieve real-time matching between remote control commands for charging piles and dynamic operating conditions at the charging site.

[0006] To achieve the above objectives, the present invention provides a remote control method for intelligent charging piles, the method comprising:

[0007] An edge intelligent acquisition terminal is deployed between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile.

[0008] Using the high-frequency electrical timing signal and the environmental physical quantity signal, an edge event trigger vector for the charging pile is generated. Based on the edge event trigger vector, an adaptive control action corresponding to the charging pile at the current moment is set.

[0009] When the adaptive control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile;

[0010] The cloud-based control platform determines the load redistribution coefficient of each charging pile in the charging group based on the edge event trigger vector, and generates differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

[0011] Optionally, the edge event trigger vector of the charging pile is generated using the high-frequency electrical timing signal and the environmental physical quantity signal, including:

[0012] Extract the spectral energy characteristics and transient response characteristics of the high-frequency electrical timing signal, and analyze the micro-meteorological characteristics and disturbance spectral characteristics of the environmental physical quantity signal;

[0013] The spectral energy features, transient response features, micro-meteorological features, and disturbance spectral features are input into a pre-trained edge event classification model, and the edge event classification model outputs the edge event trigger vector of the charging pile at the current moment.

[0014] Optionally, the edge event classification model is pre-built in the following manner:

[0015] Initialize the parameters of the edge event classification model, including the number of network layers, node weights, and bias terms;

[0016] The sample electrical state characteristic information and sample on-site environmental characteristic information of the charging pile during the historical charging process are obtained, wherein the sample electrical state characteristic information is labeled with an event type tag;

[0017] The sample electrical state feature information and the sample site environment feature information are used as inputs, and the event type label is used as a supervision signal to train the parameters of the edge event classification model, so as to obtain the trained edge event classification model based on the trained parameters.

[0018] Optionally, the parameters for training the edge event classification model include:

[0019] In the forward propagation step, the sample electrical state feature information and the sample on-site environment feature information are input into the edge event classification model to calculate and predict the event type;

[0020] In the backpropagation step, the adjustment amount of each parameter in the edge event classification model is determined based on the loss function value between the predicted event type and the event type label;

[0021] Based on the adjustment amount, the parameters of the edge event classification model are updated.

[0022] Optionally, the cloud-based control platform determines the load redistribution coefficient of each charging pile within the charging group to which the charging pile belongs, based on the edge event trigger vector, including:

[0023] Extract the on-site operating condition features of the charging pile from the edge event trigger vector;

[0024] Based on the characteristics of the on-site working conditions, the high-priority control actions are verified to obtain the target control reliability of the charging pile.

[0025] Based on the target regulation reliability, the load redistribution coefficient of each charging pile in the charging group to which the charging pile is located is calculated.

[0026] Optionally, based on the characteristics of the on-site operating conditions, the high-priority control actions are verified to obtain the target control reliability of the charging pile, including:

[0027] Based on the preset working condition-command association, the standard control command corresponding to the on-site working condition characteristics is determined as the target standard command of the charging pile;

[0028] The high-priority control action is compared with the target standard instruction to obtain the comparison result;

[0029] Based on the comparison results, the reliability of the target regulation of the charging pile is determined.

[0030] Optionally, based on the load redistribution coefficient, differentiated power adjustment instructions are generated for each charging pile within the charging group, including:

[0031] The real-time operating status data of each charging pile in the charging group is obtained. The real-time operating status data includes the current output power, power change rate and battery state of charge of each charging pile.

[0032] Based on the real-time operating status data and the load redistribution coefficient, create a power adjustment parameter table for each charging pile in the charging group;

[0033] Based on the power adjustment parameter table, differentiated power adjustment instructions are generated for each charging pile in the charging group.

[0034] Optionally, the power adjustment parameter table contains a set of data on the parameters required for each charging pile in the charging group to be executed in the next control cycle, including the target output power, power adjustment rate, adjustment execution order and adjustment time window of each charging pile.

[0035] Optionally, the edge intelligent acquisition terminal is a hardware unit deployed on the electrical circuit between the charging pile and the charging gun port, used to synchronously acquire electrical parameters and environmental parameters, including a high-frequency electrical sampling module, an environmental sensor array, and an edge processor.

[0036] To address the above problems, the present invention also provides a remote control device for intelligent charging piles, the device comprising:

[0037] The signal acquisition module is used to deploy an edge intelligent acquisition terminal between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile.

