Wall-mounted charging pile monitoring method and system with voice interaction function

Abstract

The invention relates to the technical field of charging pile monitoring, and discloses a wall-mounted charging pile monitoring method and system with a voice interaction function, and the system comprises an acquisition module which determines a charging vehicle according to a vehicle image, and a judgment module which judges whether to obtain a thermal image of the charging vehicle according to the input power and the charging power, the processing module extracts thermal inductance pixel points of the thermal inductance image, determines a thermal inductance comprehensive value of the charging vehicle based on thermal inductance feature points, compares the environment data with qualified environment data, determines an environment coefficient according to a comparison result and an environment coefficient model, adjusts the thermal inductance comprehensive value based on the environment coefficient, and sends the adjusted thermal inductance comprehensive value to the charging vehicle. And the early warning module judges whether the charging vehicle is abnormal or not according to the target thermal inductance comprehensive value and determines the abnormal level of the charging vehicle according to the judgment result. The target thermal inductance comprehensive value is determined according to the thermal inductance feature points and the environment coefficient model, and the monitoring reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile monitoring, and in particular, to a wall-mounted charging pile monitoring method and system with a voice interaction function. Background Art

[0002] Under the global trend of advocating energy conservation, emission reduction and sustainable development, with the continuous and stable growth of the economy, new energy vehicles have been widely promoted and popularized due to their advantages such as environmental protection and energy conservation. New energy vehicles are generally equipped with large-capacity batteries to meet the increasing range requirements. However, during the charging process of large-capacity batteries by charging piles, the large-capacity batteries may cause fires due to temperature rise. The existing wall-mounted charging piles with voice interaction function adopt an unattended operation mode, which leads to the inability to effectively monitor and give early warnings once the vehicle overheats during the charging process, thus triggering serious safety accidents, resulting in property losses and even casualties.

[0003] Therefore, how to provide a wall-mounted charging pile monitoring method and system with a voice interaction function is an urgent technical problem to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention proposes a wall-mounted charging pile monitoring method and system with a voice interaction function, aiming to solve the problem that once the vehicle overheats during the charging process, effective monitoring and early warning cannot be achieved.

[0005] On the one hand, the present invention proposes a wall-mounted charging pile monitoring system with a voice interaction function, including:

[0006] An acquisition module, configured to acquire vehicle images, environmental data and the charging power of the charging pile, and determine the charging vehicle according to the vehicle images;

[0007] A judgment module, configured to search for the input power of the charging vehicle in a preset vehicle lookup table, and judge whether to acquire the thermal image of the charging vehicle according to the input power and the charging power;

[0008] A processing module, configured to when the thermal image of the charging vehicle is acquired, extract the thermal pixel points of the thermal image, analyze all the thermal pixel points to determine the thermal feature points, determine the thermal comprehensive value of the charging vehicle based on the thermal feature points, compare the environmental data with the qualified environmental data, determine the environmental coefficient according to the comparison result and the environmental coefficient model, and adjust the thermal comprehensive value based on the environmental coefficient to determine the target thermal comprehensive value;

[0009] The warning module is configured to determine whether there is an abnormality in the charging vehicle according to the target thermal sensing comprehensive value, and determine the abnormality level of the charging vehicle according to the judgment result.

[0010] Further, when determining the charging vehicle according to the vehicle image, it includes:

[0011] The acquisition module performs image processing on the vehicle image. The image processing includes image denoising, contrast adjustment, and pixel value normalization, and determines the target vehicle image according to the result of the image processing.

[0012] Perform key point annotation on the front logo, headlights, intake grille, and rear logo of the target vehicle image, and use scale-invariant feature transform to extract features from the annotated key points to determine the charging vehicle.

[0013] Further, when looking up the input power of the charging vehicle in the preset vehicle lookup table and determining whether to obtain the thermal sensing image of the charging vehicle according to the input power and the charging power, it includes:

[0014] The preset vehicle lookup table includes the mapping relationship between the charging vehicle and the corresponding input power.

[0015] When the input power and the charging power are not equal, the judgment module determines to obtain the thermal sensing image of the charging vehicle.

[0016] When the input power and the charging power are equal, the judgment module determines not to obtain the thermal sensing image of the charging vehicle.

[0017] Further, when extracting the thermal sensing pixel points of the thermal sensing image, analyzing all the thermal sensing pixel points to determine the thermal sensing feature points, and determining the thermal sensing comprehensive value of the charging vehicle based on the thermal sensing feature points, it includes:

[0018] The processing module converts all the thermal sensing pixel points into thermal sensing coordinate points, and establishes a thermal sensing coordinate system according to all the thermal sensing coordinate points.

[0019] Fit all the thermal sensing coordinate points according to the least square method to determine the thermal sensing fitting curve.

[0020] Further, when converting all the thermal sensing pixel points into thermal sensing coordinate points and establishing a thermal sensing coordinate system according to all the thermal sensing coordinate points, it includes:

[0021] The processing module takes the extraction time as the X-axis coordinate value of the thermal sensing pixel points, obtains the thermal sensing index of the thermal sensing pixel points, establishes a thermal sensing index set based on the thermal sensing index, and obtains a reference thermal sensing index set corresponding to the thermal sensing index set.

