Intelligent temperature control device and method for electric tricycle converter

Abstract

The present invention discloses an intelligent temperature control device and method for an electric tricycle converter, and relates to the field of data processing technology. The method comprises: obtaining a target detection temperature, and if the target detection temperature reaches a preset temperature threshold, activating a temperature control verification mechanism, performing a multi-dimensional temperature characteristic analysis on the target converter, and obtaining a target predicted temperature; if the temperature difference between the target predicted temperature and the target detection temperature is within a preset deviation threshold, issuing a temperature control instruction, performing a temperature control simulation optimization analysis, and obtaining a target temperature control plan; and performing temperature control according to the target temperature control plan. The present invention solves the technical problem in the prior art that the heat generated by the electric tricycle converter during operation affects the operating stability and service life of the converter, and achieves the technical effect of intelligently controlling the temperature control of the electric tricycle converter through multi-dimensional temperature characteristic analysis, and improving the operating stability, reliability and service life of the converter.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent temperature control device and method for an electric tricycle converter. Background Art

[0002] The converter of an electric tricycle is usually responsible for converting the DC power provided by the battery into the voltage and current required by various systems of the vehicle, including motor drive, lighting, signal and other systems. During this process, the converter will generate a certain amount of heat. The working efficiency, stability and life of the electronic components inside the converter are all affected by temperature. Excessive temperature will cause the performance of components to decline, shorten their life, and even cause failure or damage. Summary of the invention

[0003] The present application provides an intelligent temperature control device and method for an electric tricycle converter, which is used to solve the technical problem in the prior art that the heat generated by the electric tricycle converter during operation will affect the operating stability and service life of the converter.

[0004] The first aspect of the present application provides an intelligent temperature control method for an electric tricycle converter, the method comprising: obtaining a target detection temperature, the target detection temperature refers to the temperature obtained by performing non-contact temperature detection on a target converter through a temperature detector; if the target detection temperature reaches a preset temperature threshold, activating a temperature control verification mechanism; performing a multi-dimensional temperature characteristic analysis on the target converter according to the temperature control verification mechanism to obtain a target predicted temperature; if the temperature difference between the target predicted temperature and the target detection temperature is within a preset deviation threshold, issuing a temperature control instruction; performing a temperature control simulation optimization analysis on the target converter based on the temperature control instruction to obtain a target temperature control plan; and controlling the temperature of the target converter according to the target temperature control plan.

[0005] According to a second aspect of the present application, an intelligent temperature control device for an electric tricycle converter is provided, the device comprising: a target detection temperature acquisition module, the target detection temperature acquisition module is used to acquire the target detection temperature, the target detection temperature refers to the temperature obtained by performing non-contact temperature detection on the target converter by a temperature detector; a temperature control verification mechanism activation module, the temperature control verification mechanism activation module is used to activate the temperature control verification mechanism if the target detection temperature reaches a preset temperature threshold; a multi-dimensional temperature feature analysis module, the multi-dimensional temperature feature analysis module is used to perform multi-dimensional temperature feature analysis on the target converter according to the temperature control verification mechanism to obtain a target predicted temperature; a temperature control instruction sending module, the temperature control instruction sending module is used to issue a temperature control instruction if the temperature difference between the target predicted temperature and the target detection temperature is within a preset deviation threshold; a temperature control simulation optimization module, the temperature control simulation optimization module is used to perform temperature control simulation optimization analysis on the target converter based on the temperature control instruction to obtain a target temperature control plan; a temperature control module, the temperature control module is used to perform temperature control of the target converter according to the target temperature control plan.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The intelligent temperature control device and method for an electric tricycle converter provided in the present application relate to the field of data processing technology. By acquiring the target detection temperature, if the target detection temperature reaches a preset temperature threshold, the temperature control verification mechanism is activated, and a multi-dimensional temperature characteristic analysis is performed on the target converter. If the temperature difference between the obtained target predicted temperature and the target detection temperature is within a preset deviation threshold, a temperature control simulation optimization analysis is performed, and a target temperature control plan is obtained for temperature control. The technical problem in the prior art that the heat generated by the electric tricycle converter during operation affects the operating stability and service life of the converter is solved, and the intelligent control of the temperature control of the electric tricycle converter through multi-dimensional temperature characteristic analysis is realized, and the technical effect of improving the operating stability, reliability and service life of the converter is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of an intelligent temperature control method for an electric tricycle converter provided in an embodiment of the present application.