[0038] The event processing module is used to generate an edge event trigger vector for the charging pile using the high-frequency electrical timing signal and the environmental physical quantity signal, and to set the adaptive control action of the charging pile at the current moment based on the edge event trigger vector.

[0039] An edge communication module is used to upload the edge event trigger vector corresponding to the high-priority control action to the cloud control platform of the charging pile when the adaptive control action is a preset high-priority control action.

[0040] The cloud control module is used by the cloud control platform to determine the load redistribution coefficient of each charging pile in the charging group where the charging pile is located based on the edge event trigger vector, and to generate differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

[0041] Compared to the problems described in the background technology, this embodiment of the invention deploys an edge intelligent acquisition terminal between the charging pile and the charging gun port. This terminal can simultaneously collect high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile, thereby providing a data foundation for the subsequent generation of edge trigger vectors for charging events. Furthermore, this embodiment of the invention uses the high-frequency electrical timing signals and the environmental physical quantity signals to generate the edge event trigger vector of the charging pile. The purpose of this is to characterize the dynamic operating conditions of the charging site through feature fusion, enabling the charging pile to have a comprehensive perception capability for grid fluctuations, vehicle response, and environmental changes. The adaptive... When the control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile. This allows the cloud to monitor the dynamic operating conditions of the charging site in real time, ensuring that remote control commands are consistent with actual on-site needs. Furthermore, this embodiment of the invention generates differentiated power adjustment commands for each charging pile within the charging group based on the load redistribution coefficient to execute remote control of the charging pile. This solves the problem of rigid control strategies caused by the lack of dynamic perception of grid load, vehicle status, and environmental parameters in existing methods, achieving dynamic coordination and on-demand allocation of charging power at the group level. Therefore, this invention can achieve real-time matching between remote control commands for charging piles and dynamic operating conditions of the charging site. Attached Figure Description

[0042] Figure 1This is a flowchart illustrating a remote control method for an intelligent charging pile according to an embodiment of the present invention.

[0043] Figure 2 A system module diagram of a cloud-based control platform for implementing a remote control method for intelligent charging piles, provided in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram illustrating the principle of charging group load redistribution in implementing the intelligent charging pile remote control method according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of a module for implementing a remote control device for a smart charging pile according to an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of a computer device for a remote control method for an intelligent charging pile provided in an embodiment of the present invention;

[0047] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] This application provides a remote control method for smart charging piles. The executing entity of this remote control method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the remote control method for smart charging piles can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0050] S1. Deploy an edge intelligent acquisition terminal between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile.

[0051] This invention, through the deployment of an edge intelligent acquisition terminal between the charging pile and the charging gun port, can simultaneously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile, thereby providing a data foundation for the subsequent generation of edge trigger vectors for charging events.

[0052] In detail, the edge intelligent acquisition terminal is a hardware unit deployed on the electrical circuit between the charging pile and the charging gun port, used to synchronously acquire electrical and environmental parameters. It includes a high-frequency electrical sampling module, an environmental sensor array, and an edge processor. The high-frequency electrical sampling module is used to acquire electrical signals of the charging circuit. The environmental sensor array is used to acquire environmental signals such as temperature, humidity, and vibration around the charging pile, including at least one of temperature sensors, humidity sensors, water level sensors, and MEMS vibration sensors. The edge processor is used to extract features and process the acquired signals locally. The high-frequency electrical timing signal is an electrical parameter that changes over time, acquired at a sampling frequency higher than 50Hz of the power frequency, mainly including spectral energy characteristic signals and transient response characteristic signals. The environmental physical quantity signal is sensor data characterizing the physical state of the charging pile, including micro-meteorological characteristic signals and disturbance spectral characteristic signals.

[0053] It should be noted that the edge intelligent acquisition terminal is embedded in the cable sheath of the charging gun or integrated into the grip part of the charging gun head. The terminal communicates with the main control board of the charging pile through the fieldbus and connects to the cloud control platform through the wireless communication module.

[0054] S2. Using the high-frequency electrical timing signal and the environmental physical quantity signal, generate the edge event trigger vector of the charging pile, and set the adaptive control action of the charging pile at the current moment based on the edge event trigger vector.

[0055] The purpose of generating the edge event trigger vector of the charging pile by utilizing the high-frequency electrical timing signal and the environmental physical quantity signal in this embodiment of the invention is to characterize the dynamic working conditions of the charging site in a feature fusion manner, so that the charging pile has a comprehensive perception capability for power grid fluctuations, vehicle response and environmental changes. The edge event trigger vector is a multi-dimensional numerical vector obtained by feature extraction and fusion of the high-frequency electrical timing signal and the environmental physical quantity signal. Each dimension of the multi-dimensional numerical vector corresponds to a confidence probability of a preset event type. The preset event types include power grid fluctuation events, vehicle access events, environmental anomaly events and equipment failure events.