[0022] Compare the set of thermal sensation indicators with the set of reference thermal sensation indicators;

[0023] When the thermal sensation indicator in the set of thermal sensation indicators is greater than the reference thermal sensation indicator in the set of reference thermal sensation indicators, construct the thermal sensation indicators greater than the reference thermal sensation indicator into a first thermal sensation set;

[0024] When the thermal sensation indicator in the set of thermal sensation indicators is equal to the reference thermal sensation indicator in the set of reference thermal sensation indicators, construct the thermal sensation indicators equal to the reference thermal sensation indicator into a second thermal sensation set;

[0025] When the thermal sensation indicator in the set of thermal sensation indicators is less than the reference thermal sensation indicator in the set of reference thermal sensation indicators, construct the thermal sensation indicators less than the reference thermal sensation indicator into a third thermal sensation set;

[0026] Calculate the Y-axis coordinate value of the thermal sensation pixel points according to the first thermal sensation set, the second thermal sensation set, and the third thermal sensation set;

[0027] Establish the thermal sensation coordinate system according to the X-axis coordinate value and the Y-axis coordinate value.

[0028] Further, when extracting the thermal sensation pixel points of the thermal sensation image, analyzing all the thermal sensation pixel points to determine the thermal sensation feature points, and determining the comprehensive thermal sensation value of the charging vehicle based on the thermal sensation feature points, it further includes:

[0029] The processing module removes the unfitted thermal sensation coordinate points and determines the thermal sensation coordinate points on the thermal sensation fitting curve as the thermal sensation feature points;

[0030] Obtain the thermal sensation temperature values of all the thermal sensation feature points, and determine the comprehensive thermal sensation value according to the thermal sensation temperature values.

[0031] Further, when comparing the environmental data with the qualified environmental data, determining the environmental coefficient according to the comparison result and the environmental coefficient model, and adjusting the comprehensive thermal sensation value based on the environmental coefficient to determine the target comprehensive thermal sensation value, it includes:

[0032] When the environmental data is not equal to the qualified environmental data, the processing module determines the environmental coefficient according to the environmental coefficient model, and the target comprehensive thermal sensation value is the product value of the environmental coefficient and the comprehensive thermal sensation value;

[0033] When the environmental data is equal to the qualified environmental data, the processing module determines the comprehensive thermal sensation value as the target comprehensive thermal sensation value.

[0034] Further, when the processing module determines the environmental coefficient according to the environmental coefficient model, it includes:

[0035] The processing module obtains the environmental data set, divides the environmental data set into a training set and a test set, trains a data model using the training set, tests the trained data model using the test set, and finally determines the environmental coefficient model with environmental data as the input and environmental coefficients as the output;

[0036] The data model includes a BP neural network model and an RBF neural network model.

[0037] Further, when judging whether the charging vehicle is abnormal according to the target thermal sensation comprehensive value and determining the abnormal level of the charging vehicle according to the judgment result, it includes:

[0038] A first preset target thermal sensation comprehensive value and a second preset target thermal sensation comprehensive value are preset, and the first preset target thermal sensation comprehensive value is greater than the second preset target thermal sensation comprehensive value;

[0039] A first preset abnormal level, a second preset abnormal level, and a third preset abnormal level are preset, and the emergency degrees of the first preset abnormal level, the second preset abnormal level, and the third preset abnormal level decrease in sequence;

[0040] When the target thermal sensation comprehensive value is less than the second preset target thermal sensation comprehensive value, the warning module judges that the charging vehicle is not abnormal;

[0041] When the target thermal sensation comprehensive value is greater than or equal to the second preset target thermal sensation comprehensive value, the warning module judges that the charging vehicle is abnormal;

[0042] When the target thermal sensation comprehensive value is equal to the second preset target thermal sensation comprehensive value, the abnormal level of the charging vehicle is determined as the third preset abnormal level;

[0043] When the target thermal sensation comprehensive value is greater than the second preset target thermal sensation comprehensive value and less than or equal to the first preset target thermal sensation comprehensive value, the abnormal level of the charging vehicle is determined as the second preset abnormal level;

[0044] When the target thermal sensation comprehensive value is greater than the first preset target thermal sensation comprehensive value, the abnormal level of the charging vehicle is determined as the first preset abnormal level.

[0045] Compared with the prior art, the beneficial effects of the present invention are: the input power of the charging vehicle is accurately locked from the preset vehicle lookup table, and compared with the charging power, so that the condition of the charging vehicle can be preliminarily determined, and the thermal image can be obtained in a targeted manner, avoiding blind monitoring of the charging vehicle, thereby improving the monitoring efficiency and enabling the monitoring resources to be reasonably allocated. The thermal characteristic points are determined by extracting thermal pixel points, and the thermal comprehensive value is calculated. At the same time, the environmental data is compared with the qualified environmental data. The environmental coefficient model is used to comprehensively measure the impact of environmental factors to determine the target thermal comprehensive value. The complexity of the charging environment is fully considered to ensure that the monitoring results fit the actual thermal condition of the charging vehicle. The charging vehicle is judged to be abnormal based on the target thermal comprehensive value, which makes up for the shortcomings of unattended wall-mounted charging pile monitoring, effectively reduces accident losses, and improves the reliability and stability of the system in the safety monitoring of new energy vehicle charging.