[0010] Figure 2A schematic diagram of a process for obtaining a target predicted temperature in an intelligent temperature control method for an electric tricycle converter provided in an embodiment of the present application.

[0011] Figure 3 A schematic diagram of the structure of an intelligent temperature control device for an electric tricycle converter provided in an embodiment of the present application.

[0012] Explanation of the reference numerals: target detection temperature acquisition module 11, temperature control verification mechanism activation module 12, multi-dimensional temperature characteristic analysis module 13, temperature control instruction sending module 14, temperature control simulation optimization module 15, temperature control module 16. DETAILED DESCRIPTION

[0013] The present application provides an intelligent temperature control device and method for an electric tricycle converter, which is used to solve the technical problem in the prior art that the heat generated by the electric tricycle converter during operation will affect the operating stability and service life of the converter.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0016] Embodiment 1

[0017] like Figure 1 As shown, the present application provides an intelligent temperature control method for an electric tricycle converter, the method comprising:

[0018] P10: Obtaining a target detection temperature, where the target detection temperature refers to a temperature obtained by performing non-contact temperature detection on a target converter through a temperature detector.

[0019] Specifically, the target converter is subjected to non-contact temperature detection by a temperature detector to obtain the target detection temperature. This process relies on advanced temperature detectors, such as equipment using infrared temperature measurement technology. Infrared temperature measurement technology is a non-contact measurement method that can use the infrared radiation energy emitted by the surface of an object to infer its surface temperature without direct contact with the object to be measured, thereby avoiding heat transfer, measurement errors or equipment damage that may be caused by contact. Exemplarily, the temperature detector captures the infrared radiation signals emitted from the surface of the target converter through its built-in infrared sensor. These radiation signals are then converted into electrical signals and amplified, filtered and calibrated by a built-in processing chip to eliminate interference from environmental noise and background radiation, and finally a digital value representing the current surface temperature of the target converter is obtained, namely the target detection temperature.

[0020] Since the target detection temperature adopts a non-contact measurement method, the detection accuracy is low and the detection result may be inaccurate, so it can only be used as a preliminary monitoring temperature to make a preliminary judgment on the converter temperature.

[0021] P20: If the target detection temperature reaches a preset temperature threshold, the temperature control verification mechanism is activated.

[0022] Optionally, the target detection temperature obtained by the temperature detector is continuously monitored, and the target detection temperature is compared with a preset temperature threshold. The preset temperature threshold is a primary temperature monitoring indicator determined in advance based on factors such as the design specifications of the target converter, the working environment conditions, and the material properties, and can be used to make a preliminary rough judgment on the converter temperature. If the target detection temperature reaches the preset temperature threshold, it means that the converter temperature may be abnormal and temperature control is required, that is, the temperature of the converter is adjusted through intervention. However, before executing the control intervention, it is necessary to collect relevant temperature data for temperature verification to avoid control caused by inaccurate detection of the temperature detector, which affects the operation of the converter, so the temperature control verification mechanism is activated.

[0023] The temperature control verification mechanism can be a set of complex algorithms or program modules that integrates multiple temperature management strategies and analysis tools for further diagnosis and decision-making under abnormal temperature conditions. After activating the temperature control verification mechanism, the target detection temperature can be reviewed to confirm its accuracy and reliability to avoid false alarms caused by sensor failure or interference. Based on multiple factors such as current temperature data, historical temperature records, ambient temperature changes, load conditions, etc., a comprehensive analysis and evaluation is performed to determine whether the converter is indeed in a state where temperature control measures need to be taken.

[0024] P30: Perform multi-dimensional temperature characteristic analysis on the target converter according to the temperature control verification mechanism to obtain a target predicted temperature.

[0025] Further, such as Figure 2 As shown, step P30 of the embodiment of the present application also includes:

[0026] P31: Extract the heat generation characteristic index and heat dissipation characteristic index pre-stored in the temperature control verification mechanism; wherein the heat generation characteristic index includes output power, conversion efficiency, continuous operation time and operation fluctuation index. The heat dissipation characteristic index refers to the heat dissipation condition characteristic index, including the heat dissipation design condition characteristic index and the heat dissipation environment condition characteristic index. The heat dissipation design condition characteristic index at least includes the internal circuit design index, converter material, converter heat dissipation area and converter manufacturing index. The heat dissipation environment condition characteristic index at least includes the ambient temperature and ambient wind speed.

