Independent temperature zoning regulation and control system and method for multiple zones of electric control box

Abstract

The invention provides an independent temperature zoning regulation and control system and method for multiple areas of an electric cabinet, and relates to the technical field of equipment temperature regulation and control. The layout structure of the electric cabinet is obtained, and temperature control areas are divided according to temperature control requirements; monitoring and receiving temperature sensing data of the temperature control area based on the synchronization timestamp constraint; obtaining temperature control settings, constructing a thermal buoyancy flow field, mining a fuzzy sample library in combination with historical regulation and control records, and supervising and training a fuzzy decision module; sensing data are imported, a fuzzy decision module is combined to execute a temperature control decision to carry out temperature regulation and control management on a temperature control area, the problems of low control precision, slow response speed and poor stability of temperature control of the electric control box and further reduction of industrial cost, efficiency and safety are solved, and the purposes of improving the response speed and precision of temperature control, optimizing energy use and reducing energy consumption are achieved. The operation cost is reduced, and the overall stability and safety of the internal environment of the electric cabinet are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment temperature control, and in particular to a system and method for independently controlling temperature zones in multiple areas of an electric control box. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, higher requirements are placed on the temperature management of electronic control systems, especially in equipment such as electric control boxes that integrate dense electronic components. Accurate temperature control has become a key factor in ensuring equipment performance and safety. As the core part of the industrial control system, the stability of the internal temperature of the electric control box directly affects the operating efficiency and equipment life of the entire system. However, with the continuous expansion of industrial applications and the complexity of system structure, traditional temperature control technology faces many challenges. These systems often adopt a unified temperature control strategy and lack the fine management of temperature differences in different areas of the electric control box. In addition, traditional temperature control methods often fail to meet the high efficiency, precision and safety requirements of modern industrial production in terms of response speed, control accuracy and system stability. For example, the heat generated by some high-power components inside the electric control box may increase rapidly during operation, while traditional temperature control systems may not be able to effectively manage them in time due to slow response or inaccurate regulation.

[0003] In summary, the existing temperature control of the electric control box has technical problems such as low control accuracy, slow response speed, and poor stability, which in turn reduces industrial efficiency, cost, and safety. Summary of the invention

[0004] The present application provides an independent temperature zoning control system and method for multiple zones of an electric control box, which is used to solve the technical problems in the prior art of temperature control of electric control boxes, such as low control accuracy, slow response speed, and poor stability, thereby reducing industrial efficiency, cost, and safety.

[0005] In view of the above problems, the present application provides a system and method for independent temperature zoning control of multiple areas of an electric control box.

[0006] In a first aspect of the present application, a multi-zone independent temperature zoning control system for an electric control box is provided, the system comprising: A temperature control area division unit is used to obtain the layout structure of the target electric control box, and divide multiple temperature control areas according to the temperature control requirements, wherein the layout structure includes a partition layout and a neighboring area insulation structure, and each temperature control partition includes at least one layout partition; a data monitoring unit is used to monitor and receive the temperature sensor data of the multiple temperature control areas based on a synchronization timestamp constraint, wherein each layout partition is equipped with a temperature sensor; a temperature control setting acquisition unit is used to obtain the temperature control setting, wherein the temperature control type includes active temperature control and passive temperature control; a thermal buoyancy flow field construction unit is used to construct a thermal buoyancy flow field for the multiple temperature control areas, mine a fuzzy sample library in combination with historical control records, and supervise the training of a fuzzy decision module; a control strategy determination unit is used to import the temperature sensor data, and in combination with the fuzzy decision module, execute a fuzzy temperature control decision based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraints, and determine a partition control strategy, wherein the temperature control decision is an active temperature control decision based on the passive temperature control state analysis of the temperature control setting; The control management unit is used to generate a zone temperature control signal based on the zone control strategy and perform temperature control management on the multiple temperature control areas.

