Multi-point temperature control method and system based on intelligent temperature equalization control

Abstract

The invention relates to a multi-point temperature control method and system based on intelligent temperature equalization control, and the method comprises the steps: collecting the temperature data of each region in frequency converter equipment in real time, and enabling the regions to comprise but not limited to a power module, a power module and a heat dissipation region; based on the temperature data, a deviation value between the temperature of each region and a preset target temperature is calculated through a preset analysis algorithm, and a temperature adjustment requirement of each region is generated based on the deviation value; based on the temperature adjusting requirements of all the areas, adjusting parameters of the cooling equipment are calculated through an optimization algorithm, and a corresponding control instruction is generated in combination with the load state of the frequency converter equipment; and the control instruction is transmitted to the cooling equipment, and corresponding parameters of the cooling equipment are adjusted according to the control instruction. The method has the effect of improving the temperature control precision.

Description

Technical Field

[0001] This application relates to the technical field of frequency converter temperature control, and particularly to a multi-point temperature control method and system based on intelligent temperature balance control. Background Art

[0002] Currently, with the continuous development of electronic and industrial equipment, frequency converters, as important power electronic devices, are widely used in industrial automation, power systems, air-conditioning control and other fields. During the operation of frequency converter equipment, a large amount of heat is generated due to the operation of multiple internal modules. If the temperature control is improper, it will cause the equipment to overheat, which will in turn affect its stability, increase the failure rate, and shorten the service life. Therefore, how to achieve precise temperature management and ensure that the equipment operates within a safe temperature range has become one of the key technologies in the design of frequency converter equipment.

[0003] The above-mentioned existing technical solutions have the following defects: Most of the existing frequency converter temperature control methods rely on a single temperature sensor and fixed cooling equipment, and the temperature is adjusted through a simple fan or liquid cooling system, but it cannot be accurately adjusted for different regions and different loads, so there is room for improvement. Summary of the Invention

[0004] In order to improve the temperature control accuracy, this application provides a multi-point temperature control method and system based on intelligent temperature balance control.

[0005] The first invention object of this application is achieved through the following technical solutions: A multi-point temperature control method based on intelligent temperature balance control, the multi-point temperature control method based on intelligent temperature balance control includes: Real-time collect the temperature data of each area inside the frequency converter equipment, and the areas include but are not limited to power modules, power supply modules and heat dissipation areas; Based on the temperature data, calculate the deviation value between the temperature of each area and the preset target temperature through a preset analysis algorithm, and generate the temperature adjustment requirement of each area based on the deviation value; Based on the temperature adjustment requirements of each area, calculate the adjustment parameters of the cooling equipment through an optimization algorithm, and generate corresponding control instructions in combination with the load status of the frequency converter equipment; Transmit the control instructions to the cooling equipment, and adjust the corresponding parameters of the cooling equipment according to the control instructions.

[0006] By adopting the above technical solutions, temperature sensors are installed in multiple key areas inside the frequency converter to monitor the temperature status of each area in real time, ensuring that the temperature changes of each part inside the equipment can be comprehensively and continuously grasped. Through the real-time acquisition of temperature data, temperature fluctuations or abnormal conditions can be detected in a timely manner, providing accurate basic data for subsequent temperature control adjustments, thereby effectively preventing the equipment from malfunctioning or being damaged due to overheating; through the preset algorithm, the collected temperature data is analyzed to calculate the difference between the actual temperature and the set target temperature in each area, and then the current temperature control requirements of each area are evaluated. By accurately calculating the temperature deviation, the temperature control requirements of each area can be understood in real time, ensuring that reasonable temperature control adjustments can be made according to the specific conditions of each area, and avoiding overheating or insufficient cooling in individual areas caused by overall control imbalance; through the optimized algorithm, the adjustment parameters of the cooling equipment can be accurately calculated according to the temperature adjustment requirements of each area to achieve the best cooling effect and energy use efficiency. By combining the temperature adjustment requirements with the real-time load status of the equipment, dynamic control instructions are generated, so that the working mode of the cooling equipment can be flexibly adjusted according to the load situation of the frequency converter, avoiding fixed cooling parameters, making the temperature control system more intelligent and energy-saving; by transmitting the calculated control instructions to the cooling equipment, ensuring that the cooling equipment can respond in a timely manner and adjust its own working parameters. By controlling the specific adjustment parameters of the cooling equipment, the cooling system can accurately adjust the cooling effect according to the different working states and temperature requirements of the frequency converter equipment, thereby effectively avoiding overcooling or overheating phenomena and improving the stability and service life of the equipment.

[0007] In one example, the present application can be further configured as: based on the temperature data, calculating the deviation value between the temperature of each area and the target temperature through a preset analysis algorithm, and generating the temperature adjustment requirements for each area including: Based on the comparison between the temperature of each area and the preset target temperature, calculating the deviation value of each area through a simple difference algorithm to obtain the deviation values of each area; According to the deviation value, combining the operation load and working environment data of the frequency converter equipment, evaluating the temperature control priority of each area to generate an evaluation result; Based on the evaluation result, dynamically generating the temperature adjustment requirements for each area.

[0008] By adopting the above technical solution, through a simple difference calculation method, the temperature deviation of each area can be quickly obtained, thereby providing a basic quantitative basis for subsequent adjustment steps to ensure that the magnitude and direction of the temperature deviation can be efficiently identified; by combining the temperature deviation, the actual load of the frequency converter device, and the surrounding environment data, the temperature control urgency of each area can be comprehensively evaluated. Through this evaluation process, it can be dynamically determined which areas need to be preferentially temperature-regulated, thereby ensuring that critical areas are timely temperature-regulated; through the dynamic analysis of the evaluation results, the system can flexibly generate the temperature control requirements for each area according to the temperature control priorities of different areas. The dynamically generated adjustment requirements can automatically adjust the temperature control strategy according to the real-time load of the frequency converter device and the changes in the working environment, ensuring that the temperature regulation is not only accurate but also flexible to respond to different working conditions and environmental changes, thereby realizing efficient and intelligent temperature control management.

[0009] In one example of the present application, it can be further configured that: according to the deviation value, combining the operating load and working environment data of the frequency converter device, evaluating the temperature control priority of each area, and the generated evaluation result includes: According to the load formula: Calculate the load situation of each area and generate a load coefficient L i , where P actual is the actual power of the area, and P max is the maximum power capacity of the area; According to the environment formula: E i = f(T ambient , H ambient , V air ) Calculate the working environment data of each area and generate an environment coefficient E i , where T ambient is the external environmental temperature, H ambient is the environmental humidity, and V air is the air flow rate; Through the priority formula: P i = ω1×|ΔT i | + ω2×L i + ω3×E i , calculate the temperature control priority of each area and generate the evaluation result, where ω1, ω2, ω3 are weight coefficients, and P i is the temperature control priority of each area, and ΔT i is the temperature deviation value of each area.