[0046] On the other hand, the present application also provides a wall-mounted charging pile monitoring method with voice interaction function, which is used to apply the above-mentioned wall-mounted charging pile monitoring system with voice interaction function, including:

[0047] Acquire a vehicle image, environmental data, and a charging power of a charging pile, and determine a charging vehicle according to the vehicle image;

[0048] Searching for the input power of the charging vehicle in a preset vehicle search table, and determining whether to obtain a thermal image of the charging vehicle according to the input power and the charging power;

[0049] When acquiring the thermal image of the charging vehicle, extracting the thermal pixels of the thermal image, analyzing all the thermal pixels to determine the thermal feature points, determining the thermal comprehensive value of the charging vehicle based on the thermal feature points, comparing the environmental data with the qualified environmental data, determining the environmental coefficient according to the comparison result and the environmental coefficient model, adjusting the thermal comprehensive value based on the environmental coefficient, and determining the target thermal comprehensive value;

[0050] It is determined whether the charging vehicle is abnormal according to the target thermal sensitivity comprehensive value, and the abnormality level of the charging vehicle is determined according to the determination result.

[0051] It can be understood that the above-mentioned wall-mounted charging pile monitoring method and system with voice interaction function have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0053] Figure 1 This is a functional block diagram of a wall-mounted charging pile monitoring system with voice interaction function provided by an embodiment of the present invention;

[0054] Figure 2 This is a flowchart of a method for monitoring a wall-mounted charging pile with voice interaction function provided by an embodiment of the present invention. Detailed implementation manners

[0055] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0056] Refer to Figure 1 As shown, in some embodiments of the present application, this embodiment provides a wall-mounted charging pile monitoring system with voice interaction function, including:

[0057] An acquisition module, configured to acquire vehicle images, environmental data, and the charging power of the charging pile, and determine the charging vehicle according to the vehicle images.

[0058] A judgment module, configured to look up the input power of the charging vehicle in a preset vehicle lookup table, and judge whether to acquire the thermal image of the charging vehicle according to the input power and the charging power.

[0059] A processing module, configured to extract the thermal pixel points of the thermal image when acquiring the thermal image of the charging vehicle, analyze all the thermal pixel points to determine the thermal feature points, determine the thermal comprehensive value of the charging vehicle based on the thermal feature points, compare the environmental data with the qualified environmental data, determine the environmental coefficient according to the comparison result and the environmental coefficient model, adjust the thermal comprehensive value based on the environmental coefficient, and determine the target thermal comprehensive value.

[0060] An early warning module, configured to judge whether there is an abnormality in the charging vehicle according to the target thermal comprehensive value, and determine the abnormality level of the charging vehicle according to the judgment result.

[0061] Specifically, the acquisition module obtains vehicle images through image acquisition devices such as cameras, and uses temperature sensors, humidity sensors, and air pressure sensors to obtain environmental data. It also obtains the charging power from the charging pile according to the power collector, and determines the charging vehicle model based on features such as the body contour and brand logo according to the recognition technology. There are differences in the characteristics and safety parameters of the batteries of different vehicle models. Accurate identification is the prerequisite for ensuring the effectiveness of monitoring and lays the foundation for subsequent targeted monitoring. The judgment module searches for the input power of the charging vehicle in the preset vehicle lookup table, and compares the current charging power with the input power in the lookup table. If there is a difference, it means that the charging state of the battery may deviate and there is a risk of battery overheating. At this time, an instruction to obtain a thermal image is triggered. Based on the characteristic that the power is relatively stable during normal battery charging, the condition of the charging vehicle can be quickly determined, avoiding resource waste caused by continuous acquisition of thermal images. The thermal image is collected by the thermal imaging sensor and obtained and processed by the processing module. In the actual environment of the charging pile, there may be various reflection sources around the charging vehicle, such as walls, the ground, other vehicles, etc. When the thermal imaging sensor captures the thermal image, these reflected infrared radiations will be received together. For example, when the infrared radiation reflected after sunlight shines on a nearby metal object enters the thermal imaging sensor, some false thermal pixels will be formed in the image. They do not come from the thermal radiation of the charging vehicle itself and do not represent the true thermal state of the vehicle, belonging to invalid thermal pixels. Therefore, the processing module extracts and analyzes all the thermal pixels in the thermal image, identifies the thermal feature points. The thermal feature points represent the effective thermal pixels in the thermal image. By analyzing and calculating the thermal feature points, a comprehensive thermal value is obtained. The comprehensive thermal value reflects the overall thermal state of the charging vehicle. At the same time, the environmental data is compared with the qualified environmental data, and the environmental coefficient is determined according to the environmental coefficient model (a model considering the influence of different environmental factors on battery heating). For example, a high-temperature environment accelerates battery temperature rise, etc. The comprehensive thermal value is adjusted according to the environmental coefficient to obtain the target comprehensive thermal value, making the result more in line with the actual condition of the charging vehicle. The warning module judges whether the charging vehicle is abnormal according to the target comprehensive thermal value, determines the corresponding abnormal level, and issues a corresponding voice reminder according to the abnormal level. When the urgency of the abnormal level is high, the voice reminder will use a rapid and loud tone and directly issue a serious warning, enabling the vehicle owner and surrounding personnel to take corresponding actions in time. When the urgency of the abnormal level is low, the voice reminder will use a relatively gentle tone to guide the vehicle owner to pay attention. When the warning module determines the abnormal level, the voice reminder can be issued in a short time.Whether the vehicle suddenly encounters an abnormality during the charging process or the abnormality level changes during the monitoring process, the voice reminder can follow up in real time to effectively ensure the safety of the charging process. In addition, considering the needs of different user groups, the voice reminder has a multi-language switching function. When facing car owners with different language backgrounds, the system provides voice reminders in Chinese, English, Japanese and other languages ​​to ensure that every car owner can clearly understand the reminder content so that they can take timely measures, effectively reducing accident losses, thereby making up for the real-time and reliability of monitoring in unattended mode.

