Dynamic calculation method of threshold value for overload protection of intelligent charging box and related equipment

Abstract

The present invention relates to the field of box transformer control technology, and specifically discloses a dynamic calculation method for the threshold of overload protection of an intelligent charging box transformer and related equipment, wherein the method includes the steps of: continuously acquiring load information, box temperature information, and ambient temperature information of the intelligent charging box transformer; identifying and acquiring a parameter set of a dynamic thermal model to determine the dynamic thermal model; using the identified dynamic thermal model to predict the box temperature at the next moment based on the load information and ambient temperature information; calculating and acquiring a temperature margin based on the predicted box temperature and a preset safety temperature threshold; adjusting the overload protection threshold according to the temperature margin; the method realizes adaptive adjustment of the overload protection threshold, so that the overload protection strategy can dynamically change according to the actual operating status of the box transformer, which not only ensures the safe operation of the box transformer under conditions of slow heat dissipation and load fluctuation, but also makes full use of the overload capacity of the box transformer, thereby improving the continuity of charging service.

Description

Technical Field

[0001] The present application relates to the field of box transformer control technology, and in particular to a threshold dynamic calculation method for overload protection of an intelligent charging box transformer and related equipment. Background Art

[0002] With the increasing number of electric vehicles in urban core areas, smart charging box transformers in underground spaces have become critical infrastructure for alleviating urban charging pressures. However, the enclosed underground environment limits the heat dissipation of the charging box transformers. Long-term high-load operation can easily cause the internal temperature of the transformers to rise continuously, leading to heat accumulation. This, combined with the fluctuations in urban grid load and the peaks and valleys in electric vehicle charging, significantly increases the risk of thermal runaway for smart charging box transformers.

[0003] Currently, existing dynamic overload protection technology primarily relies on load prediction, adjusting the overload protection threshold based on this prediction. However, this approach has inherent technical flaws when applied to underground smart charging box transformers. First, due to the thermal hysteresis effect of the underground environment, changes in the box transformer temperature often lag behind load fluctuations, making it difficult for traditional prediction models to accurately capture the complex nonlinear thermal dynamic characteristics within the box transformer. Second, to simplify computational complexity, existing models typically employ linear models, ignoring the coupling relationship between the box transformer temperature field and the load. This results in delayed and inaccurate adjustment of the overload protection threshold, or in frequent false triggering. This fails to effectively guarantee the safe and stable operation of the smart charging box transformer and severely impacts the continuity of charging services for electric vehicle users, resulting in a poor user experience. Summary of the Invention

[0004] The purpose of this application is to provide a dynamic calculation method for the threshold of overload protection of an intelligent charging box transformer and related equipment, so as to adjust the overload protection threshold on the premise of capturing the complex nonlinear thermal dynamic characteristics inside the box transformer and the coupling relationship between the box transformer temperature field and the load, thereby improving the accuracy and time efficiency of the overload protection threshold.

[0005] In a first aspect, the present application provides a method for dynamically calculating a threshold value for overload protection of a smart charging box transformer, which is used to adjust the overload protection threshold value to provide overload protection for the smart charging box transformer. The method comprises the following steps:

[0006] S1. Continuously obtain the load information, internal temperature information, and ambient temperature information of the smart charging box;

[0007] S2. Based on the load information, the internal temperature information, the ambient temperature information, and the historical operation information, identify and obtain a parameter set of a dynamic thermal model to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the load change of the box transformer, the ambient temperature change, and the internal temperature change of the box transformer;

[0008] S3. Using the identified dynamic thermal model, based on the load information and ambient temperature information, predict the temperature inside the box at the next moment;

[0009] S4. Calculating and obtaining a temperature margin based on the predicted temperature inside the box and a preset safety temperature threshold;

[0010] S5. Adjust the overload protection threshold according to the temperature margin.

[0011] The dynamic calculation method of the threshold value of the overload protection of the intelligent charging box transformer of the present application is based on the dynamic identification of the dynamic thermal model based on the load information, the temperature information inside the box and the ambient temperature information to predict the temperature inside the box at the next moment, and calculates the temperature margin based on the temperature inside the box at the next moment to adjust the overload protection threshold, realizing the adaptive adjustment of the overload protection threshold, so that the overload protection strategy can change dynamically according to the actual operating status of the box transformer, which not only ensures the safe operation of the box transformer under conditions of slow heat dissipation and load fluctuations, but also fully utilizes the overload capacity of the box transformer and improves the continuity of the charging service. Compared with the traditional fixed threshold overload protection method, the dynamic calculation method of the threshold value of the overload protection of the intelligent charging box transformer of the present application can more effectively deal with the overload protection problem under the complex working conditions of the intelligent charging box transformer in the underground space, and improve the safety and reliability of the system.

[0012] The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box, wherein step S2 includes:

[0013] S21. Based on the online parameter identification algorithm, the parameter set of the dynamic thermal model is identified according to the load information, the temperature information inside the box, the ambient temperature information and the historical operation information. The parameter set includes the baseline thermal time constant, the load influence coefficient, the heat gain coefficient, the nonlinear dissipation coefficient and the baseline delay step number. The historical operation information includes: historical load information, historical temperature information inside the box and historical ambient temperature information.

[0014] Step S21 incorporates historical operating information for identification, enhancing the accuracy of parameter identification because historical data provides information on long-term trends and characteristics of the transformer's operation. The resulting dynamic thermal model can more accurately predict transformer temperature, overcoming the inability of traditional models to accurately capture nonlinear thermal dynamic characteristics. This provides a more reliable model foundation for subsequent adjustments to the overload protection threshold.

[0015] The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box, wherein step S4 includes:

[0016] S41, calculating and obtaining an initial temperature margin based on the difference between the predicted temperature inside the box and a preset safety temperature threshold;

[0017] S42 . Smoothing the initial temperature margin using a first-order lag filtering algorithm to obtain the temperature margin.

[0018] Through this processing method, the sudden change of the temperature margin is effectively reduced, ensuring that the subsequent overload protection threshold adjustment process based on the temperature margin is more stable and reliable. The smoothed temperature margin can more accurately reflect the safe status of the transformer temperature, providing a more reliable basis for adjusting the overload protection threshold in the subsequent step S5, thereby ensuring the stability of the overload protection threshold adjustment process.