[0010] By adopting the above technical solution, by calculating the ratio of the actual power of each area to the maximum power capacity, the load level of the area can be evaluated, and the generated load factor can reflect the load pressure of each area, so that the areas with higher loads can be preferentially adjusted in the temperature control strategy to ensure that the temperatures of these areas are controlled in a timely manner and overheating caused by excessive load is avoided; by comprehensively considering factors such as the external environmental temperature, humidity, and air flow velocity, the working environmental conditions of each area can be accurately evaluated; by combining the load factor, environmental factor, and temperature deviation value, and using weight coefficients to weight each factor, the temperature control priority of each area is calculated, and the temperature control intensity of different areas is adjusted according to actual needs to ensure that the areas with larger temperature deviations and heavier loads or poorer environments are preferentially adjusted. This priority calculation can dynamically adjust the temperature control strategy according to various factors to ensure the accuracy and efficiency of temperature control.

[0011] In one example of the present application, it can be further configured that: based on the temperature adjustment requirements of each area, calculating the adjustment parameters of the cooling device through an optimization algorithm, and generating corresponding control instructions in combination with the load status of the frequency converter device includes: Based on the temperature adjustment requirements of each area, using a genetic algorithm to calculate the adjustment parameters of the cooling device; When generating the adjustment parameters of the cooling device, correcting the adjustment parameters of the cooling device through a load adjustment factor, and the calculation formula of the load adjustment factor is: Wherein, P actual is the actual power of the area, P max is the maximum power capacity of the area, F load is the load adjustment factor, and β is the dynamic adjustment coefficient; According to the adjustment parameters of the cooling device and the load adjustment factor, generating the control instructions of the cooling device through a fuzzy control algorithm.

[0012] By adopting the above technical solution, the adjustment parameters of the cooling equipment are optimized through a genetic algorithm, which can find the optimal solution among various possible parameter combinations to achieve the best cooling effect, enabling it to dynamically adapt to the temperature control requirements of each region, thereby improving the overall heat dissipation efficiency and temperature control accuracy of the system; through the load adjustment factor, the parameters of the cooling equipment can be dynamically adjusted according to the load conditions of the region, enabling the cooling equipment to make appropriate adjustments according to different load states, avoiding over-cooling or under-cooling. The load adjustment factor is determined by calculating the ratio of the actual power of the region to the maximum power capacity, so as to increase the cooling intensity when the equipment load is heavy and ensure the stability of the equipment temperature; through the fuzzy control algorithm, the control process of the cooling equipment can be fuzzified to cope with uncertain factors such as load and temperature changes, making the control smoother and more intelligent. By comprehensively processing the input data of the adjustment parameters of the cooling equipment and the load adjustment factor, control instructions that meet the current actual needs are generated, avoiding the traditional binary control method, enabling the cooling equipment to smoothly adjust its state, avoiding excessive fluctuations, and ensuring stable operation of the equipment within the optimal temperature range.

[0013] In one example, the present application can be further configured as follows: Based on the temperature adjustment requirements of each region, calculating the adjustment parameters of the cooling equipment by using a genetic algorithm includes: Construct an initial population according to the temperature adjustment requirements, and set the number of individuals to N; Calculate the fitness of each individual in the initial population according to the fitness function to generate fitness values; According to the fitness values, select individuals for crossover and mutation operations. When the fitness values reach a preset threshold, terminate the crossover and mutation operations and output the adjustment parameters of the cooling equipment.

[0014] By adopting the above technical solution, by constructing an initial population and setting the number of individuals in the population to N, multiple possible combinations of cooling device adjustment parameters can be generated, which provides a rich selection space for the optimization process of the genetic algorithm. Each individual in the initial population represents a combination of cooling device parameters, thus ensuring comprehensive exploration at the beginning to find the most suitable adjustment parameters; by evaluating each individual through a fitness function, the quality of the cooling device adjustment parameters can be quantified to generate a fitness value. The higher the fitness value, the closer or the more the cooling effect of the combination approaches or reaches the expected goal. The fitness function is calculated based on factors such as the temperature control requirements of the device, the actual power, and the temperature deviation, ensuring that each adjustment parameter combination can match the actual requirements during the optimization process, thereby improving the temperature control accuracy of the system; by selecting individuals with stronger adaptability according to the fitness value for crossover and mutation operations, new combinations of cooling device adjustment parameters can be continuously generated. This process simulates the process of natural selection, and the best adjustment scheme is found through multiple generations of evolution. When the fitness value reaches the preset threshold, it means that the adjustment parameters of the cooling device have been optimized enough to meet the temperature control requirements of the system. At this time, the crossover and mutation operations are stopped, and the final adjustment parameters of the cooling device are output to ensure that the cooling system can work accurately and efficiently.

[0015] In one example, the present application can be further configured as: the multi-point temperature control method based on intelligent temperature balance control further includes: Performing temperature anomaly detection on the temperature data of each area of the frequency converter device with a preset safe temperature threshold to generate an anomaly detection result; If the anomaly detection result is that the area temperature reaches the preset safe temperature threshold, an alarm instruction is triggered; According to the alarm instruction, a reminder notification is generated to the user terminal, and the operating state of the cooling device is adjusted.

[0016] By adopting the above technical solution, by comparing the temperature data of each area with the safety temperature threshold, it is possible to detect in real time whether there is a situation where the temperature exceeds the standard. This process can timely identify the potential overheating risk of the device, prevent the device from being damaged or its performance from degrading due to abnormal temperature. The generation of the abnormal detection result helps the system to respond quickly and take measures, thereby ensuring the stability and safety of the device and avoiding failures caused by excessive temperature; by setting a preset safety temperature threshold, when the temperature of a certain area exceeds this threshold, the system automatically triggers an alarm instruction, which can timely alert the device operator to avoid the device overheating problem caused by the failure to detect excessive temperature; by generating a reminder notice to the user terminal according to the alarm instruction, it is possible to timely notify relevant personnel of the temperature control abnormality of the device, ensure that the operator can immediately take necessary maintenance or treatment measures. At the same time, by adjusting the operating state of the cooling device, the temperature of the overheating area can be effectively reduced, ensuring that the frequency converter device operates within the safe temperature range and improving the stability and reliability of the system.