[0056] As an embodiment of the present invention, the edge event trigger vector of the charging pile is generated using the high-frequency electrical timing signal and the environmental physical quantity signal, including:

[0057] Extract the spectral energy characteristics and transient response characteristics of the high-frequency electrical timing signal, and analyze the micro-meteorological characteristics and disturbance spectral characteristics of the environmental physical quantity signal;

[0058] The spectral energy features, transient response features, micro-meteorological features, and disturbance spectral features are input into a pre-trained edge event classification model, and the edge event classification model outputs the edge event trigger vector of the charging pile at the current moment.

[0059] The spectral energy characteristics are used to reflect the energy distribution of each harmonic in the charging circuit; the transient response characteristics are used to reflect the rate of change and fluctuation amplitude of voltage or current during the transient process; the micro-meteorological characteristics are used to reflect the temperature, humidity and water accumulation around the charging pile; and the disturbance spectral characteristics are used to reflect the mechanical vibration frequency and impact intensity of the charging pile body.

[0060] Furthermore, the edge event classification model is pre-built in the following manner:

[0061] Initialize the parameters of the edge event classification model, including the number of network layers, node weights, and bias terms;

[0062] The sample electrical state characteristic information and sample on-site environmental characteristic information of the charging pile during the historical charging process are obtained, wherein the sample electrical state characteristic information is labeled with an event type tag;

[0063] The sample electrical state feature information and the sample site environment feature information are used as inputs, and the event type label is used as a supervision signal to train the parameters of the edge event classification model, so as to obtain the trained edge event classification model based on the trained parameters.

[0064] Optionally, the sample electrical state characteristic information and the sample site environment characteristic information can be obtained by feature extraction from historical operating data; the event type label can be manually labeled by experts based on the changing patterns of historical operating data.

[0065] Furthermore, the parameters for training the edge event classification model include:

[0066] In the forward propagation step, the sample electrical state feature information and the sample on-site environment feature information are input into the edge event classification model to calculate and predict the event type;

[0067] In the backpropagation step, the adjustment amount of each parameter in the edge event classification model is determined based on the loss function value between the predicted event type and the event type label;

[0068] Based on the adjustment amount, the parameters of the edge event classification model are updated.

[0069] In this embodiment, the edge event classification model adopts a multi-layer feedforward neural network structure. The number of nodes in the input layer is determined by the dimensions of the input electrical state feature information and the on-site environmental feature information, and the number of nodes in the output layer is determined by the preset total number of event types. The number of hidden layers and the number of nodes per layer can be adjusted according to the constraints of actual computing resources. For example, in edge scenarios with limited computing power, 2-3 hidden layers can be used, with 16-64 nodes per layer; in scenarios with sufficient computing power, deeper networks can be used to improve classification accuracy. The loss function value is used to measure the difference between the predicted event type and the actual event type label of the edge event classification model. Specifically, the loss function value is calculated using the cross-entropy loss function, and its calculation formula is: ,in, Indicates the number of training samples. The number of categories representing event types. Indicates the first The sample belongs to the first Real labels for event types The model predicts the first The sample belongs to the first The probability of an event type, where i represents the training sample index and j represents the event type index.

[0070] It should be noted that the iterative training of the edge event classification model should be repeated based on the above forward propagation and backward propagation steps until the preset training termination condition is met. The training termination condition includes at least one of the following: the loss function value is less than the preset convergence threshold, the preset maximum number of iterations is reached, or the classification accuracy on the validation set no longer improves.

[0071] This invention, through its embodiment, sets the adaptive control action of the charging pile at the current moment based on the edge event trigger vector. This enables the charging pile to autonomously perform emergency protection when it senses a safety risk, and to dynamically adjust its power output when it detects fluctuations in grid load. This solves the problem of spatiotemporal mismatch between remote control commands and the actual working conditions at the charging site.

[0072] In detail, the adaptive control action is a control action corresponding to the local control command generated at the current moment by a preset local decision engine based on the edge event trigger vector. The local decision engine is deployed on the edge processor of the edge intelligent acquisition terminal and is implemented using a lightweight rule engine. Its core function is to quickly match the preset control strategy according to the input edge event trigger vector and generate an adaptive control action. The adaptive control action includes a flexible power adjustment action and an emergency shutdown protection action. The flexible power adjustment action is used to dynamically adjust the output power of the charging pile according to the grid load fluctuation or vehicle charging demand. The emergency shutdown protection action is used to immediately cut off the charging circuit when a safety risk is detected.