[0027] It should be understood that the temperature control verification mechanism is used to conduct a comprehensive multi-dimensional temperature characteristic analysis of the target converter. First, the key heat generation characteristic indicators and heat dissipation characteristic indicators are extracted from the database pre-stored by the temperature control verification mechanism. Among them, the heat generation characteristic indicators are directly related to the energy conversion process of the converter, including output power, conversion efficiency, continuous operation time and operation fluctuation index. The output power reflects the energy output of the converter when it is working; the conversion efficiency measures the loss in the energy conversion process; the continuous operation time and the operation fluctuation index provide information on the workload and usage mode of the converter. These factors together determine the heat generation rate and total amount of the converter.

[0028] On the other hand, the heat dissipation characteristic index refers to the heat dissipation condition characteristic index, which focuses on the heat dissipation capacity of the converter and the influence of environmental conditions on its heat dissipation effect. The heat dissipation condition characteristic index is subdivided into heat dissipation design condition characteristic index and heat dissipation environment condition characteristic index. Among them, the heat dissipation design condition characteristic index covers the physical design factors of the converter itself, such as internal circuit design index, converter material, converter heat dissipation area and converter manufacturing index. The internal circuit design index reflects the influence of circuit layout on heat dissipation, the converter material can reflect the thermal conductivity of the material), the converter heat dissipation area directly affects the heat dissipation efficiency, and the converter manufacturing index can reflect the indirect influence of the manufacturing process on the heat dissipation performance. The heat dissipation environment condition characteristic index focuses on the effect of the external environment on the heat dissipation effect, including ambient temperature and ambient wind speed. The ambient temperature directly affects the heat exchange efficiency, and the increase in ambient wind speed can improve the convection heat dissipation effect.

[0029] Furthermore, to obtain the operation fluctuation index, step P31 of the embodiment of the present application further includes:

[0030] P31-1: Obtain a target fluctuation record, which refers to a time series record of the operation fluctuation data of the target converter; the target fluctuation record includes an operation voltage fluctuation time series, an operation current fluctuation time series, and an operation load fluctuation time series. P31-2: Extract the first fluctuation time series in the target fluctuation record, and analyze the first fluctuation time series to obtain a first fluctuation frequency; P31-3: Record the fluctuation frequency mean calculated based on the first fluctuation frequency as the operation fluctuation index.

[0031] Optionally, by accurately analyzing the operating stability of the target converter to quantify the fluctuations in its operation, the specific process of obtaining the operating fluctuation index may be to first collect and process the target fluctuation record, that is, a time series data record that can reflect the voltage, current and load changes of the converter during operation. Exemplarily, the operating fluctuation data of the target converter can be captured in real time or periodically through a preset data acquisition mechanism. These data are saved in the form of time series records, specifically including the operating voltage fluctuation time series, the operating current fluctuation time series and the operating load fluctuation time series, which record the voltage, current and load status of the converter at different time points, and are the basic data source for subsequent analysis of fluctuation characteristics.

[0032] Furthermore, any fluctuation sequence in the target fluctuation record is analyzed as the first fluctuation sequence. The first fluctuation sequence here can be any one of voltage, current or load, and the specific selection depends on the evaluation requirements and priorities. By processing the sequence data, such as using Fourier transform, wavelet analysis or other signal processing techniques, the fluctuation frequency in the sequence, i.e., the first fluctuation frequency, is identified and calculated. This frequency reflects the speed of the fluctuation within the cycle of the sequence data and is an important parameter for evaluating the stability of the converter.

[0033] Furthermore, in order to obtain a more representative operation fluctuation index, all first fluctuation frequencies within a period of time (such as one day, one week or one month) are statistically processed, and their average, that is, the average fluctuation frequency, is calculated and used as the operation fluctuation index of the converter. The level of the operation fluctuation index is directly related to the operating stability and efficiency of the converter, and is an important basis for evaluating its performance and maintenance needs.