[0017] The second invention object of the present application is achieved by the following technical solutions: A multi-point temperature control system based on intelligent temperature balance control, the multi-point temperature control system based on intelligent temperature balance control includes: A data acquisition module for real-time collecting temperature data of each area inside the frequency converter device, and the areas include but are not limited to a power module, a power supply module, and a heat dissipation area; A calculation module for calculating the deviation value between the temperature of each area and the preset target temperature based on the temperature data through a preset analysis algorithm, and generating the temperature adjustment requirement of each area based on the deviation value; A control instruction generation module for calculating the adjustment parameters of the cooling device through an optimization algorithm based on the temperature adjustment requirements of each area, and generating corresponding control instructions in combination with the load state of the frequency converter device; An adjustment module for transmitting the control instruction to the cooling device and adjusting the corresponding parameters of the cooling device according to the control instruction.

[0018] By adopting the above technical solution, temperature sensors are installed in multiple key areas inside the frequency converter to monitor the temperature status of each area in real time, ensuring that the temperature changes of each part inside the device can be comprehensively and continuously grasped. Through the real-time collection of temperature data, temperature fluctuations or abnormal conditions can be detected in a timely manner, providing accurate basic data for subsequent temperature control adjustments, thereby effectively preventing the device from malfunctioning or being damaged due to excessive temperature; through a preset algorithm, the collected temperature data is analyzed to calculate the difference between the actual temperature and the set target temperature in each area, and then the current temperature control requirements of each area are evaluated. By accurately calculating the temperature deviation, the temperature control requirements of each area can be understood in real time, ensuring that reasonable temperature control adjustments can be made according to the specific conditions of each area, and avoiding overheating or insufficient cooling in individual areas caused by overall control imbalance;

[0019] In summary, the present application includes the following beneficial technical effects: 1. By installing temperature sensors in multiple key areas inside the frequency converter, the temperature status of each area is monitored in real time, ensuring that the temperature changes of each part inside the device can be comprehensively and continuously grasped. Through the real-time collection of temperature data, temperature fluctuations or abnormal conditions can be detected in a timely manner, providing accurate basic data for subsequent temperature control adjustments, thereby effectively preventing the device from malfunctioning or being damaged due to excessive temperature; through a preset algorithm, the collected temperature data is analyzed to calculate the difference between the actual temperature and the set target temperature in each area, and then the current temperature control requirements of each area are evaluated. By accurately calculating the temperature deviation, the temperature control requirements of each area can be understood in real time, ensuring that reasonable temperature control adjustments can be made according to the specific conditions of each area, and avoiding overheating or insufficient cooling in individual areas caused by overall control imbalance; 2. By optimizing the algorithm, the adjustment parameters of the cooling equipment can be accurately calculated according to the temperature regulation requirements of each area to achieve the best cooling effect and energy use efficiency. By combining the temperature regulation requirements with the real-time load status of the equipment, dynamic control instructions are generated, so as to flexibly adjust the working mode of the cooling equipment according to the load situation of the frequency converter, avoiding fixed cooling parameters and making the temperature control system more intelligent and energy-saving. By transmitting the calculated control instructions to the cooling equipment, it is ensured that the cooling equipment can respond in a timely manner and adjust its own working parameters. By controlling the specific adjustment parameters of the cooling equipment, the cooling system can accurately adjust the cooling effect according to the different working states and temperature requirements of the frequency converter equipment, thus effectively avoiding overcooling or overheating phenomena and improving the stability and service life of the equipment. Description of the Drawings

[0020] Figure 1 is a flowchart of a multi-point temperature control method based on intelligent temperature balance control in an embodiment of the present application; Figure 2 is a flowchart for implementing step S20 in a multi-point temperature control method based on intelligent temperature balance control in an embodiment of the present application; Figure 3 is a flowchart for implementing step S22 in a multi-point temperature control method based on intelligent temperature balance control in an embodiment of the present application; Figure 4 is a flowchart for implementing step S30 in a multi-point temperature control method based on intelligent temperature balance control in an embodiment of the present application; Figure 5 is a flowchart for implementing step S31 in a multi-point temperature control method based on intelligent temperature balance control in an embodiment of the present application; Figure 6 is an implementation flowchart of a multi-point temperature control method based on intelligent temperature balance control in an embodiment of the present application; Figure 7 is a schematic block diagram of a multi-point temperature control system based on intelligent temperature balance control in an embodiment of the present application; Detailed Description of the Embodiment

[0021] The present application will be further described in detail below with reference to the accompanying drawings.

[0022] In an embodiment, as Figure 1 shown, the present application discloses a multi-point temperature control method based on intelligent temperature balance control, which specifically includes the following steps: S10: Real-time collect the temperature data of each area inside the frequency converter equipment, and the areas include but are not limited to the power module, the power supply module, and the heat dissipation area.

[0023] Specifically, temperature acquisition is carried out by installing multiple temperature sensors in various areas of the frequency converter device, such as the power module, power supply module, and heat dissipation area. The temperature sensors can be thermocouples, RTDs, or infrared sensors, and the specific selection depends on the requirements of the actual use environment. For example, in the power module area, by installing the temperature sensor on the surface or surrounding area of the power module, the temperature change in this area can be monitored in real time to ensure the accuracy and real-time nature of temperature acquisition. Similar temperature monitoring is also carried out in the power supply module and heat dissipation area to ensure that the temperatures in different areas of the entire device are fully monitored, and the temperature data is transmitted to the data acquisition unit through a wired or wireless network.

[0024] S20: Based on the temperature data, calculate the deviation value between the temperature of each area and the preset target temperature through a preset analysis algorithm, and generate the temperature adjustment requirement for each area based on the deviation value.

[0025] Specifically, the deviation value is calculated by comparing the collected temperature data with the preset target temperature through a preset analysis algorithm. The preset target temperature is usually the optimal temperature range set according to the working conditions of the device, the ambient temperature, and the performance characteristics of the device. For example, the target temperature of the power module is set to 60 °C. When the actually collected temperature is 65 °C, the deviation value is 5 °C, which is obtained through simple subtraction calculation. Then, based on the deviation value, judge the temperature adjustment requirement for each area. Assuming the deviation value is positive, it means the actual temperature is higher than the target temperature, so the temperature adjustment requirement for this area is to lower the temperature. If the deviation value is negative, it means the temperature is too low and the temperature needs to be increased. In this process, the adjustment requirement can be weighted according to factors such as the heat dissipation conditions and load conditions of different areas to ensure the rationality and effectiveness of the temperature adjustment requirement.

