Control method and device of intelligent charging box transformer substation, electronic equipment and storage medium

Abstract

The invention relates to the technical field of box-type transformers, and particularly discloses a control method and device for an intelligent charging box transformer substation, electronic equipment and a storage medium, and the method comprises the steps: collecting operation parameters and environment parameters of internal parts of the charging box transformer substation; according to the flying dust concentration information and the vibration intensity information, calculating an environment severity degree index; according to the environmental severity index, dynamically adjusting a preset initial fault threshold matched with the operation parameter type by using a nonlinear function to obtain an adjusted fault threshold; monitoring operation parameters of the corresponding parts based on the fault threshold values to perform early warning control; according to the method, the environment severe degree index determined based on the environment parameters is introduced to compensate and adjust the fault threshold value, the adjusted fault threshold value is utilized to perform fault early warning control on the intelligent charging box transformer substation, the fault early warning control mode can be dynamically adjusted according to the environment severe degree, the interference of environment factors on the intelligent charging box transformer substation is reduced, and the fault early warning control efficiency is improved. And the operation reliability of the charging box transformer substation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of box-type transformers, and more specifically, to a control method, device, electronic device, and storage medium for an intelligent charging box transformer. Background Art

[0002] In the complex and ever-changing environment of construction sites, smart charging boxes have become an essential component to efficiently meet the power supply needs of various construction equipment and vehicles. As the core energy supply facility for construction machinery and electric transport vehicles, their stable operation directly affects project progress and safety.

[0003] However, the harsh environmental conditions unique to construction sites, such as the high concentration of dust in the air, not only exacerbate the pollution and aging of electrical equipment, but may also affect the sensitivity of sensors and electronic components. In addition, the frequent ground vibrations caused by construction activities further pose a challenge to the stable operation of the smart box transformer.

[0004] Although the intelligent box-type transformer is designed with a fault warning control system based on preset conditions, aiming to identify and deal with potential problems in advance, its fault warning mechanism is easily interfered with by the combined effects of the above-mentioned environmental factors, resulting in misjudgment, which not only affects charging efficiency but may also have an adverse impact on the overall construction progress and safety management of the construction site.

[0005] There is currently no effective technical solution to the above problems. Summary of the Invention

[0006] The purpose of this application is to provide a control method, device, electronic device and storage medium for an intelligent charging box transformer, so as to dynamically adjust the fault threshold used for fault warning control under harsh environmental conditions, avoid interference with the fault warning mechanism, and improve the reliability of the operation of the charging box transformer.

[0007] In a first aspect, the present application provides a control method for an intelligent charging box transformer, for performing fault early warning control on the charging box transformer, the method comprising the following steps: S1. Collecting operating parameters and environmental parameters of internal components of the charging box transformer, wherein the environmental parameters include dust concentration information and vibration intensity information; S2. Calculating an environmental severity index based on the dust concentration information and the vibration intensity information; S3. For each component inside the charging box transformer, dynamically adjust, based on the environmental severity index, a preset initial fault threshold that matches the operating parameter type using a nonlinear function to obtain an adjusted fault threshold; S4. Monitoring the operating parameters of corresponding components based on the fault threshold and performing early warning control.

[0008] The control method of the smart charging box transformer of the present application introduces an environmental severity index determined based on environmental parameters to compensate and adjust the fault threshold, so as to use the adjusted fault threshold to perform fault warning control on the smart charging box transformer. It can dynamically adjust the fault warning control method according to the severity of the environment, significantly improve the adaptability and robustness of the smart charging box transformer in harsh environments such as dust and vibration, effectively reduce the interference of environmental factors on the smart charging box transformer, and can realize effective monitoring and early warning of the operating status of the smart charging box transformer in harsh environments, thereby improving the reliability of the operation of the charging box transformer.

[0009] The control method of the intelligent charging box transformer, wherein step S2 includes: S21. Normalize the dust concentration information and the vibration intensity information respectively to obtain a normalized dust concentration value and a normalized vibration intensity value; S22. Performing a weighted summation of the normalized dust concentration value and the normalized vibration intensity value according to a predetermined dust weight coefficient and a vibration weight coefficient to obtain an initial environmental severity index; S23 , using a first-order lag filtering algorithm to filter the initial environmental severity index to obtain an environmental severity index.

[0010] The above processing process can comprehensively consider the joint impact of dust and vibration on the severity of the environment to obtain a stable initial index of environmental severity, and can provide reliable basic data for the subsequent dynamic adjustment of the fault threshold based on the environmental severity index, thereby improving the early warning and control effect of the charging box transformer in harsh environments.

[0011] The control method of the intelligent charging box transformer, wherein step S21 includes: S211. Perform fast Fourier transform on the dust concentration information and the vibration intensity information to obtain a dust concentration spectrum and a vibration intensity spectrum; S212. Extracting main periodic components within a preset time window based on the dust concentration spectrum and the vibration intensity spectrum to obtain a main period of dust concentration and a main period of vibration intensity; S213: Filter the main period of dust concentration and the main period of vibration intensity based on a pre-built periodic filter to eliminate periodic fluctuations; S214. The filtered dust concentration main period and the vibration intensity main period are normalized using a maximum and minimum value scaling method to obtain a normalized dust concentration value and a normalized vibration intensity value.

[0012] The above steps can effectively remove the periodic fluctuations in the original dust concentration information and vibration intensity information, improve the accuracy of the normalization processing, and thus improve the accuracy of the environmental severity index.