[0019] In the method for dynamically calculating the threshold value of overload protection for a smart charging box transformer, the filter coefficient of the first-order lag filter algorithm is adaptively adjusted according to the degree of fluctuation of the load information of the box transformer. The adaptive adjustment process includes:

[0020] S421. Calculate and obtain a current load change rate based on the load information change;

[0021] S422. Determine the filter coefficient using a piecewise function according to the current load change rate.

[0022] The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box further includes the following steps:

[0023] S6. Online monitoring of the voltage and current of the smart charging box transformer to see if there is any abnormal fluctuation. When such abnormal fluctuation occurs, an adaptive factor is generated according to the degree of the abnormal fluctuation, and the adjusted overload protection threshold is compensated according to the adaptive factor.

[0024] The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box, wherein step S6 includes:

[0025] S61. Monitor the voltage and current information of the smart charging box transformer in real time, and calculate the voltage fluctuation rate and the current fluctuation rate respectively according to the voltage information and the current information;

[0026] S62: determining whether the voltage fluctuation rate and / or the current fluctuation rate exceeds a corresponding preset fluctuation threshold; if so, determining that abnormal voltage and current fluctuations occur;

[0027] S63. When the abnormal fluctuation occurs, calculating an exceeding threshold ratio according to the voltage fluctuation rate and the current fluctuation rate;

[0028] S64, calculating an adaptive factor according to the exceeding threshold ratio and a preset adaptive coefficient;

[0029] S65. Compensate the adjusted overload protection threshold according to the adaptive factor.

[0030] The method for dynamically calculating the threshold value of the intelligent charging box variable overload protection, wherein step S5 includes:

[0031] S51, determining an adjustment step size and an adjustment direction according to the temperature margin, and calculating and obtaining a plurality of candidate overload protection thresholds according to the adjustment step size and the adjustment direction and an initial overload protection threshold;

[0032] S52. Calculate the load margin of the box-type transformer according to the load information and each candidate overload protection threshold;

[0033] S53: Obtain a historical load fluctuation frequency and a historical overload duration based on the ambient temperature information and the load margin of each box-type transformer, and evaluate a risk level of the box-type transformer operation under each candidate overload protection threshold based on the historical load fluctuation frequency and the historical overload duration;

[0034] S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

[0035] In a second aspect, the present application further provides a device for dynamically calculating a threshold value for overload protection of a smart charging box transformer, which is used to adjust the overload protection threshold value to provide overload protection for the smart charging box transformer, the device comprising:

[0036] The acquisition module is used to continuously obtain the load information, internal temperature information, and ambient temperature information of the smart charging box;

[0037] The model configuration module is used to identify and obtain the parameter set of the dynamic thermal model based on load information, internal temperature information, ambient temperature information, and historical operation information to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the load change of the box transformer, the change in ambient temperature, and the change in internal temperature of the box transformer;

[0038] The temperature prediction module is used to use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on load information and ambient temperature information;

[0039] A margin calculation module is used to calculate and obtain a temperature margin based on the predicted temperature inside the box and a preset safety temperature threshold;

[0040] A threshold adjustment module is used to adjust the overload protection threshold according to the temperature margin.

[0041] The threshold dynamic calculation device for overload protection of the intelligent charging box transformer of the present application dynamically identifies a dynamic thermal model based on load information, temperature information inside the box, and ambient temperature information to predict the temperature inside the box at the next moment, and calculates the temperature margin based on the temperature inside the box at the next moment to adjust the overload protection threshold, thereby realizing adaptive adjustment of the overload protection threshold, so that the overload protection strategy can dynamically change according to the actual operating status of the box transformer, which not only ensures the safe operation of the box transformer under conditions of slow heat dissipation and load fluctuations, but also fully utilizes the overload capacity of the box transformer and improves the continuity of charging services. Compared with traditional fixed threshold overload protection devices, the threshold dynamic calculation device for overload protection of the intelligent charging box transformer of the present application can more effectively deal with the overload protection problem under complex working conditions of the intelligent charging box transformer in underground space, thereby improving the safety and reliability of the system.

[0042] In a third aspect, the present application further provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps of the method provided in the first aspect are executed.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps of the method provided in the first aspect above.

[0044] From the above, it can be seen that the present application provides a method for dynamic calculation of the threshold value of overload protection of an intelligent charging box transformer and related equipment, wherein the method dynamically identifies a dynamic thermal model based on load information, box temperature information and ambient temperature information to predict the box temperature at the next moment, and calculates the temperature margin based on the box temperature at the next moment to adjust the overload protection threshold, thereby realizing adaptive adjustment of the overload protection threshold value, so that the overload protection strategy can dynamically change according to the actual operating status of the box transformer, which not only ensures the safe operation of the box transformer under conditions of slow heat dissipation and load fluctuations, but also fully utilizes the overload capacity of the box transformer and improves the continuity of charging services. Compared with the traditional fixed threshold overload protection method, the dynamic calculation method of the threshold value of overload protection of the intelligent charging box transformer of the present application can more effectively deal with the overload protection problem under complex working conditions of the intelligent charging box transformer in the underground space, thereby improving the safety and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flowchart of a method for dynamically calculating the threshold value of the overload protection of the smart charging box provided in an embodiment of the present application.

[0046] Figure 2 This is a schematic diagram of the structure of the threshold dynamic calculation device for the overload protection of the smart charging box provided in an embodiment of the present application.

[0047] Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0048] Reference numerals: 201, acquisition module; 202, model configuration module; 203, temperature prediction module; 204, margin calculation module; 205, threshold adjustment module; 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0050] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0051] First, please refer to Figure 1 Some embodiments of the present application provide a method for dynamically calculating an overload protection threshold for a smart charging box transformer, which is used to adjust the overload protection threshold to provide overload protection for the smart charging box transformer. The method includes the following steps:

[0052] S1. Continuously obtain the load information, internal temperature information, and ambient temperature information of the smart charging box;

[0053] S2. Based on the load information, the internal temperature information, the ambient temperature information, and the historical operation information, identify and obtain a parameter set of a dynamic thermal model to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the load change of the box transformer, the ambient temperature change, and the internal temperature change of the box transformer;

[0054] S3. Using the identified dynamic thermal model, based on the load information and ambient temperature information, predict the temperature inside the box at the next moment;

[0055] S4. Calculating and obtaining a temperature margin based on the predicted temperature inside the box and a preset safety temperature threshold;

[0056] S5. Adjust the overload protection threshold according to the temperature margin.