[0034] P32: Based on the heat generation characteristic index, the characteristics of the target converter are collected to obtain target heat generation characteristic parameters; P33: The target heat generation characteristic parameters are input into the heat generation prediction model to obtain predicted target heat generation values; P34: Based on the heat dissipation characteristic index, the characteristics of the target converter are collected to obtain target heat dissipation characteristic parameters; P35: The target heat dissipation characteristic parameters are analyzed to obtain predicted target heat dissipation values, and the target predicted temperature is obtained in combination with the predicted target heat generation values.

[0035] Specifically, after extracting the heat generation characteristic indicators and heat dissipation characteristic indicators pre-stored in the temperature control verification mechanism, the characteristics of the target converter are collected based on the heat generation characteristic indicators. For example, the specific values ​​of the heat generation characteristic indicators are collected through sensor monitoring, data recording or model estimation, and processed and integrated to finally obtain a set of target heat generation characteristic parameters that can comprehensively describe the heat generation characteristics of the converter.

[0036] Furthermore, the target heat generation characteristic parameters are used as input and input into a pre-trained heat generation prediction model for heat generation prediction. The heat generation prediction model can be constructed based on thermodynamic principles, machine learning algorithms or empirical formulas, and can quickly calculate the expected heat generation of the converter under specific working conditions based on the input characteristic parameters, that is, predict the target heat generation value. At the same time, corresponding to the heat generation analysis, the heat dissipation performance of the converter is also concerned. Identify and collect characteristic indicators related to heat dissipation, such as ambient temperature, radiator efficiency, wind speed or fan speed, etc. These indicators jointly determine the strength of the heat dissipation capacity of the converter. By collecting and processing these data, the target heat dissipation characteristic parameters are obtained, laying the foundation for the subsequent heat dissipation value prediction.

[0037] Finally, the target heat dissipation characteristic parameters are used to estimate the expected heat dissipation of the converter under given working conditions through a heat dissipation calculation model or empirical formula, that is, the predicted target heat dissipation value. The predicted target heat dissipation value is compared with the predicted target heat generation value. According to the principle of energy conservation, the difference between the two is the net heat accumulation inside the converter, which is further used to calculate the target predicted temperature of the converter. This process comprehensively considers the factors of heat generation and heat dissipation, ensuring the accuracy and comprehensiveness of temperature prediction.

[0038] Furthermore, to obtain the heat generation prediction model, step P33 of the embodiment of the present application further includes:

[0039] P33-1: Obtain a historical heat generation monitoring log, and extract a first historical heat generation record from the historical heat generation monitoring log, wherein the first historical heat generation record includes a first historical heat generation characteristic parameter, a first historical initial temperature, and a first historical target temperature; P33-2: Obtain a first historical heat generation value based on the first historical initial temperature and the first historical target temperature, and form a first training data group with the first historical heat generation characteristic parameter; P33-3: Perform machine learning and verification on the first training data group to obtain the heat generation prediction model.

[0040] It should be understood that the construction process of the heat generation prediction model may be to first retrieve and obtain detailed historical heat generation monitoring logs from the historical database, which record the heat generation of the converter under different operating conditions. Subsequently, a representative first historical heat generation record is extracted from the log. The first historical heat generation record includes the first historical heat generation characteristic parameters (such as current, voltage, load, etc.), and the corresponding first historical initial temperature (i.e., the temperature before the heat generation process begins) and the first historical target temperature (i.e., the stable temperature after the heat generation process ends).

[0041] Furthermore, using the principle of thermodynamics or the law of conservation of energy, the first historical heat generation value in the time period, that is, the heat actually generated by the converter under the working condition, is inferred based on the initial temperature and the target temperature in the first historical heat generation record. Subsequently, the first historical heat generation value is paired with its corresponding heat generation characteristic parameter to form a plurality of first training data groups. These data groups are used as inputs for machine learning to train the heat generation prediction model.

[0042] Similarly, after obtaining a sufficient number of training data sets, start a machine learning algorithm, such as a regression tree, a neural network, or a support vector machine, to train these training data sets. During the training process, the algorithm will continuously adjust the internal parameters to minimize the difference between the predicted heat generation value and the actual heat generation value. After the training is completed, the model is verified and tested, such as cross-validation, leave-one-out validation, etc., to ensure its generalization ability and prediction accuracy on unknown data, and obtain the heat generation prediction model. This model can accurately predict the heat generation of the converter based on the current heat generation characteristic parameters, providing strong support for subsequent temperature prediction and thermal management strategy formulation.

