Optimization method of thermal management system for industrial equipment with multiple heat sources coupled

Abstract

The present invention relates to the technical field of thermal management of industrial equipment, and discloses a method for optimizing a thermal management system of industrial equipment with multiple heat sources coupled. First, the heat source parameters and environmental data of the industrial equipment are obtained, and structured heat source data is generated according to preset dimension classification; a multi-dimensional thermodynamic constraint model including a time axis, a space axis, a heat flow axis, and an energy consumption axis is constructed; the initial thermal constraint conditions are determined according to the heat source type and the temperature gradient, and after heat flow simulation and thermal conflict detection, a mixed integer programming algorithm is used for optimization to obtain a conflict-free optimized thermal management solution. The solution synchronizes the model parameters with the real-time operation data of the equipment, generates an instruction set, and pushes it to the control terminal and execution unit. In addition, a visual monitoring interface is constructed to receive manual control instructions and update the instruction set. This method can accurately handle the problem of multiple heat source coupling, improve the performance of the thermal management system, realize efficient and intelligent thermal management, and ensure the stable operation of industrial equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of industrial equipment, and in particular to an optimization method for a thermal management system of industrial equipment coupled with multiple heat sources. Background Art

[0002] In modern industrial production, the complexity and integration of industrial equipment continue to increase, and the coupling of multiple heat sources is becoming increasingly common. For example, in large data centers, multiple heat sources, such as server chips and power modules, operate simultaneously, generating significant amounts of heat. If this heat is not effectively managed, equipment temperatures will continue to rise, seriously impacting performance, reliability, and service life.

[0003] Early thermal management methods for industrial equipment were relatively simple. In equipment with a single heat source or a small number of heat sources, a single cooling method, such as natural cooling or simple fan cooling, was often used. However, this method is not effective in complex scenarios with multiple heat sources coupled, and it is impossible to accurately control the temperature of each heat source. With the development of industrial technology, some equipment has begun to adopt active cooling technologies, such as liquid cooling systems. However, when multiple heat sources are coupled, the temperature requirements, power levels, and coupling modes of different heat sources vary, and traditional liquid cooling systems are difficult to flexibly adjust to these differences. For example, some heat sources may require lower temperatures to ensure performance, while others can still work normally at higher temperatures. A unified cooling strategy will result in energy waste and insufficient cooling of some heat sources.

[0004] From the perspective of heat source parameter processing, there has been a lack of systematic classification and structured processing methods. Engineers often simply record basic parameters such as heat source temperature and power, without considering multiple factors such as heat source type, temperature gradient, power range, and coupling mode. This makes it difficult to fully analyze the interactions between heat sources when developing thermal management plans, resulting in insufficiently targeted and effective thermal management solutions.

[0005] When it comes to modeling thermal management systems, traditional models are mostly simple two-dimensional, considering only a single dimension, either spatial or temporal. These models are unable to accurately describe the complex thermodynamic processes involved in the coupling of multiple heat sources. For example, when describing heat transfer, they fail to consider the dynamic changes in heat flow over time and space, as well as the relationship between energy consumption and heat flow. Consequently, thermal management strategies developed based on these models are unable to adapt to changing operating conditions and achieve efficient thermal management.

[0006] Thermal management system optimization algorithms also have limitations. Traditional optimization algorithms tend to fall into local optimal solutions when dealing with the complex constraints of multiple coupled heat sources, failing to find the globally optimal thermal management solution. For example, when addressing heat flow path conflicts and energy consumption allocation, they fail to comprehensively consider the impact of multiple factors, resulting in insufficient improvement in thermal management system performance. Summary of the Invention

[0007] The object of the present invention is to provide a method for optimizing a thermal management system of industrial equipment with multiple heat sources coupled, so as to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled, the method comprising:

[0009] Obtaining heat source parameters and environmental data of industrial equipment, and generating structured heat source data by classifying the heat source parameters according to preset dimensions, including heat source type, temperature gradient, power range, and coupling mode;

[0010] Construct a multidimensional thermodynamic constraint model and define a thermodynamic coordinate system based on the classification dimension of heat source data. The thermodynamic coordinate system includes a time axis, a space axis, a heat flow axis, and an energy consumption axis.

[0011] Determining initial thermal constraints based on the heat source type and temperature gradient, wherein the thermal constraints include heat exchange thresholds, heat transfer path conflict rules, and energy consumption allocation priorities;

[0012] Perform heat flow simulation on the heat source scheduling task based on the thermodynamic constraint model, simulate the dynamic distribution of heat sources within the equipment using the Monte Carlo method, and generate a preliminary thermal management plan;

[0013] Perform thermal conflict detection on the preliminary thermal management plan, identify heat source nodes with overlapping heat flow paths or conflicting rules, and mark the conflict type and thermal imbalance intensity;

[0014] A mixed integer programming algorithm is used to iteratively optimize the conflicting nodes, adjust the spatiotemporal parameters or energy consumption allocation strategy of the heat source nodes, and generate a conflict-free optimized thermal management solution.