[0062] It is understandable that the system not only pays attention to the temperature of the charging vehicle, but also adjusts the comprehensive thermal sensitivity value based on environmental data, taking into account the impact of the environment on battery heating and thus the temperature of the charging vehicle, making the monitoring results more in line with reality and improving the reliability and stability of monitoring.

[0063] In some embodiments of the present application, when determining a charging vehicle based on a vehicle image, it includes: an acquisition module performs image processing on the vehicle image, the image processing includes image denoising, adjusting contrast and normalizing pixel values, determining the target vehicle image based on the image processing result, annotating key points of the front logo, lights, air intake grille and rear logo of the target vehicle image, and using scale-invariant feature transformation to extract features from the annotated key points to determine the charging vehicle.

[0064] Specifically, the collected vehicle images will generate noise due to factors such as environmental electromagnetic interference, which will blur the features of the vehicle. Image denoising can remove these interferences, making the outline and details of the vehicle clear, providing clean and accurate data for subsequent key point annotation and feature extraction. Adjusting the contrast improves the recognizability of information in the image. The images collected by cameras of different brands and models have different pixel value ranges and distributions. Normalized pixel values ​​can unify the pixel values ​​of various images to a standard range, eliminating the impact of equipment differences on image analysis, improving the stability and accuracy of identifying charging vehicles in different scenarios, and enhancing the system's adaptability to various environments. The front logo is an intuitive reflection of the vehicle brand. Different brand logos have unique shapes and styles. The shape, position and arrangement of the headlights vary from model to model. The style of the air intake grille can reflect the style and model of the vehicle, and the rear logo contains specific information about the model. By marking the key points of these parts, the core features of the charging vehicle are accurately locked, providing a basis for vehicle model identification. The scale-invariant feature transform (SIFT) feature extraction has scale invariance and rotation invariance. The SIFT algorithm can stably extract feature descriptors of key points under complex conditions, so that the system can still accurately identify charging vehicles in complex scenarios such as densely packed parking lots and various parking angles, further enhancing the adaptability of the system and laying the foundation for the subsequent determination of input power.

[0065] In some embodiments of the present application, when looking up the input power of a charging vehicle in a preset vehicle lookup table and determining whether to obtain a thermal image of the charging vehicle based on the input power and the charging power, it includes: The preset vehicle lookup table includes the mapping relationship between the charging vehicle and the corresponding input power. When the input power and the charging power are not equal, the judgment module determines to obtain the thermal image of the charging vehicle; when the input power and the charging power are equal, the judgment module determines not to obtain the thermal image of the charging vehicle.

[0066] Specifically, look up the corresponding input power in the vehicle lookup table according to the obtained charging vehicle. The preset vehicle lookup table is obtained based on the vehicle usage instructions of different new energy vehicles. The acquisition and processing of thermal images require a certain amount of computing resources and time. If thermal images of the charging vehicle are continuously obtained, it will cause waste of resources. Especially when there are a large number of vehicles in the charging station, by comparing the input power and the charging power, limited resources can be accurately invested in the monitoring of vehicles that may have overheating risks, avoiding unnecessary thermal image acquisition of normal charging vehicles, thereby improving the utilization efficiency of system resources and ensuring the stable operation of the system.

[0067] In some embodiments of the present application, when extracting the thermal pixel points of the thermal image, analyzing all the thermal pixel points to determine the thermal feature points, and determining the thermal comprehensive value of the charging vehicle based on the thermal feature points, it includes: The processing module converts all the thermal pixel points into thermal coordinate points, establishes a thermal coordinate system according to all the thermal coordinate points, and fits all the thermal coordinate points according to the least squares method to determine the thermal fitting curve.

[0068] Specifically, convert the thermal pixel points into thermal coordinate points, and then construct a thermal coordinate system, which gives a clear structure to the complex thermal data. Under the thermal coordinate system, it can be arranged and analyzed in an orderly manner. Use the least squares method to fit the thermal coordinate points, and simplify a large amount of discrete data into a thermal fitting curve. The processing from thermal pixel points to thermal coordinate system and then to thermal fitting curve simplifies the complexity of data processing, improves the operation efficiency, enables the system to complete the analysis of a large amount of thermal data in a short time, and by removing invalid thermal pixel points through the fitting method, comprehensively considers the distribution law of thermal coordinate points, and then quantifies the dynamic changes of the thermal state of the charging vehicle, comprehensively and deeply considering the overall trend and change details of the thermal distribution. It provides data support for the subsequent warning module to accurately judge whether the vehicle is abnormal and the abnormal level division, thereby ensuring the stability and reliability of the monitoring.