[0073] As an embodiment of the present invention, based on the edge event trigger vector, the adaptive control action of the charging pile at the current moment is set, including:

[0074] The edge event trigger vector is input into a preset local decision engine;

[0075] The local decision engine obtains the control action identifier corresponding to the edge event trigger vector from a preset control action mapping table based on the edge event trigger vector.

[0076] Based on the control action identifier, an adaptive control action for the charging pile at the current moment is generated.

[0077] Optionally, the process of constructing the control action mapping table is as follows: obtaining multiple event type samples that occurred during the charging process of the charging pile in history and the standard control actions corresponding to each event type sample; establishing the association between the event type samples and the standard control actions; and generating the control action mapping table based on the association.

[0078] It should be noted that the association relationship is a mapping rule between event types and standard control actions, including one-to-one correspondence and one-to-many relationships. For one-to-many relationships, the execution order of different control actions can be further determined according to preset priority rules. The priority rules are set according to the safety level. Among them, the priority of emergency shutdown protection actions is higher than that of flexible power adjustment actions, and the priority of power adjustment actions with larger derating is higher than that of power adjustment actions with smaller derating.

[0079] S3. When the adaptive control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile.

[0080] In this embodiment of the invention, when the adaptive control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile. This allows the cloud to monitor the dynamic operating conditions of the charging site in real time, ensuring that remote control commands are consistent with the actual needs on site. The high-priority control action is a protective control action triggered by the edge intelligent acquisition terminal and required to be executed immediately when an emergency event occurs at the charging site that may endanger personal safety, equipment safety, or power grid stability. This includes emergency shutdown protection actions, rigid derating protection actions, and physical isolation protection actions.

[0081] In detail, the cloud-based control platform is deployed in a remote data center and serves as a central control system for centralized monitoring, global strategy optimization, and remote command issuance of at least one charging pile and its associated charging group. Figure 2 The diagram shown illustrates a system module diagram of a cloud-based control platform for implementing a remote control method for intelligent charging piles according to an embodiment of the present invention. The cloud-based control platform includes the following functional modules: a data integration module for receiving operational data, edge event trigger vectors, and adaptive control action execution results uploaded by each charging pile; a strategy optimization module for analyzing the overall load status of the charging group, grid constraints, and user needs based on aggregated multi-source data, and generating a global collaborative control strategy; an instruction issuance module for converting the generated collaborative control strategy into specific remote control instructions and issuing them to the edge intelligent acquisition terminals of each charging pile; and a model update module for iteratively optimizing the edge event classification model or control action mapping table based on the uploaded edge event trigger vectors, and synchronizing the updated model parameters to the edge.

[0082] S4. The cloud control platform determines the load redistribution coefficient of each charging pile in the charging group where the charging pile is located based on the edge event trigger vector, and generates differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

[0083] According to the embodiments of the present invention, the cloud control platform determines the load redistribution coefficient of each charging pile in the charging group where the charging pile is located based on the edge event trigger vector. This can provide the cloud remote control platform with the basis for power allocation of each charging pile in the group, so as to realize dynamic collaborative scheduling of charging resources.

[0084] In detail, the charging group to which the charging pile belongs is a collection of multiple charging piles that are geographically adjacent and managed by the same cloud control platform, all within the power supply range of the same transformer area. Each charging pile in the charging group shares the available capacity of the transformer and has a coordinated scheduling relationship in power allocation. The load redistribution coefficient is a value used to characterize the allocation ratio of the released capacity of the remaining charging piles in the group when a charging pile in the group releases power capacity due to triggering a high-priority control action. The sum of the load redistribution coefficients of each charging pile is 1, and the load redistribution coefficient of the charging pile that triggers the high-priority control action is 0.

[0085] As an embodiment of the present invention, the cloud control platform determines the load redistribution coefficient of each charging pile within the charging group to which the charging pile is located based on the edge event trigger vector, including:

[0086] Extract the on-site operating condition features of the charging pile from the edge event trigger vector;

[0087] Based on the characteristics of the on-site working conditions, the high-priority control actions are verified to obtain the target control reliability of the charging pile.

[0088] Based on the target regulation reliability, the load redistribution coefficient of each charging pile in the charging group to which the charging pile is located is calculated.

