Charging pile regulation and control system based on digital twinning

Abstract

The invention belongs to the technical field of digital twinning, and discloses a charging pile regulation and control system based on digital twinning. Comprising a data acquisition module used for acquiring charging pile state data, vehicle information data, environment data and safety monitoring data; the data processing module is used for carrying out preprocessing and correlation fusion on the collected charging pile state data, the vehicle information data, the environment data and the safety monitoring data to obtain a comprehensive feature data set; the digital twinning module is used for constructing a digital twinning model, inputting the comprehensive feature data set into a virtual model, simulating the operation process of the charging pile, and predicting to obtain operation data of the charging pile; the state monitoring module is used for constructing a state monitoring model and importing the predicted operation data of the charging pile into the trained state monitoring model to obtain the safety state of the charging pile; the operation condition of the charging pile can be effectively predicted, so that potential problems are recognized in advance, and system faults are avoided.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and more specifically, the present invention relates to a charging pile control system based on digital twin. Background Art

[0002] As the world's awareness of environmental protection grows, reducing carbon emissions and promoting green travel have become a global consensus. As a representative of low-carbon travel, the popularity of electric vehicles has become a general trend. In order to cope with environmental challenges and energy transformation, governments have introduced policies to support the development of the electric vehicle industry. The rapid growth in the number of electric vehicles has put forward higher requirements for charging infrastructure, which has promoted the research and development and application of charging pile control systems.

[0003] The patent with the patent publication number CN118082585A discloses a charging pile safety charging control system and method, which provides a charging pile safety charging control system and method that can eliminate potential safety hazards in a timely manner, ensure the safety of vehicle charging, make safety measures more reasonable, and provide more humane operation services, and can reduce the probability of station accidents. The charging pile safety charging control system includes an Internet of Things communication gateway, a data preprocessing unit, a charging safety feature model, a risk assessment engine, a strategy distributor, and a scheduling control unit. The present invention uses a charging safety control system to collect and analyze risks in real time based on data from multiple aspects such as vehicles, piles, and batteries, and implements different classifications and strategy modes based on the control measures generated by the evaluation, implements differentiated regulation and control for different risk levels, and outputs through comprehensive charging strategy control and health reports. The present invention is applied to the technical field of safe charging of charging piles.

[0004] The existing charging pile control system mainly has the following problems:

[0005] Without considering the dynamic adjustment of feature weights, the prediction results of the model may not be accurate enough because they fail to reflect the changing environment and status of the charging pile during operation; low-precision predictions may cause managers to rely on wrong information when making decisions, thus affecting operational efficiency and safety; failure to dynamically monitor and adjust feature weights may lead to excessive maintenance or unnecessary downtime, increasing operating costs, affecting the overall economic benefits and challenging the sustainability of charging pile operations; if existing technologies do not adopt dynamic monitoring and intelligent weight adjustment, they may not be able to keep up with the trend of intelligent development, which will cause technology to lag behind market demand and affect innovation and progress in the industry;

[0006] Failure to effectively analyze and manage the thermal effects of charging piles may cause the equipment to operate for a long time in a high temperature environment; resulting in poor heat dissipation of the charging pile, increasing the risk of failure, and in serious cases, may cause fire or explosion; failure to use the thermal conductivity limit formula to control heat conduction may cause the charging pile to overheat; overheating will not only shorten the service life of the equipment, but may also cause permanent damage to the equipment, resulting in expensive repair or replacement costs; failure to consider the impact of thermal conductivity on temperature, the safe operation of the charging pile cannot be guaranteed; increased safety risks of the equipment, especially in high load or extreme environments, may pose a threat to users and the surrounding environment;

[0007] Failure to monitor and evaluate the operating status of charging piles in real time may lead to the neglect of potential risks; potential faults or abnormal conditions may continue to develop without being discovered in time, ultimately leading to accidents; there is no dynamically adjusted risk assessment model that cannot adapt to environmental changes; the operating safety of charging piles under different environmental conditions may not be guaranteed, resulting in excessive risks in some cases; lack of effective risk monitoring and management, potential risks are not discovered in time, which may lead to accidents and increased maintenance costs.

[0008] In view of this, the present invention proposes a charging pile control system based on digital twin to solve the above problems. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a charging pile control system based on digital twin, comprising:

[0010] Data acquisition module, used to collect charging pile status data, vehicle information data, environmental data and safety monitoring data;

[0011] The data processing module is used to pre-process and correlate the collected charging pile status data, vehicle information data, environmental data and safety monitoring data to obtain a comprehensive feature data set;

[0012] The digital twin module is used to build a digital twin model, input the comprehensive feature data set into the virtual model, simulate the operation process of the charging pile, and predict the operation data of the charging pile;

[0013] The status monitoring module is used to build a status monitoring model, import the predicted charging pile operation data into the trained status monitoring model, and obtain the safety status of the charging pile;

[0014] A status assessment module is used to compare the predicted safety status of the charging pile with a preset safety status threshold of the charging pile to assess whether the safety status of the charging pile meets the standard;

[0015] The intelligent scheduling module is used to optimize the scheduling and usage strategy of charging piles; each module is connected via wired and / or wireless means.