[0007] A second aspect of the present application provides a method for independent temperature zoning control of multiple zones of an electric control box, the method comprising: Acquire the layout structure of the target electric control box, and divide it into multiple temperature control areas according to the temperature control requirements, wherein the layout structure includes a partition layout and an adjacent zone insulation structure, and each temperature control partition includes at least one layout partition; based on the synchronization timestamp constraint, monitor and receive the temperature sensor data of the multiple temperature control areas, wherein each layout partition is equipped with a temperature sensor; acquire the temperature control settings, wherein the temperature control type includes active temperature control and passive temperature control; for the multiple temperature control areas, construct a thermal buoyancy flow field, mine a fuzzy sample library in combination with historical control records, and supervise the training of a fuzzy decision module; import the temperature sensor data, and in combination with the fuzzy decision module, execute a fuzzy temperature control decision based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraints, and determine a partition control strategy, wherein the temperature control decision is an active temperature control decision based on the passive temperature control state analysis of the temperature control setting; generate a partition temperature control signal based on the partition control strategy, and perform temperature control management on the multiple temperature control areas.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The independent temperature zoning control system for multiple areas of an electric control box provided by the present application obtains the layout structure of the target electric control box and divides multiple temperature control areas in combination with temperature control requirements, wherein the layout structure includes a zoning layout and an adjacent area temperature isolation structure, and each temperature control zone includes at least one layout zone; based on a synchronization timestamp constraint, monitors and receives temperature sensor data of the multiple temperature control zones, wherein each layout zone is equipped with a temperature sensor; obtains temperature control settings, wherein the temperature control type includes active temperature control and passive temperature control; for the multiple temperature control zones, constructs a thermal buoyancy flow field, mines a fuzzy sample library in combination with historical control records, and supervises the training of a fuzzy decision module; imports the temperature sensor data, and in combination with the fuzzy decision module, executes fuzzy temperature control decisions based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraints, and determines a zoning control strategy. Among them, the temperature control decision is an active temperature control decision under the passive temperature control state analysis based on the temperature control setting; a partition temperature control signal based on the partition control strategy is generated, and temperature control management is performed on the multiple temperature control areas, which solves the technical problems of low control accuracy, slow response speed and poor stability in the temperature control of the electric control box, thereby reducing industrial costs, efficiency and safety. By accurately monitoring and controlling the temperature of each independent area in the electric control box, not only the response speed and accuracy of the temperature control are improved, but also energy use is optimized and operating costs are reduced through intelligent decision support. At the same time, by real-time monitoring of the temperature changes of various areas inside the electric control box, and using fuzzy logic and flow field analysis technology, refined temperature control is implemented according to the specific needs of each area, thereby ensuring the overall stability and safety of the internal environment of the electric control box. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of the structure of a multi-zone independent temperature zoning control system for an electric control box provided in an embodiment of the present application.

[0010] Figure 2 A schematic flow chart of a method for independent temperature zoning and controlling multiple zones of an electric control box provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: temperature control area division unit 11 , data monitoring unit 12 , temperature control setting acquisition unit 13 , thermal buoyancy flow field construction unit 14 , control strategy determination unit 15 , control management unit 16 . DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1As shown, the present application provides an independent temperature zoning control system for multiple areas of an electric control box, the system comprising: The temperature control area division unit 11 is used to obtain the layout structure of the target electric control box and divide it into multiple temperature control areas according to the temperature control requirements, wherein the layout structure includes a partition layout and an adjacent zone insulation structure, and each temperature control zone includes at least one layout zone.

[0014] Specifically, in order to achieve independent temperature zoning control of multiple areas of the electric control box, the layout structure of the target electric control box is first obtained. The layout structure refers to the physical partition inside the electric control box, including the spatial distribution of various components such as electronic control elements and circuit boards. It is directly related to the reasonable division of subsequent temperature control areas and the temperature management effect. Then, based on the obtained layout structure, combined with the temperature control requirements of the electric control box, multiple temperature control areas are further divided. Among them, the temperature control requirement refers to the temperature maintenance range required for each component or area inside the electric control box. These temperature requirements may vary depending on the function and sensitivity of the components. For example, a power module with higher power may require stronger cooling measures, while a storage device may require a stable temperature environment to ensure data security.

[0015] The specific area division can be combined with the partition layout and the adjacent area insulation structure. The partition layout refers to dividing the interior of the electric control box into several temperature control zones with actual physical boundaries according to the spatial structure and component distribution inside the electric control box. Each temperature control zone will contain at least one layout zone to ensure the locality and effectiveness of temperature control. The adjacent area insulation structure refers to the use of insulation materials or the design of specific air flow paths between adjacent temperature control zones to reduce heat exchange between zones and ensure that the temperature of each temperature control zone is independently controlled and not affected by adjacent areas. For example, if there is a particularly sensitive sensor module in the electric control box, tighter insulation materials and special cooling channels may be designed around it to form a separate temperature control zone to isolate the heat generated by other high-power components. Through such design and division, a more accurate and effective temperature control strategy can be formulated for the specific needs of each area, so as to achieve the optimization of the temperature management of the entire electric control box.

[0016] The data monitoring unit 12 is used to monitor and receive the temperature sensing data of the multiple temperature control areas based on the synchronization timestamp constraint, wherein each layout partition is equipped with a temperature sensor.

[0017] Optionally, in order to realize independent temperature monitoring and control of multiple areas of the electric control box, a method based on synchronous timestamp constraints is adopted to monitor and receive temperature sensor data of multiple temperature control areas. The synchronous timestamp constraint refers to that all temperature sensors collect and transmit data on the same time basis to ensure that the collected data is consistent in time, thereby improving the accuracy and efficiency of data processing and temperature control. Furthermore, in each temperature control partition, at least one temperature sensor is installed according to its specific layout structure. These temperature sensors are responsible for real-time monitoring of the temperature conditions of their respective areas and continuously transmitting the temperature data to the central control system. For example, a high heat generation area such as a power management module may be equipped with multiple temperature sensors distributed in different locations to accurately monitor possible hot spots and ensure timely response of temperature control measures.

[0018] Through the above steps, the system can obtain comprehensive temperature distribution information in real time. The central control system will collect and analyze the temperature data received from different sensors, and implement corresponding temperature adjustment measures according to the preset temperature control strategy and algorithm. In this process, the application of synchronized timestamps ensures the time consistency of all data, so that every step from data analysis to strategy implementation is based on accurate and real-time data. In addition, synchronized timestamps also help the system to conduct retrospective analysis of historical temperature data, optimize temperature control strategies, and improve the system's self-learning ability and intelligent response speed.