[0089] The on-site operating condition characteristics include electrical anomaly level and environmental risk level. The electrical anomaly level represents the degree of electrical disturbance in the charging circuit, and the environmental risk level represents the degree of physical safety risk around the charging pile. The target control credibility refers to the target control credibility of the charging pile that triggers high-priority control actions, which is used to characterize the degree of matching between the high-priority control actions and the actual on-site operating conditions.

[0090] Optionally, a threshold-based method can be used to extract the on-site operating condition features of the charging pile from the edge event trigger vector. Specifically, this includes: inputting the edge event trigger vector into a preset feature mapping table, which has multiple feature dimensions and corresponding judgment thresholds for each dimension; comparing the feature values ​​of each dimension in the edge event trigger vector with their corresponding judgment thresholds; when the feature value of a certain dimension exceeds its corresponding judgment threshold, extracting the operating condition feature label corresponding to that dimension as the on-site operating condition feature; when the feature values ​​of multiple dimensions simultaneously exceed their respective thresholds, extracting the combination of the operating condition feature labels corresponding to each dimension as the on-site operating condition feature.

[0091] Furthermore, based on the aforementioned on-site operating conditions, the high-priority control actions are verified to obtain the target control reliability of the charging pile, including:

[0092] Based on the preset working condition-command association, the standard control command corresponding to the on-site working condition characteristics is determined as the target standard command of the charging pile;

[0093] The high-priority control action is compared with the target standard instruction to obtain the comparison result;

[0094] Based on the comparison results, the reliability of the target regulation of the charging pile is determined.

[0095] It should be noted that the working condition-command association is pre-stored in the form of a mapping table on the cloud control platform or the local edge intelligent acquisition terminal. The mapping table includes multiple records, each containing a working condition feature identifier field and a corresponding standard command field. The working condition feature identifier field uniquely identifies a specific on-site working condition feature, and the standard command field indicates the standard control command that should theoretically be executed under that working condition feature. The comparison result uses a binary classification result, i.e., "consistent" or "inconsistent." When the high-priority control action completely matches the type and parameters of the target standard command, the comparison result is determined to be "consistent"; otherwise, the comparison result is determined to be "inconsistent."

[0096] For example, the operating condition-command association can be set as follows: when the on-site operating condition is an electrical disturbance, the corresponding standard control command is a flexible derating protection action; when the on-site operating condition is a transient impact, the corresponding standard control command is a rigid derating protection action; when the on-site operating condition is a mechanical impact, the corresponding standard control command is an emergency shutdown protection action; and when the on-site operating condition is environmental water accumulation, the corresponding standard control command is a physical isolation protection action.

[0097] In another embodiment of the present invention, the load redistribution coefficient of each charging pile within the charging group to which the charging pile is located is calculated by the following formula:

[0098]

[0099] in, Let represent the load redistribution coefficient of the i-th charging pile. This represents the priority weight of the i-th charging pile. This represents the priority weight of the j-th charging pile within the charging group, excluding those that triggered high-priority control actions. N represents the total number of charging piles within the charging group, excluding those that triggered high-priority control actions. Represents the total distribution coefficient of the group. This indicates the credibility of the target regulation.

[0100] It should be noted that the above formula allows the subsequently generated differentiated power adjustment commands to respond to actual on-site needs while being constrained by the grid's carrying capacity, thus balancing fairness and priority among charging piles within the group. Specifically: the priority weight of the charging piles... By integrating multi-source information such as vehicle remaining charging time, battery state of charge, and equipment historical reliability, the urgency of different charging needs can be quantitatively characterized; target control reliability. By verifying the true nature of events reported at the edge, it is ensured that only trusted events can trigger power redistribution; the group total allocation coefficient γ introduces transformer capacity constraints to limit power allocation behavior within the boundaries of safe grid operation.

[0101] In detail, the priority weight of the charging pile The total allocation coefficient of the group can be determined based on at least one of the following: the estimated remaining charging time of the vehicle corresponding to each charging pile, the battery state of charge, and the historical reliability score of each charging pile; This is a quantitative representation of the capacity constraint of the transformer in the area where the charging group is located, with a value range of [0,1]. Optionally, the specific process for obtaining the total allocation coefficient of the group is as follows: real-time acquisition of the current total load and rated capacity of the transformer in the area where the charging group is located to calculate the idle capacity corresponding to the charging group; synchronous acquisition of the rated power of the charging pile that triggers high-priority actions; and calculation of the total allocation coefficient of the group based on the idle capacity and the rated power. The total allocation coefficient of the group is calculated using the following formula: ,in, The idle capacity is represented by the difference between the current total load and the rated capacity of the transformer in the area where the charging group is located. This indicates the rated power of the charging station that triggered the high-priority action, which can be read from the charging station's equipment file.