[0069] In some embodiments of the present application, when converting all thermal sensing pixel points into thermal sensing coordinate points and establishing a thermal sensing coordinate system based on all the thermal sensing coordinate points, the following steps are included: The processing module takes the extraction time as the X-axis coordinate value of the thermal sensing pixel point, obtains the thermal sensing index of the thermal sensing pixel point, establishes a thermal sensing index set based on the thermal sensing index, obtains a corresponding reference thermal sensing index set for the thermal sensing index set, compares the thermal sensing index set with the reference thermal sensing index set. When the thermal sensing index in the thermal sensing index set is greater than the reference thermal sensing index in the reference thermal sensing index set, the thermal sensing index greater than the reference thermal sensing index is constructed into a first thermal sensing set. When the thermal sensing index in the thermal sensing index set is equal to the reference thermal sensing index in the reference thermal sensing index set, the thermal sensing index equal to the reference thermal sensing index is constructed into a second thermal sensing set. When the thermal sensing index in the thermal sensing index set is less than the reference thermal sensing index in the reference thermal sensing index set, the thermal sensing index less than the reference thermal sensing index is constructed into a third thermal sensing set. Calculate the Y-axis coordinate value of the thermal sensing pixel point according to the first thermal sensing set, the second thermal sensing set, and the third thermal sensing set, and establish a thermal sensing coordinate system based on the X-axis coordinate value and the Y-axis coordinate value.

[0070] Specifically, the Y-axis coordinate value is obtained from the following formula:

[0071]

[0072] Where Y is the Y-axis coordinate value of the thermal sensing pixel point, n represents the number of thermal sensing indices in the first thermal sensing set, Fi represents the i-th thermal sensing index in the first thermal sensing set, Di represents the reference thermal sensing index corresponding to the i-th thermal sensing index, m represents the number of thermal sensing indices in the third thermal sensing set, Rj represents the reference thermal sensing index corresponding to the j-th thermal sensing index, and Kj represents the j-th thermal sensing index in the third thermal sensing set.

[0073] Specifically, the extraction times are 1 second, 1.5 seconds, 3 seconds, etc., which form an arithmetic sequence of extractions, representing the extraction time interval for each thermal sensing pixel point. One thermal sensing pixel point is extracted every 1.5 seconds. The thermal sensing indices include thermal sensing brightness, thermal sensing saturation, etc. Moreover, the reference thermal sensing indices in the reference thermal sensing index set correspond one-to-one with the thermal sensing indices. By comparing the thermal sensing indices of the thermal sensing pixel points with the reference thermal sensing indices and constructing different thermal sensing sets accordingly, the differences between each thermal sensing index and the reference thermal sensing index can be clearly represented. Taking the extraction time as the X-axis coordinate value of the thermal sensing pixel point makes the thermal sensing data closely related to the time dimension. By establishing a thermal sensing coordinate system with the Y-axis coordinate value determined by the thermal sensing indices, the dynamic changes of the thermal sensing data over time can be fully reflected, improving the accuracy of thermal sensing image analysis and enhancing the reliability of the entire monitoring system.

[0074] In some embodiments of the present application, when extracting the thermal pixels of the thermal image, analyzing all the thermal pixels to determine the thermal feature points, and determining the comprehensive thermal value of the charging vehicle based on the thermal feature points, it further includes: the processing module removes the unfitted thermal coordinate points, determines the thermal coordinate points on the thermal fitting curve as the thermal feature points, obtains the thermal temperature values of all the thermal feature points, and determines the comprehensive thermal value according to the thermal temperature values.

[0075] Specifically, the average thermal temperature of all the thermal feature points is determined as the comprehensive thermal value. The thermal temperature values of individual abnormal thermal pixels will not cause excessive interference to the average thermal temperature, so that the comprehensive thermal value can still remain relatively stable when facing complex and changeable thermal image data, reducing the situation where the comprehensive thermal value fluctuates greatly due to accidental factors, improving the reliability and stability of the monitoring results, and helping to accurately judge whether there is an abnormality in the charging vehicle. Moreover, removing the unfitted thermal coordinate points can screen out the abnormal data in the thermal image that may be caused by factors such as noise interference and environmental reflection. These thermal coordinate points may deviate from the true thermal distribution law of the charging vehicle. If included in the calculation, it will affect the accuracy of the comprehensive thermal value. Only retaining the thermal coordinate points on the thermal fitting curve as the thermal feature points ensures that the data used to calculate the comprehensive thermal value can represent the thermal characteristics of the charging vehicle, making the comprehensive thermal value accurately reflect the actual thermal state of the charging vehicle and improving the accuracy and reliability of the system monitoring.

[0076] In some embodiments of the present application, when comparing the environmental data with the qualified environmental data, determining the environmental coefficient according to the comparison result and the environmental coefficient model, and adjusting the comprehensive thermal value based on the environmental coefficient to determine the target comprehensive thermal value, it includes: when the environmental data is not equal to the qualified environmental data, the processing module determines the environmental coefficient according to the environmental coefficient model, and the target comprehensive thermal value is the product value of the environmental coefficient and the comprehensive thermal value; when the environmental data is equal to the qualified environmental data, the processing module determines the comprehensive thermal value as the target comprehensive thermal value.