[0019] The temperature control setting acquisition unit 13 is used to acquire the temperature control setting, wherein the temperature control type includes active temperature control and passive temperature control.

[0020] For example, in order to ensure that the multiple temperature control areas in the electric control box can achieve accurate and efficient temperature management, the temperature control settings must first be obtained to determine the temperature control strategy to be adopted. The temperature control settings are the basic configuration of the temperature management system of the electric control box, which includes two types: active temperature control and passive temperature control. The active temperature control refers to the control method in which the system actively adjusts the temperature according to the preset temperature target. For example, the system can actively reduce the temperature of a certain area by adjusting the speed of the internal fan or starting the cooling device, or start the heater when the temperature is lower than the set threshold. Active temperature control is usually used for key component areas with fast temperature changes and high temperature sensitivity to ensure that the temperature of these areas is always kept within the optimal operating range. The corresponding passive temperature control is a way of temperature regulation that relies more on environmental natural conditions. For example, the natural ventilation structure in the design of the electric control box is used to adjust the temperature, or the thermal isolation design is used to reduce the impact of external environmental temperature changes on the internal temperature. Passive temperature control is usually suitable for areas where temperature requirements are not very strict, which can effectively save energy while ensuring the basic temperature control requirements of the system.

[0021] In actual operation, the system will use a combination of active and passive temperature control strategies based on the specific needs of each area inside the electric control box and the external environmental conditions. For example, in the electric control box, the processor area may require active temperature control due to its high heat output, while the storage device area may use more passive temperature control measures due to its high thermal stability requirements but less heat generation. In this way, not only can the temperature stability of each area be ensured, but also the energy efficiency can be optimized while ensuring performance.

[0022] The thermal buoyancy flow field construction unit 14 is used to construct a thermal buoyancy flow field for the multiple temperature control areas, mine a fuzzy sample library in combination with historical control records, and supervise the training of a fuzzy decision module.

[0023] Furthermore, the thermal buoyancy flow field construction technology is adopted, and the historical control records are used to mine the fuzzy sample library, so as to supervise the training of the fuzzy decision module to optimize the temperature control strategy. The thermal buoyancy flow field refers to the hot air flow pattern generated by the temperature difference inside the electric control box. The construction of this flow field is based on the temperature distribution and physical layout inside the electric control box. By simulating the thermal flow characteristics in the temperature control area, the system can accurately predict and control the temperature change, thereby achieving more refined temperature management. Next, the existing historical temperature control records are used to mine the fuzzy sample library. The historical control records include previous temperature data, control behaviors, environmental variables and other information. These data are used to generate fuzzy samples, that is, to convert continuous numerical data into a set that can express uncertainty and ambiguity. For example, fuzzy concepts such as "high", "medium" and "low" temperatures can be defined according to specific temperature control requirements and historical performance.

[0024] These fuzzy samples are used to train the fuzzy decision module, which uses fuzzy logic technology to parse and process the input fuzzy sample data through a set fuzzy rule base. The fuzzy rule base is built based on expert knowledge and historical data analysis, and can describe the fuzzy logic relationship between input (such as temperature deviation, rate of change) and output (such as control strategy). For example, if the temperature in an area is "high" and "rising fast", the decision module may decide to "enhance cooling".

[0025] Through the above steps, the system is able to build a dynamic and adaptive temperature management strategy, which not only improves the accuracy and efficiency of temperature control, but also allows the system to make intelligent adjustments based on actual conditions, optimize energy use and reduce unnecessary energy consumption, thereby achieving the dual goals of economy and environmental protection while ensuring the safe operation of equipment.

[0026] The control strategy determination unit 15 is used to import the temperature sensor data, combine with the fuzzy decision module, execute fuzzy temperature control decision based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraint, and determine the partition control strategy, wherein the temperature control decision is an active temperature control decision based on the passive temperature control state analysis of the temperature control setting.

[0027] Optionally, temperature sensor data is imported from the temperature sensors installed in each temperature control zone. These data provide real-time temperature information and are the basis for temperature control decisions. Next, these real-time data are input into the fuzzy decision module, in which the temperature data are first converted into fuzzy values, which are classified and processed according to the preset temperature range (such as low, medium, and high) to meet the needs of fuzzy logic processing. Further, combined with the thermal buoyancy flow analysis, the system analyzes the hot air flow caused by temperature differences inside the electric control box, predicts possible temperature change trends and heat distribution, and this analysis can more accurately understand and predict the heat exchange and influence between the zones. At the same time, the neighborhood temperature mutual conductance constraint is also taken into account. This constraint involves the temperature influence between adjacent temperature control zones. By calculating the temperature conduction coefficient between adjacent zones, the corresponding control strategy is adjusted to reduce unnecessary temperature interference. Finally, based on the above analysis and data, the fuzzy decision module executes temperature control decisions and converts fuzzy inputs into specific control commands. These decisions take into account environmental factors and internal conditions under passive temperature control, and also implement active temperature control decisions, such as starting fans or adjusting cooling equipment, to ensure that the temperature of each zone can reach the set optimal state. This decision-making method not only improves the response speed and accuracy of temperature control, but also maximizes the energy efficiency of the entire system, ensuring the stable operation and long-term reliability of the electrical control box and its internal equipment.