[0102] Furthermore, this embodiment of the invention generates differentiated power adjustment commands for each charging pile within the charging group based on the load redistribution coefficient, thereby executing remote control of the charging piles. This solves the problem of rigid control strategies caused by the lack of dynamic perception of grid load, vehicle status, and environmental parameters in existing methods, and realizes dynamic coordination and on-demand allocation of charging power at the group level, enabling remote control commands to match the dynamic operating conditions of the charging site in real time.

[0103] In detail, the differentiated power adjustment instruction refers to the set of personalized power adjustment instructions generated by the cloud control platform for each charging pile in the charging group based on the load redistribution coefficient. This set includes the target output power, power adjustment rate and adjustment priority of each charging pile in the next control cycle.

[0104] As an embodiment of the present invention, based on the load redistribution coefficient, a differentiated power adjustment command is generated for each charging pile within the charging group, including:

[0105] The real-time operating status data of each charging pile in the charging group is obtained. The real-time operating status data includes the current output power, power change rate and battery state of charge of each charging pile.

[0106] Based on the real-time operating status data and the load redistribution coefficient, create a power adjustment parameter table for each charging pile in the charging group;

[0107] Based on the power adjustment parameter table, differentiated power adjustment instructions are generated for each charging pile in the charging group.

[0108] The current output power is the actual active power output of the charging pile at the current moment, which is obtained by the edge intelligent acquisition terminal through real-time sampling. The power change rate refers to the change in the output power of the charging pile within a unit of time, which is obtained by the edge intelligent acquisition terminal through differential calculation of the continuously sampled power values. The battery state of charge refers to the percentage value of the current remaining power of the electric vehicle's power battery relative to its rated capacity, with a value range of 0% to 100%, which is read in real-time by the charging pile from the vehicle battery management system through the vehicle communication protocol. The power adjustment parameter table contains a set of data on the parameters required for each charging pile in the charging group to be executed in the next control cycle, including the target output power, power adjustment rate, adjustment execution order, and adjustment time window of each charging pile.

[0109] Furthermore, based on the real-time operating status data and the load redistribution coefficient, a power adjustment parameter table corresponding to each charging pile within the charging group is created, including:

[0110] Calculate the target output power of each charging pile based on the load redistribution coefficient and the current output power;

[0111] The power adjustment rate of each charging pile is determined based on the difference between the target output power and the current output power.

[0112] Based on the battery state of charge, determine the adjustment execution sequence for each charging station;

[0113] The current total load and rated capacity of the transformer in the area where the charging group is located are obtained in real time, so as to calculate the real-time idle capacity of the transformer in the area.

[0114] Based on the real-time idle capacity, determine the adjustment time window for each charging pile;

[0115] By integrating the target output power, the power adjustment rate, the adjustment execution sequence, and the adjustment time window, a power adjustment parameter table corresponding to each charging pile in the charging group is obtained.

[0116] Wherein, the target output power refers to the output power value that the charging pile needs to achieve in the next control cycle; the power adjustment rate refers to the power change per unit time during the process of adjusting the charging pile from the current output power to the target output power; the current total load of the transformer in the distribution area refers to the total active power carried by the transformer in the distribution area where the charging group is located at the current moment, which is collected in real time by the monitoring terminal deployed on the transformer side; the rated capacity of the transformer in the distribution area refers to the maximum active power that the transformer in the distribution area where the charging group is located can withstand during long-term stable operation, which is the inherent nameplate parameter of the transformer; the real-time idle capacity refers to the remaining available active power capacity of the transformer in the distribution area at the current moment, which is calculated by subtracting the current total load from the rated capacity, and is used to constrain the total power adjustment of the charging group.

[0117] Optionally, the target output power of each charging station is calculated using the following formula: ,in, This represents the target output power of the i-th charging pile. This represents the current output power of the i-th charging pile. This represents the power adjustment increment of the i-th charging pile. The process for obtaining the power adjustment increment is as follows: Based on the power released by the charging piles that trigger high-priority control actions within the charging group and the real-time idle capacity of the transformer in the distribution area, determine the total allocatable power of the charging group; calculate the power adjustment increment of each charging pile based on the load redistribution coefficient and the total allocatable power. The total allocatable power of the charging group is calculated using the following formula: , ,in, This indicates the total allocatable power of the charging group. This indicates the power released by the charging pile that triggered the high-priority control action. This indicates the real-time idle capacity of the transformer in the aforementioned distribution area. This represents the load redistribution coefficient of the i-th charging pile.