[0077] In some embodiments of the present application, when the processing module determines the environmental coefficient according to the environmental coefficient model, it includes: the processing module obtains the environmental data set, divides the environmental data set into a training set and a test set, uses the training set to train the data model, uses the test set to test the trained data model, and finally determines the environmental coefficient model with the environmental data as the input and the environmental coefficient as the output. The data model includes a BP neural network model and an RBF neural network model.

[0078] Specifically, environmental data includes data such as environmental temperature, environmental humidity, and environmental air pressure. Qualified environmental data is the standard data for the charging pile's environmental requirements. When there is a situation where the environmental data is inconsistent with the qualified environmental data, it indicates that the current environment will affect the thermal sensation comprehensive value. For example, a high-temperature environment causes the thermal sensation comprehensive value to increase, and a high-humidity environment causes the thermal sensation comprehensive value to decrease. Then, the environmental coefficient is determined according to the environmental coefficient model, so that the monitoring result closely conforms to the changes in the actual environment, avoiding misjudgment caused by ignoring environmental factors. When the environmental data is equal to the qualified environmental data, the thermal sensation comprehensive value is directly determined as the target thermal sensation comprehensive value. In an ideal and stable environmental condition, unnecessary calculations are reduced, enabling the system to process data and output results under different environmental conditions, improving the adaptability of the system monitoring. The environmental data set includes key data such as air pressure, temperature, and humidity. These data record the operating conditions of the charging pile and the charging vehicle at different times. The environmental data set is divided into a training set and a test set. Usually, 70%-80% of the data is used as the training set, and the rest is used as the test set. Ensure that both the training set and the test set contain data on various operating conditions to improve the generalization ability of the model. By training and testing the BP neural network model and the RBF neural network model, the finally determined environmental coefficient model has high accuracy. These neural network models can learn and capture the relationships between data during the training process. For example, under the combined action of various environmental factors such as different temperatures, humidities, and air pressures, the model outputs the corresponding environmental coefficient. Adjusting the thermal sensation comprehensive value based on the environmental coefficient improves the accuracy of the target thermal sensation comprehensive value, effectively avoiding false alarms and missed alarms caused by environmental factors.

[0079] It can be understood that with the passage of time and the increase in environmental diversity, the environmental data set can be continuously updated, and the environmental coefficient model can be retrained and optimized, enabling the system to adapt to environmental changes in different regions, different seasons, and different time periods. For example, during the high-temperature period in summer and the low-temperature period in winter, the environmental coefficient model can adjust the output result according to the new data to ensure that the system can stably and accurately monitor the status of the charging pile and the charging vehicle in various environments.

[0080] In some embodiments of the present application, when judging whether there is an abnormality in the charging vehicle according to the target thermal sense comprehensive value, and determining the abnormality level of the charging vehicle according to the judgment result, it includes: presetting a first preset target thermal sense comprehensive value and a second preset target thermal sense comprehensive value, and the first preset target thermal sense comprehensive value is greater than the second preset target thermal sense comprehensive value, presetting a first preset abnormality level, a second preset abnormality level and a third preset abnormality level, and the urgency of the first preset abnormality level, the second preset abnormality level and the third preset abnormality level decreases in sequence, and when the target thermal sense comprehensive value is less than the second preset target thermal sense comprehensive value, the early warning module judges that There is no abnormality in the charging vehicle. When the target thermal sense comprehensive value is greater than or equal to the second preset target thermal sense comprehensive value, the early warning module determines that there is an abnormality in the charging vehicle. When the target thermal sense comprehensive value is equal to the second preset target thermal sense comprehensive value, the abnormality level of the charging vehicle is determined as the third preset abnormality level. When the target thermal sense comprehensive value is greater than the second preset target thermal sense comprehensive value and less than or equal to the first preset target thermal sense comprehensive value, the abnormality level of the charging vehicle is determined as the second preset abnormality level. When the target thermal sense comprehensive value is greater than the first preset target thermal sense comprehensive value, the abnormality level of the charging vehicle is determined as the first preset abnormality level.

[0081] Specifically, by setting the first preset target thermal sense comprehensive value and the second preset target thermal sense comprehensive value, as well as the first preset abnormal level, the second preset abnormal level and the third preset abnormal level, the status of the charging vehicle can be finely graded. When the target thermal sense comprehensive value is in different intervals, it corresponds to abnormal levels of different urgency. For example: when the target thermal sense comprehensive value is greater than the first preset target thermal sense comprehensive value, it is determined to be the first preset abnormal level, indicating that the charging vehicle is at a high risk of overheating and emergency measures need to be taken immediately. The graded early warning mechanism enables relevant managers to quickly and accurately understand the degree of abnormality of the charging vehicle, so as to arrange response strategies in a targeted manner so that resources can be reasonably allocated. The efficiency of handling abnormalities in charging vehicles is improved, and the stability and reliability of monitoring are improved.