[0026] S30: Based on the temperature adjustment requirements of each area, calculate the adjustment parameters of the cooling device through an optimization algorithm, and generate corresponding control instructions in combination with the load status of the frequency converter device.

[0027] Specifically, an optimization algorithm with strong adaptability and fast calculation speed is used to calculate the best adjustment parameters of the cooling device. For example, assuming that the fan speed, coolant flow rate, etc. of the cooling system are adjustable parameters, the optimization algorithm will calculate the optimal cooling parameters according to the temperature adjustment requirements of each area (such as lowering the temperature or increasing heat dissipation). At the same time, the load status is also taken into account. If the frequency converter device is in a high-load state, its heat generation is large, then a larger cooling capacity is required to maintain temperature stability. On the contrary, when the load is low, the cooling capacity can be appropriately reduced. For example, when the temperature deviation of the power module is large, the cooling system may need to increase the fan speed to more quickly lower the temperature and generate corresponding control instructions to control the various parameters of the cooling device, so as to ensure that the temperature of the device is always maintained within the safe range.

[0028] S40: Transmit the control instruction to the cooling device, and adjust the corresponding parameters of the cooling device according to the control instruction.

[0029] Specifically, the control instruction is transmitted to the cooling device through a communication interface, such as I2C, RS485, CAN bus, etc. The instruction contains specific adjustment parameters, such as fan speed, coolant flow rate, refrigeration intensity, etc. After receiving the control instruction, the cooling device immediately adjusts its working parameters to respond to the temperature change. For example, after the fan control circuit receives the instruction to increase the fan speed, it will adjust the voltage or frequency of the fan to increase the speed and enhance the cooling effect. The coolant flow control device adjusts the flow rate of the pump according to the instruction to increase or decrease the flow rate of the coolant, thereby effectively reducing the device temperature and ensuring that the temperature control system of the frequency converter can automatically respond and keep the device temperature within the safe range when the load changes.

[0030] By adopting the above technical solutions, by installing temperature sensors in multiple key areas inside the frequency converter to monitor the temperature status of each area in real time, it is ensured that the temperature changes of each part inside the device can be comprehensively and continuously grasped. Through the real-time acquisition of temperature data, temperature fluctuations or abnormal conditions can be detected in a timely manner, providing accurate basic data for subsequent temperature control adjustments, thereby effectively preventing the device from malfunctioning or being damaged due to overheating; by analyzing the collected temperature data through a preset algorithm, calculating the difference between the actual temperature of each area and the set target temperature, and then evaluating the current temperature control requirements of each area. By accurately calculating the temperature deviation, the temperature control requirements of each area can be understood in real time, ensuring that reasonable temperature control adjustments can be made according to the specific conditions of each area, and avoiding overheating or insufficient cooling of individual areas caused by overall control imbalance; through an optimized algorithm, the adjustment parameters of the cooling device can be accurately calculated according to the temperature adjustment requirements of each area to achieve the best cooling effect and energy use efficiency. By combining the temperature adjustment requirements with the real-time load status of the device, dynamic control instructions are generated, so as to flexibly adjust the working mode of the cooling device according to the load situation of the frequency converter, avoiding fixed cooling parameters, making the temperature control system more intelligent and energy-saving; by transmitting the calculated control instruction to the cooling device, it is ensured that the cooling device can respond in a timely manner and adjust its own working parameters. By controlling the specific adjustment parameters of the cooling device, the cooling system can accurately adjust the cooling effect according to the different working states and temperature requirements of the frequency converter device, thereby effectively avoiding overcooling or overheating phenomena and improving the stability and service life of the device. In one embodiment, such as Figure 2As shown, in step S20, based on the temperature data, the deviation value between the temperature of each region and the target temperature is calculated through a preset analysis algorithm, and the temperature adjustment requirements for each region are generated based on the deviation value, specifically including: S21: Compare the temperature of each region with the preset target temperature, and calculate the deviation value of each region through a simple difference algorithm to obtain the deviation values of each region.

[0031] Specifically, first, measure the actual temperature of each region in the frequency converter device, and compare these temperatures with the preset target temperature, such as the power module, power supply module, and heat dissipation area. The target temperature is the ideal temperature preset based on the operating characteristics and design requirements of the device. For example, if the target temperature of the power module is 60°C and the actual measured temperature is 65°C, then the temperature deviation is +5°C. If the actual temperature is 55°C, the temperature deviation is -5°C. Then, use a simple difference algorithm, that is, by calculating the difference between the actual temperature and the target temperature, to obtain the temperature deviation value of each region. The larger the deviation value, the more significant the temperature difference, and the higher the temperature adjustment requirement.

[0032] S22: According to the deviation value, combined with the operating load and working environment data of the frequency converter device, evaluate the temperature control priority of each region and generate an evaluation result.

[0033] Specifically, calculate the temperature control priority based on the temperature deviation value of each region, combined with the operating load of the device and external environmental conditions, such as ambient temperature, humidity, air flow rate, etc. For example, in a high-load state, the power module generates more heat due to high-power output, resulting in an increase in its temperature and a large temperature deviation. At this time, the temperature control priority of this region should be increased, that is, cooling measures need to be taken more urgently; on the contrary, if the device is in a low-load state, the power module generates less heat, and even if its temperature has a deviation, the temperature control priority is relatively low. In addition, the influence of the working environment is also an important factor in evaluating the priority. For example, a high-temperature and high-humidity environment will reduce the cooling effect of the cooling equipment. Therefore, it is necessary to perform a weight analysis on environmental factors, calculate the temperature control priority of each region, and obtain an evaluation result. The region with a higher priority will be adjusted first.

[0034] S23: Dynamically generate the temperature adjustment requirements for each region based on the evaluation result.

[0035] Specifically, according to the evaluation results, that is, the temperature control priorities of each region, the temperature adjustment requirements for each region are dynamically generated. For example, if the temperature deviation of the power module is large and the temperature control priority is high, the temperature adjustment requirements for this region may be measures such as increasing the rotation speed of the cooling fan and improving the working efficiency of the heat dissipation system; if the temperature deviation of the power supply module is small and the priority is low, the adjustment requirements may only be to slightly adjust the speed of the cooling fan or not to perform excessive adjustment. The dynamically generated temperature adjustment requirements will be continuously adjusted according to the temperature control priorities and actual needs of each region, and will be fed back to the cooling equipment in real time to ensure that the frequency converter equipment can efficiently and stably maintain within the optimal working temperature range during operation.