[0118] like Figure 3 The diagram illustrates the principle of load redistribution in a charging group according to an embodiment of the intelligent charging pile remote control method of the present invention. The left side represents the initial state before triggering the high-priority control action, and the right side represents the target state after adjustment by the load redistribution coefficient. Specifically, when charging pile 1 in charging group 1 triggers the high-priority control action, the released power capacity... Based on the load redistribution coefficient of each pile The power is then redistributed to the remaining charging stations within the group. Taking station 2 as an example, its target output power is determined by its current output power. Adjusted to ,in This enables dynamic and coordinated scheduling of charging resources at the group level.

[0119] It should be noted that the remote control described in this embodiment of the invention is a process in which the cloud control platform generates and issues differentiated power adjustment commands to each charging pile in the charging group based on the high-priority control actions uploaded from the edge and their corresponding edge event trigger vectors, through credibility verification and load redistribution coefficient calculation. This process takes the edge event trigger vector as input and the differentiated power adjustment command as output, forming a complete control closed loop of "edge perception - cloud decision-making - group execution" to achieve real-time matching between remote control commands and dynamic working conditions at the charging site.

[0120] like Figure 4 The diagram shown is a functional block diagram of a remote control device for an intelligent charging pile according to the present invention.

[0121] The intelligent charging pile remote control device 400 described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent charging pile remote control device includes a signal acquisition module 401, an event processing module 402, an edge communication module 403, and a cloud control module 404. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and is stored in the memory of the electronic device.

[0122] In this embodiment of the invention, the functions of each module / unit are as follows:

[0123] The signal acquisition module 401 is used to deploy an edge intelligent acquisition terminal between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile.

[0124] The event processing module 402 is used to generate an edge event trigger vector for the charging pile using the high-frequency electrical timing signal and the environmental physical quantity signal, and to set the adaptive control action of the charging pile at the current moment based on the edge event trigger vector.

[0125] The edge communication module 403 is used to upload the edge event trigger vector corresponding to the high-priority control action to the cloud control platform of the charging pile when the adaptive control action is a preset high-priority control action.

[0126] The cloud control module 404 is used by the cloud control platform to determine the load redistribution coefficient of each charging pile in the charging group where the charging pile is located according to the edge event trigger vector, and to generate differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

[0127] In detail, the modules in the intelligent charging pile remote control device 400 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method used is the same as the one described in the article for remote control of smart charging piles, and can produce the same technical effect, so it will not be repeated here.

[0128] In one embodiment, a computer device is provided, which may be a server or a client, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a remote control method for smart charging piles on the server or client side.

[0129] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0130] S1. Deploy an edge intelligent acquisition terminal between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile.

[0131] S2. Using the high-frequency electrical timing signal and the environmental physical quantity signal, generate the edge event trigger vector of the charging pile, and set the adaptive control action of the charging pile at the current moment based on the edge event trigger vector;

[0132] S3. When the adaptive control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile.

[0133] S4. The cloud control platform determines the load redistribution coefficient of each charging pile in the charging group where the charging pile is located based on the edge event trigger vector, and generates differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0135] S1. Deploy an edge intelligent acquisition terminal between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile.

[0136] S2. Using the high-frequency electrical timing signal and the environmental physical quantity signal, generate the edge event trigger vector of the charging pile, and set the adaptive control action of the charging pile at the current moment based on the edge event trigger vector;

[0137] S3. When the adaptive control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile.

[0138] S4. The cloud control platform determines the load redistribution coefficient of each charging pile in the charging group where the charging pile is located based on the edge event trigger vector, and generates differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

[0139] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0140] 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 computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program 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 may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of 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.

[0141] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0142] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0143] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for remotely controlling an intelligent charging pile, characterized in that, The method includes: An edge intelligent acquisition terminal is deployed between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile. Using the high-frequency electrical timing signal and the environmental physical quantity signal, an edge event trigger vector for the charging pile is generated. Based on the edge event trigger vector, an adaptive control action corresponding to the charging pile at the current moment is set. When the adaptive control action is a preset high-priority control action, the edge event trigger vector corresponding to the high-priority control action is uploaded to the cloud control platform of the charging pile; The cloud-based control platform determines the load redistribution coefficient of each charging pile in the charging group based on the edge event trigger vector, and generates differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