[0028] The control management unit 16 is used to generate a zone temperature control signal based on the zone control strategy, and perform temperature control management on the multiple temperature control areas.

[0029] Specifically, after completing the temperature control decision, a partition temperature control signal based on the determined partition control strategy is further generated to actually perform temperature control management of multiple temperature control areas. This step is a key link in achieving accurate and effective temperature management, ensuring that each temperature control area can reach its optimal working state. The partition temperature control signal refers to a specific operation signal generated by the system control center according to the control instruction output by the fuzzy decision module. These signals correspond to specific temperature control devices, such as fans, cooling systems, heaters, etc., and their operations include switch control, power adjustment, operating speed changes, etc. The process of generating temperature control signals includes several key steps: First, the system identifies the temperature control area that needs to be adjusted and the required temperature adjustment direction (such as cooling or heating) based on the information provided by the fuzzy decision module. Then, the equipment operation intensity and duration required to reach the target temperature are calculated. These calculations are based on the current temperature of the area, the expected temperature, and the influencing factors of various environmental and operating conditions. Next, these calculation results are converted into specific equipment operation instructions to form temperature control signals. These signals are sent to the corresponding temperature control devices through the control network to achieve operations such as increasing the fan speed, enhancing the cooling system, or activating the heater. Each signal corresponds precisely to a specific device and operation, ensuring the accuracy and efficiency of temperature control. Finally, the system monitors the operating effects of these temperature control devices and adjusts the control strategy in real time to respond to any temperature control results that do not meet expectations. In this way, temperature management can be continuously optimized to ensure that each area in the electric control box can operate at the most suitable temperature, thereby ensuring the stability of the equipment and extending its service life, significantly improving the operating efficiency and safety of the electric control box.

[0030] Furthermore, the temperature control area division unit 11 is further used to divide multiple temperature control areas according to the temperature control requirements: The temperature control free time difference is set, and based on the temperature control synchronization division, a first division area is determined, wherein the temperature control free time difference is the allowable time difference under the synchronization constraint.

[0031] A temperature control free temperature difference is set, and a second division area is determined based on the temperature control homoparametric division, where the temperature control free temperature difference is the allowable temperature difference under the homoparametric constraint.

[0032] The first divided area and the second divided area are fitted to determine the multiple temperature control areas.

[0033] Exemplarily, in order to manage the temperature in the electric control box more finely, the concepts of temperature control free time difference and temperature control free temperature difference are used to meet the specific temperature control requirements of each area. First, set the temperature control free time difference, which is used to divide the area based on temperature control synchronization, wherein the temperature control synchronization refers to the time range within which different parts of the electric control box should reach the predetermined temperature control target to ensure the coordinated operation of the system. The temperature control free time difference is the maximum time difference allowed under the synchronization constraint, which allows the system to make flexible time adjustments between different areas without affecting the overall temperature control effect. For example, if an area contains sensitive equipment that needs to respond quickly to temperature changes, its temperature control free time difference will be set smaller. Next, set the temperature control free temperature difference, which is the key parameter for the second area division based on temperature control syntropy. Temperature control syntropy focuses on the maximum temperature difference allowed between different areas, which usually depends on the thermal sensitivity and heat capacity of the components in each area. The temperature control free temperature difference is the temperature difference allowed under the syntropy constraint. Such a setting allows the system to make more detailed temperature adjustments to specific areas while ensuring overall temperature balance. For example, the storage area may allow a larger temperature difference, while the power supply area needs to maintain stricter temperature control. Finally, the above two division criteria are combined to fit the first division area and the second division area, and finally determine the multiple temperature control areas in the electric control box. This process involves analyzing the specific temperature control requirements and actual operating conditions of each area, and then optimizing the boundaries and control strategies of each area according to the parameter settings of the temperature control free time difference and the temperature control free temperature difference. In this way, each temperature control area can get the temperature management that best suits its specific needs, thereby improving the efficiency and reliability of the entire electric control box. This division method not only improves the flexibility and responsiveness of the system, but also makes the temperature control more accurate and efficient.

[0034] Furthermore, in constructing the thermal buoyancy flow field, the thermal buoyancy flow field constructing unit 14 is further used for: The multiple temperature control areas are traversed to determine the flow field distribution.

[0035] For the adjacent area thermal insulation structure, the thermal insulation coefficient of the adjacent area heat conduction is determined based on the adjacent area thermal insulation material and the geometric layout.

[0036] Based on the thermal insulation coefficient, the flow field distribution is mapped and marked to determine the thermal buoyancy flow field.