[0016] Furthermore, the charging pile status data includes operation data and basic information data; the operation data includes the power, current, voltage, timestamp, charging duration, utilization frequency and usage frequency of the charging pile generated during the operation of the charging pile; the basic information data includes the physical characteristics, satellite positioning, design drawings, service life and charging type of the charging pile;

[0017] Vehicle information data includes vehicle type data and battery data; battery data includes battery type, rated capacity, health status, charge status and battery temperature;

[0018] Environmental data include ambient temperature, ambient humidity, ambient brightness and traffic flow;

[0019] Safety monitoring data includes smoke monitoring data, fire monitoring data, temperature monitoring data, electrical monitoring data, insulation resistance detection data, short circuit monitoring data and overload protection data.

[0020] Furthermore, the method for preprocessing the collected charging pile status data, vehicle information data, environmental data and safety monitoring data includes:

[0021] Use clustering algorithm to detect outliers in charging pile status data, vehicle information data, environmental data and safety monitoring data, and remove the identified outliers to obtain charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set;

[0022] The acquired charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set are standardized and normalized, and converted into a standard normal distribution form unified according to timestamps to obtain the final charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set.

[0023] Furthermore, the method for obtaining the comprehensive feature data set includes:

[0024] Calculate the Jaccard similarity between different features in the charging pile status dataset, vehicle information dataset, environment dataset, and safety monitoring dataset and the charging pile operation data, and assign weights to the features in each dataset through the Jaccard similarity;

[0025] After each feature is processed by standardization and weight assignment, a quantized charging pile status data set, a vehicle information data set, an environmental data set, and a safety monitoring data set are formed, and a comprehensive feature data set is formed through weighted model fusion; the charging pile status data set is recorded as D1, the vehicle information data set is recorded as D2, the environmental data set is recorded as D3, and the safety monitoring data set is recorded as D4;

[0026] The weighted model is: YR=D1·ω1+D2·ω2+D3·ω3+D4·ω4; where YR is the comprehensive feature data set; ω1 is the weight coefficient of the charging pile status data set; ω2 is the weight coefficient of the vehicle information data set; ω3 is the weight coefficient of the environmental data set; and ω4 is the weight coefficient of the safety monitoring data set.

[0027] Furthermore, the method of assigning weights to features in each data set by using Jaccard similarity includes:

[0028] Define a time window W. At the end of each time window, collect the data from the latest charging pile status data set, vehicle information data set, environmental data set, and safety monitoring data set within the window. For each feature in the data, at the end of the time window W, use the latest data set to calculate the Jaccard similarity. Let the value set of the charging pile operation data be C and the feature data be F. g , the Jaccard similarity calculation formula is: Among them, f g is the value set of the feature; J is the similarity; |f g ∩C| is the value set f of the feature g The size of the intersection with the value set C of the charging pile operation data; |f g ∪C| is the value set f of the feature g The size of the union of the value set C of the charging pile operation data;

[0029] According to the calculated Jaccar similarity, a weight factor σ is assigned to each feature in the charging pile status dataset, vehicle information dataset, environment dataset, and safety monitoring dataset. h , the model is adjusted by adjusting the weight factor of each feature;

[0030] The weight factor adjustment model is: Among them, σ′ h is the adjusted weight factor; r h is the adjustment coefficient for adjusting the hth weight factor; is the accumulation of the h-th feature at time t′; is the accumulation of all features at time t′; K is the type of feature; T′ is the number of time windows; t′ is the index of time; h is the index of weight factor.

[0031] Furthermore, the method for constructing the digital twin model includes:

[0032] S61, physical structure module; use CAD software to build a 3D physical model of the charging pile; the 3D physical model is built based on the basic information data of the charging pile to reflect the physical structure of the charging pile; differential equations are used to simulate the electrical behavior of the charging pile, and the mathematical expression is: Where, I(t) is the charging current, V(t) is the charging voltage, and R(t) is the resistance;

[0033] The finite element method is used to simulate the thermal behavior of the charging pile, and the heat conduction equation is used to model it. The mathematical expression is: Where T is the temperature distribution, ρ is the density, c p is the specific heat capacity, t is the time, k is the thermal conductivity, Q is the heat source; ▽ is the vector differential operator; is the differential symbol;

[0034] The thermal conductivity k in the heat conduction equation is limited by the thermal conductivity limitation formula, which is: Wherein, k(t) is the thermal conductivity after limitation; T(t) is the temperature distribution function that changes with time; α is the influence coefficient of temperature on thermal conductivity; β is the influence coefficient of charging time on thermal conductivity;

[0035] S62, operation mechanism module; the electromagnetic induction law is used to construct the power equation, thereby describing the power transmission process; the mathematical formula of the power equation is specifically: Where V is voltage, L is inductance, I is current, R is resistance, dI is current increment, and dt is time increment;

[0036] S63, dynamic response module; construct a dynamic response model to accurately model the input-output relationship and state change of the charging pile, thereby ensuring that the dynamic behavior of the charging pile under different conditions is consistent with reality; use the state space equation to construct the dynamic response model, the specific mathematical formula is: Where x(t) is the state vector, u(t) is the input vector, y(t) is the output vector, A, B, C and D are the parameters of the state space model.