[0036] In one embodiment, as Figure 3 shown, in step S22, that is, according to the deviation value, combined with the operating load and working environment data of the frequency converter equipment, the temperature control priority of each region is evaluated to generate an evaluation result, specifically including: S221: According to the load formula: Calculate the load situation of each region to generate a load coefficient L i , where P actual is the actual power of the region, and P max is the maximum power capacity of this region.

[0037] Specifically, first, measure the actual power output of each region in the frequency converter equipment. P actual is the power actually consumed by this region in the current working state. For example, in the power module region, assume that the actually measured power is 15 kW; then, according to the design and specifications of the frequency converter equipment, obtain the maximum power capacity P max of this region. For example, the maximum power of the power module is 20 kW; next, substitute these two values into the load formula to calculate the load coefficient L i . The load coefficient L i =P actual / P max . In this way, the working state of this region under the current load can be obtained. The higher the load coefficient, the heavier the load of this region, and more heat dissipation and temperature control measures are required. For example, if P actual is 15 kW and Pmax is 20 kW, then L i =15 / 20 = 0.75, which means that the load of this region is 75% of the maximum capacity and requires a higher temperature control priority.

[0038] S222: According to the environment formula: E i =f(T ambient , H ambient , V air ) Calculate the working environment data of each region to generate an environment coefficient E i, where T ambient is the external environmental temperature, H ambient is the environmental humidity, V air is the air flow rate.

[0039] Specifically, by measuring the temperature, humidity and air flow rate of the working environment where the frequency converter device is located, T ambient (external environmental temperature), H ambient (environmental humidity), V air (air flow rate) are obtained respectively. For example, assume that the current environmental temperature T ambient is 35 °C, the humidity H ambient is 60%, and the air flow rate V air is 2 m / s. These environmental factors will all affect the heat dissipation efficiency of the cooling equipment. Then, by setting an environmental formula: E i = a×T ambient + b×H ambient c×V air , where a, b, c are weight coefficients, which can be adjusted according to the operating characteristics of the equipment in different environments. For example, a higher T ambient will increase the heat dissipation burden, so its weight a will be larger; H ambient (humidity) has an impact on the heat dissipation of some equipment, and higher humidity may reduce the heat dissipation efficiency, so b may be negative; V air (air flow rate) directly affects the cooling efficiency, and a higher air flow rate usually can improve the heat dissipation efficiency, so the weight of c can be set as a positive value. These coefficients reflect the influence of environmental factors on the temperature control of the equipment. Substitute these environmental data into the calculation to generate the environmental coefficient E i . The environmental coefficient reflects the degree of influence of the current working environment on the heat dissipation of the equipment. For example, higher temperature and humidity will reduce the cooling effect, resulting in the need for additional temperature control measures, and higher air flow rate may help to improve the heat dissipation efficiency. Assume the environmental formula is Ei = f(T ambient , H ambient , V air ), where E i represents the environmental coefficient. The larger the value of E i calculated according to the formula, the greater the influence of the environmental conditions on the cooling, and the working state of the cooling equipment needs to be adjusted to ensure that the temperature of the equipment is within the safe range.

[0040] S223: Through the priority formula: P i = ω1×|ΔT i |+ ω2×L i + ω3×E i , calculate the temperature control priority of each area to generate an evaluation result, where ω1, ω2, ω3 are weight coefficients, P iis the temperature control priority of each area, ΔT i is the temperature deviation value of each area.

[0041] Specifically, according to the load factor L i and the environmental factor E i , combined with the temperature deviation ΔT i of each area, use the priority formula to calculate the temperature control priority P i of this area. The priority formula is: P i = ω1×|ΔT i | + ω2×L i + ω3×E i , where ω1, ω2, and ω3 in the formula are the weight coefficients of each factor, and are usually adjusted according to the actual situation to reflect the relative importance of different factors to the temperature control priority. For example, if the load factor L i is large, it means that this area is in a high-load state and the temperature needs to be controlled preferentially. Therefore, the weight of ω2 is large; if the environmental temperature is high and the heat dissipation condition is poor, the environmental factor E i has a greater impact on the temperature control priority. Therefore, the weight of ω3 should also be increased appropriately; if the temperature deviation ΔT i of this area is large, it indicates that the temperature control requirement is urgent, and the weight coefficient ω1 should also be increased accordingly. Through this formula, the temperature control priority P i of each area can be dynamically generated. The higher the priority, the more urgent the temperature control measures are required for this area. In this way, the load situation, environmental conditions, and temperature deviation jointly act on the temperature control priority evaluation of each area, ensuring that the cooling equipment can be flexibly adjusted according to specific requirements, and improving the stability and safety of the frequency converter equipment.

[0042] In an embodiment, as Figure 4 shown, in step S30, that is, based on the temperature adjustment requirements of each area, calculate the adjustment parameters of the cooling equipment through an optimization algorithm, and generate corresponding control instructions in combination with the load status of the frequency converter equipment, specifically including: S31: Based on the temperature adjustment requirements of each area, use the genetic algorithm to calculate the adjustment parameters of the cooling equipment.

[0043] Specifically, initially construct an initial population based on the temperature adjustment requirements of each area, such as the temperature adjustment requirements of areas like the power module, power supply module, and heat dissipation area. Each individual represents a possible combination of cooling device adjustment parameters. The genetic algorithm optimizes the adjustment parameters by evolving these initial individuals. Each individual contains multiple parameters, such as the rotation speed of the cooling fan, the flow rate of the liquid cooling system, and the refrigeration capacity of the refrigeration system. Then, calculate the fitness value of these parameters under the current temperature adjustment requirements. An individual with a higher fitness value represents more suitable adjustment parameters. The genetic algorithm continuously optimizes the population through operations such as crossover and mutation, and finally selects the most suitable cooling device adjustment parameters. For example, assume that an individual in the initial population represents a fan rotation speed of 2000 revolutions per minute, a liquid cooling flow rate of 10 L / min, and a refrigeration capacity of 5 kW. Through the evolution of the algorithm, the adjustment parameters of a fan rotation speed of 3000 revolutions per minute, a liquid cooling flow rate of 12 L / min, and a refrigeration capacity of 6 kW may be finally obtained, and these parameters will better meet the current area's temperature adjustment requirements.