[0015] Preferably, the method further comprises:

[0016] Synchronize the optimized thermal management plan with the real-time operation data of the equipment to dynamically modify the parameters of the thermodynamic constraint model;

[0017] Generate a final thermal management instruction set based on the revised thermodynamic constraint model, the instruction set including heat source start and stop timing, heat transfer path planning, and energy consumption allocation information;

[0018] The thermal management instruction set is pushed to the equipment control terminal and the heat source execution unit to trigger the automated thermal management process.

[0019] Preferably, the construction of a multidimensional thermodynamic constraint model includes:

[0020] Define grid partitions of the spatial axis based on the equipment structure layout, with each partition associated with the heat dissipation channel capacity, heat source installation location, and cooling medium distribution;

[0021] Divide the time axis into continuous or discrete thermal cycles, and associate each thermal cycle with a priority weight for starting and stopping the heat source;

[0022] Define the dynamic distribution status of equipment energy consumption based on the energy consumption axis, including power consumption, cooling energy consumption and waste heat recovery efficiency.

[0023] Preferably, the mixed integer programming algorithm adopts an improved branch and bound method, including:

[0024] The thermodynamic parameters of conflicting nodes are encoded as decision variables, and an objective function is defined to evaluate the thermal imbalance intensity, energy utilization, and thermal management delay cost.

[0025] A new set of thermal management solutions is generated by relaxing constraints, and the feasible region pruning strategy is used to retain the optimal solution set.

[0026] Preferably, the heat flow simulation includes:

[0027] Introducing a real-time temperature monitoring module to dynamically receive equipment surface temperature data, cooling medium flow rate, and heat source power fluctuations;

[0028] Adjust the boundary conditions of the simulation model based on real-time data to generate fault-tolerant thermal management solutions.

[0029] Preferably, the thermal conflict detection includes:

[0030] Establish a heat flow path occupancy matrix and detect the overlapping areas of heat source nodes in thermal cycles and spatial partitions through matrix operations;

[0031] Based on the rule engine matching the preset conflict judgment logic, conflict events that violate heat transfer priority or safe temperature difference are identified.

[0032] Preferably, the heat transfer path planning adopts an ant colony optimization algorithm, including:

[0033] Model the topology of the device's heat dissipation channel as a graph network, and define heat flow diffusion actions as state transitions;

[0034] Path selection is guided by a pheromone renewal strategy to minimize thermal resistance and avoid local overheating.

[0035] Preferably, the definition of the energy consumption axis includes:

[0036] Integrate environmental temperature and humidity factors into energy allocation strategies, including seasonal changes, diurnal temperature differences, and air mobility;

[0037] Dynamically adjust the cooling medium circulation rate and heat source power adjustment threshold according to environmental temperature and humidity factors.

[0038] Preferably, the energy consumption allocation strategy includes:

[0039] Use dynamic weight queue to manage heat source tasks and adjust the queue order according to equipment load rate and heat source efficiency;

[0040] Reserve redundant capacity for critical heat dissipation channels to ensure that high-priority heat sources take priority in resource conflicts.

[0041] Preferably, the method further comprises:

[0042] Build a visual thermal management monitoring interface to map the thermodynamic constraint model, conflict detection results, and optimized thermal management solutions into a three-dimensional thermal field distribution map;

[0043] Receive manual control instructions through the interactive operation interface and update the thermal management instruction set in real time.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] In terms of heat source parameter processing, by classifying heat source parameters according to preset dimensions such as heat source type, temperature gradient, power range, and coupling mode, structured heat source data is generated, which can comprehensively and meticulously analyze the heat source characteristics. This makes the formulation of subsequent thermal management plans more targeted. Instead of treating heat sources in a general way, it is precisely regulated according to the unique properties of each heat source. For example, in semiconductor manufacturing equipment, the heat source characteristics of different process links vary greatly. This classification method can formulate exclusive thermal management strategies for heat sources in different processes such as lithography and etching, greatly improving the accuracy and effectiveness of thermal management.

[0046] The constructed multi-dimensional thermodynamic constraint model covers the time axis, space axis, heat flow axis, and energy consumption axis, breaking through the limitations of traditional models. By defining the grid partitioning of the space axis based on the equipment structure layout and associating information such as the capacity of the heat dissipation channel, the distribution of heat dissipation resources within the equipment can be accurately grasped. By dividing the time axis thermal cycle and associating the start and stop priority weights of the heat sources, the working sequence of the heat sources can be rationally arranged according to the operating rules of the equipment. In the multi-heat source system of the automobile engine, the start and stop of the heat sources are optimized according to the time sequence of different operating conditions such as engine start-up, acceleration, and constant speed, combined with the priority of each heat source, to improve thermal management efficiency. Starting from the energy consumption axis, considering power consumption, cooling energy consumption, and waste heat recovery efficiency, and integrating environmental temperature and humidity factors to adjust the energy consumption allocation strategy, it can effectively reduce the overall energy consumption of the equipment and achieve efficient energy utilization.