[0013] The control method of the intelligent charging box transformer, wherein step S3 includes: S31, obtaining a preset environmental sensitivity steepness coefficient and a preset maximum attenuation ratio of a fault threshold; S32: Calculate an environmental impact factor according to the maximum attenuation ratio of the fault threshold, the environmental sensitivity steepness coefficient, and the environmental severity index, where the environmental impact factor is used to characterize the degree of influence of the environmental severity index on the initial fault threshold; S33: Calculate an adjusted fault threshold according to the initial fault threshold and the environmental impact factor.

[0014] In the control method of the smart charging box transformer, step S3 further includes the following steps executed between step S32 and step S33: S3A, obtaining the preset environmental sensitivity coefficient corresponding to each component; Step S33 includes: S331. Compensate and adjust the environmental impact factor according to the environmental sensitivity coefficient, where the environmental sensitivity coefficient is used to adjust the degree of influence of the environmental impact factor on the initial fault threshold; S332: Calculate an adjusted fault threshold according to the initial fault threshold and the compensated and adjusted environmental impact factor.

[0015] The control method of the intelligent charging box transformer, wherein step S4 includes: S41. Determine whether the number of consecutive times that the real-time operating parameter of the component exceeds the fault threshold reaches a set number, where the set number is determined based on the use environment and component type of the charging box transformer; S42. If the set number of times is reached, the early warning mechanism is triggered, which includes local sound and light alarms and remote data upload.

[0016] In the control method of the intelligent charging box transformer, the initial fault threshold is determined based on the following method: C1. Query the preset component initial fault threshold table based on the component type to obtain the initial fault threshold corresponding to each component; C2. For components whose corresponding initial failure thresholds cannot be found in the component initial failure threshold table, obtain historical operating parameter data of the component, and based on the historical operating parameter data, use a statistical analysis method to calculate the distribution range of the operating parameters of the component under normal operating conditions, and use the upper limit value of the distribution range as the initial failure threshold of the component.

[0017] In a second aspect, the present application further provides a control device for an intelligent charging box transformer, for performing fault warning control on the charging box transformer, the device comprising: A collection module, configured to collect operating parameters and environmental parameters of the internal components of the charging box transformer, wherein the environmental parameters include dust concentration information and vibration intensity information; A first calculation module is used to calculate an environmental severity index based on the dust concentration information and the vibration intensity information; a second calculation module, configured to dynamically adjust, for each component inside the charging box transformer, a preset initial fault threshold that matches the operating parameter type and is matched with the environmental severity index using a nonlinear function to obtain an adjusted fault threshold; The control module is used to monitor the operating parameters of the corresponding components based on the fault threshold and perform early warning control.

[0018] The control device of the smart charging box transformer of the present application introduces an environmental severity index determined based on environmental parameters to compensate and adjust the fault threshold, so as to use the adjusted fault threshold to perform fault warning control on the smart charging box transformer. It can dynamically adjust the fault warning control mode according to the severity of the environment, significantly improving the adaptability and robustness of the smart charging box transformer in harsh environments such as dust and vibration, effectively reducing the interference of environmental factors on the smart charging box transformer, and can realize effective monitoring and early warning of the operating status of the smart charging box transformer in harsh environments, thereby improving the reliability of the operation of the charging box transformer.

[0019] 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.

[0020] 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.

[0021] From the above, it can be seen that the present application provides a control method, device, electronic device and storage medium for an intelligent charging box transformer, wherein the control method of the intelligent charging box transformer introduces an environmental severity index determined based on environmental parameters to compensate and adjust the fault threshold, so as to use the adjusted fault threshold to perform fault warning control on the intelligent charging box transformer, and can dynamically adjust the fault warning control mode according to the severity of the environment, significantly improving the adaptability and robustness of the intelligent charging box transformer in harsh environments such as dust and vibration, effectively reducing the interference of environmental factors on the intelligent charging box transformer, and can realize effective monitoring and early warning of the operating status of the intelligent charging box transformer in harsh environments, thereby improving the reliability of the operation of the charging box transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a control method for a smart charging box transformer provided in an embodiment of the present application.

[0023] Figure 2 This is a schematic diagram of the structure of the control device of the smart charging box transformer provided in an embodiment of the present application.

[0024] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0025] Reference numerals: 201, acquisition module; 202, first calculation module; 203, second calculation module; 204, control module; 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0026] 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.

[0027] 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.

[0028] First, please refer to Figure 1Some embodiments of the present application provide a control method for an intelligent charging box transformer, which is used to perform fault early warning control on the charging box transformer. The method includes the following steps: S1. Collect operating parameters and environmental parameters of the internal components of the charging box transformer, including dust concentration information and vibration intensity information; S2. Calculate the environmental severity index based on the dust concentration information and vibration intensity information; S3. For each component inside the charging box transformer, according to the environmental severity index, a nonlinear function is used to dynamically adjust the preset initial fault threshold that matches the operating parameter type to obtain an adjusted fault threshold; S4. Monitor the operating parameters of corresponding components based on the fault threshold and perform early warning control.

[0029] Specifically, in step S1, the operating parameters may include but are not limited to temperature, current, voltage, insulation status, and other parameters that can reflect the operating status of the internal components of the charging box transformer. Environmental parameters are limited to dust concentration information and vibration intensity information. This is to take into account the typical harsh environmental characteristics of urban fringe areas such as temporary construction sites, and is used to evaluate the impact of the environment on the charging box transformer. The components are the main operating devices inside the charging box transformer, such as the main transformer, circuit breaker, contactor, etc. These parameter information can be obtained by setting various sensors for real-time collection. Among them, the operating parameters are collected by various sensors deployed inside the charging box transformer, such as temperature sensors, current sensors, voltage sensors, insulation sensors, etc. The dust concentration information in the environmental parameters is collected by the dust sensor, and the vibration intensity information is collected by the vibration sensor.