[0057] Specifically, in step S1, the load information can be measured in real time by the current sensor and voltage sensor or power detection device installed in the box transformer, the temperature information inside the box can be collected in real time by the thermistor or temperature sensor deployed inside the box transformer, and the ambient temperature information can be monitored in real time by the temperature sensor installed outside or near the box transformer. The real-time acquisition of this information provides a data basis for the subsequent identification of the dynamic thermal model and the adjustment of the overload protection threshold.

[0058] More specifically, step S2 is used to identify the parameter set of the dynamic thermal model based on the real-time information obtained in step S1 and historical operating information to determine the dynamic thermal model. This identification process can use online parameter identification algorithms such as extended Kalman filtering and recursive least squares. Historical operating information can include historical load information, historical box temperature information, and historical ambient temperature information. This historical operating information can be stored in a local database or cloud platform for identification and updating of model parameters. The establishment of the dynamic thermal model achieves accurate modeling of the box transformer's thermal characteristics, overcomes the limitations of traditional linear models, and provides model support for subsequent temperature prediction and threshold adjustment.

[0059] More specifically, step S3 uses the load and ambient temperature information acquired in step S1 and substitutes it into the dynamic thermal model identified in step S2 to calculate the predicted internal temperature at the next moment. The accuracy of the temperature prediction directly affects the rationality of the overload protection threshold adjustment. Using a dynamic thermal model can more accurately predict the temperature trend in the box, providing a reliable basis for subsequent threshold adjustments.

[0060] More specifically, step S4 compares the next-moment internal temperature predicted in step S3 with a preset safety temperature threshold. The difference between the two is the initial temperature margin. The safety temperature threshold can be pre-set based on factors such as the insulation level and heat dissipation conditions of the box-type transformer. For example, it can be set to the heat resistance temperature of the box-type transformer's insulation material. The size of the temperature margin reflects the safety level of the box-type transformer's operation. A larger temperature margin indicates safer operation and greater potential for improvement in overload capacity.

[0061] More specifically, step S5 can preset a correspondence between the temperature margin and the overload protection threshold adjustment step size and adjustment direction based on the temperature margin. For example, when the temperature margin is large, the overload protection threshold can be increased to allow the box transformer to operate under overload conditions within a certain range; when the temperature margin is small, the overload protection threshold can be decreased to strengthen overload protection. This dynamic adjustment of the overload protection threshold allows the box transformer overload protection strategy to adapt to the box transformer's operating status, ensuring safe operation while also ensuring the continuity of charging services.

[0062] The method for dynamically calculating the threshold value of overload protection for the smart charging box transformer in the embodiment of the present application dynamically identifies a dynamic thermal model based on load information, temperature information inside the box, and ambient temperature information to predict the temperature inside the box at the next moment, and calculates the temperature margin based on the temperature inside the box at the next moment to adjust the overload protection threshold value, thereby realizing adaptive adjustment of the overload protection threshold value, so that the overload protection strategy can dynamically change according to the actual operating status of the box transformer, which not only ensures the safe operation of the box transformer under conditions of slow heat dissipation and load fluctuations, but also fully utilizes the overload capacity of the box transformer and improves the continuity of the charging service. Compared with the traditional fixed threshold overload protection method, the method for dynamically calculating the threshold value of overload protection for the smart charging box transformer in the embodiment of the present application can more effectively deal with the overload protection problem under complex working conditions of the smart charging box transformer in the underground space, thereby improving the safety and reliability of the system.

[0063] It should be noted that the overload protection threshold may be a current threshold, a power threshold or a temperature threshold, which preferably corresponds to the load information type. In the embodiment of the present application, it is further preferably an overload current threshold.

[0064] In some preferred embodiments, step S2 includes:

[0065] S21. Based on the online parameter identification algorithm, the parameter set of the dynamic thermal model is identified according to the load information, the temperature information inside the box, the ambient temperature information and the historical operation information. The parameter set includes the baseline thermal time constant, the load influence coefficient, the heat gain coefficient, the nonlinear dissipation coefficient and the baseline delay step number. The historical operation information includes: historical load information, historical temperature information inside the box and historical ambient temperature information.

[0066] Specifically, the online parameter identification algorithm dynamically identifies the parameters of the dynamic thermal model based on real-time and historical data from the transformer's operation. Real-time data refers to load information, real-time internal temperature information, and real-time ambient temperature information, while historical data refers to historical operating information, including historical load information, historical internal temperature information, and historical ambient temperature information. The dynamic thermal model's parameter set specifically includes six key parameters: a baseline thermal time constant, which characterizes the transformer's temperature response speed; a load influence coefficient, which describes the degree of influence of a unit load change on the internal temperature; a heat gain coefficient, which reflects the effective proportion of the ambient temperature on the internal temperature; a nonlinear dissipation coefficient, which quantifies the intensity of nonlinear heat dissipation; and a baseline delay step number, which represents the number of delay steps affected by the load. Together, these parameters form the core of the dynamic thermal model, which accurately describes the transformer's thermal characteristics. They characterize the transformer's thermal characteristics from different dimensions, enabling the dynamic thermal model to more comprehensively and precisely describe the transformer's thermal behavior.

[0067] More specifically, during the identification process, the algorithm continuously iteratively updates parameter values ​​to minimize the error between the model's predicted output and the actual temperature inside the transformer. Through online identification, the parameters of the dynamic thermal model can be dynamically adjusted as the transformer's operating status changes, ensuring that the model accurately reflects the transformer's real-time thermal characteristics.

[0068] More specifically, step S21 incorporates historical operating information for identification, enhancing the accuracy of parameter identification. This historical data provides information on long-term trends and characteristics of the transformer's operation. The resulting dynamic thermal model can more accurately predict transformer temperature, overcoming the inability of traditional models to accurately capture nonlinear thermal dynamics. This provides a more reliable model foundation for subsequent adjustments to the overload protection threshold.