[0037] S64, data driven module; the data driven module is composed of a status monitoring model, which predicts the operating status of the charging pile.

[0038] Furthermore, the training method of the condition monitoring model includes:

[0039] The data set is divided into a training set and a test set to build a state monitoring model; the sample set is a subset of the data set, and each sample set includes historical charging pile operation data and the corresponding safety status of the charging pile; the input data of the model is the historical charging pile operation data; the output label is the safety status of the charging pile; the state monitoring model is a random forest regressor model;

[0040] Initialize the condition monitoring model and set the number and depth of trees as hyperparameters; use the training set to train the condition monitoring model, use the k-fold cross-validation method to adjust the model's hyperparameters, and tune the parameters of the initial settings; use the test set data to evaluate the performance of the model, and use the determination coefficient to evaluate the difference between the calculated prediction results and the true label;

[0041] According to the model performance feedback, the number of trees and the depth hyperparameters are adjusted to tune the model. The model is retrained using the adjusted hyperparameters. When the training reaches the preset model complexity, it is stopped to obtain the final trained state monitoring model. The trained state monitoring model is used to predict the current charging pile operation data to predict the safety status of the charging pile.

[0042] Furthermore, the method of adjusting the number of trees and the depth hyperparameters to tune the model includes:

[0043] The number and depth hyperparameters of the trees are adjusted through the genetic algorithm. During the iteration of the genetic algorithm, the number and depth of the trees corresponding to the chromosome with the highest fitness are recorded as the optimal hyperparameter combination;

[0044] For the number and depth hyperparameters of trees, randomly preset the minimum number threshold and the minimum depth threshold; at the same time, randomly preset the maximum number and depth thresholds; gradually increase or decrease the thresholds until the optimal threshold is found, judge the performance of the model on the test set, and obtain the optimal number and depth hyperparameters of trees.

[0045] Furthermore, the method of comparing the predicted safety status of the charging pile with a preset safety status threshold of the charging pile to evaluate whether the safety status of the charging pile meets the standard includes:

[0046] If the predicted safety status of the charging pile is consistent with the preset safety status threshold of the charging pile, it is determined that the safety status of the charging pile meets the standard;

[0047] If the predicted safety status of the charging pile does not match the preset safety status threshold of the charging pile, it is determined that the safety status of the charging pile does not meet the standard.

[0048] Furthermore, the method for optimizing the scheduling and use strategy of charging piles includes:

[0049] If the safety status of the charging pile meets the standard, the charging pile intelligent control terminal generates a safety instruction to indicate that the charging pile is in normal status;

[0050] If the safety status of the charging pile does not meet the standard, the charging pile intelligent control terminal will generate a danger command, automatically generate warning information, and collect parameter data of the charging pile that does not meet the standard;

[0051] The non-standard parameter data include the power generated during the operation of the charging pile, the non-standard current, the non-standard voltage, the charging pile operation time and the charging pile temperature itself;

[0052] A charging pile status risk assessment model is constructed based on the substandard parameter data. The charging pile status risk is assessed through the charging pile status risk assessment model to obtain the charging pile status hazard coefficient; the charging pile risk assessment model is: Among them, RE is the dangerous coefficient of the charging pile state; P is the power generated during the operation of the charging pile; τ is the substandard current; V' is the substandard voltage; T sf is the charging pile operation time; T run is the temperature of the charging pile itself; δ1 is the power weight factor generated during the operation of the charging pile; δ2 is the substandard current weight factor; δ3 is the substandard voltage weight factor; δ4 is the charging pile operation time weight factor; δ5 is the charging pile temperature weight factor.

[0053] The technical effects and advantages of the charging pile control system based on digital twins of the present invention are as follows:

[0054] The present invention can realize dynamic monitoring of the operation status of the charging pile by collecting the latest charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set at the end of each time window, and timely reflect the actual situation of the system; calculate the Jaccard similarity of each feature and assign weight factors to the features based on the similarity, and flexibly adjust the feature weights according to the actual operation conditions, thereby enhancing the model's adaptability to changing environments;

[0055] The finite element method is used to model the thermal behavior of the charging pile through the heat conduction equation to ensure that the thermal effect in actual operation is effectively analyzed and managed; the effectiveness of heat conduction is controlled by the thermal conductivity limit formula to avoid equipment damage caused by overheating; a dynamic response model is constructed, and the state space equation is used to accurately model the input-output relationship and state change of the charging pile; the dynamic behavior of the charging pile under different conditions is ensured to be consistent with reality, and the stability and reliability of the system are improved; by limiting the thermal conductivity, the temperature of the charging pile during operation can be effectively controlled to prevent overheating and ensure that the charging pile works within a safe range;