[0030] More specifically, the purpose of calculating the environmental severity index in step S2 is to integrate multi-dimensional environmental information into a single quantitative indicator, providing a basis for the subsequent dynamic adjustment of the fault threshold. The environmental severity index can be calculated using a weighted summation method. For example, weights are assigned to the normalized dust concentration and vibration intensity values, and then the sum is used to obtain the environmental severity index.

[0031] More specifically, in step S3, the fault threshold is adjusted dynamically for each component. The application of a nonlinear function allows for more precise adaptation of the fault threshold to changes in environmental severity. The initial fault threshold is a baseline value, set for each component type based on the component's rated parameters, historical operating data, and empirical data. A sigmoid function can be used as the nonlinear function. By adjusting the sigmoid function's parameters, the degree of influence of the environmental severity index on the fault threshold and the curve of its change can be controlled.

[0032] More specifically, in step S4, early warning control based on the adjusted fault threshold can improve the accuracy and timeliness of fault warnings in harsh environments. Once a component's operating parameters are detected to have exceeded the adjusted fault threshold, an early warning mechanism is triggered, such as emitting an audible or visual alarm signal, and uploading the warning information to a remote monitoring platform, allowing operations personnel to take timely measures to prevent the failure.

[0033] More specifically, to adapt to environmental changes, this solution uses a nonlinear function to dynamically adjust the initial fault threshold based on the calculated environmental severity index, resulting in an adjusted fault threshold that better matches the current environmental conditions. For example, when the environmental severity index is high, the adjusted fault threshold will be relatively low, and vice versa.

[0034] The control method of the smart charging box transformer in the embodiment of the present application introduces an environmental severity index determined based on environmental parameters to compensate and adjust the fault threshold, so as to use the adjusted fault threshold to perform fault warning control on the smart charging box transformer. It can dynamically adjust the fault warning control method according to the severity of the environment, significantly improve the adaptability and robustness of the smart charging box transformer in harsh environments such as dust and vibration, effectively reduce the interference of environmental factors on the smart charging box transformer, and can realize effective monitoring and early warning of the operating status of the smart charging box transformer in harsh environments, thereby improving the reliability of the charging box transformer operation.

[0035] In some preferred embodiments, step S2 includes: S21. Normalize the dust concentration information and the vibration intensity information respectively to obtain a normalized dust concentration value and a normalized vibration intensity value; S22. Performing a weighted summation of the normalized dust concentration value and the normalized vibration intensity value based on a predetermined dust weight coefficient and a vibration weight coefficient to obtain an initial environmental severity index; S23. Use a first-order lag filtering algorithm to filter the initial environmental severity index to obtain an environmental severity index.

[0036] Specifically, in step S21, the normalization process is to eliminate the problems of inconsistent units and large differences in numerical ranges between the dust concentration information and the vibration intensity information, to ensure the comparability of the data, and to unify the dust concentration information and vibration intensity information with different dimensions and numerical ranges into the same numerical range interval, thereby eliminating the impact of the differences in dimensions and numerical ranges on the subsequent calculation of the environmental severity index.

[0037] More specifically, in step S22, the dust weight coefficient and the vibration weight coefficient are used to distinguish the different degrees of influence of dust and vibration on the severity of the environment. The determination of the weight coefficient can be based on empirical data, experimental analysis, or simulation methods. For example, in some application scenarios, dust may have a greater impact on the charging box. In this case, the dust weight coefficient can be set to a value greater than the vibration weight coefficient. The sum of the dust weight coefficient and the vibration weight coefficient is preferably 1. This step comprehensively evaluates the severity of the environment by distinguishing the different effects of dust and vibration on the severity of the environment according to predetermined weight coefficients.

[0038] More specifically, in step S23, a first-order lag filtering algorithm is used to filter the initial index of environmental severity, which can effectively smooth the fluctuations of the initial index of environmental severity and weaken the interference of noise data on the calculation results of the environmental severity index, thereby improving the accuracy and stability of the environmental severity index.

[0039] More specifically, the above processing process can comprehensively consider the joint impact of dust and vibration on the severity of the environment to obtain a stable initial index of environmental severity, and can provide reliable basic data for the subsequent dynamic adjustment of the fault threshold based on the environmental severity index, thereby improving the early warning and control effect of the charging box transformer in harsh environments.

[0040] In some preferred embodiments, the dust weight coefficient and the vibration weight coefficient are determined based on the following steps: A1. Obtain historical meteorological data within a preset statistical period, including wind speed and rainfall; A2. Calculate the dust attenuation coefficient and vibration attenuation coefficient based on the distance between the charging box transformer and the main dust and vibration sources; A3. Based on the obtained wind speed, rainfall, dust attenuation coefficient, and vibration attenuation coefficient, the adjusted dust weight coefficient and vibration weight coefficient are calculated according to the preset formula.

[0041] Specifically, in step A1, the preset statistical period can be a time span of half a day, 1 day, 3 days, 5 days, 1 week, etc. Historical meteorological data can be obtained through meteorological API interface, historical meteorological database or local meteorological station, etc. The meteorological data includes at least the average wind speed and cumulative rainfall within the statistical period.

[0042] More specifically, in step A2, the distance between the charging box transformer and the main dust source and vibration source can be determined through GPS positioning, on-site survey or design drawings, and the dust attenuation coefficient and vibration attenuation coefficient can be calculated based on the distance attenuation model, empirical formula or experimental data. The attenuation coefficient characterizes the degree to which the dust and vibration intensity decreases with increasing distance.