[0043] Further, to obtain the predicted target heat dissipation value, step P35 of the embodiment of the present application further includes:

[0044] P35-1: Constructing a target digital model of the target converter according to the characteristic indicators of the heat dissipation design conditions; P35-2: Rendering the monitoring data corresponding to the characteristic indicators of the heat dissipation environment conditions to the target digital model and performing heat dissipation simulation to obtain the predicted target heat dissipation value.

[0045] Specifically, the specific process of obtaining the predicted target heat dissipation value may be, first, to construct a detailed digital model of the target converter based on the characteristic indicators of the heat dissipation design conditions. The characteristic indicators of the heat dissipation design conditions include but are not limited to the geometric dimensions, material properties, layout methods, air duct design, and specifications and configurations of heat dissipation components (such as fans, heat pipes, etc.) of the radiator. By inputting these design parameters into professional 3D modeling software or thermal simulation platforms, a target digital model that highly restores the physical structure and heat dissipation characteristics of the target converter is generated, which serves as the basis for subsequent heat dissipation simulation analysis and can accurately reflect the performance of the converter under different heat dissipation conditions.

[0046] Furthermore, real-time monitoring data of characteristic indicators of the heat dissipation environment conditions are collected and sorted, such as ambient temperature, wind speed, wind direction, and air humidity. These monitoring data are used as boundary conditions or input parameters and rendered into the previously constructed target digital model. By starting the heat dissipation simulation program, the heat dissipation process of the converter in the actual operating environment is simulated, including heat transfer, convection, radiation, and the working state of the heat dissipation components. The simulation results are output as predicted target heat dissipation values, which represent the expected heat dissipation capacity of the converter under given heat dissipation environment conditions, and can provide data support for subsequent temperature prediction and thermal management strategy formulation.

[0047] P40: If the temperature difference between the target predicted temperature and the target detected temperature is within a preset deviation threshold, a temperature control instruction is issued.

[0048] Optionally, the difference between the target predicted temperature and the target detected temperature is calculated, that is, the difference between the actual measured temperature and the temperature predicted based on the model. The calculated temperature difference is compared with a preset deviation threshold. The preset deviation threshold is a temperature range set according to factors such as the converter design specifications, operating environment, and safety standards, and can be used to determine the acceptable degree of deviation between the predicted temperature and the measured temperature.

[0049] If the temperature difference is within the preset deviation threshold, it indicates that the current temperature state of the converter is in line with expectations and no additional temperature control operation is required. Once the temperature difference exceeds the preset deviation threshold, the system immediately identifies it as an abnormal temperature state and triggers the corresponding temperature control instruction. The specific content of the temperature control instruction depends on the design characteristics of the converter, the current operating environment and the temperature control strategy. It may include adjusting the workload, starting or adjusting the speed of the heat dissipation device (such as fans, radiators, etc.), changing the operating voltage or current, etc., to achieve precise control of the converter temperature. In addition, in order to ensure the timeliness and effectiveness of the temperature control instruction, the system may also integrate a real-time monitoring and feedback mechanism. Once the temperature control instruction is issued, the system will continue to monitor the temperature changes of the converter, and adjust the instruction parameters or execute a new temperature control strategy as needed until the temperature returns to a safe and efficient operating range.

[0050] P50: Based on the temperature control instruction, a temperature control simulation optimization analysis is performed on the target converter to obtain a target temperature control plan.

[0051] Furthermore, step P50 of the embodiment of the present application also includes:

[0052] P51: Obtain the threshold constraints of each indicator in the heat generation characteristic index, and form a first optimization space; P52: Obtain the threshold constraints of each indicator in the heat dissipation environment condition characteristic index, and form a second optimization space; P53: Using the preset temperature threshold as the optimization constraint, perform temperature control simulation optimization analysis in the first optimization space or the second optimization space to obtain the target temperature control plan.