[0069] In some preferred embodiments, step S21 includes:

[0070] S211, obtaining historical load information, historical box temperature information, and historical ambient temperature information;

[0071] S212. Based on the extended Kalman filter online parameter identification algorithm, the parameter set of the dynamic thermal model is identified according to the historical load information, the historical box temperature information, the historical ambient temperature information, the load information, the real-time box temperature information, and the real-time ambient temperature information. The dynamic thermal model is a dynamic thermal model that takes into account nonlinear dissipation and delay effects. Its function form is as follows:

[0072] T in (k+1)=e -Δt / τ T in (k)+K P ·P(k-dbase)+K G (1-e -Δt / τ )T amb (k)-β(Tin(k)-T amb (k)) 2 (1)

[0073] Where Δt is the sampling time interval, τ is the reference thermal time constant, K P is the load influence coefficient; K G is the heat gain coefficient, β is the nonlinear dissipation coefficient, d base is the number of baseline delay steps;

[0074] Among them, T in (k) is the temperature information inside the box at time k, T in (k+1) is the temperature information inside the box at time k+1, P(k) is the load information at time k, so P(kd base ) is kd base Load information at time T amb(k) is the ambient temperature information at time k;

[0075] S213. Output the parameter set obtained by identification to determine the dynamic thermal model.

[0076] Specifically, the identification process of step S212 uses historical load information and load information as observation data of P(k), historical box temperature information and real-time box temperature information as T in (k) and T in (k+1) observation data, historical ambient temperature information and real-time ambient temperature information as T amb (k) is identified by using the observation data of the extended Kalman filter online parameter identification algorithm, that is, based on the time series relationship, historical load information, historical box temperature information and historical ambient temperature information form one type of observation data group, and based on the time series relationship, continuously collected load information, real-time box temperature information and real-time ambient temperature information form another type of observation data group, which work as the observation data of the extended Kalman filter online parameter identification algorithm to identify the parameter set of the dynamic thermal model; among them, the extended Kalman filter online parameter identification algorithm belongs to the existing identification algorithm, and its identification process will not be elaborated here.

[0077] More specifically, in the above dynamic thermal model, e -Δt / τ T in Item (k) reflects the thermal inertia of the system and describes the self-attenuation characteristics of the box-type transformer temperature, K P The term P(k-dbase) reflects the effect of load on the temperature inside the box, and takes into account the delayed effect of load influence, K G (1-e -Δt / τ )T amb Term (k) represents the immediate effect of the environment on the temperature, β(T in (k)-T amb (k)) 2 The term describes the nonlinear heat dissipation caused by the temperature difference; the load effect has a baseline delay step (d base ), indicating that the load change needs to take several time steps to fully affect the temperature; the dynamic thermal model also introduces a nonlinear relationship of the square of the temperature difference through the nonlinear dissipation term (β term) to simulate the complex heat exchange phenomenon in the actual heat dissipation process, and determines the speed of temperature response by introducing a reference thermal time constant (τ): the larger τ is, the slower the system temperature changes; in addition, the load influence coefficient (K P ) and ambient heat gain coefficient (K G ) quantifies the contribution weights of load and ambient temperature to the system respectively.

[0078] More specifically, the traditional linear model usually ignores the nonlinear dissipation term, while the above dynamic thermal model uses β(T in (k)-Tamb (k)) 2 The quadratic term more accurately describes the heat dissipation effect in high temperature difference scenarios, and also sets the benchmark delay step number (d base ) realizes the segmentation effect of time series and can predict the temperature information inside the box at the next moment by inputting continuously acquired load information, ambient temperature information and box temperature information.

[0079] More specifically, after identifying the parameter set consisting of the baseline thermal time constant, load influence coefficient, heat gain coefficient, nonlinear dissipation coefficient, and baseline delay step number in step S212, step S213 determines the dynamic thermal model based on this information. Through these steps, the dynamic thermal model more accurately reflects the thermal characteristics of the underground smart charging box transformer, improving model identification accuracy and temperature prediction accuracy.

[0080] More specifically, the design of the above-mentioned dynamic thermal model fully considers the characteristics of the operating conditions of the underground space intelligent charging box transformer. Nonlinear dissipation terms and delay effects are introduced into the model to more comprehensively describe the thermal characteristics of the box transformer. The nonlinear dissipation term can reflect the nonlinear characteristics of heat dissipation, and the delay effect takes into account the lag of the load's impact on temperature. These considerations enable the dynamic thermal model to more accurately capture the thermal behavior of the box transformer. By extending the Kalman filter online parameter identification algorithm, the model parameters can be identified and updated online based on real-time operating data, ensuring that the model can adapt to changes in operating conditions and improving the identification accuracy and robustness of the model. The final dynamic thermal model can provide a more accurate basis for subsequent box transformer temperature prediction, thereby improving the accuracy and reliability of the overload protection threshold adjustment.

[0081] In some preferred embodiments, step S4 includes:

[0082] S41, calculating and obtaining an initial temperature margin based on the difference between the predicted temperature inside the box and a preset safety temperature threshold;

[0083] S42. Use a first-order lag filtering algorithm to smooth the initial temperature margin to obtain a temperature margin.

[0084] Specifically, in step S41, the initial temperature margin is calculated by subtracting the predicted internal temperature from the preset safety temperature threshold. This initial temperature margin directly reflects the difference between the predicted internal temperature and the safety temperature. To prevent sudden changes in the initial temperature margin, the method for dynamically calculating the threshold for overload protection in the smart charging box of this embodiment of the present application adds step S42 to smooth the initial temperature margin.

[0085] More specifically, in the embodiment of the present application, the formula of the first-order lag filtering algorithm is preferably:

[0086] Margin(k)=α(k)*Margin(k-1)+(1-α(k))*Margin inikial (k) (2)

[0087] Among them, Margin(k) is the temperature margin at the kth moment, that is, the temperature margin at the current moment, Margin(k-1) is the temperature margin at the k-1th moment, that is, the temperature margin at the previous moment, and Margin inikial (k) is the initial temperature margin at time k, that is, the initial temperature margin at the current moment, and α(k) is the filter coefficient at time k, which can be a preset constant value or an adaptive value set according to actual conditions, and is used to set the intensity of the adjustment filter, thereby controlling the smoothness of the temperature margin.

[0088] More specifically, the hysteresis filtering of the first-order hysteresis filtering algorithm comprehensively considers the temperature margin at the previous moment and the current initial temperature margin to determine the current temperature margin, so that the fluctuation of the temperature margin can be effectively suppressed, thereby obtaining a smoother temperature margin.