[0043] More specifically, step A3 preferably calculates the dust weight coefficient and the vibration weight coefficient based on the following formula: W da =W d *(1+α*V-β*R)*D d (1) W va =W v *D v (2) Among them, W da is the adjusted dust weight coefficient, W va is the adjusted vibration weight coefficient, W d is the initial dust weight coefficient, Wv is the initial vibration weight coefficient, V is the wind speed, R is the rainfall, α is the wind speed influence factor, β is the rainfall influence factor, D d is the dust attenuation coefficient, D v is the vibration attenuation coefficient.

[0044] More specifically, the wind speed influence factor α and the rainfall influence factor β are pre-set parameters used to adjust the degree of influence of wind speed and rainfall on the weight coefficient. For example, α can be set to 0.1 and β can be set to 0.05. The numerical values of these factors can be determined based on actual application scenarios and empirical data.

[0045] More specifically, the dust attenuation coefficient D d and vibration attenuation coefficient D v The value range of can be set between 0 and 1, and the smaller the value, the greater the attenuation degree. Step A2 can extract the dust attenuation coefficient and the vibration attenuation coefficient based on a preset linear relationship or mapping table between distance and attenuation coefficient.

[0046] More specifically, the initial dust weight coefficient W d and the initial vibration weight coefficient W v The weight coefficients can be pre-set based on experience or experimental data. For example, if dust and vibration have similar impacts on environmental severity, both can be set to 0.5. Through the above steps, an adjusted dust weight coefficient and vibration weight coefficient that reflect the current environmental meteorological conditions and attenuation characteristics can be obtained.

[0047] More specifically, to address the issue of static weight coefficients in calculating the environmental severity index, the control method for the smart charging box transformer in the embodiment of the present application proposes a method for dynamically adjusting the weight coefficients based on steps A1-A3 to address the issue of inaccurate calculation of the environmental severity index. Through the above-described processing method, the dust weight coefficient and the vibration weight coefficient can achieve the following adaptive adjustments based on environmental changes: 1) In terms of meteorological conditions, the greater the wind speed, the more easily dust is dispersed, so the adjusted dust weight coefficient also increases accordingly; the greater the rainfall, the more dust is suppressed, so the adjusted dust weight coefficient decreases accordingly; 2) In terms of attenuation characteristics, the farther the charging box transformer is from the dust or vibration source, the less the impact of dust or vibration, so the dust attenuation coefficient and the vibration attenuation coefficient also decrease accordingly, and thus the adjusted weight coefficient also decreases. Therefore, by dynamically adjusting the weight coefficients, the calculation of the environmental severity index can more accurately reflect the actual environmental conditions, providing a more reliable basis for subsequent fault warning control, thereby improving the effectiveness of fault warning control based on the environmental severity index.

[0048] In some preferred embodiments, step S23 includes: S231. Calculate the environmental severity index based on a first-order lag filtering algorithm. The first-order lag filtering algorithm is: ESI=a*ESI0+(1-a)*ESI pre (3) Among them, ESI is the environmental severity index, ESI0 is the initial environmental severity index at the current moment, and ESI pre is the environmental severity index at the previous moment, a is the filter coefficient, and 0 <a<1。

[0049] It should be noted that if the current moment is the first time to calculate the environmental severity index, the environmental severity initial index ESI0 is assigned to the environmental severity index ESI at the previous moment. pre To ensure that the first environmental severity index can be generated smoothly, thereby ensuring the normal operation of the filtering algorithm; if the current moment is not the first time to calculate the environmental severity index, the currently calculated environmental severity index ESI is assigned to the environmental severity index ESI of the previous moment pre , in order to facilitate the next filtering calculation.

[0050] Specifically, step S231 adopts a first-order lag filtering algorithm, and uses a formula to weightedly fuse the initial environmental severity index at the current moment and the environmental severity index at the previous moment to calculate the filtered environmental severity index at the current moment.

[0051] More specifically, the filtering coefficient controls the filtering intensity, and its value range is 0 < a < 1. A smaller value of a produces a stronger filtering effect, the environmental severity index is more affected by the previous moment's value, with less volatility, but may lead to a reduced response speed to environmental changes; a larger value of a has the opposite effect, and it can be set according to the usage requirements. By adjusting the filtering coefficient, the intensity and response speed of filtering can be controlled, while ensuring the filtering effect, taking into account the sensitivity to environmental changes.

[0052] More specifically, this first-order lag filtering algorithm obtains the environmental severity index at the current moment in a weighted average manner by comprehensively considering the initial environmental severity index at the current moment and the environmental severity index at the previous moment, realizing the smoothing of the environmental severity index, effectively reducing the volatility of the environmental severity index, enabling the environmental severity index to more stably reflect the overall trend of the environmental severity, providing a more stable input for dynamically adjusting the fault threshold based on the environmental severity index subsequently, and thus improving the accuracy and reliability of the charging box transformer fault warning control.

[0053] In some preferred embodiments, step S21 includes: S211. Perform fast Fourier transforms on the dust concentration information and the vibration intensity information respectively to obtain the dust concentration spectrum and the vibration intensity spectrum; S212. According to the dust concentration spectrum and the vibration intensity spectrum, respectively extract the main periodic components within a preset time window to obtain the dust concentration main period and the vibration intensity main period; S213. Perform filtering processing on the dust concentration main period and the vibration intensity main period based on a pre-constructed periodic filter to eliminate the periodic fluctuations; S214. For the dust concentration main period and the vibration intensity main period after the filtering processing, perform normalization processing using the maximum-minimum scaling method to obtain the normalized dust concentration value and the normalized vibration intensity value.