[0053] It should be understood that after receiving the temperature control instruction, the system does not directly execute the instruction, but first performs a temperature control simulation optimization analysis, simulates the temperature response under different temperature control strategies in a virtual environment, and finds the optimal temperature control plan through comparative analysis to ensure the safe and stable operation of the converter while maximizing its performance or extending its service life.

[0054] Specifically, we first need to clarify the scope of the optimization search, that is, the optimization space. By obtaining the threshold constraints of each indicator in the heat generation characteristic index, the range of heat that the converter may generate under different working conditions is defined, and the first optimization space is constructed. This space contains all possible heat generation state adjustment schemes, such as adjusting the workload, optimizing the circuit layout, etc. Similarly, the threshold constraints of each indicator in the heat dissipation environment condition characteristic index are obtained, that is, the heat dissipation capacity of the environment in which the converter is located. Based on these constraints, the second optimization space is constructed, which contains all possible heat dissipation condition adjustment schemes, such as changing the fan speed, optimizing the air duct design, etc.

[0055] Furthermore, the preset temperature threshold is used as the optimization constraint, and a temperature control simulation optimization analysis is performed in the first optimization space or the second optimization space (or a combination of the two). By simulating different temperature control strategies, observing and recording their impact on the converter temperature, and comparing and analyzing factors such as temperature change trends, stability, and energy consumption under different strategies, combined with the global optimization algorithm for screening, the optimal temperature control plan is determined. This plan can not only ensure that the temperature of the converter remains within the preset threshold range, but also achieve the best balance between multiple performance indicators.

[0056] P60: Control the temperature of the target converter according to the target temperature control plan.

[0057] Specifically, the temperature of the target converter is controlled according to the target temperature control plan. First, the specific instructions and control parameters in the target temperature control plan are parsed. These instructions may include adjusting the workload, changing the working state of the heat dissipation element (such as fan speed, radiator opening and closing state, etc.), optimizing the circuit layout to reduce heat source generation, etc. At the same time, the control parameters specify the specific values ​​or ranges of each adjustment in detail to ensure the accurate execution of the control measures.

[0058] Furthermore, specific instructions and parameters are sent to the control system of the target converter. After receiving the instructions, the control system adjusts the operating state of the converter in real time according to the preset logic and algorithm. For example, if the plan requires reducing the workload to reduce heat generation, the output power of the converter is automatically adjusted or switched to a low power mode. If the plan requires enhancing the heat dissipation effect, the fan speed is increased, the heat dissipation area of ​​the radiator is increased, or the air duct design is adjusted to improve the air circulation efficiency.

[0059] At the same time, during the temperature control process, the temperature changes of the converter are continuously monitored. The current temperature of the converter is obtained in real time through monitoring equipment such as temperature sensors, and compared with the preset temperature threshold. If the temperature deviates from the expected range, it is immediately adjusted dynamically according to the adjustment strategy in the target temperature control plan to ensure that the converter always operates within a safe temperature range. By accurately executing the instructions and parameters in the target temperature control plan, the target converter is effectively temperature controlled. Ensure the stable operation and performance optimization of the converter, and improve its reliability and service life.

[0060] In summary, the embodiments of the present application have at least the following technical effects:

[0061] The present application obtains the target detection temperature. If the target detection temperature reaches a preset temperature threshold, the temperature control verification mechanism is activated, and a multi-dimensional temperature characteristic analysis is performed on the target converter. If the temperature difference between the obtained target predicted temperature and the target detection temperature is within a preset deviation threshold, a temperature control simulation optimization analysis is performed to obtain a target temperature control plan for temperature control.

[0062] The technical effect of intelligently controlling the temperature of the electric tricycle converter through multi-dimensional temperature characteristic analysis and improving the operating stability, reliability and service life of the converter is achieved.