[0044] S32: When generating the adjustment parameters of the cooling device, correct the adjustment parameters of the cooling device through a load adjustment factor. The calculation formula of the load adjustment factor is: where, P actual is the actual power of the area, P max is the maximum power capacity of this area, F load is the load adjustment factor, and β is the dynamic adjustment coefficient.

[0045] Specifically, after calculating the preliminary adjustment parameters of the cooling device, correct these parameters through the load adjustment factor to further improve the adjustment accuracy and efficiency. First, measure the actual power P actual of this area, and compare it with the maximum power capacity P max of this area. Calculate the load adjustment factor F load through the load formula. The load adjustment factor reflects the impact of the change in the area load on the adjustment of the cooling device. For example, if P actual is 18 kW, and P max is 20 kW, then the load adjustment factor F load = P actual / P max = 18 / 20 = 0.9, indicating that the area load is close to the maximum capacity and stronger cooling measures may be required. Then, use the dynamic adjustment coefficient β (dynamically adjusted according to the actual usage of the device, such as factors like changes in the device's working environment and load fluctuations), and multiply F loadCombine with β to further correct the adjustment parameters of the cooling device. If the dynamic adjustment coefficient β is set to 1.2, the adjusted parameters of the load adjustment factor will be appropriately adjusted according to the correction factor to ensure the best cooling effect under different load conditions.

[0046] S33: Generate a control command for the cooling device through a fuzzy control algorithm based on the adjustment parameters of the cooling device and the load adjustment factor.

[0047] Specifically, use the fuzzy control algorithm to generate specific control commands based on the adjustment parameters of the cooling device and the load adjustment factor. The fuzzy control algorithm can generate control outputs with uncertainty and flexibility according to the input fuzzy variables, and achieve precise adjustment of the device by defining fuzzy rules. For example, if the temperature deviation in the area is large and the load is high, the fuzzy control algorithm will generate a control command according to the preset rules, such as "if the temperature deviation is large and the load is high, then increase the rotation speed of the cooling fan". When calculating, first convert parameters such as temperature and load into fuzzy values. For example, "large temperature deviation" can be mapped to "high", and "high load" can also be mapped to "high", and then derive the corresponding output value according to the fuzzy rules, that is, adjust the fan rotation speed, increase the liquid cooling flow rate, etc., and finally generate specific control commands. In this way, through the fuzzy control algorithm, the parameters of the cooling device can be flexibly adjusted according to the load conditions and temperature requirements of the frequency converter to ensure that the cooling effect remains in the best state under changing working environments and load conditions.

[0048] In one embodiment, as Figure 5 shown, in step S31, that is, based on the temperature adjustment requirements of each area, use the genetic algorithm to calculate the adjustment parameters of the cooling device, specifically including: S311: Construct an initial population according to the temperature adjustment requirements, and set the number of individuals to N.

[0049] Specifically, after obtaining the temperature adjustment requirements, first construct an initial population. Each individual in the population represents a combination of cooling device adjustment parameters, and these parameters include but are not limited to fan rotation speed, liquid cooling flow rate, refrigeration capacity, etc. These parameter combinations are initial values designed according to the target temperature and load requirements. Set the number of individuals in the initial population to N, and N is a pre-determined value. Usually, this value is adjusted according to needs in practical applications. A larger number of individuals may be selected to ensure the breadth of the search space, or a smaller number of individuals may be selected according to the actual computing power limitations. For example, if the temperature adjustment requirement is to increase the temperature of the area from 30°C to the target temperature of 35°C, then each individual in the initial population can be set to contain different combinations of fan rotation speed, liquid cooling flow rate, etc. The individuals in the population can be initial combinations of different wind speeds and liquid cooling flow rates, such as a fan rotation speed of 1500 revolutions per minute and a liquid cooling flow rate of 10 L / min.

[0050] S312: Calculate the fitness of each individual in the initial population according to the fitness function to generate fitness values.

[0051] Specifically, for each initial individual, evaluate its performance in meeting the temperature regulation requirements according to the fitness function. The fitness function is a criterion for measuring the quality of the combination of cooling equipment adjustment parameters, and usually factors such as temperature deviation, energy consumption, and operating efficiency are considered. For example, the fitness function can be designed to evaluate the quality of each individual by calculating the error of temperature regulation, that is, the difference between the current temperature and the target temperature, and minimizing the energy consumption. If the adjustment parameters of the cooling equipment of an individual can minimize the temperature deviation and consume less energy, then the fitness value of this individual is higher. On the contrary, if the adjustment parameters fail to effectively reduce the temperature deviation or consume more energy, the fitness value is lower. For example, assume that the adjustment parameters of the cooling equipment of an initial individual are set to a fan speed of 3000 revolutions per minute and a liquid cooling flow rate of 12 L / min, and the measured temperature regulation effect is good, with a fitness value of 0.95; while another individual with a fan speed of 1500 revolutions per minute and a liquid cooling flow rate of 5 L / min has a poor temperature regulation effect and a fitness value of 0.5.

[0052] S313: Select individuals for crossover and mutation operations according to the fitness values. When the fitness value reaches a preset threshold, terminate the crossover and mutation operations and output the adjustment parameters of the cooling equipment.

[0053] Specifically, according to the fitness values, select individuals with higher fitness for crossover and mutation operations. The crossover operation refers to randomly selecting some genes (adjustment parameters) from two individuals and combining them into a new individual, which can explore better combinations of adjustment parameters; the mutation operation refers to randomly changing some gene values of an individual, such as changing the fan speed or liquid cooling flow rate, to increase the diversity of solutions and avoid the emergence of local optimal solutions. Through these operations, new candidate individuals are continuously generated. The goal of the crossover and mutation operations is to continuously optimize the combination of cooling equipment adjustment parameters so that it can better meet the temperature regulation requirements. The fitness value is used to guide the selection of crossover and mutation operations. For example, individuals with higher fitness values have a higher probability of being selected as the parents for crossover, while individuals with lower fitness values may be eliminated during the crossover and mutation processes. As the crossover and mutation operations continue, the fitness value gradually increases. Finally, when the fitness value reaches a preset threshold (such as above 0.9), stop the crossover and mutation operations, and consider that the current combination of adjustment parameters has been optimized enough. At this time, output this individual as the final adjustment parameters of the cooling equipment. For example, when the fan speed of an individual is 3500 revolutions per minute and the liquid cooling flow rate is 15 L / min, the fitness value reaches 0.95 and the optimization effect no longer improves significantly, then this individual is the final adjustment parameter.