[0054] Specifically, step S211 analyzes the frequency components in the dust concentration information and the vibration intensity information through fast Fourier transform to obtain the corresponding spectra, so as to successfully identify the frequencies corresponding to the periodic fluctuations existing in the data. Among them, the fast Fourier transform can be implemented using the Cooley-Tukey algorithm, which can efficiently convert the time-domain signal to the frequency domain.

[0055] More specifically, in step S212, the extraction of the main periodic components can be achieved by setting a threshold, such as regarding the frequency components with energy higher than the threshold in the spectrum as the main periodic components, or by using a peak detection algorithm to automatically identify the significant peaks in the spectrum, and these peaks represent the main periodic components in the data.

[0056] More specifically, in step S213, the periodic filter may be a Butterworth filter or a Chebyshev filter, which have good filtering characteristics in the frequency domain and can effectively eliminate periodic fluctuations within a specific frequency range, thereby reducing the impact of periodic interference on data.

[0057] More specifically, in step S214, the Min-Max Scaling method is used to implement normalization processing, which is expressed as X normalized =(XX min ) / (X max -X min ), where X normalized represents the normalized value, X represents the original data, and X min and X max The normalization process ensures the comparability of data of different dimensions, providing a data basis for the accurate calculation of the subsequent environmental severity index.

[0058] More specifically, the above steps can effectively remove the periodic fluctuations in the original dust concentration information and vibration intensity information, improve the accuracy of the normalization processing, and thus improve the accuracy of the environmental severity index.

[0059] In some preferred embodiments, step S3 includes: S31, obtaining a preset environmental sensitivity steepness coefficient and a preset maximum attenuation ratio of a fault threshold; S32. Calculate an environmental impact factor based on the maximum attenuation ratio of the fault threshold, the environmental sensitivity steepness coefficient, and the environmental severity index. The environmental impact factor is used to characterize the degree of influence of the environmental severity index on the initial fault threshold. S33. Calculate an adjusted fault threshold based on the initial fault threshold and the environmental impact factor, which satisfies: T a，i =T in,i *H (4) Among them, H satisfies: H=(1-γ / (1+exp(-k*ESI))) (5) Among them, H is the environmental impact factor, T a，i is the fault threshold of the i-th component after adjustment, T in,i is the initial fault threshold of the i-th component, γ is the maximum attenuation ratio of the fault threshold, k is the environmental sensitivity steepness coefficient, and ESI is the environmental severity index.

[0060] Specifically, in step S31, the environmental sensitivity steepness coefficient and the maximum attenuation ratio of the fault threshold are pre-set parameters used to control the adjustment characteristics of the nonlinear function. The environmental sensitivity steepness coefficient determines the sensitivity of the fault threshold to changes in environmental severity. A high value indicates that the fault threshold changes rapidly with the environmental severity index, while a low value indicates that the fault threshold changes slowly with the environmental severity index. The maximum attenuation ratio of the fault threshold limits the maximum extent to which the initial fault threshold can be reduced, ensuring that the adjusted fault threshold is within a reasonable range. The values of these two parameters can be determined through experiments or simulation analysis based on the component type, application scenario, and historical operating data of the charging box transformer.

[0061] More specifically, in step S32, the environmental impact factor is used to quantify the impact of environmental severity on the initial fault threshold. The environmental impact factor is an intermediate parameter calculated based on the maximum attenuation ratio of the fault threshold, the environmental sensitivity coefficient, and the environmental severity index. Its magnitude directly determines the magnitude of the fault threshold adjustment.

[0062] More specifically, in step S33, the adjusted fault threshold is calculated using the nonlinear function composed of formulas (4) and (5), where the initial fault threshold represents the inherent threshold of the component under an ideal environment. The above calculation method uses the environmental impact factor to adjust the initial fault threshold to obtain a dynamic fault threshold that adapts to the severity of the current environment, so that the adjusted fault threshold decays nonlinearly with the increase of the environmental severity index. When the environmental severity index is small, the adjustment amplitude is small; when the environmental severity index is large, the adjustment amplitude increases, which is more in line with the actual situation of the impact of environmental factors on component aging. In this way, the fault threshold can be adaptively adjusted according to the severity of the environment.

[0063] It should be noted that the nonlinear function in step S3 refers to a nonlinear relationship between the adjusted fault threshold and the environmental severity index.

[0064] More specifically, the adjusted fault threshold adaptively adjusts to changes in environmental severity. In harsh environments, the threshold is appropriately lowered to improve warning sensitivity. In benign environments, the threshold approaches the initial value to avoid false alarms. This dynamic adjustment mechanism allows the warning system to more accurately reflect the actual health status of components, improving the accuracy and reliability of fault warnings.

[0065] In some preferred embodiments, step S3 further includes the following steps performed between step S32 and step S33: S3A, obtaining the preset environmental sensitivity coefficient corresponding to each component; Step S33 includes: S331. Compensate and adjust the environmental impact factor according to the environmental sensitivity coefficient, where the environmental sensitivity coefficient is used to adjust the degree of influence of the environmental impact factor on the initial fault threshold; S332: Calculate an adjusted fault threshold based on the initial fault threshold and the compensated environmental impact factor, satisfying: T a，i =T in,i *H βi (6) Where βi is the environmental sensitivity coefficient of the i-th component, and the environmental impact factor H is calculated using formula (5).

[0066] Specifically, to address the differences in environmental sensitivity among different components, the control method for the smart charging box transformer in the embodiment of the present application further adds step S3A between step S32 and step S33 to obtain a preset environmental sensitivity coefficient for each component. This environmental sensitivity coefficient reflects the degree to which the corresponding component is affected by the environment, that is, it characterizes the component's sensitivity to environmental harshness. Components with high environmental sensitivity have a relatively high environmental sensitivity coefficient value, while components with low environmental sensitivity have a relatively low environmental sensitivity coefficient value. This environmental sensitivity coefficient value can be determined based on factors such as component type, material, structure, or historical failure data.