[0089] More specifically, through this processing method, the sudden change of the temperature margin is effectively reduced, ensuring that the subsequent overload protection threshold adjustment process based on the temperature margin is more stable and reliable. The smoothed temperature margin can more accurately reflect the safe state of the box transformer temperature, providing a more reliable basis for the subsequent step S5 to adjust the overload protection threshold, thereby ensuring the stability of the overload protection threshold adjustment process.

[0090] In some preferred embodiments, the filter coefficient of the first-order lag filter algorithm is adaptively adjusted according to the degree of fluctuation of the load information of the box transformer. The adaptive adjustment process includes:

[0091] S421. Calculate and obtain the current load change rate based on the load information change;

[0092] S422. Determine the filter coefficient using a piecewise function according to the current load change rate.

[0093] Specifically, step S421 calculates the current load change rate to quantify the degree of load fluctuation. The calculation process can be to calculate the difference between the load value at the current moment and the previous moment, and then compare the difference with the load value at the previous moment to obtain the load change rate. Step S422 determines the filter coefficient based on the load change rate based on a piecewise function. For example, the load change rate is divided into multiple intervals, each interval corresponds to a specific filter coefficient. When the calculated load change rate falls into a certain interval, the piecewise function outputs the filter coefficient corresponding to the interval as the current filter coefficient. When the load changes drastically, a smaller filter coefficient is used to speed up the filter response speed; when the load changes smoothly, a larger filter coefficient is used to enhance the filter stability. Adaptive adjustment of the filter coefficient enables the first-order lag filter algorithm to take into account both rapid tracking of load changes and suppression of noise interference, improve the accuracy and reliability of temperature margin calculation, and provide a more accurate temperature margin basis for the subsequent adaptive adjustment of the overload protection threshold. Therefore, through the adaptive adjustment of the filter coefficient, the calculation of the temperature margin can not only quickly track the load changes but also effectively suppress the noise, thereby providing a more reliable and accurate temperature margin basis for the subsequent adaptive adjustment of the overload protection threshold.

[0094] More specifically, the interval division and coefficient values ​​of the piecewise function can be adjusted according to actual application scenarios and requirements.

[0095] In some preferred embodiments, step S421 calculates the load change rate based on the following formula:

[0096] ΔL(k)=|L(k)-L(k-1)| / L max (3)

[0097] Among them, ΔL(k) is the load change rate at time k, L(k) is the load information at time k, L(k-1) is the load information at time k-1, and L max It is the maximum load capacity of the box transformer.

[0098] Specifically, the load change rate ΔL(t) can directly reflect the fluctuation range of the current load information relative to the previous load information.

[0099] In some preferred embodiments, the piecewise function is expressed as follows: when ΔL(k)≤θ1, α(k)=α max ;When θ1<ΔL(k)<θ2, α(k)=α max -(α max -α min )*(ΔL(k)-θ1) / (θ2-θ1); when ΔL(k)≥θ2, α(k)=α min ; Among them, α max is the maximum filter coefficient, α minis the minimum filter coefficient, θ1 and θ2 are the first boundary and the second boundary of the preset load change rate threshold, and θ1<θ2.

[0100] Specifically, in this embodiment, the determination of the filter coefficient α(k) is based on a piecewise function of the load change rate ΔL(k). When ΔL(k) is less than or equal to the threshold θ1, the filter coefficient α(k) is set to the maximum filter coefficient α max , the filtering effect is the strongest at this time, and the temperature margin smoothness is the highest. When ΔL(k) is between the thresholds θ1 and θ2, the filter coefficient α(k) changes from α max Linear transition to the minimum filter coefficient α min , achieving a gradual weakening of the filtering effect. When ΔL(k) is greater than or equal to the threshold θ2, the filter coefficient α(k) is set to the minimum filter coefficient α min , the filtering effect is weakest at this point, and the response to load changes is fastest. Through this piecewise function approach, the filter coefficient α(k) can be adaptively adjusted based on the degree of load fluctuation, thereby ensuring smoothness of the temperature margin while also taking into account a rapid response to load changes.

[0101] In some preferred embodiments, the method further comprises the following steps:

[0102] S6. Online monitoring is performed to determine whether the voltage and current of the smart charging box transformer fluctuate abnormally. When abnormal fluctuations occur, an adaptive factor is generated according to the degree of the abnormal fluctuations, and the adjusted overload protection threshold is compensated according to the adaptive factor.

[0103] Specifically, due to the influence of weather conditions or other irresistible factors, the voltage and current of the smart charging box transformer input may fluctuate violently and frequently. The fluctuations in voltage and current will cause corresponding fluctuations in load information, which will increase the randomness and suddenness of the load, greatly increasing the difficulty of predicting the temperature inside the box at the next moment and significantly reducing the prediction accuracy. Therefore, the dynamic calculation method of the threshold value of the overload protection of the smart charging box transformer in the embodiment of the present application introduces step S6 to monitor whether the voltage and current of the smart charging box transformer input fluctuate abnormally, and temporarily take specific safety measures when abnormal fluctuations occur.

[0104] More specifically, step S6 aims to address the issue of delayed overload protection due to abnormal voltage and current fluctuations. By further adjusting the overload protection threshold, the overload protection is achieved more quickly and accurately. Voltage and current monitoring can be achieved using devices such as voltage and current sensors, which are configured to collect real-time voltage and current data during the transformer's operation.

[0105] More specifically, the generation of the adaptive factor can be determined based on the relationship between a preset fluctuation threshold and the actual degree of fluctuation. For example, the greater the degree of fluctuation, the greater the adaptive factor. The adaptive factor is used to compensate for the overload protection threshold that was previously adjusted based on the temperature margin. The compensation adjustment process is intended to enable the overload protection threshold to respond more quickly to abnormal fluctuations in voltage and current, thereby starting the overload protection mechanism more promptly. In this way, even when temperature changes lag behind load fluctuations, or when the simplified model fails to fully consider sudden changes in voltage and current, the system can improve the response speed and accuracy of overload protection through real-time monitoring and adaptive adjustment of voltage and current, ensure the safe operation of the smart charging box, effectively avoid potential risks caused by abnormal fluctuations in voltage and current, and ensure the safety and continuity of charging services.