[0054] In one embodiment, as Figure 6 shown, in step S50, the multi-point temperature control method based on intelligent temperature balance control further includes: S50: Perform temperature anomaly detection on the temperature data of each area of the frequency converter device and the preset safety temperature threshold, and generate an anomaly detection result.

[0055] Specifically, first, the temperature data of each area of the frequency converter device is collected in real time, and devices such as temperature sensors are used to monitor each area to obtain temperature values. For example, the temperature sensor can be set on the power module to read the operating temperature of the power module in real time. Then, these temperature data are compared with the preset safety temperature threshold, and the threshold can be set according to the maximum operating temperature of the device, the normal operating range, and the temperature standard given by the device manufacturer. For example, if the safety temperature threshold of the power module is 85°C, when the collected temperature data reaches or exceeds this threshold, it can be determined that the temperature is abnormal. By comparing, an anomaly detection result is obtained. When the temperature of a certain area exceeds the set safety temperature threshold, it is determined as "too high temperature", and if it does not exceed the threshold, it is "normal". This detection process uses the temperature data collected by the temperature sensor and the preset threshold for real-time comparison, and the generated result can be "normal" or "abnormal", further triggering subsequent operations.

[0056] S60: If the anomaly detection result is that the area temperature reaches the preset safety temperature threshold, then trigger an alarm instruction.

[0057] Specifically, when it is determined through temperature anomaly detection that the temperature of a certain area exceeds the preset safety temperature threshold, the system will immediately trigger an alarm mechanism and send an alarm instruction. This instruction can be an electronic signal, transmitted to the control system, indicating the area where the temperature is too high. The actions triggered by the alarm can include forms such as sound warnings and light prompts. For example, if the temperature monitoring result of the power module exceeds the safety threshold of 85°C, the alarm instruction will be triggered. This instruction is not only used to warn the operator to pay attention to the temperature problem of the device, but may also include notifying the operator to check the cooling system or take other protective measures. The generation of the alarm instruction is based on the anomaly detection and is processed according to the comparison result when the temperature data reaches or exceeds the preset threshold.

[0058] S70: According to the alarm instruction, generate a reminder notification to the user terminal and adjust the operating state of the cooling device.

[0059] Specifically, after the alarm instruction is generated, it will be sent to the user terminal device through the communication network. Usually, a reminder notice of abnormal temperature will pop up on the display screen, informing the operator or maintenance personnel that the temperature in a certain area is abnormal and that immediate measures may be needed to make adjustments. The notice can transmit the information to the user terminal through methods such as text messages, emails, and APP push notifications, ensuring that the abnormal situation is known in a timely manner and corresponding responses are made. At the same time, the cooling equipment will also be adjusted according to the alarm instruction. If it is found that the temperature in a certain area is too high, the cooling equipment will be adjusted to a more efficient working state, such as increasing the fan speed or increasing the coolant flow rate, to accelerate the heat dissipation process and reduce the temperature. The adjustment process is comprehensively considered based on the current temperature of the equipment, the load situation, and the environmental parameters to ensure that the equipment can be promptly restored to the safe temperature range.

[0060] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0061] In one embodiment, a multi-point temperature control system based on intelligent temperature balance control is provided. This multi-point temperature control system based on intelligent temperature balance control corresponds one-to-one with the multi-point temperature control method based on intelligent temperature balance control in the above embodiment. As Figure 7 shown, this multi-point temperature control system based on intelligent temperature balance control includes a data acquisition module, a calculation module, a generation control instruction module, and an adjustment module. The detailed description of each functional module is as follows: The data acquisition module is used to collect the temperature data of each area inside the frequency converter device in real time. The areas include but are not limited to the power module, the power supply module, and the heat dissipation area; The calculation module is used to calculate the deviation value between the temperature of each area and the preset target temperature based on the temperature data through a preset analysis algorithm, and generate the temperature adjustment requirement for each area based on the deviation value; The generation control instruction module is used to calculate the adjustment parameters of the cooling equipment through an optimization algorithm based on the temperature adjustment requirements of each area, and generate corresponding control instructions in combination with the load status of the frequency converter device; The adjustment module is used to transmit the control instruction to the cooling equipment and adjust the corresponding parameters of the cooling equipment according to the control instruction.

[0062] Optionally, this multi-point temperature control system based on intelligent temperature balance control further includes: The abnormal detection module is used to perform temperature abnormal detection on the temperature data of each area of the frequency converter device with a preset safety temperature threshold, and generate an abnormal detection result; An alarm module, which is used to trigger an alarm instruction if the abnormal detection result is that the regional temperature reaches the preset safe temperature threshold; a notification module, which is used to generate a reminder notification to the user terminal according to the alarm instruction and adjust the operating state of the cooling equipment.

[0063] Optionally, the calculation module includes: A temperature comparison sub-module, which is used to compare each regional temperature with the preset target temperature, calculate the deviation value of each region through a simple difference algorithm, and obtain the deviation values of each region; An evaluation sub-module, which is used to evaluate the temperature control priority of each region according to the deviation value, combined with the operating load and working environment data of the frequency converter equipment, and generate an evaluation result; A generation requirement sub-module, which is used to dynamically generate the temperature adjustment requirements of each region based on the evaluation result.

[0064] Optionally, the evaluation sub-module includes: A load calculation unit, which is used to calculate the load situation of each region according to the load formula: Calculate the load situation of each region and generate a load coefficient L i , where P actual is the actual power of the region, and P max is the maximum power capacity of the region; An environment calculation unit, which is used to calculate the working environment data of each region according to the environment formula: E i = f(T ambient , H ambient , V air ) and generate an environment coefficient E i , where T ambient is the external environment temperature, H ambient is the environmental humidity, and V air is the air flow rate; A priority calculation unit, which is used to calculate the temperature control priority of each region through the priority formula: P i = ω1×|ΔT i | + ω2×L i + ω3×E i , generate an evaluation result, where ω1, ω2, ω3 are weight coefficients, and P i is the temperature control priority of each region, and ΔT i is the temperature deviation value of each region.

[0065] Optionally, the generation control instruction module includes: A genetic calculation sub-module, which is used to calculate the adjustment parameters of the cooling equipment based on the temperature adjustment requirements of each region using the genetic algorithm; a load adjustment sub-module, which is used to correct the adjustment parameters of the cooling equipment by a load adjustment factor when generating the adjustment parameters of the cooling equipment. The calculation formula of the load adjustment factor is: Among them, P actual is the actual power of the region, and P max is the maximum power capacity of the region, F load is the load adjustment factor, and β is the dynamic adjustment coefficient; The instruction generation sub-module is used to generate the control instruction of the cooling device according to the adjustment parameters of the cooling device and the load adjustment factor through the fuzzy control algorithm.