[0037] Furthermore, the heat flow pattern of multiple temperature control areas is analyzed and simulated in detail to further improve the accuracy and efficiency of temperature control. Specifically, the flow field distribution is determined by traversing the multiple temperature control areas, that is, the system analyzes the specific heat source layout and air flow path of each temperature control area, and how these factors affect the flow of hot air. For example, the system will take into account the location of high heat generating equipment such as power units, as well as the layout of vents and fans, to determine the preliminary pattern of hot air flow. Next, in order to accurately control and isolate the heat exchange between different areas, the insulation coefficient of the neighboring heat conduction is determined based on the neighboring insulation material and geometric layout for the neighboring insulation structure. This step evaluates the insulation material and geometric layout to understand the conduction ability of heat energy to be transferred to the adjacent area through the solid material or air layer. The insulation coefficient is a key parameter that reflects the ability of the material and design structure to block the heat flow, thereby ensuring more independent and specific temperature control. Finally, the flow field distribution is mapped and marked based on the insulation coefficient determined above to determine the thermal buoyancy flow field. Specifically, advanced simulation technology is used to combine the actual heat flow pattern and theoretical thermal insulation capacity, and the thermal buoyancy flow field is reproduced in the computational model through mapping and labeling technology. This allows the system to not only predict the thermal behavior of each area, but also adjust the temperature control strategy in real time in response to the actual measured temperature changes.

[0038] By establishing a detailed and highly optimized thermal management system in the electric control box, we can ensure that all equipment operates under optimal temperature conditions, thereby improving equipment efficiency and extending service life. The construction of this thermal buoyancy flow field not only improves the accuracy of temperature control, but also enhances the system's ability to adapt to complex thermal environments.

[0039] Furthermore, the supervised training fuzzy decision module and the thermal buoyancy flow field construction unit 14 are also used for: The historical control records are obtained, a plurality of control sequences are integrated and fuzzified, and a fuzzy set is determined, wherein the fuzzy set includes an input set and an output set, and the input data type at least includes the fuzzified temperature deviation and the deviation change rate.

[0040] Based on the fuzzy set and the multiple control sequences, a fuzzy rule base is mined, wherein the fuzzy rule base represents the fuzzy relationship between input and output.

[0041] Taking the thermal buoyancy flow field as a benchmark, sample-driven training based on the fuzzy set and the fuzzy rule base is performed to generate the fuzzy decision module.

[0042] Optionally, in order to improve the temperature control accuracy and efficiency of multiple temperature control areas of the electric control box, supervised training is performed on the fuzzy decision module, which uses historical control records and thermal buoyancy flow field data to achieve a more sophisticated temperature control strategy through fuzzy logic methods. First, the system obtains historical control records, which include the temperature data of each temperature control area in the past and its control response. By integrating these data, multiple control sequences are formed, and these sequences are fuzzified to generate the required fuzzy sets. In fuzzy logic, fuzzification is the conversion of precise numerical data into fuzzy set representations, such as converting temperature deviation and deviation change rate into fuzzy levels such as "low", "medium", and "high". Fuzzy sets mainly include input sets and output sets, where the input data type at least includes fuzzified temperature deviation and deviation change rate, and the output set corresponds to the control measures taken. Next, based on these fuzzy sets and control sequences, the system mines the fuzzy rule base, which is the core of the fuzzy logic system and defines the fuzzy relationship between input (such as fuzzified temperature deviation) and output (such as control measures). By analyzing historical data, it is possible to identify which fuzzy input conditions usually lead to specific output responses, thereby constructing a set of rules to guide future decision-making processes. Finally, the thermal buoyancy flow field is used as a benchmark for sample-driven training based on the fuzzy set and the fuzzy rule base to generate the fuzzy decision module. At this stage, the system uses the data of the thermal buoyancy flow field to simulate different temperature control scenarios, and then verifies and optimizes the fuzzy rule base. In this way, the fuzzy decision module can not only reflect past experience, but also adapt to actual physical thermal flow conditions, making temperature control decisions more accurate and real-time.

[0043] Through the above steps, the supervised training fuzzy decision-making module can provide more accurate and intelligent temperature control for multiple temperature control areas in the electric control box, ensuring the efficient operation of the system and the long-term stability of the equipment.

[0044] Furthermore, in determining the partition control strategy, the control strategy determination unit 15 is further used to: The temperature sensing data is identified, and the thermal buoyancy flow field is initialized.

[0045] Based on the thermal buoyancy flow field, the passive temperature control state evolution prediction is performed in combination with the passive temperature control setting to determine the first flow field trend.

[0046] Based on the first flow field trend and in combination with the active temperature control setting, a temperature adjustment decision is made to determine the zone control strategy.

[0047] Furthermore, the temperature sensing data is identified, and real-time data is collected from the temperature sensors in each temperature control area. The collected data is used to initialize the thermal buoyancy flow field, that is, to simulate the hot air flow inside the electric control box based on the current temperature distribution. This flow field simulation can understand and predict how the temperature propagates and changes inside the electric control box, which is the basis for subsequent decision-making. Next, based on the initialized thermal buoyancy flow field, the system will combine the passive temperature control setting to predict the evolution of the passive temperature control state. Passive temperature control settings usually rely on natural cooling or heating processes and do not involve active intervention of additional energy consumption. At this stage, the system predicts how the temperature of the temperature control area will evolve naturally under the current settings, thereby determining the first flow field trend, which includes the trend of temperature increase or decrease, and the possible impact of these trends on the performance of the equipment inside the electric control box. Finally, based on the determined first flow field trend, the temperature adjustment decision is made in combination with the active temperature control setting. In this step, it is evaluated whether active temperature control measures are needed to adjust the temperature, such as turning on the fan, starting the cooling system or the heater, etc. These decisions are based on the evolution trend of the thermal buoyancy flow field and the effect of passive temperature control to ensure that the temperature of each temperature control area can reach the ideal state, thereby determining the zoning control strategy.