[0056] The charging pile status risk assessment model converts different parameters into a quantified risk factor, making the status risk of the charging pile easier to understand and manage. Real-time monitoring and evaluation of the operating status of the charging pile can promptly identify potential risks and take necessary early warning measures to reduce the possibility of accidents. The model can be dynamically adjusted according to real-time data to reflect changes in risks under different environmental conditions and improve the operating safety of the charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the process of a charging pile control system based on digital twin of the present invention;

[0058] Figure 2 It is a schematic flow chart of a charging pile control method based on digital twin of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1

[0061] See also Figure 1 As shown, the charging pile control system based on digital twin described in this embodiment includes:

[0062] Data acquisition module, used to collect charging pile status data, vehicle information data, environmental data and safety monitoring data;

[0063] The data processing module is used to pre-process and correlate the collected charging pile status data, vehicle information data, environmental data and safety monitoring data to obtain a comprehensive feature data set;

[0064] The digital twin module is used to build a digital twin model, input the comprehensive feature data set into the virtual model, simulate the operation process of the charging pile, and predict the operation data of the charging pile;

[0065] The status monitoring module is used to build a status monitoring model, import the predicted charging pile operation data into the trained status monitoring model, and obtain the safety status of the charging pile;

[0066] A status assessment module is used to compare the predicted safety status of the charging pile with a preset safety status threshold of the charging pile to assess whether the safety status of the charging pile meets the standard;

[0067] The intelligent scheduling module is used to optimize the scheduling and usage strategy of charging piles; each module is connected via wired and / or wireless means.

[0068] Charging pile status data includes operation data and basic information data; operation data includes power, current, voltage, timestamp, charging duration, utilization frequency and usage frequency of charging piles generated during operation; basic information data includes physical characteristics, satellite positioning, design drawings, service life and charging type of charging piles;

[0069] Vehicle information data includes vehicle type data and battery data; battery data includes battery type, rated capacity, health status, charge status and battery temperature;

[0070] Environmental data include ambient temperature, ambient humidity, ambient brightness and traffic flow;

[0071] Safety monitoring data includes smoke monitoring data, fire monitoring data, temperature monitoring data, electrical monitoring data, insulation resistance detection data, short circuit monitoring data and overload protection data.

[0072] The method for preprocessing the collected charging pile status data, vehicle information data, environmental data and safety monitoring data includes:

[0073] Use clustering algorithm to detect outliers in charging pile status data, vehicle information data, environmental data and safety monitoring data, and remove the identified outliers to obtain charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set;

[0074] The acquired charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set are standardized and normalized, and converted into a standard normal distribution form unified according to timestamps to ensure that the data can be integrated and analyzed in chronological order; the final charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set are obtained.

[0075] The method for obtaining the comprehensive feature data set includes:

[0076] Calculate the Jaccard similarity between different features in the charging pile status dataset, vehicle information dataset, environment dataset, and safety monitoring dataset and the charging pile operation data, and assign weights to the features in each dataset through the Jaccard similarity;

[0077] After each feature is processed by standardization and weight assignment, a quantized charging pile status data set, a vehicle information data set, an environmental data set, and a safety monitoring data set are formed, and a comprehensive feature data set is formed through weighted model fusion; the charging pile status data set is recorded as D1, the vehicle information data set is recorded as D2, the environmental data set is recorded as D3, and the safety monitoring data set is recorded as D4;

[0078] The weighted model is: YR=D1·ω1+D2·ω2+D3·ω3+D4·ω4; where YR is the comprehensive feature data set; ω1 is the weight coefficient of the charging pile status data set; ω2 is the weight coefficient of the vehicle information data set; ω3 is the weight coefficient of the environmental data set; and ω4 is the weight coefficient of the safety monitoring data set.

[0079] Methods for assigning weights to features in each dataset using Jaccard similarity include:

[0080] Define a time window W. At the end of each time window, collect the data from the latest charging pile status data set, vehicle information data set, environmental data set, and safety monitoring data set within the window. For each feature in the data, at the end of the time window W, use the latest data set to calculate the Jaccard similarity. Let the value set of the charging pile operation data be C and the feature data be F. g , the Jaccard similarity calculation formula is: Among them, f g is the value set of the feature; J is the similarity; |f g ∩C| is the value set f of the feature g The size of the intersection with the value set C of the charging pile operation data; |f g ∪C| is the value set f of the feature g The size of the union of the value set C of the charging pile operation data;

[0081] According to the calculated Jaccar similarity, a weight factor σ is assigned to each feature in the charging pile status dataset, vehicle information dataset, environment dataset, and safety monitoring dataset. h , the model is adjusted by adjusting the weight factor of each feature;

[0082] The weight factor adjustment model is: Among them, σ′ h is the adjusted weight factor; r h is the adjustment coefficient for adjusting the hth weight factor; is the accumulation of the h-th feature at time t′; is the accumulation of all features at time t′; K is the type of feature; T′ is the number of time windows; t′ is the index of time; h is the index of weight factor.