[0063] Embodiment 2

[0064] Based on the same inventive concept as the intelligent temperature control method for the electric tricycle converter in the aforementioned embodiment, Figure 3 As shown, the present application provides an intelligent temperature control device for an electric tricycle converter, and the device and method embodiments in the present application embodiments are based on the same inventive concept. The device includes:

[0065] A target detection temperature acquisition module 11, wherein the target detection temperature acquisition module 11 is used to acquire a target detection temperature, wherein the target detection temperature refers to a temperature obtained by performing non-contact temperature detection on a target converter by a temperature detector;

[0066] A temperature control verification mechanism activation module 12, wherein the temperature control verification mechanism activation module 12 is used to activate the temperature control verification mechanism if the target detection temperature reaches a preset temperature threshold;

[0067] A multi-dimensional temperature characteristic analysis module 13, the multi-dimensional temperature characteristic analysis module 13 is used to perform a multi-dimensional temperature characteristic analysis on the target converter according to the temperature control verification mechanism to obtain a target predicted temperature;

[0068] A temperature control instruction sending module 14, the temperature control instruction sending module 14 is used to send a temperature control instruction if the temperature difference between the target predicted temperature and the target detected temperature is within a preset deviation threshold;

[0069] A temperature control simulation optimization module 15, wherein the temperature control simulation optimization module 15 is used to perform a temperature control simulation optimization analysis on the target converter based on the temperature control instruction to obtain a target temperature control plan;

[0070] The temperature control module 16 is used to control the temperature of the target converter according to the target temperature control plan.

[0071] Furthermore, the multi-dimensional temperature characteristic analysis module 13 is also used to perform the following steps:

[0072] Extract the heat generation characteristic index and the heat dissipation characteristic index pre-stored in the temperature control verification mechanism; collect characteristics of the target converter based on the heat generation characteristic index to obtain target heat generation characteristic parameters; input the target heat generation characteristic parameters into the heat generation prediction model to obtain predicted target heat generation values; collect characteristics of the target converter based on the heat dissipation characteristic index to obtain target heat dissipation characteristic parameters; analyze the target heat dissipation characteristic parameters to obtain predicted target heat dissipation values, and obtain the target predicted temperature in combination with the predicted target heat generation value.

[0073] Furthermore, the multi-dimensional temperature characteristic analysis module 13 is also used to perform the following steps:

[0074] Obtain a target fluctuation record, wherein the target fluctuation record refers to a time series record of the operation fluctuation data of the target converter; extract a first fluctuation time series in the target fluctuation record, and analyze the first fluctuation time series to obtain a first fluctuation frequency; and record a fluctuation frequency mean value calculated based on the first fluctuation frequency as the operation fluctuation index.

[0075] Furthermore, the multi-dimensional temperature characteristic analysis module 13 is also used to perform the following steps:

[0076] Obtain a historical heat generation monitoring log, and extract a first historical heat generation record in the historical heat generation monitoring log, wherein the first historical heat generation record includes a first historical heat generation characteristic parameter, a first historical initial temperature, and a first historical target temperature; obtain a first historical heat generation value according to the first historical initial temperature and the first historical target temperature, and form a first training data group with the first historical heat generation characteristic parameter; perform machine learning and verification on the first training data group to obtain the heat generation prediction model.

[0077] Furthermore, the multi-dimensional temperature characteristic analysis module 13 is also used to perform the following steps:

[0078] A target digital model of the target converter is constructed according to the heat dissipation design condition characteristic index; monitoring data corresponding to the heat dissipation environment condition characteristic index is rendered to the target digital model and heat dissipation simulation is performed to obtain the predicted target heat dissipation value.

[0079] Furthermore, the temperature control simulation optimization module 15 is also used to perform the following steps:

[0080] The threshold constraints of each indicator in the heat generation characteristic index are obtained, and a first optimization space is formed; the threshold constraints of each indicator in the heat dissipation environment condition characteristic index are obtained, and a second optimization space is formed; with the preset temperature threshold as the optimization constraint, a temperature control simulation optimization analysis is performed in the first optimization space or the second optimization space to obtain the target temperature control plan.