[0066] Optionally, the calculation module includes: The population setting sub-module is used to construct an initial population according to the temperature adjustment requirement and set the number of individuals to N; The fitness calculation sub-module is used to calculate the fitness of each individual in the initial population according to the fitness function and generate fitness values; The output sub-module is used to select individuals for crossover and mutation operations according to the fitness values. When the fitness values reach the preset threshold, the crossover and mutation operations are terminated, and the adjustment parameters of the cooling device are output.

[0067] For the specific limitations of a multi-point temperature control system based on intelligent temperature equalization control, reference can be made to the limitations of a multi-point temperature control method based on intelligent temperature equalization control in the above text, which will not be elaborated here. Each module in the above multi-point temperature control system based on intelligent temperature equalization control can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in the processor of the computer device in the form of hardware or independent of it, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0068] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above.

[0069] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-point temperature control method based on intelligent temperature balance control, characterized in that: The multi-point temperature control method based on intelligent temperature balanced control includes: Collect temperature data of various areas inside the inverter device in real time, including but not limited to power module, power supply module and heat dissipation area; Based on the temperature data, a deviation value between the temperature of each area and a preset target temperature is calculated by a preset analysis algorithm, and a temperature adjustment requirement of each area is generated based on the deviation value; Based on the temperature adjustment requirements of each area, the adjustment parameters of the cooling device are calculated by an optimization algorithm, and corresponding control instructions are generated in combination with the load state of the inverter device; The control instruction is transmitted to the cooling device, and the corresponding parameters of the cooling device are adjusted according to the control instruction.

2. A multi-point temperature control method based on intelligent temperature balanced control according to claim 1, characterized in that: The step of calculating the deviation between the temperature of each area and the target temperature based on the temperature data by a preset analysis algorithm, and generating the temperature adjustment requirements of each area based on the deviation includes: Based on the comparison between the temperature of each area and the preset target temperature, the deviation value of each area is calculated by a simple difference algorithm to obtain the deviation value of each area; According to the deviation value, combined with the operating load and working environment data of the inverter device, the temperature control priority of each area is evaluated to generate an evaluation result; Based on the evaluation results, temperature adjustment requirements of the various areas are dynamically generated.

3. The multi-point temperature control method based on intelligent temperature balanced control according to claim 2, characterized in that: The step of evaluating the temperature control priority of each area according to the deviation value and in combination with the operating load and working environment data of the inverter device, and generating the evaluation result includes: According to the load formula: Calculate the load condition of each area and generate the load factor L i , where P actual is the actual power of the area, P max is the maximum power capacity of the area; According to the environmental formula: E i =f(T ambient ,H ambient ,V air ) Calculate the working environment data of each area and generate the environmental coefficient E i , where T ambient is the external ambient temperature, H ambient is the ambient humidity, V air is the air velocity; By priority formula: P i =ω1×|ΔT i |+ω2×L i +ω3×E i , calculate the temperature control priority of each area and generate the evaluation result, where ω1, ω2, ω3 are weight coefficients, P i is the temperature control priority of each zone, ΔT i is the temperature deviation value of each area.

4. The multi-point temperature control method based on intelligent temperature balanced control according to claim 1, characterized in that: The step of calculating the adjustment parameters of the cooling device based on the temperature adjustment requirements of each area by using an optimization algorithm and generating corresponding control instructions in combination with the load state of the inverter device includes: Based on the temperature adjustment requirements of each area, using a genetic algorithm to calculate the adjustment parameters of the cooling device; When generating the adjustment parameters of the cooling device, the adjustment parameters of the cooling device are corrected by the load adjustment factor, and the calculation formula of the load adjustment factor is: Among them, P actual is the actual power of the area, P max is the maximum power capacity of the area, F load is the load adjustment factor, β is the dynamic adjustment coefficient; According to the adjustment parameters of the cooling device and the load adjustment factor, a control instruction of the cooling device is generated through a fuzzy control algorithm.

5. The multi-point temperature control method based on intelligent temperature balanced control according to claim 4, characterized in that: The calculating the adjustment parameters of the cooling device by using a genetic algorithm based on the temperature adjustment requirements of each area includes: According to the temperature regulation requirements, an initial population is constructed, and the number of individuals is set to N; Calculating the fitness of each individual in the initial population according to the fitness function to generate a fitness value; According to the fitness value, an individual is selected to perform a crossover mutation operation. When the fitness value reaches a preset threshold, the crossover mutation operation is terminated and the adjustment parameters of the cooling device are output.

6. The multi-point temperature control method based on intelligent temperature balanced control according to claim 1, characterized in that: The multi-point temperature control method based on intelligent temperature balance control also includes: Perform temperature anomaly detection on the temperature data of each area of ​​the inverter device and the preset safety temperature threshold to generate an anomaly detection result; If the abnormal detection result is that the regional temperature reaches the preset safety temperature threshold, an alarm instruction is triggered; According to the alarm instruction, a reminder notification is generated to the user end, and the operating status of the cooling device is adjusted.

7. A multi-point temperature control system based on intelligent temperature balance control, characterized in that: The multi-point temperature control system based on intelligent temperature balance control comprises: A data acquisition module, used to collect temperature data of various areas inside the inverter device in real time, including but not limited to the power module, power supply module and heat dissipation area; A calculation module, used to calculate the deviation between the temperature of each area and the preset target temperature based on the temperature data by a preset analysis algorithm, and generate the temperature adjustment requirement of each area based on the deviation; A control instruction generation module is used to calculate the adjustment parameters of the cooling device through an optimization algorithm based on the temperature adjustment requirements of each area, and generate corresponding control instructions in combination with the load state of the inverter device; The adjustment module is used to transmit the control instruction to the cooling device and adjust the corresponding parameters of the cooling device according to the control instruction.

8. The multi-point temperature control system based on intelligent temperature balance control according to claim 7, characterized in that: The multi-point temperature control system based on intelligent temperature balance control also includes: An anomaly detection module, used to perform temperature anomaly detection on the temperature data of each area of ​​the inverter device and a preset safety temperature threshold, and generate an anomaly detection result; An alarm module is used to trigger an alarm instruction if the abnormal detection result is that the regional temperature reaches a preset safety temperature threshold; The notification module is used to generate a reminder notification to the user end according to the alarm instruction and adjust the operating status of the cooling device.

Images