[0083] The construction methods of digital twin models include:

[0084] S61, physical structure module; use CAD software to build a 3D physical model of the charging pile; the 3D physical model is built based on the basic information data of the charging pile to reflect the physical structure of the charging pile; differential equations are used to simulate the electrical behavior of the charging pile, and the mathematical expression is: Among them, I(t) is the charging current, V(t) is the charging voltage, and R(t) is the resistance, which depends on the changes of time and temperature factors. The finite element method is used to simulate the thermal behavior of the charging pile, and the heat conduction equation is used to model it. The mathematical expression is: Where T is the temperature distribution, ρ is the density, cp is the specific heat capacity, t is the time, k is the thermal conductivity, Q is the heat source; ▽ is the vector differential operator; is the differential symbol;

[0085] The thermal conductivity k in the heat conduction equation is limited by the thermal conductivity limitation formula, which is: Wherein, k(t) is the thermal conductivity after limitation; T(t) is the temperature distribution function that changes with time; α is the influence coefficient of temperature on thermal conductivity; β is the influence coefficient of charging time on thermal conductivity;

[0086] For example, the temperature T changes with time during the charging process and is related to the charging time; the function can be described as: T(t)=2+0.5t, in degrees Celsius, the initial temperature is 20°C, and the temperature increases by 5°C per hour.

[0087] The influence coefficient α of temperature on thermal conductivity is 0.2, and the influence coefficient β of charging time on thermal conductivity is 0.1;

[0088] When t is 0, That is, when charging starts, the thermal conductivity is 40%;

[0089] When t is 2 hours, That is, after charging for 2 hours, the thermal conductivity is 50%;

[0090] When t is 5 hours, That is, after charging for 5 hours, the thermal conductivity reaches 60%;

[0091] When t is 10, That is, after charging for 10 hours, the thermal conductivity reaches 70%;

[0092] When the temperature T increases, the thermal conductivity k will increase, indicating that the device needs better thermal conductivity when the temperature rises; when the charging time t increases, the denominator increases and the thermal conductivity k decreases accordingly, indicating that long-term charging may reduce the thermal conductivity effect.

[0093] S62, operation mechanism module; the electromagnetic induction law is used to construct the power equation, thereby describing the power transmission process; the mathematical formula of the power equation is specifically: Where V is voltage, L is inductance, I is current, R is resistance, dI is current increment, and dt is time increment;

[0094] S63, dynamic response module; construct a dynamic response model, accurately model the input-output relationship and state change of the charging pile, and track the behavior of the charging pile under different loads and external conditions in real time, so as to ensure that the dynamic behavior of the charging pile under different conditions is consistent with reality; use the state space equation to construct the dynamic response model, and the specific mathematical formula is: Among them, x(t) is the state vector (physical quantities such as current and voltage), u(t) is the input vector (external power or control signal); y(t) is the output vector (monitored charging pile status, such as temperature and current); A, B, C and D are the parameters of the state space model;

[0095] S64, data driven module; the data driven module is composed of a status monitoring model, which predicts the operating status of the charging pile.

[0096] The training methods of the condition monitoring model include:

[0097] The data set is divided into a training set and a test set to build a state monitoring model; the sample set is a subset of the data set, and each sample set includes historical charging pile operation data and the corresponding safety status of the charging pile; the input data of the model is the historical charging pile operation data; the output label is the safety status of the charging pile; the state monitoring model is a random forest regressor model;

[0098] Initialize the condition monitoring model and set the number and depth of trees as hyperparameters; use the training set to train the condition monitoring model, use the k-fold cross-validation method to adjust the model's hyperparameters, and tune the parameters of the initial settings; use the test set data to evaluate the performance of the model, and use the determination coefficient to evaluate the difference between the calculated prediction results and the true label;

[0099] According to the model performance feedback, the number of trees and the depth hyperparameters are adjusted to tune the model. The model is retrained using the adjusted hyperparameters. When the training reaches the preset model complexity, it is stopped to obtain the final trained state monitoring model. The trained state monitoring model is used to predict the current charging pile operation data to predict the safety status of the charging pile.

[0100] Methods for tuning the model include adjusting the number and depth hyperparameters of the tree:

[0101] The number and depth hyperparameters of the trees are adjusted through the genetic algorithm. During the iteration of the genetic algorithm, the number and depth of the trees corresponding to the chromosome with the highest fitness are recorded as the optimal hyperparameter combination;

[0102] For the number and depth hyperparameters of trees, randomly preset the minimum number threshold and the minimum depth threshold; at the same time, randomly preset the maximum number and depth thresholds; gradually increase or decrease the thresholds until the optimal threshold is found, judge the performance of the model on the test set, and obtain the optimal number and depth hyperparameters of trees.