[0081] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0083] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent temperature control method for an electric tricycle converter, characterized in that: include: Acquiring a target detection temperature, wherein the target detection temperature refers to a temperature obtained by performing non-contact temperature detection on a target converter by a temperature detector; If the target detection temperature reaches a preset temperature threshold, a temperature control verification mechanism is activated; Performing a multi-dimensional temperature characteristic analysis on the target converter according to the temperature control verification mechanism to obtain a target predicted temperature; If the temperature difference between the target predicted temperature and the target detected temperature is within a preset deviation threshold, a temperature control instruction is issued; Based on the temperature control instruction, a temperature control simulation optimization analysis is performed on the target converter to obtain a target temperature control plan; Performing temperature control of the target converter according to the target temperature control plan; The method further comprises: Extracting the heat generation characteristic index and the heat dissipation characteristic index pre-stored in the temperature control verification mechanism, wherein the heat generation characteristic index includes output power, conversion efficiency, continuous operation time and operation fluctuation index; Based on the heat generation characteristic index, characteristics of the target converter are collected to obtain target heat generation characteristic parameters; Inputting the target heat generation characteristic parameter into a heat generation prediction model to obtain a predicted target heat generation value; Based on the heat dissipation characteristic index, characteristics of the target converter are collected to obtain target heat dissipation characteristic parameters; Analyzing the target heat dissipation characteristic parameters to obtain a predicted target heat dissipation value, and combining the predicted target heat generation value to obtain the target predicted temperature; Acquire a target fluctuation record, wherein the target fluctuation record refers to a time series record of operation fluctuation data of the target converter; Extracting a first fluctuation time sequence in the target fluctuation record, and analyzing the first fluctuation time sequence to obtain a first fluctuation frequency; Recording the fluctuation frequency mean value calculated based on the first fluctuation frequency as the operation fluctuation index; The target fluctuation record includes the operation voltage fluctuation time sequence, the operation current fluctuation time sequence and the operation load fluctuation time sequence; The method further comprises: Acquire a historical heat generation monitoring log, and extract a first historical heat generation record in the historical heat generation monitoring log, wherein the first historical heat generation record includes a first historical heat generation characteristic parameter, a first historical initial temperature, and a first historical target temperature; Obtain a first historical heat generation value according to the first historical initial temperature and the first historical target temperature, and form a first training data group with the first historical heat generation characteristic parameter; The first training data set is subjected to machine learning and testing to obtain the heat generation prediction model.

2. The intelligent temperature control method for an electric tricycle converter according to claim 1, characterized in that: The heat dissipation characteristic index refers to the heat dissipation condition characteristic index, including the heat dissipation design condition characteristic index and the heat dissipation environment condition characteristic index. The heat dissipation design condition characteristic index at least includes the internal circuit design index, the converter material, the converter heat dissipation area and the converter manufacturing index. The heat dissipation environment condition characteristic index at least includes the ambient temperature and the ambient wind speed.

3. The intelligent temperature control method for an electric tricycle converter according to claim 2, characterized in that: include: Constructing a target digital model of the target converter according to the heat dissipation design condition characteristic index; The monitoring data corresponding to the characteristic index of the heat dissipation environment condition is rendered to the target digital model and heat dissipation simulation is performed to obtain the predicted target heat dissipation value.

4. The intelligent temperature control method for an electric tricycle converter according to claim 3, characterized in that: include: Obtaining the threshold constraints of each indicator in the heat generation characteristic indicator and forming a first optimization space; Obtaining the threshold constraints of each indicator in the heat dissipation environment condition characteristic indicator, and forming a second optimization space; Taking the preset temperature threshold as an optimization constraint, a temperature control simulation optimization analysis is performed in the first optimization space or the second optimization space to obtain the target temperature control plan.

5. An intelligent temperature control device for an electric tricycle converter, characterized in that: The device is used to implement the intelligent temperature control method for an electric tricycle converter according to any one of claims 1 to 4, the device comprising: A target detection temperature acquisition module, the target detection temperature acquisition module is used to acquire the target detection temperature, the target detection temperature refers to the temperature obtained by performing non-contact temperature detection on the target converter by a temperature detector; A temperature control verification mechanism activation module, the temperature control verification mechanism activation module is used to activate the temperature control verification mechanism if the target detection temperature reaches a preset temperature threshold; A multi-dimensional temperature characteristic analysis module, the multi-dimensional temperature characteristic analysis module is used to perform a multi-dimensional temperature characteristic analysis on the target converter according to the temperature control verification mechanism to obtain a target predicted temperature; a temperature control instruction sending module, the temperature control instruction sending module being used to send a temperature control instruction if the temperature difference between the target predicted temperature and the target detected temperature is within a preset deviation threshold; A temperature control simulation optimization module, the temperature control simulation optimization module is used to perform temperature control simulation optimization analysis on the target converter based on the temperature control instruction to obtain a target temperature control plan; A temperature control module is used to control the temperature of the target converter according to the target temperature control plan.

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