[0048] The above steps ensure that the temperature management of the electric control box can not only utilize the natural temperature control mechanism, but also intervene through active control when necessary to achieve the best temperature management effect. Through such a strategy, the system can not only effectively control the temperature inside the electric control box, but also optimize energy use, improve the overall efficiency of the system and the reliability of the equipment.

[0049] Furthermore, the system further comprises: The input data determining unit is used to determine fuzzy input data based on the initialized thermal buoyancy flow field.

[0050] The matching and reasoning unit is used to combine the fuzzy rule base, perform fuzzy rule matching and reasoning based on the fuzzy input data, and determine the fuzzy output strategy.

[0051] A strategy integration unit is used to integrate the fuzzy output strategy based on the layout structure to determine the partition control strategy.

[0052] Optionally, in order to realize the refined temperature management of multiple areas of the electric control box, a fuzzy logic control strategy based on the thermal buoyancy flow field is used, from the initialization of the thermal buoyancy flow field to the matching and reasoning of fuzzy rules, and finally the output strategy is integrated to determine the entire process of the partition control strategy. Specifically, based on the initialized thermal buoyancy flow field, the fuzzy input data is determined, which means that the system uses the data obtained from the thermal buoyancy flow field simulation, such as temperature distribution, flow trend, etc., to convert these specific values ​​into input variables in fuzzy logic. For example, the specific temperature deviation is converted into fuzzy levels such as "low", "medium", and "high". This fuzzification process enables the system to handle uncertainty and complexity more flexibly. Then, in combination with the fuzzy rule base, fuzzy rule matching and reasoning are performed based on the fuzzy input data to determine the fuzzy output strategy. The fuzzy rule base is pre-defined and contains the relationship between various possible fuzzy inputs and corresponding control outputs. By matching the current fuzzy input data with the conditions in the rule base, fuzzy logic can be used to infer the appropriate control output, such as adjusting the fan speed, starting a specific cooling device, etc. Finally, the fuzzy output strategy is integrated based on the layout structure to determine the partition control strategy, that is, to combine the fuzzy logic control output with the physical layout of the electric control box and the actual needs of each area to ensure that each area can get the most suitable temperature control treatment. For example, for areas with more heat generation, stronger cooling measures may be required; while for heat-sensitive equipment areas, more stable temperature control may be required.

[0053] Through the above steps, not only can the temperature in the electric control box be effectively managed, but also the implementation of the temperature control strategy can be ensured to meet the specific needs of each area, optimize energy use, and enhance the overall performance and reliability of the system. This fuzzy logic-based temperature management strategy provides an efficient and flexible solution for complex and changing industrial application environments.

[0054] Furthermore, after performing temperature control management on the multiple temperature control areas, the control management unit 16 is further used to: Combined with the temperature sensor, temperature control adjustment tracking is performed to determine the temperature change trend.

[0055] Determine whether the temperature change trend meets the standard, wherein the unit temperature change vector and temperature change rate based on a single area and the temperature change trend based on the global temperature field are used as the judgment criteria.

[0056] If the standard is not met, a feedback temperature adjustment instruction is generated.

[0057] Specifically, in order to ensure that the temperature control of multiple temperature control areas of the electric control box achieves the predetermined effect, the system adopts a comprehensive temperature control management and monitoring process, which includes tracking, evaluating and adjusting the temperature change trend when necessary to ensure that the temperature control of each area meets the set standards. Specifically, by combining the temperature sensor, temperature control adjustment tracking is performed to determine the temperature change trend, that is, the temperature sensors installed in each area are used to collect temperature data in real time, and the temperature change trend is analyzed based on these data. For example, the system can monitor the temperature increase or decrease rate in a specific area, as well as the temperature change pattern over time, so as to obtain the temperature dynamics in the area. Further, it is determined whether the temperature change trend meets the standard, where the standard refers to whether the temperature change meets the preset temperature control target and parameters. The judgment criteria include the unit temperature change vector and temperature change rate based on a single area and the temperature change trend based on the global temperature field. Among them, the unit temperature change vector takes into account the direction and magnitude of the temperature change, while the temperature change rate focuses on the speed of the temperature change. The temperature change trend of the global temperature field provides an overall observation of the overall temperature change of the electric control box, identifying possible hot spots or areas with insufficient cooling. If the temperature change trend does not meet the standard, that is, it is found that the temperature control of a certain area does not meet the predetermined standard, the system will generate feedback temperature adjustment instructions. These instructions are based on previous temperature monitoring data and the output of the fuzzy logic decision module to adjust the corresponding temperature control equipment operations, such as increasing the cooling intensity, adjusting the fan speed or starting the heating element to correct the temperature trend that deviates from the target.

[0058] Through the above steps, it is possible to ensure that the temperature of each temperature control area in the electric control box is effectively managed, and to achieve efficient and accurate temperature control, thereby optimizing the performance of the electric control box and extending the service life of the equipment. This systematic temperature management method not only improves the accuracy of temperature control, but also improves the system's responsiveness and reliability to environmental changes.