[0067] More specifically, as a possible implementation, the environmental sensitivity coefficient βi can be pre-stored in a control system database. In the database, each component is associated with a specific environmental sensitivity coefficient βi. When adjusting the fault threshold, the system first identifies the component requiring threshold adjustment and then retrieves the preset environmental sensitivity coefficient βi corresponding to that component from the database.

[0068] More specifically, the control method of the smart charging box transformer in the embodiment of the present application uses this βi to perform exponential adjustment on the environmental impact factor, and then calculates the final adjusted fault threshold based on the adjusted environmental impact factor and the initial fault threshold, forming a refined threshold adjustment strategy, improving the accuracy of fault warning, avoiding the problems of excessive threshold adjustment of environmentally insensitive components and insufficient threshold adjustment of environmentally sensitive components, and realizing differentiated threshold adjustment for the environmental sensitivity of different components, making the warning control more reasonable and effective, and reducing the false alarm rate.

[0069] In some preferred embodiments, step S4 includes: S41. Determine whether the number of consecutive times that the real-time operating parameters of the component exceed the fault threshold reaches a set number, where the set number is determined based on the use environment and component type of the charging box transformer; S42. If the set number of times is reached, the early warning mechanism is triggered, which includes local sound and light alarms and remote data upload.

[0070] Specifically, in order to further improve the accuracy of early warning control, the control method of the smart charging box transformer in the embodiment of the present application introduces a continuous over-threshold judgment mechanism to perform control judgment through the above steps; wherein, the early warning is triggered by judging whether the number of times the component operating parameters continuously exceed the fault threshold reaches a set number, the purpose is to avoid false alarms caused by accidental factors. When performing early warning control, the operating parameters of the component are first monitored in real time. When it is detected that the operating parameters exceed the preset fault threshold, the system will not trigger an early warning immediately, but will start recording the number of times the parameters continuously exceed the threshold. The system will compare this continuous exceedance number with the pre-set number. Only when the continuous exceedance number reaches or exceeds the set number, the component operating state is determined to be abnormal and the early warning mechanism is triggered. On the contrary, if the operating parameter falls back to within the fault threshold before the continuous exceedance number reaches the set number, the continuous count will be cleared, avoiding false alarms caused by instantaneous parameter fluctuations.

[0071] More specifically, the number of set times is adjustable and depends on the actual environment in which the charging box is used and the type of component being monitored. For example, at a temporary construction site with a harsh operating environment, where there are many environmental interference factors and frequent parameter fluctuations, the set number of times can be appropriately increased to reduce false alarms caused by environmental interference. Conversely, in relatively stable environments, the set number of times can be appropriately reduced to increase the sensitivity of the warning.

[0072] More specifically, by adjusting the set number of times, the warning triggering conditions can be adjusted according to actual conditions, thereby improving the flexibility and accuracy of the warning system.

[0073] More specifically, the early warning mechanism includes two methods: local audible and visual alarms and remote data upload. Local audible and visual alarms can promptly alert on-site personnel to address faults; remote data uploads can send early warning information to the monitoring center, facilitating remote monitoring and management by maintenance personnel. These two warning methods complement each other to ensure the safe operation of the charging box transformer.

[0074] More specifically, through the above-mentioned processing method, the early warning control can effectively filter out instantaneous parameter fluctuations caused by environmental interference or accidental factors, and only issue early warnings for persistent parameter anomalies that may actually reflect component failures, thereby reducing the false alarm rate and improving the accuracy of the early warning.

[0075] More specifically, this application triggers an early warning by determining whether a component's operating parameters have exceeded a set number of consecutive fault thresholds. This effectively addresses the problem of false alarms in early warning control and improves the accuracy and reliability of early warnings. By adapting the set number of faults to the charging box transformer's operating environment and component type, the early warning system can better adapt to the needs of different application scenarios, reducing the workload of maintenance personnel, improving the credibility of the early warning system, and ensuring the safe and stable operation of the charging box transformer in various environments.

[0076] In some preferred embodiments, the set number of times is determined based on the following steps: B1. Based on the vibration frequency information and dust concentration information, query the pre-established environmental impact factor table to obtain the comprehensive environmental impact factor; B2. Determine the environmental sensitivity level of the component based on the component type; B3. Calculate and determine the set times corresponding to the component based on the comprehensive impact factor, environmental sensitivity level, and preset times, and satisfy the following requirements: Setting times = [initial setting times * (1 + comprehensive environmental impact factor * environmental sensitivity level)].

[0077] Specifically, a pre-established environmental impact factor table stores the mapping between vibration frequency information, dust concentration information, and comprehensive environmental impact factors. By querying the table, the comprehensive environmental impact factor corresponding to the current environmental state can be quickly obtained. The environmental impact factor table can be established by dividing the vibration frequency information and dust concentration information into multiple intervals, with each interval corresponding to a comprehensive environmental impact factor. The magnitude of the comprehensive environmental impact factor is positively correlated with the severity of the environment.

[0078] More specifically, the environmental sensitivity level of a component is determined by classifying different components according to their sensitivity to environmental factors. For example, a component that is easily affected by vibration has a higher environmental sensitivity level.

[0079] More specifically, in step B2, the environmental sensitivity level of each component within the charging box transformer can be assessed in advance, with each component classified into multiple levels, such as high, medium, and low. A higher level indicates a component is more sensitive to environmental factors. Step B2 queries a preset component environmental sensitivity level table based on the type of component being monitored to obtain the corresponding environmental sensitivity level.