[0103] The method of comparing the predicted safety status of the charging pile with the preset safety status threshold of the charging pile and evaluating whether the safety status of the charging pile meets the standard includes:

[0104] If the predicted safety status of the charging pile is consistent with the preset safety status threshold of the charging pile, it is determined that the safety status of the charging pile meets the standard;

[0105] If the predicted safety status of the charging pile does not match the preset safety status threshold of the charging pile, it is determined that the safety status of the charging pile does not meet the standard.

[0106] Methods for optimizing the scheduling and use strategy of charging piles include:

[0107] If the safety status of the charging pile meets the standard, the charging pile intelligent control terminal generates a safety instruction to indicate that the charging pile is in normal status;

[0108] If the safety status of the charging pile does not meet the standard, the charging pile intelligent control terminal will generate a danger command, automatically generate warning information, and collect parameter data of the charging pile that does not meet the standard;

[0109] The non-standard parameter data include the power generated during the operation of the charging pile, the non-standard current, the non-standard voltage, the charging pile operation time and the charging pile temperature itself;

[0110] A charging pile status risk assessment model is constructed based on the substandard parameter data. The charging pile status risk is assessed through the charging pile status risk assessment model to obtain the charging pile status hazard coefficient; the charging pile risk assessment model is: Among them, RE is the dangerous coefficient of the charging pile state; P is the power generated during the operation of the charging pile; τ is the substandard current; V' is the substandard voltage; T sf is the charging pile operation time; T run is the temperature of the charging pile itself; δ1 is the power weight factor generated during the operation of the charging pile; δ2 is the substandard current weight factor; δ3 is the substandard voltage weight factor; δ4 is the charging pile operation time weight factor; δ5 is the charging pile temperature weight factor;

[0111] For example, the power generated by the charging pile during operation is 2 kilowatts, the substandard current is 5A, the substandard voltage is 300V, the charging pile operation time is 10 hours, the charging pile's own temperature is 75°C, δ1 is 0.3, δ2 is 0.3, δ3 is 0.2, δ4 is 0.1, and δ5 is 0.1, then the charging pile status hazard factor is 1.2.

[0112] The preset safety status threshold of the charging pile is set by the staff. The safety status of different charging piles is collected through the charging pile intelligent control terminal, and the average value of the safety status of multiple charging piles is taken as the preset safety status threshold of the charging pile.

[0113] In this embodiment, by collecting the latest charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set at the end of each time window, dynamic monitoring of the charging pile operation status can be achieved, and the actual situation of the system can be reflected in a timely manner; the Jaccard similarity of each feature is calculated and a weight factor is assigned to the feature based on the similarity, and the feature weight can be flexibly adjusted according to the actual operation situation, thereby enhancing the model's adaptability to a changing environment;

[0114] The finite element method is used to model the thermal behavior of the charging pile through the heat conduction equation to ensure that the thermal effect in actual operation is effectively analyzed and managed; the effectiveness of heat conduction is controlled by the thermal conductivity limit formula to avoid equipment damage caused by overheating; a dynamic response model is constructed, and the state space equation is used to accurately model the input-output relationship and state change of the charging pile; the dynamic behavior of the charging pile under different conditions is ensured to be consistent with reality, and the stability and reliability of the system are improved; by limiting the thermal conductivity, the temperature of the charging pile during operation can be effectively controlled to prevent overheating and ensure that the charging pile works within a safe range;

[0115] The charging pile status risk assessment model converts different parameters into a quantified risk factor, making the status risk of the charging pile easier to understand and manage. Real-time monitoring and evaluation of the operating status of the charging pile can promptly identify potential risks and take necessary early warning measures to reduce the possibility of accidents. The model can be dynamically adjusted according to real-time data to reflect changes in risks under different environmental conditions and improve the operating safety of the charging pile.

[0116] Embodiment 2

[0117] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a charging pile control method based on digital twin is provided, including:

[0118] S1. Collect charging pile status data, vehicle information data, environmental data and safety monitoring data;

[0119] S2, preprocessing and associating the collected charging pile status data, vehicle information data, environmental data and safety monitoring data to obtain a comprehensive feature data set;

[0120] S3. Build a digital twin model, input the comprehensive feature data set into the virtual model, simulate the operation process of the charging pile, and predict the operation data of the charging pile;

[0121] S4. Build a status monitoring model, import the predicted charging pile operation data into the trained status monitoring model, and obtain the safety status of the charging pile;

[0122] S5. Compare the predicted safety status of the charging pile with a preset safety status threshold of the charging pile to evaluate whether the safety status of the charging pile meets the standard;

[0123] S6. Optimize the scheduling and usage strategies of charging piles.

[0124] Since the electronic device introduced in this embodiment is an electronic device used to implement a charging pile control system based on digital twins in the embodiment of this application, based on the charging pile control system based on digital twins introduced in the embodiment of this application, the technical personnel of this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as the technical personnel of this field implement the electronic device used in the charging pile control system based on digital twins in the embodiment of this application, it belongs to the scope of protection of this application.