[0059] Through the technical solutions of the above embodiments, the independent temperature zoning control system for multiple areas of the electric control box provided by the present application solves the technical problems of low control accuracy, slow response speed and poor stability in the temperature control of the electric control box, thereby reducing industrial costs, efficiency and safety. By accurately monitoring and controlling the temperature of each independent area in the electric control box, not only the response speed and accuracy of the temperature control are improved, but also energy use is optimized and operating costs are reduced through intelligent decision support. At the same time, by real-time monitoring of the temperature changes of each area inside the electric control box and using fuzzy logic and flow field analysis technology, refined temperature control is implemented according to the specific needs of each area, thereby ensuring the overall stability and safety of the internal environment of the electric control box.

[0060] Embodiment 2 is based on the same inventive concept as the independent temperature zone control system for multiple zones of the electric control box in the previous embodiment. Figure 2As shown, the embodiment of the present application provides a method for independent temperature zoning control of multiple areas of an electric control box, the method comprising: The layout structure of the target electric control box is obtained, and multiple temperature control areas are divided according to the temperature control requirements, wherein the layout structure includes a partition layout and an adjacent zone insulation structure, and each temperature control zone includes at least one layout zone.

[0061] Based on a synchronization timestamp constraint, temperature sensing data of the multiple temperature control zones are monitored and received, wherein each layout partition is equipped with a temperature sensor.

[0062] Get the temperature control settings, where the temperature control types include active temperature control and passive temperature control.

[0063] For the multiple temperature control areas, a thermal buoyancy flow field is constructed, and a fuzzy sample library is mined in combination with historical control records to supervise the training of a fuzzy decision module.

[0064] The temperature sensing data is imported, combined with the fuzzy decision module, and fuzzy temperature control decision based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraint is executed to determine the partition control strategy, wherein the temperature control decision is an active temperature control decision based on the passive temperature control state analysis of the temperature control setting.

[0065] A zone temperature control signal based on the zone control strategy is generated to perform temperature control management on the multiple temperature control areas.

[0066] Furthermore, the method of dividing multiple temperature control areas according to temperature control requirements includes: The temperature control free time difference is set, and based on the temperature control synchronization division, a first division area is determined, wherein the temperature control free time difference is the allowable time difference under the synchronization constraint.

[0067] A temperature control free temperature difference is set, and a second division area is determined based on the temperature control homoparametric division, where the temperature control free temperature difference is the allowable temperature difference under the homoparametric constraint.

[0068] The first divided area and the second divided area are fitted to determine the multiple temperature control areas.

[0069] Furthermore, the step of constructing a thermal buoyancy flow field comprises: The multiple temperature control areas are traversed to determine the flow field distribution.

[0070] For the adjacent area thermal insulation structure, the thermal insulation coefficient of the adjacent area heat conduction is determined based on the adjacent area thermal insulation material and the geometric layout.

[0071] Based on the thermal insulation coefficient, the flow field distribution is mapped and marked to determine the thermal buoyancy flow field.

[0072] Furthermore, the supervised training fuzzy decision module includes: The historical control records are obtained, a plurality of control sequences are integrated and fuzzified, and a fuzzy set is determined, wherein the fuzzy set includes an input set and an output set, and the input data type at least includes the fuzzified temperature deviation and the deviation change rate.

[0073] Based on the fuzzy set and the multiple control sequences, a fuzzy rule base is mined, wherein the fuzzy rule base represents the fuzzy relationship between input and output.

[0074] Taking the thermal buoyancy flow field as a benchmark, sample-driven training based on the fuzzy set and the fuzzy rule base is performed to generate the fuzzy decision module.

[0075] Furthermore, the determining of the partition control strategy includes: The temperature sensing data is identified, and the thermal buoyancy flow field is initialized.

[0076] Based on the thermal buoyancy flow field, the passive temperature control state evolution prediction is performed in combination with the passive temperature control setting to determine the first flow field trend.

[0077] Based on the first flow field trend and in combination with the active temperature control setting, a temperature adjustment decision is made to determine the zone control strategy.

[0078] Furthermore, the method further comprises: Based on the initialized thermal buoyancy flow field, fuzzy input data is determined.

[0079] In combination with the fuzzy rule base, fuzzy rule matching and reasoning are performed based on the fuzzy input data to determine the fuzzy output strategy.

[0080] The fuzzy output strategy is integrated based on the layout structure to determine the partition control strategy.

[0081] Furthermore, after the temperature control management of the multiple temperature control areas is performed, it includes: Combined with the temperature sensor, temperature control adjustment tracking is performed to determine the temperature change trend.

[0082] Determine whether the temperature change trend meets the standard, wherein the unit temperature change vector and temperature change rate based on a single area and the temperature change trend based on the global temperature field are used as the judgment criteria.

[0083] If the standard is not met, a feedback temperature adjustment instruction is generated.

[0084] 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. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] 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 shall be included in the protection scope of the present application.