[0106] In some preferred embodiments, step S6 includes:

[0107] S61. Monitor the voltage and current information of the smart charging box in real time, and calculate the voltage fluctuation rate and the current fluctuation rate based on the voltage and current information respectively;

[0108] S62: Determine whether the voltage fluctuation rate and / or current fluctuation rate exceeds the corresponding preset fluctuation threshold. If so, determine that abnormal voltage and current fluctuation occurs.

[0109] S63. When abnormal fluctuation occurs, calculate the threshold value exceeding ratio according to the voltage fluctuation rate and the current fluctuation rate;

[0110] S64, calculating an adaptive factor according to the threshold-exceeding ratio and a preset adaptive coefficient;

[0111] S65. Compensate the adjusted overload protection threshold according to the adaptive factor.

[0112] Specifically, in step S61, the voltage fluctuation rate is the standard deviation of the voltage change per unit time, and the current fluctuation rate is the standard deviation of the current change per unit time. The voltage fluctuation rate and the current fluctuation rate can be calculated using a sliding window standard deviation calculation method. For example, a time window is set, such as 30 seconds, and the standard deviation of the voltage and current sampling values ​​within this 30-second window is calculated to obtain the voltage fluctuation rate and the current fluctuation rate.

[0113] More specifically, in step S62, the preset fluctuation threshold is a pre-set benchmark for determining whether voltage and current fluctuations are abnormal. The preset fluctuation threshold can be set based on the historical operating data and actual operating conditions of the smart charging box transformer.

[0114] More specifically, in step S63, the over-threshold ratio is used to quantify the extent to which the voltage fluctuation rate and the current fluctuation rate exceed the preset fluctuation threshold. It can respectively calculate the extent to which the voltage fluctuation rate exceeds the preset voltage fluctuation threshold and the extent to which the current fluctuation rate exceeds the preset current fluctuation threshold, and then take the larger value as the over-threshold ratio.

[0115] More specifically, in step S64, the adaptive coefficient can be a preset value, and the adaptive coefficient can be set according to actual needs, and is used to determine the adaptive factor in combination with the over-threshold ratio. In the embodiment of the present application, it is preferred to use the product of the over-threshold ratio and the adaptive coefficient as the adaptive factor, which reflects the severity of the abnormal fluctuations in voltage and current.

[0116] More specifically, in step S65, the overload protection threshold value is adjusted based on the compensation of the adaptive factor. The compensation adjustment method is not limited to multiplication or subtraction. Nonlinear methods such as table lookup and function fitting can also be used to establish a mapping relationship between the adaptive factor and the overload protection threshold value to achieve more refined threshold adjustment. In the embodiment of the present application, it is preferred to subtract the adaptive factor from the original overload protection threshold value to obtain the compensated overload protection threshold value. In this way, when abnormal fluctuations in voltage and current occur, the overload protection threshold value can be adaptively lowered, allowing the smart charging box to enter the overload protection mode more quickly, thereby ensuring the safe operation of the device.

[0117] In some preferred embodiments, the preset fluctuation threshold includes a preset voltage fluctuation threshold and a preset current fluctuation threshold, and the step of calculating the threshold-exceeding ratio based on the voltage fluctuation rate and the current fluctuation rate includes:

[0118] S631. When the voltage fluctuation rate exceeds a preset voltage fluctuation threshold, calculating the difference between the voltage fluctuation rate and the preset voltage fluctuation threshold to obtain a voltage exceeding threshold difference; when the current fluctuation rate exceeds a preset current fluctuation threshold, calculating the difference between the current fluctuation rate and the preset current fluctuation threshold to obtain a current exceeding threshold difference;

[0119] S632: Divide the voltage exceeding threshold value difference by the preset voltage fluctuation threshold value to obtain a voltage exceeding threshold value ratio; divide the current exceeding threshold value difference by the preset current fluctuation threshold value to obtain a current exceeding threshold value ratio;

[0120] S633: The larger value of the voltage exceeding threshold ratio and the current exceeding threshold ratio is used as the exceeding threshold ratio.

[0121] Specifically, in step S631 , when the voltage fluctuation rate does not exceed the preset voltage fluctuation threshold, the calculation of the voltage over-threshold difference is skipped; when the current fluctuation rate does not exceed the preset current fluctuation threshold, the calculation of the current over-threshold difference is skipped.

[0122] More specifically, step S632 divides the voltage over-threshold difference obtained in step S631 by the preset voltage fluctuation threshold to calculate the voltage over-threshold ratio, and divides the current over-threshold difference obtained in step S631 by the preset current fluctuation threshold to calculate the current over-threshold ratio.

[0123] More specifically, step S633 compares the voltage over-threshold ratio and the current over-threshold ratio, and selects the larger one as the final over-threshold ratio. The over-threshold ratio will be used for subsequent adaptive factor calculation, thereby affecting the adjustment of the overload protection threshold.

[0124] More specifically, step S631 quantifies the degree to which the voltage fluctuation rate and the current fluctuation rate exceed the safety threshold by calculating the over-threshold difference, avoiding the loss of accuracy that may be caused by using only simple Boolean judgment. Step S632 introduces normalization processing to convert the over-threshold difference of voltage and current into a ratio relative to their respective preset fluctuation thresholds, so that the over-threshold degree between different physical quantities can be directly compared. Step S633 selects the larger one as the final over-threshold ratio, ensuring that the system responds to more serious abnormal fluctuations in the two dimensions of voltage and current, and improving the sensitivity and reliability of the triggering of the rapid overload protection mode. As a result, the obtained over-threshold ratio can more comprehensively and accurately reflect the comprehensive abnormal fluctuations of the voltage and current of the smart charging box transformer, providing more effective data support for the subsequent adaptive factor generation and overload protection threshold adjustment, solving the problem that the over-threshold ratio calculation method in the prior art is one-sided and may lead to a reduction in the accuracy and timeliness of the rapid overload protection mode, and improving the safe operation performance of the smart charging box transformer under abnormal voltage and current fluctuation conditions.

[0125] In some preferred embodiments, step S5 includes:

[0126] S51. Determine an adjustment step size and an adjustment direction according to the temperature margin, and calculate and obtain multiple candidate overload protection thresholds according to the adjustment step size, the adjustment direction, and the initial overload protection threshold;

[0127] S52. Calculate the load margin of the box-type transformer according to the load information and each candidate overload protection threshold;

[0128] S53. Obtain historical load fluctuation frequency and historical overload duration based on the load margin of each box-type transformer, and evaluate the risk level of the box-type transformer operation under each candidate overload protection threshold based on the ambient temperature information, historical load fluctuation frequency, and historical overload duration.