[0082] In summary, the beneficial effects of the present invention are: the input power of the charging vehicle is accurately locked from the preset vehicle lookup table, and compared with the charging power, so that the condition of the charging vehicle can be preliminarily determined, and the thermal image can be obtained in a targeted manner, avoiding blind monitoring of the charging vehicle, thereby improving the monitoring efficiency and enabling the monitoring resources to be reasonably allocated. The thermal characteristic points are determined by extracting thermal pixel points, and the thermal comprehensive value is calculated. At the same time, the environmental data is compared with the qualified environmental data. The environmental coefficient model is used to comprehensively measure the impact of environmental factors to determine the target thermal comprehensive value. The complexity of the charging environment is fully considered to ensure that the monitoring results fit the actual thermal condition of the charging vehicle. The charging vehicle is judged to be abnormal based on the target thermal comprehensive value, which makes up for the shortcomings of unattended wall-mounted charging pile monitoring, effectively reduces accident losses, and improves the reliability and stability of the system in the safety monitoring of new energy vehicle charging.

[0083] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a wall-mounted charging pile monitoring method with voice interaction function, which is used to apply the above-mentioned wall-mounted charging pile monitoring system with voice interaction function, including:

[0084] S100: Acquire a vehicle image, environmental data, and the charging power of a charging pile, and determine a charging vehicle according to the vehicle image.

[0085] S200: Searching for the input power of the charging vehicle in a preset vehicle search table, and determining whether to obtain a thermal image of the charging vehicle according to the input power and the charging power.

[0086] S300: When acquiring a thermal image of a charging vehicle, extract the thermal pixels of the thermal image, analyze all the thermal pixels to determine the thermal feature points, determine the thermal comprehensive value of the charging vehicle based on the thermal feature points, compare the environmental data with the qualified environmental data, determine the environmental coefficient based on the comparison result and the environmental coefficient model, adjust the thermal comprehensive value based on the environmental coefficient, and determine the target thermal comprehensive value.

[0087] S400: judging whether there is an abnormality in the charging vehicle according to the target thermal sensitivity comprehensive value, and determining the abnormality level of the charging vehicle according to the judgment result.

[0088] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0089] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or one or more blocks.

[0090] These computer program instructions can also be stored in a computer-readable storage that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable storage generate a manufactured article including instruction means, and the instruction means implements the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or one or more blocks.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or one or more blocks.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A wall-mounted charging pile monitoring system with voice interaction function, characterized in that: include: A collection module is configured to obtain a vehicle image, environmental data and a charging power of a charging pile, and determine a charging vehicle according to the vehicle image; A determination module is configured to search for the input power of the charging vehicle in a preset vehicle search table, and determine whether to obtain a thermal image of the charging vehicle according to the input power and the charging power; a processing module configured to, when acquiring the thermal image of the charging vehicle, extract the thermal pixels of the thermal image, analyze all the thermal pixels to determine the thermal feature points, determine the thermal comprehensive value of the charging vehicle based on the thermal feature points, compare the environmental data with the qualified environmental data, determine the environmental coefficient according to the comparison result and the environmental coefficient model, adjust the thermal comprehensive value based on the environmental coefficient, and determine the target thermal comprehensive value; The early warning module is configured to judge whether there is an abnormality in the charging vehicle according to the target thermal sensitivity comprehensive value, and determine the abnormality level of the charging vehicle according to the judgment result.

2. The wall-mounted charging pile monitoring system with voice interaction function according to claim 1 is characterized in that: When determining the charging vehicle according to the vehicle image, the method includes: The acquisition module performs image processing on the vehicle image, wherein the image processing includes image denoising, contrast adjustment and pixel value normalization, and determines the target vehicle image according to the result of the image processing; The front logo, headlights, air intake grille and rear logo of the target vehicle image are annotated with key points, and scale-invariant feature transformation is used to extract features from the annotated key points to determine the charging vehicle.

3. The wall-mounted charging pile monitoring system with voice interaction function according to claim 2 is characterized in that: Searching the input power of the charging vehicle in a preset vehicle search table, and determining whether to acquire the thermal image of the charging vehicle according to the input power and the charging power, including: The preset vehicle lookup table includes a mapping relationship between charging vehicles and corresponding input powers; When the input power and the charging power are not equal, the judging module determines to acquire a thermal image of the charging vehicle; When the input power is equal to the charging power, the judgment module determines not to acquire the thermal image of the charging vehicle.

4. The wall-mounted charging pile monitoring system with voice interaction function according to claim 3 is characterized in that: When extracting the thermosensitive pixels of the thermosensitive image, analyzing all the thermosensitive pixels to determine the thermosensitive feature points, and determining the thermosensitive comprehensive value of the charging vehicle based on the thermosensitive feature points, the method includes: The processing module converts all the thermal pixel points into thermal coordinate points, and establishes a thermal coordinate system according to all the thermal coordinate points; All thermal coordinate points are fitted according to the least squares method to determine the thermal fitting curve.