[0086] This specification and 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. The multi-zone independent temperature control system of the electric control box is characterized by: The system comprises: A temperature control area division unit is used to obtain the layout structure of the target electric control box and divide multiple temperature control areas according to the temperature control requirements, wherein the layout structure includes a partition layout and an adjacent zone temperature isolation structure, and each temperature control zone includes at least one layout zone; a data monitoring unit, configured to monitor and receive temperature sensing data of the multiple temperature control zones based on a synchronization timestamp constraint, wherein each layout partition is equipped with a temperature sensor; A temperature control setting acquisition unit, used to acquire temperature control settings, wherein the temperature control type includes active temperature control and passive temperature control; A thermal buoyancy flow field construction unit is used to construct a thermal buoyancy flow field for the multiple temperature control areas, mine a fuzzy sample library in combination with historical control records, and supervise the training of a fuzzy decision module; A control strategy determination unit, used for importing the temperature sensing data, combining with the fuzzy decision module, executing fuzzy temperature control decision based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraint, and determining a partition control strategy, wherein the temperature control decision is an active temperature control decision based on the passive temperature control state analysis of the temperature control setting; The control management unit is used to generate a zone temperature control signal based on the zone control strategy and perform temperature control management on the multiple temperature control areas.

2. The multi-zone independent temperature zoning control system of the electric control box according to claim 1, characterized in that: The temperature control area division unit is also used for: Setting a temperature control free time difference, and determining a first division area based on the temperature control synchronization division, wherein the temperature control free time difference is an allowable time difference under the synchronization constraint; A temperature control free temperature difference is set, and a second partition area is determined based on the temperature control homoparametric partition, wherein the temperature control free temperature difference is the allowable temperature difference under the homoparametric constraint; The first divided area and the second divided area are fitted to determine the multiple temperature control areas.

3. The multi-zone independent temperature zoning control system of the electric control box according to claim 1, characterized in that: The thermal buoyancy flow field construction unit is also used for: Traversing the multiple temperature control areas to determine the flow field distribution; For the adjacent area insulation structure, based on the adjacent area insulation material and geometric layout, the insulation coefficient of the adjacent area heat conduction is determined; Based on the thermal insulation coefficient, the flow field distribution is mapped and marked to determine the thermal buoyancy flow field.

4. The multi-zone independent temperature zoning control system of the electric control box according to claim 3, characterized in that: The thermal buoyancy flow field construction unit is also used for: Acquire the historical control records, integrate multiple control sequences and perform fuzzification to determine a fuzzy set, wherein the fuzzy set includes an input set and an output set, and the input data type at least includes a fuzzified temperature deviation and a deviation change rate; Based on the fuzzy set and the multiple control sequences, mining a fuzzy rule base, wherein the fuzzy rule base represents a fuzzy relationship between an input and an output; Taking the thermal buoyancy flow field as a benchmark, sample-driven training based on the fuzzy set and the fuzzy rule base is performed to generate the fuzzy decision module.

5. The multi-zone independent temperature zone control system of the electric control box according to claim 4, characterized in that: The control strategy determination unit is also used for: Identifying the temperature sensing data and initializing the thermal buoyancy flow field; Based on the thermal buoyancy flow field, the passive temperature control state evolution is predicted in combination with the passive temperature control setting to determine the first flow field trend; Based on the first flow field trend and in combination with the active temperature control setting, a temperature adjustment decision is made to determine the zone control strategy.

6. The multi-zone independent temperature zone control system of the electric control box according to claim 5, characterized in that: The system further comprises: an input data determining unit, configured to determine fuzzy input data based on the initialized thermal buoyancy flow field; A matching and reasoning unit, used to combine the fuzzy rule base, perform fuzzy rule matching and reasoning based on the fuzzy input data, and determine a fuzzy output strategy; A strategy integration unit is used to integrate the fuzzy output strategy based on the layout structure to determine the partition control strategy.

7. The multi-zone independent temperature zone control system of the electric control box according to claim 1, characterized in that: The control management unit is also used for: Combined with the temperature sensor, temperature control and adjustment tracking is performed to determine the temperature change trend; Determining whether the temperature change trend meets the standard, wherein the unit temperature change vector and temperature change rate based on a single region and the temperature change trend based on the global temperature field are used as the determination criteria; If the standard is not met, a feedback temperature adjustment instruction is generated.

8. An independent temperature zoning control method for multiple areas of an electric control box, characterized in that: The method is applied to the independent temperature zoning control system for multiple zones of an electric control box according to any one of claims 1 to 7, and the method comprises: Obtaining the layout structure of the target electric control box, and dividing it into multiple temperature control areas according to the temperature control requirements, wherein the layout structure includes a partition layout and an adjacent zone temperature isolation structure, and each temperature control zone includes at least one layout zone; Based on a synchronization timestamp constraint, monitoring and receiving temperature sensing data of the multiple temperature control zones, wherein each layout partition is equipped with a temperature sensor; Get temperature control settings, where temperature control types include active temperature control and passive temperature control; For the multiple temperature control areas, a thermal buoyancy flow field is constructed, a fuzzy sample library is mined in combination with historical control records, and a fuzzy decision module is supervised and trained; Importing the temperature sensing data, combining with the fuzzy decision module, executing fuzzy temperature control decision based on thermal buoyancy flow analysis and neighborhood temperature mutual conductance constraint, and determining the partition control strategy, wherein the temperature control decision is an active temperature control decision based on the passive temperature control state analysis of the temperature control setting; A zone temperature control signal based on the zone control strategy is generated to perform temperature control management on the multiple temperature control areas.

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