[0080] More specifically, in step B3, the initial set number is used as a base value, weightedly calculated with the comprehensive environmental impact factor and the environmental sensitivity level, and rounded to obtain the final set number.

[0081] More specifically, this application dynamically adjusts the set times based on comprehensive environmental impact factors and environmental sensitivity levels, thereby improving the sensitivity and accuracy of the early warning mechanism. In harsh environments or sensitive components, the set times are increased, reducing the probability of false alarms. In benign environments or insensitive components, the set times are reduced, improving the timeliness of early warnings.

[0082] In some preferred embodiments, the initial failure threshold is determined based on the following: C1. Query the preset component initial fault threshold table based on the component type to obtain the initial fault threshold corresponding to each component; C2. For components whose corresponding initial failure thresholds cannot be found in the component initial failure threshold table, obtain the historical operating parameter data of the component, and based on the historical operating parameter data, use statistical analysis methods to calculate the distribution range of the operating parameters of the component under normal operating conditions, and use the upper limit value of the distribution range as the initial failure threshold of the component.

[0083] Specifically, for step C1, the component types may include various electrical components and mechanical components inside the charging box transformer, such as transformers, circuit breakers, contactors, fans, etc. The preset component initial fault threshold table is a database or file that stores different types of components and their corresponding initial fault thresholds. The table can be established in advance and stored in the memory of the control system. When determining the initial fault threshold, the control system will first query the preset component initial fault threshold table based on the component type for which the threshold currently needs to be set. If there is a record of the component type in the table, the preset initial fault threshold in the table is directly read as the initial fault threshold of the component.

[0084] More specifically, in step C2, if a component with a corresponding initial fault threshold cannot be found in the component initial fault threshold table, this typically refers to new or special models. These components may not be recorded in the preset component initial fault threshold table. In this case, a data-driven approach is needed to determine the initial fault threshold. Specifically, historical operating parameter data for the component over a period of time must be obtained. This data should be collected when the component is operating normally. These operating parameter data can include parameters that reflect the component's operating status, such as temperature, voltage, current, and vibration frequency. Statistical analysis methods are then used to analyze this historical operating parameter data and calculate the distribution range of the component's operating parameters under normal operating conditions. Statistical analysis methods can include calculating the mean, standard deviation, and quantiles. The operating parameter distribution range can be determined in various ways, such as by defining the operating parameter distribution range as the mean plus or minus a number of standard deviations, or by defining the operating parameter distribution range as a confidence interval at a certain confidence level. Finally, the upper limit of this distribution range is used as the initial fault threshold for the component. The advantage of this method is that it can fully consider the operating characteristics of the component itself. By analyzing the historical operating data of the component, the distribution range of the operating parameters of the component under normal operating conditions can be obtained, and the upper limit value of the distribution range is used as the initial fault threshold. The setting of the initial fault threshold is more in line with the actual operating conditions of the component, thereby improving the accuracy of fault warning.

[0085] More specifically, the above processing method, through the combined use of step C1 and step C2, can provide more accurate and reasonable initial fault thresholds for different types of components, improve the effectiveness and reliability of fault warning control, and thus ensure the stable operation of the smart charging box in harsh environments.

[0086] In some preferred embodiments, the operating parameters include temperature information, voltage information, current information, and vibration intensity information, and the fault threshold matches the type of the operating parameter.

[0087] Specifically, the operating parameters are limited to temperature information, voltage information, current information and vibration intensity information, which can fully reflect the operating status of the internal components of the charging box transformer.

[0088] More specifically, temperature information can reflect the heating of internal equipment in the charging box transformer, such as transformers and power devices. Excessively high temperatures usually indicate potential faults such as overload or poor heat dissipation. Voltage and current information can directly reflect the operating load and electrical connection status of electrical equipment. Abnormal voltage fluctuations or current overloads, short circuits, etc. can be monitored. Vibration intensity information can be used to monitor the operating status of mechanical components, such as abnormal fan vibration and loose fasteners. By simultaneously monitoring these types of operating parameters, comprehensive monitoring of the operating status of the charging box transformer's electrical and mechanical systems can be achieved.

[0089] More specifically, the fault threshold matches the type of operating parameter, which means that for each operating parameter, a fault threshold that is appropriate to its characteristics needs to be set. For example, for temperature information, an upper temperature threshold can be set. Once the measured temperature exceeds the threshold, it is determined to be abnormal. For voltage information, an upper voltage threshold and a lower voltage threshold can be set. Deviations from the normal voltage range are considered abnormal. For current information, an upper current threshold can be set, and overcurrent is considered abnormal. For vibration intensity information, an upper vibration intensity threshold can be set, and vibration exceeding the standard is determined to be abnormal. When any operating parameter exceeds the corresponding fault threshold, the system can immediately issue a warning signal to prompt the operation and maintenance personnel to check and handle it in time, thereby avoiding the occurrence or expansion of the fault.

[0090] More specifically, this matching threshold setting method ensures the pertinence and effectiveness of early warning control, avoids false alarms or missed alarms caused by improper threshold settings, and improves the accuracy of fault early warning.

[0091] Second, please refer to Figure 2 Some embodiments of the present application further provide a control device for an intelligent charging box transformer, for performing fault early warning control on the charging box transformer, the device comprising: The collection module 201 is used to collect operating parameters and environmental parameters of the internal components of the charging box transformer, wherein the environmental parameters include dust concentration information and vibration intensity information; A first calculation module 202 is configured to calculate an environmental severity index based on the dust concentration information and the vibration intensity information; A second calculation module 203 is configured to dynamically adjust, for each component inside the charging box transformer, a preset initial fault threshold that matches the operating parameter type and is matched with the environmental severity index using a nonlinear function to obtain an adjusted fault threshold; The control module 204 is configured to monitor the operating parameters of the corresponding components based on the fault threshold and perform early warning control.