[0129] S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

[0130] Specifically, step S51 can divide the range from zero to the temperature margin into multiple intervals according to a preset number, and use the upper limit of each interval to correspond to a different adjustment step size, which is used to adjust the initial overload protection threshold to obtain multiple candidate overload protection thresholds.

[0131] More specifically, in step S52, the load margin of the box transformer can be understood as the remaining space between the actual load of the box transformer and the overload protection threshold under the current candidate overload protection threshold. The larger the value, the higher the operating safety of the box transformer, but it may also limit the full utilization of the load capacity of the box transformer.

[0132] More specifically, in step S53, the risk level assessment process is as follows: first, based on the ambient temperature information, the preset ambient temperature and heat dissipation capacity correspondence table is queried to obtain the heat dissipation capacity level corresponding to the current ambient temperature. Then, based on the historical load data, the historical load fluctuation frequency and historical overload duration of the intelligent charging box transformer within the preset time window when different box transformer load margins are set are searched and counted. The historical load fluctuation frequency is obtained by counting the number of times the load changes exceed the preset threshold per unit time, and the historical overload duration is obtained by accumulating the time that the load exceeds the candidate overload protection threshold. Finally, based on the heat dissipation capacity level, historical load fluctuation frequency and historical overload duration, a pre-trained risk assessment model or a pre-set risk assessment table is used to obtain the risk level of the box transformer operation under each candidate overload protection threshold.

[0133] More specifically, in step S54 , by comparing the risk levels corresponding to the candidate overload protection thresholds, the candidate overload protection threshold with the lowest risk level is selected as the final adjusted overload protection threshold.

[0134] More specifically, the candidate overload protection threshold with the lowest risk level is selected as the final adjustment result, achieving optimal adjustment of the overload protection threshold. By introducing a risk assessment mechanism, the adjustment of the overload protection threshold is no longer simply determined by the temperature margin alone. Instead, it comprehensively considers multiple factors, including the environment and load. This makes the adjustment more scientific and reasonable, and achieves a balance between safety and load requirements.

[0135] In some preferred embodiments, step S53 includes:

[0136] S531. According to the ambient temperature information, query a preset ambient temperature and heat dissipation capacity correspondence table to obtain a heat dissipation capacity level corresponding to the current ambient temperature;

[0137] S532. Based on the transformer load margin and in combination with historical load data, calculate the historical load fluctuation frequency and historical overload duration within a preset time window when setting different transformer load margins. The historical load fluctuation frequency is obtained by counting the number of times the load change exceeds a preset threshold per unit time, and the historical overload duration is obtained by accumulating the time the load exceeds the candidate overload protection threshold. The preset threshold is positively correlated with the transformer load margin.

[0138] S533. Based on the heat dissipation capability level, historical load fluctuation frequency, and historical overload duration, a pre-trained risk assessment model is used to calculate the risk level of the box transformer operation under each candidate overload protection threshold. The risk assessment model uses the heat dissipation capability level, historical load fluctuation frequency, and historical overload duration as inputs and the risk level as output. The risk assessment model is periodically updated based on historical data to adapt to changes in load characteristics.

[0139] Specifically, in step S531, the table of correspondences between ambient temperature and heat dissipation capacity can be preset as a lookup table. This lookup table records the heat dissipation capacity levels of the box-type transformer at different ambient temperatures. Ambient temperature is a key factor affecting the heat dissipation capacity of the box-type transformer. Higher ambient temperatures generally indicate reduced heat dissipation capacity and increased overload risk. By considering the impact of ambient temperature on heat dissipation capacity, risk assessments can be more accurately tailored to actual operating conditions.

[0140] More specifically, step S532 combines the transformer's load margin with historical load data to calculate the historical load fluctuation frequency and historical overload duration within a preset time window. The historical load fluctuation frequency reflects the severity of load fluctuations, while the historical overload duration reflects the duration of the transformer's operation under high load. These two parameters represent the transformer's operating pressure and potential risks from the load side.

[0141] More specifically, the risk assessment model is trained using historical operating data to learn the mapping between heat dissipation capacity, historical load fluctuation frequency, and historical overload duration, and the risk level of the box-type transformer. The risk assessment model comprehensively considers multiple factors, including ambient temperature, load fluctuation, and overload duration, to provide a comprehensive assessment of the box-type transformer's operational risk. By selecting the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold, the box-type transformer's overload capacity can be fully utilized while ensuring safe operation.

[0142] Second, please refer to Figure 2 Some embodiments of the present application further provide a device for dynamically calculating an overload protection threshold of a smart charging box transformer, which is used to adjust the overload protection threshold to provide overload protection for the smart charging box transformer. The device includes:

[0143] The acquisition module 201 is used to continuously acquire the load information, internal temperature information, and ambient temperature information of the smart charging box;

[0144] The model configuration module 202 is used to identify and obtain a set of parameters of a dynamic thermal model based on load information, internal temperature information, ambient temperature information, and historical operation information to determine a dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between changes in load and ambient temperature of the transformer and changes in internal temperature of the transformer;

[0145] The temperature prediction module 203 is used to use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on the load information and the ambient temperature information;

[0146] The margin calculation module 204 is configured to calculate and obtain a temperature margin based on the predicted temperature inside the box and a preset safety temperature threshold;

[0147] The threshold adjustment module 205 is configured to adjust the overload protection threshold according to the temperature margin.

[0148] The threshold dynamic calculation device for overload protection of the intelligent charging box transformer in the embodiment of the present application dynamically identifies a dynamic thermal model based on load information, temperature information inside the box, and ambient temperature information to predict the temperature inside the box at the next moment, and calculates the temperature margin based on the temperature inside the box at the next moment to adjust the overload protection threshold, thereby realizing adaptive adjustment of the overload protection threshold, so that the overload protection strategy can dynamically change according to the actual operating status of the box transformer, which not only ensures the safe operation of the box transformer under conditions of slow heat dissipation and load fluctuations, but also fully utilizes the overload capacity of the box transformer and improves the continuity of charging services. Compared with the traditional fixed threshold overload protection device, the threshold dynamic calculation device for overload protection of the intelligent charging box transformer in the embodiment of the present application can more effectively deal with the overload protection problem of the intelligent charging box transformer in the complex working conditions of the underground space, thereby improving the safety and reliability of the system.