[0125] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0126] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A charging pile control system based on digital twin, characterized in that: include: Data acquisition module, used to collect charging pile status data, vehicle information data, environmental data and safety monitoring data; The data processing module is used to pre-process and correlate the collected charging pile status data, vehicle information data, environmental data and safety monitoring data to obtain a comprehensive feature data set; The digital twin module is used to build a digital twin model, input the comprehensive feature data set into the virtual model, simulate the operation process of the charging pile, and predict the operation data of the charging pile; The status monitoring module is used to build a status monitoring model, import the predicted charging pile operation data into the trained status monitoring model, and obtain the safety status of the charging pile; A status assessment module is used to compare the predicted safety status of the charging pile with a preset safety status threshold of the charging pile to assess whether the safety status of the charging pile meets the standard; The intelligent scheduling module is used to optimize the scheduling and usage strategy of charging piles; each module is connected via wired and / or wireless means.

2. According to the digital twin-based charging pile control system of claim 1, it is characterized in that: The charging pile status data includes operation data and basic information data; the operation data includes the power, current, voltage, timestamp, charging time, utilization frequency and usage frequency of the charging pile generated during the operation of the charging pile; the basic information data includes the physical characteristics, satellite positioning, design drawings, service life and charging type of the charging pile; Vehicle information data includes vehicle type data and battery data; battery data includes battery type, rated capacity, health status, charge status and battery temperature; Environmental data include ambient temperature, ambient humidity, ambient brightness and traffic flow; Safety monitoring data includes smoke monitoring data, fire monitoring data, temperature monitoring data, electrical monitoring data, insulation resistance detection data, short circuit monitoring data and overload protection data.

3. The charging pile control system based on digital twin according to claim 2 is characterized in that: The method for preprocessing the collected charging pile status data, vehicle information data, environmental data and safety monitoring data includes: Use density clustering algorithm to detect outliers on charging pile status data, vehicle information data, environmental data and safety monitoring data, and remove the identified outliers to obtain charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set; The acquired charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set are standardized and normalized, and converted into a standard normal distribution form unified according to timestamps to obtain the final charging pile status data set, vehicle information data set, environmental data set and safety monitoring data set.

4. The charging pile control system based on digital twin according to claim 3 is characterized in that: The method for obtaining the comprehensive feature data set includes: Calculate the Jaccard similarity between different features in the charging pile status dataset, vehicle information dataset, environment dataset, and safety monitoring dataset and the charging pile operation data, and assign weights to the features in each dataset through the Jaccard similarity; After each feature is processed by standardization and weight assignment, a quantized charging pile status data set, a vehicle information data set, an environmental data set, and a safety monitoring data set are formed, and a comprehensive feature data set is formed through weighted model fusion; the charging pile status data set is recorded as D1, the vehicle information data set is recorded as D2, the environmental data set is recorded as D3, and the safety monitoring data set is recorded as D4; The weighted model is: YR=D1·ω1+D2·ω2+D3·ω3+D4·ω4; where YR is the comprehensive feature data set; ω1 is the weight coefficient of the charging pile status data set; ω2 is the weight coefficient of the vehicle information data set; ω3 is the weight coefficient of the environmental data set; and ω4 is the weight coefficient of the safety monitoring data set.

5. The charging pile control system based on digital twin according to claim 4 is characterized in that: The method of assigning weights to features in each data set by using Jaccard similarity includes: Define a time window W. At the end of each time window, collect the data from the latest charging pile status data set, vehicle information data set, environmental data set, and safety monitoring data set within the window. For each feature in the data, at the end of the time window W, use the latest data set to calculate the Jaccard similarity. Let the value set of the charging pile operation data be C and the feature data be F. g , the Jaccard similarity calculation formula is: Among them, f g is the value set of the feature; J is the similarity; |f g ∩C| is the value set f of the feature g The size of the intersection with the value set C of the charging pile operation data; |f g ∪C| is the value set f of the feature g The size of the union of the value set C of the charging pile operation data; According to the calculated Jaccar similarity, a weight factor σ is assigned to each feature in the charging pile status dataset, vehicle information dataset, environment dataset, and safety monitoring dataset. h , the model is adjusted by adjusting the weight factor of each feature; The weight factor adjustment model is: Among them, σ′ h is the adjusted weight factor; r h is the adjustment coefficient for adjusting the hth weight factor; is the accumulation of the h-th feature at time t′; is the accumulation of all features at time t′; K is the type of feature; T′ is the number of time windows; t′ is the index of time; h is the index of weight factor.