[0092] The control device of the smart charging box transformer in the embodiment of the present application introduces an environmental severity index determined based on environmental parameters to compensate and adjust the fault threshold, so as to use the adjusted fault threshold to perform fault warning control on the smart charging box transformer. It can dynamically adjust the fault warning control method according to the severity of the environment, significantly improve the adaptability and robustness of the smart charging box transformer in harsh environments such as dust and vibration, effectively reduce the interference of environmental factors on the smart charging box transformer, and can realize effective monitoring and early warning of the operating status of the smart charging box transformer in harsh environments, thereby improving the reliability of the charging box transformer operation.

[0093] In some preferred embodiments, the control device of the smart charging box transformer of the embodiment of the present application is used to execute the control method of the smart charging box transformer provided in the first aspect above.

[0094] Thirdly, please refer to Figure 3 Some 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 control method for an intelligent charging box transformer, used for fault early warning control of the charging box transformer, characterized in that: The method comprises the following steps: S1. Collecting operating parameters and environmental parameters of internal components of the charging box transformer, wherein the environmental parameters include dust concentration information and vibration intensity information; S2. Calculating an environmental severity index based on the dust concentration information and the vibration intensity information; S3. For each component inside the charging box transformer, dynamically adjust, based on the environmental severity index, a preset initial fault threshold that matches the operating parameter type using a nonlinear function to obtain an adjusted fault threshold; S4. Monitoring the operating parameters of corresponding components based on the fault threshold and performing early warning control.

2. The control method of a smart charging box transformer according to claim 1, characterized in that: Step S2 includes: S21. Normalize the dust concentration information and the vibration intensity information respectively to obtain a normalized dust concentration value and a normalized vibration intensity value; S22. Performing a weighted summation of the normalized dust concentration value and the normalized vibration intensity value according to a predetermined dust weight coefficient and a vibration weight coefficient to obtain an initial environmental severity index; S23 , using a first-order lag filtering algorithm to filter the initial environmental severity index to obtain an environmental severity index.

3. The control method of a smart charging box transformer according to claim 2, characterized in that: Step S21 includes: S211. Perform fast Fourier transform on the dust concentration information and the vibration intensity information to obtain a dust concentration spectrum and a vibration intensity spectrum; S212. Extracting main periodic components within a preset time window based on the dust concentration spectrum and the vibration intensity spectrum to obtain a main period of dust concentration and a main period of vibration intensity; S213: Filter the main period of dust concentration and the main period of vibration intensity based on a pre-built periodic filter to eliminate periodic fluctuations; S214. The filtered dust concentration main period and the vibration intensity main period are normalized using a maximum and minimum value scaling method to obtain a normalized dust concentration value and a normalized vibration intensity value.

4. The control method of a smart charging box transformer according to claim 1, characterized in that: Step S3 includes: S31, obtaining a preset environmental sensitivity steepness coefficient and a preset maximum attenuation ratio of a fault threshold; S32: Calculate an environmental impact factor according to the maximum attenuation ratio of the fault threshold, the environmental sensitivity steepness coefficient, and the environmental severity index, where the environmental impact factor is used to characterize the degree of influence of the environmental severity index on the initial fault threshold; S33: Calculate an adjusted fault threshold according to the initial fault threshold and the environmental impact factor.

5. The control method of a smart charging box transformer according to claim 4, characterized in that: Step S3 also includes the following steps performed between step S32 and step S33: S3A, obtaining the preset environmental sensitivity coefficient corresponding to each component; Step S33 includes: S331. Compensate and adjust the environmental impact factor according to the environmental sensitivity coefficient, where the environmental sensitivity coefficient is used to adjust the degree of influence of the environmental impact factor on the initial fault threshold; S332: Calculate an adjusted fault threshold according to the initial fault threshold and the compensated and adjusted environmental impact factor.

6. The control method of a smart charging box transformer according to claim 1, characterized in that: Step S4 includes: S41. Determine whether the number of consecutive times that the real-time operating parameter of the component exceeds the fault threshold reaches a set number, where the set number is determined based on the use environment and component type of the charging box transformer; S42. If the set number of times is reached, the early warning mechanism is triggered, which includes local sound and light alarms and remote data upload.

7. The control method of a smart charging box transformer according to claim 1, characterized in that: The initial failure threshold is determined based on: C1. Query the preset component initial fault threshold table based on the component type to obtain the initial fault threshold corresponding to each component; C2. For components whose corresponding initial failure thresholds cannot be found in the component initial failure threshold table, obtain historical operating parameter data of the component, and based on the historical operating parameter data, use a statistical analysis method to calculate the distribution range of the operating parameters of the component under normal operating conditions, and use the upper limit value of the distribution range as the initial failure threshold of the component.

8. A control device for an intelligent charging box transformer, used for fault early warning control of the charging box transformer, characterized in that: The device includes: A collection module, configured to collect operating parameters and environmental parameters of the internal components of the charging box transformer, wherein the environmental parameters include dust concentration information and vibration intensity information; A first calculation module is used to calculate an environmental severity index based on the dust concentration information and the vibration intensity information; a second calculation module, configured to dynamically adjust, for each component inside the charging box transformer, a preset initial fault threshold that matches the operating parameter type and is matched with the environmental severity index using a nonlinear function to obtain an adjusted fault threshold; The control module is used to monitor the operating parameters of the corresponding components based on the fault threshold and perform early warning control.

9. 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 of the method according to any one of claims 1 to 7 are executed.

10. 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 7 are executed.

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