[0149] In some preferred embodiments, the threshold adjustment module 205 is also used to monitor online whether the voltage and current of the smart charging box transformer have abnormal fluctuations. When abnormal fluctuations occur, an adaptive factor is generated according to the degree of fluctuation of the abnormal fluctuation, and the overload protection threshold is adjusted according to the adaptive factor compensation.

[0150] In some preferred embodiments, the device for dynamically calculating the threshold value of the intelligent charging box variable overload protection of the embodiment of the present application is used to execute the method for dynamically calculating the threshold value of the intelligent charging box variable overload protection provided in the first aspect above.

[0151] Thirdly, please refer to Figure 3Some embodiments of the present application also provide a structural diagram of an electronic device. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not marked). The memory 302 stores computer-readable instructions executable by the processor 301. When the electronic device is running, the processor 301 executes the computer-readable instructions to execute the method in any optional implementation of the above embodiments.

[0152] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method of any optional implementation of the above embodiment is executed. The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0153] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0154] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0156] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0157] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for dynamically calculating the threshold value of overload protection for a smart charging box transformer, which is used to adjust the overload protection threshold value to provide overload protection for the smart charging box transformer, characterized in that: The method comprises the following steps: S1. Continuously obtain the load information, internal temperature information, and ambient temperature information of the smart charging box; S2. Based on the load information, the internal temperature information, the ambient temperature information, and the historical operation information, identify and obtain a parameter set of a dynamic thermal model to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the load change of the box transformer, the ambient temperature change, and the internal temperature change of the box transformer; S3. Using the identified dynamic thermal model, based on the load information and ambient temperature information, predict the temperature inside the box at the next moment; S4. Calculating and obtaining a temperature margin based on the predicted temperature inside the box and a preset safety temperature threshold; S5. Adjusting the overload protection threshold according to the temperature margin; Step S5 includes: S51, determining an adjustment step size and an adjustment direction according to the temperature margin, and calculating and obtaining a plurality of candidate overload protection thresholds according to the adjustment step size and the adjustment direction and an initial overload protection threshold; S52. Calculate the load margin of the box-type transformer according to the load information and each candidate overload protection threshold; S53: Obtain a historical load fluctuation frequency and a historical overload duration based on the ambient temperature information and the load margin of each box-type transformer, and evaluate a risk level of the box-type transformer operation under each candidate overload protection threshold based on the historical load fluctuation frequency and the historical overload duration; S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

2. The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box according to claim 1 is characterized in that: Step S2 includes: S21. Based on the online parameter identification algorithm, the parameter set of the dynamic thermal model is identified according to the load information, the temperature information inside the box, the ambient temperature information and the historical operation information. The parameter set includes the baseline thermal time constant, the load influence coefficient, the heat gain coefficient, the nonlinear dissipation coefficient and the baseline delay step number. The historical operation information includes: historical load information, historical temperature information inside the box and historical ambient temperature information.

3. The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box according to claim 1 is characterized in that: Step S4 includes: S41, calculating and obtaining an initial temperature margin based on the difference between the predicted temperature inside the box and a preset safety temperature threshold; S42 . Smoothing the initial temperature margin using a first-order lag filtering algorithm to obtain the temperature margin.

4. The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box according to claim 3 is characterized in that: The filter coefficient of the first-order lag filter algorithm is adaptively adjusted according to the fluctuation degree of the load information of the box transformer. The adaptive adjustment process includes: S421. Calculate and obtain a current load change rate based on the load information change; S422. Determine the filter coefficient using a piecewise function according to the current load change rate.

5. The method for dynamically calculating the threshold value of the overload protection of the intelligent charging box according to claim 1 is characterized in that: The method further comprises the following steps: S6. Online monitoring of the voltage and current of the smart charging box transformer to see if there is any abnormal fluctuation. When such abnormal fluctuation occurs, an adaptive factor is generated according to the degree of the abnormal fluctuation, and the adjusted overload protection threshold is compensated according to the adaptive factor.

6. A dynamic calculation method for threshold value of overload protection of intelligent charging box according to claim 5, characterized in that: Step S6 includes: S61. Monitor the voltage and current information of the smart charging box transformer in real time, and calculate the voltage fluctuation rate and the current fluctuation rate respectively according to the voltage information and the current information; S62: determining whether the voltage fluctuation rate and / or the current fluctuation rate exceeds a corresponding preset fluctuation threshold; if so, determining that abnormal voltage and current fluctuations occur; S63. When the abnormal fluctuation occurs, calculating an exceeding threshold ratio according to the voltage fluctuation rate and the current fluctuation rate; S64, calculating an adaptive factor according to the exceeding threshold ratio and a preset adaptive coefficient; S65. Compensate the adjusted overload protection threshold according to the adaptive factor.

7. A device for dynamically calculating the threshold value of overload protection for a smart charging box transformer, used to adjust the overload protection threshold value to provide overload protection for the smart charging box transformer, characterized in that: The device comprises: The acquisition module is used to continuously obtain the load information, internal temperature information, and ambient temperature information of the smart charging box; The model configuration module is used to identify and obtain the parameter set of the dynamic thermal model based on load information, internal temperature information, ambient temperature information, and historical operation information to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the load change of the box transformer, the change in ambient temperature, and the change in internal temperature of the box transformer; The temperature prediction module is used to use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on load information and ambient temperature information; A margin calculation module is used to calculate and obtain a temperature margin based on the predicted temperature inside the box and a preset safety temperature threshold; A threshold adjustment module, configured to adjust an overload protection threshold according to the temperature margin; The step of adjusting the overload protection threshold according to the temperature margin includes: S51, determining an adjustment step size and an adjustment direction according to the temperature margin, and calculating and obtaining a plurality of candidate overload protection thresholds according to the adjustment step size and the adjustment direction and an initial overload protection threshold; S52. Calculate the load margin of the box-type transformer according to the load information and each candidate overload protection threshold; S53: Obtain a historical load fluctuation frequency and a historical overload duration based on the ambient temperature information and the load margin of each box-type transformer, and evaluate a risk level of the box-type transformer operation under each candidate overload protection threshold based on the historical load fluctuation frequency and the historical overload duration; S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 6 are executed.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are executed.

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