2. The intelligent charging pile remote control method of claim 1, wherein, Using the high-frequency electrical timing signal and the environmental physical quantity signal, an edge event trigger vector for the charging pile is generated, including: Extract the spectral energy characteristics and transient response characteristics of the high-frequency electrical timing signal, and analyze the micro-meteorological characteristics and disturbance spectral characteristics of the environmental physical quantity signal; The spectral energy features, transient response features, micro-meteorological features, and disturbance spectral features are input into a pre-trained edge event classification model, and the edge event classification model outputs the edge event trigger vector of the charging pile at the current moment. 3.The intelligent charging pile remote control method of claim 2, wherein, The edge event classification model is pre-built in the following manner: Initialize the parameters of the edge event classification model, including the number of network layers, node weights, and bias terms; The sample electrical state characteristic information and sample on-site environmental characteristic information of the charging pile during the historical charging process are obtained, wherein the sample electrical state characteristic information is labeled with an event type tag; The sample electrical state feature information and the sample site environment feature information are used as inputs, and the event type label is used as a supervision signal to train the parameters of the edge event classification model, so as to obtain the trained edge event classification model based on the trained parameters.

4. The intelligent charging pile remote control method of claim 3, wherein, The parameters for training the edge event classification model include: In the forward propagation step, the sample electrical state feature information and the sample on-site environment feature information are input into the edge event classification model to calculate and predict the event type; In the backpropagation step, the adjustment amount of each parameter in the edge event classification model is determined based on the loss function value between the predicted event type and the event type label; Based on the adjustment amount, the parameters of the edge event classification model are updated.

5. The intelligent charging pile remote control method of claim 1, wherein, The cloud-based control platform determines the load redistribution coefficients for each charging pile within the charging group based on the edge event trigger vector, including: Extract the on-site operating condition features of the charging pile from the edge event trigger vector; Based on the characteristics of the on-site working conditions, the high-priority control actions are verified to obtain the target control reliability of the charging pile. Based on the target regulation reliability, the load redistribution coefficient of each charging pile in the charging group to which the charging pile is located is calculated.

6. The intelligent charging pile remote control method of claim 5, wherein, Based on the characteristics of the on-site working conditions, the high-priority control actions are verified to obtain the target control reliability of the charging pile, including: Based on the preset working condition-command association, the standard control command corresponding to the on-site working condition characteristics is determined as the target standard command of the charging pile; The high-priority control action is compared with the target standard instruction to obtain the comparison result; Based on the comparison results, the reliability of the target regulation of the charging pile is determined.

7. The intelligent charging pile remote control method of claim 1, wherein, Based on the load redistribution coefficient, differentiated power adjustment instructions are generated for each charging pile within the charging group, including: The real-time operating status data of each charging pile in the charging group is obtained. The real-time operating status data includes the current output power, power change rate and battery state of charge of each charging pile. Based on the real-time operating status data and the load redistribution coefficient, create a power adjustment parameter table for each charging pile in the charging group; Based on the power adjustment parameter table, differentiated power adjustment instructions are generated for each charging pile in the charging group. 8.The intelligent charging pile remote control method of claim 7, wherein, The power adjustment parameter table contains a set of data on the parameters required for each charging pile in the charging group to be executed in the next control cycle, including the target output power, power adjustment rate, adjustment execution order and adjustment time window of each charging pile. 9.The intelligent charging pile remote control method of claim 1, wherein, The edge intelligent acquisition terminal is a hardware unit deployed on the electrical circuit between the charging pile and the charging gun port, used to synchronously acquire electrical parameters and environmental parameters. It includes a high-frequency electrical sampling module, an environmental sensor array, and an edge processor.

10. A method for remotely controlling an intelligent charging pile, characterized in that, The device includes: The signal acquisition module is used to deploy an edge intelligent acquisition terminal between the charging pile and the charging gun port. The edge intelligent acquisition terminal is used to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile. The event processing module is used to generate an edge event trigger vector for the charging pile using the high-frequency electrical timing signal and the environmental physical quantity signal, and to set the adaptive control action of the charging pile at the current moment based on the edge event trigger vector. An edge communication module is used to upload the edge event trigger vector corresponding to the high-priority control action to the cloud control platform of the charging pile when the adaptive control action is a preset high-priority control action. The cloud control module is used by the cloud control platform to determine the load redistribution coefficient of each charging pile in the charging group where the charging pile is located based on the edge event trigger vector, and to generate differentiated power adjustment commands for each charging pile in the charging group based on the load redistribution coefficient, so as to execute the remote control of the charging pile.

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