6. A charging pile control system based on digital twin according to claim 5, characterized in that: The method for constructing the digital twin model includes: S61, physical structure module; use CAD software to build a 3D physical model of the charging pile; the 3D physical model is built based on the basic information data of the charging pile to reflect the physical structure of the charging pile; differential equations are used to simulate the electrical behavior of the charging pile, and the mathematical expression is: Where, I(t) is the charging current, V(t) is the charging voltage, and R(t) is the resistance; The finite element method is used to simulate the thermal behavior of the charging pile, and the heat conduction equation is used to model it. The mathematical expression is: Where T is the temperature distribution, ρ is the density, c p is the specific heat capacity, t is the time, k is the thermal conductivity, Q is the heat source; ▽ is the vector differential operator; is the differential symbol; The thermal conductivity k in the heat conduction equation is limited by the thermal conductivity limitation formula, which is: Wherein, k(t) is the thermal conductivity after limitation; T(t) is the temperature distribution function that changes with time; α is the influence coefficient of temperature on thermal conductivity; β is the influence coefficient of charging time on thermal conductivity; S62, operation mechanism module; the electromagnetic induction law is used to construct the power equation, thereby describing the power transmission process; the mathematical formula of the power equation is specifically: Where V is voltage, L is inductance, I is current, R is resistance, dI is current increment, and dt is time increment; S63, dynamic response module; construct a dynamic response model to accurately model the input-output relationship and state change of the charging pile, thereby ensuring that the dynamic behavior of the charging pile under different conditions is consistent with reality; use the state space equation to construct the dynamic response model, the specific mathematical formula is: Where x(t) is the state vector, u(t) is the input vector, y(t) is the output vector, A, B, C and D are the parameters of the state space model. S64, data driven module; the data driven module is composed of a status monitoring model, which predicts the operating status of the charging pile.

7. The charging pile control system based on digital twin according to claim 6 is characterized in that: The training method of the condition monitoring model includes: The data set is divided into a training set and a test set to build a state monitoring model; the sample set is a subset of the data set, and each sample set includes historical charging pile operation data and the corresponding safety status of the charging pile; the input data of the model is the historical charging pile operation data; the output label is the safety status of the charging pile; the state monitoring model is a random forest regressor model; Initialize the condition monitoring model and set the number and depth of trees as hyperparameters; use the training set to train the condition monitoring model, use the k-fold cross-validation method to adjust the model's hyperparameters, and tune the parameters of the initial settings; use the test set data to evaluate the performance of the model, and use the determination coefficient to evaluate the difference between the calculated prediction results and the true label; According to the model performance feedback, the number of trees and the depth hyperparameters are adjusted to tune the model. The model is retrained using the adjusted hyperparameters. When the training reaches the preset model complexity, it is stopped to obtain the final trained state monitoring model. The trained state monitoring model is used to predict the current charging pile operation data to predict the safety status of the charging pile.

8. The charging pile control system based on digital twin according to claim 7 is characterized in that: The method of adjusting the number and depth hyperparameters of the trees to optimize the model includes: The number and depth hyperparameters of the trees are adjusted through the genetic algorithm. During the iteration of the genetic algorithm, the number and depth of the trees corresponding to the chromosome with the highest fitness are recorded as the optimal hyperparameter combination; For the number and depth hyperparameters of trees, randomly preset the minimum number threshold and the minimum depth threshold; at the same time, randomly preset the maximum number and depth thresholds; gradually increase or decrease the thresholds until the optimal threshold is found, judge the performance of the model on the test set, and obtain the optimal number and depth hyperparameters of trees.

9. A charging pile control system based on digital twin according to claim 8, characterized in that: The method of comparing the predicted safety status of the charging pile with a preset safety status threshold of the charging pile to evaluate whether the safety status of the charging pile meets the standard includes: If the predicted safety status of the charging pile is consistent with the preset safety status threshold of the charging pile, it is determined that the safety status of the charging pile meets the standard; If the predicted safety status of the charging pile does not match the preset safety status threshold of the charging pile, it is determined that the safety status of the charging pile does not meet the standard.

10. A charging pile control system based on digital twin according to claim 9, characterized in that: The method for optimizing the scheduling and use strategy of charging piles includes: If the charging pile safety status meets the standard, the charging pile intelligent control terminal generates a safety instruction to indicate that the charging pile status is normal; If the safety status of the charging pile does not meet the standard, the charging pile intelligent control terminal will generate a danger command, automatically generate warning information, and collect parameter data of the charging pile that does not meet the standard; The non-standard parameter data include the power generated during the operation of the charging pile, the non-standard current, the non-standard voltage, the charging pile operation time and the charging pile temperature itself; A charging pile status risk assessment model is constructed based on the substandard parameter data. The charging pile status risk is assessed through the charging pile status risk assessment model to obtain the charging pile status hazard coefficient; the charging pile risk assessment model is: Among them, RE is the dangerous coefficient of the charging pile state; P is the power generated during the operation of the charging pile; τ is the substandard current; V' is the substandard voltage; T sf is the charging pile operation time; T run is the temperature of the charging pile itself; δ1 is the power weight factor generated during the operation of the charging pile; δ2 is the substandard current weight factor; δ3 is the substandard voltage weight factor; δ4 is the charging pile operation time weight factor; δ5 is the charging pile temperature weight factor.

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