5. The wall-mounted charging pile monitoring system with voice interaction function according to claim 4 is characterized in that: When all thermal pixels are converted into thermal coordinate points and a thermal coordinate system is established based on all thermal coordinate points, it includes: The processing module uses the extraction time as the X-axis coordinate value of the thermal pixel point, obtains the thermal index of the thermal pixel point, establishes a thermal index set based on the thermal index, and obtains a reference thermal index set corresponding to the thermal index set; Comparing the thermal sensitivity index set with the reference thermal sensitivity index set; When the thermal sensation index in the thermal sensation index set is greater than the reference thermal sensation index in the reference thermal sensation index set, constructing the thermal sensation index greater than the reference thermal sensation index into a first thermal sensation set; When the thermal sensation index in the thermal sensation index set is equal to the reference thermal sensation index in the reference thermal sensation index set, constructing the thermal sensation index equal to the reference thermal sensation index into a second thermal sensation set; When the thermal sensation index in the thermal sensation index set is smaller than the reference thermal sensation index in the reference thermal sensation index set, constructing the thermal sensation index smaller than the reference thermal sensation index into a third thermal sensation set; Calculate the Y-axis coordinate value of the thermal-sensitive pixel point according to the first thermal-sensitive set, the second thermal-sensitive set, and the third thermal-sensitive set; The thermal coordinate system is established according to the X-axis coordinate value and the Y-axis coordinate value.

6. The wall-mounted charging pile monitoring system with voice interaction function according to claim 5 is characterized in that: When extracting the thermosensitive pixels of the thermosensitive image, analyzing all the thermosensitive pixels to determine the thermosensitive feature points, and determining the thermosensitive comprehensive value of the charging vehicle based on the thermosensitive feature points, the method further includes: The processing module removes the unfitted thermosensitive coordinate points and determines the thermosensitive coordinate points on the thermosensitive fitting curve as the thermosensitive feature points; The thermal temperature values ​​of all thermal characteristic points are obtained, and the thermal comprehensive value is determined according to the thermal temperature values.

7. The wall-mounted charging pile monitoring system with voice interaction function according to claim 6 is characterized in that: When comparing the environmental data with qualified environmental data, determining the environmental coefficient according to the comparison result and the environmental coefficient model, adjusting the thermal sensation comprehensive value based on the environmental coefficient, and determining the target thermal sensation comprehensive value, the method includes: When the environmental data and the qualified environmental data are not equal, the processing module determines the environmental coefficient according to the environmental coefficient model, and the target thermal sensitivity comprehensive value is the product of the environmental coefficient and the thermal sensitivity comprehensive value; When the environmental data and the qualified environmental data are equal, the processing module determines the thermal sensation comprehensive value as the target thermal sensation comprehensive value.

8. The wall-mounted charging pile monitoring system with voice interaction function according to claim 7 is characterized in that: When the processing module determines the environmental coefficient according to the environmental coefficient model, it includes: The processing module obtains an environmental data set, and divides the environmental data set into a training set and a test set, uses the training set to train a data model, and uses the test set to test the trained data model, and finally determines the environmental coefficient model whose input is environmental data and output is environmental coefficient; The data model includes a BP neural network model and a RBF neural network model.

9. The wall-mounted charging pile monitoring system with voice interaction function according to claim 8 is characterized in that: When judging whether the charging vehicle is abnormal according to the target thermal sensitivity comprehensive value, and determining the abnormality level of the charging vehicle according to the judgment result, it includes: A first preset target thermal sensation comprehensive value and a second preset target thermal sensation comprehensive value are preset, and the first preset target thermal sensation comprehensive value is greater than the second preset target thermal sensation comprehensive value; A first preset abnormality level, a second preset abnormality level and a third preset abnormality level are preset, and the urgency of the first preset abnormality level, the second preset abnormality level and the third preset abnormality level is sequentially decreased; When the target thermal sense comprehensive value is less than the second preset target thermal sense comprehensive value, the early warning module determines that there is no abnormality in the charging vehicle; When the target thermal sense comprehensive value is greater than or equal to the second preset target thermal sense comprehensive value, the early warning module determines that there is an abnormality in the charging vehicle; When the target thermal sense comprehensive value is equal to the second preset target thermal sense comprehensive value, the abnormality level of the charging vehicle is determined to be the third preset abnormality level; When the target thermal sense comprehensive value is greater than the second preset target thermal sense comprehensive value and less than or equal to the first preset target thermal sense comprehensive value, the abnormality level of the charging vehicle is determined to be the second preset abnormality level; When the target thermal sense comprehensive value is greater than the first preset target thermal sense comprehensive value, the abnormality level of the charging vehicle is determined to be the first preset abnormality level.

10. A method for monitoring a wall-mounted charging pile with voice interaction function, used for applying the wall-mounted charging pile monitoring system with voice interaction function as claimed in any one of claims 1 to 9, characterized in that: include: Acquire a vehicle image, environmental data, and a charging power of a charging pile, and determine a charging vehicle according to the vehicle image; Searching for the input power of the charging vehicle in a preset vehicle search table, and determining whether to obtain a thermal image of the charging vehicle according to the input power and the charging power; When acquiring the thermal image of the charging vehicle, extracting the thermal pixels of the thermal image, analyzing all the thermal pixels to determine the thermal feature points, determining the thermal comprehensive value of the charging vehicle based on the thermal feature points, comparing the environmental data with the qualified environmental data, determining the environmental coefficient according to the comparison result and the environmental coefficient model, adjusting the thermal comprehensive value based on the environmental coefficient, and determining the target thermal comprehensive value; It is determined whether the charging vehicle is abnormal according to the target thermal sensitivity comprehensive value, and the abnormality level of the charging vehicle is determined according to the determination result.

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