[0047] Thermal flow simulation, combined with a real-time temperature monitoring module, adjusts the simulation model's boundary conditions based on equipment surface temperature data, cooling medium flow rate, and heat source power fluctuations, generating a fault-tolerant thermal management solution. This enables the thermal management system to rapidly respond to various changes during equipment operation, enhancing system stability and adaptability. For example, when operating furnace equipment in the metallurgical industry, the furnace temperature and heat source power may fluctuate due to changes in raw materials and processes. This simulation method can adjust the thermal management strategy in real time to ensure stable furnace operation.

[0048] Thermal conflict detection uses a heat flow path occupancy matrix and a rule engine to accurately identify heat source nodes with overlapping heat flow paths or conflicting rules, and labels the conflict type and thermal imbalance intensity. Based on this, a modified branch-and-bound method using a mixed integer programming algorithm is used to iteratively optimize conflicting nodes. This method adjusts the spatiotemporal parameters or energy allocation strategies of the heat source nodes to generate a conflict-free, optimized thermal management solution, effectively avoiding conflicts during the thermal management process and ensuring stable equipment operation.

[0049] Heat transfer path planning utilizes an ant colony optimization algorithm, modeling the topology of the device's cooling channels as a graph network. A pheromone update strategy guides path selection, minimizing thermal resistance and preventing localized overheating, further enhancing the thermal management system's cooling performance. The energy allocation strategy employs a dynamic weighted queue to manage heat source tasks and reserves redundant capacity for critical cooling channels, ensuring that high-priority heat sources receive priority in resource allocation when conflicts arise, balancing device performance and energy consumption.

[0050] A visual thermal management monitoring interface is constructed, presenting complex thermodynamic models, conflict detection results, and optimization solutions as a three-dimensional thermal field distribution diagram, allowing operators to intuitively understand the thermal status of the equipment. The interactive interface receives manual control commands and updates the thermal management instruction set in real time, achieving human-machine collaborative optimization and improving the flexibility and operability of the thermal management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a working principle diagram of the multi-heat source coupled industrial equipment thermal management system optimization method of the present invention;

[0052] Figure 2 Flowcharts for optimization solution application and instruction generation;

[0053] Figure 3 Improve the execution graph of branch-and-bound method for mixed integer programming algorithm;

[0054] Figure 4 This is the workflow diagram for thermal flow simulation. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] See also Figures 1-4 The present invention provides a method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled. The specific implementation method thereof will be described in detail below.

[0057] In the industrial equipment operation scenario, the first step is to obtain the heat source parameters and environmental data of the industrial equipment. Heat source parameters include information such as the heat source's heating power and initial temperature, while environmental data includes ambient temperature, humidity, etc. After obtaining this data, the heat source parameters are classified according to preset dimensions. The preset dimensions include heat source type, such as resistive heating source, inductive heating source, etc.; temperature gradient, that is, the rate of temperature change of the heat source at different locations; power range, which specifies the power range of each heat source; coupling mode, such as series coupling, parallel coupling, etc. After classification is completed, structured heat source data is generated for subsequent processing.

[0058] Construct a multidimensional thermodynamic constraint model. Based on the classification dimensions of heat source data, a thermodynamic coordinate system is defined. This coordinate system consists of a time axis, a spatial axis, a heat flow axis, and an energy consumption axis. The time axis is used to record the thermal management status of industrial equipment at different times during operation; the spatial axis is divided according to the actual structural layout of the equipment to determine the specific location of the heat source within the equipment; the heat flow axis is used to indicate the direction and flow rate of heat transfer; and the energy consumption axis focuses on energy consumption throughout the entire thermal management process.

[0059] Initial thermal constraints are determined based on the heat source type and temperature gradient. The heat exchange threshold determines the temperature limit for heat exchange between different heat sources. For example, heat exchange is allowed only when the temperature of one heat source reaches a certain value and the temperature difference with another heat source meets the heat exchange threshold. Heat transfer path conflict rules are designed to avoid confusion during heat transfer and ensure that heat is transferred along a reasonable path. Energy allocation priorities determine which heat sources should be prioritized for energy supply when energy is limited.

[0060] Based on a thermodynamic constraint model, a heat flow simulation is performed for the heat source scheduling task. The Monte Carlo method is used to simulate the dynamic distribution of heat sources within the equipment. This method uses multiple random samplings to simulate the distribution of heat sources at different times and locations, thereby generating a preliminary thermal management plan. This plan includes the heat source scheduling strategy and preliminary planning of heat transfer paths.

[0061] Perform thermal conflict detection on the preliminary thermal management plan. Identify heat source nodes with overlapping heat flow paths or conflicting rules, and mark the conflict type and thermal imbalance intensity. Overlapping heat flow paths can lead to localized overheating or poor heat transfer, while rule conflicts refer to violations of previously defined heat transfer path conflict rules or energy allocation priorities. Marking the conflict type and thermal imbalance intensity facilitates subsequent targeted optimization.

[0062] A mixed integer programming algorithm is used to iteratively optimize conflicting nodes. Adjustments to the spatiotemporal parameters or energy allocation strategies of heat source nodes are made, such as changing the start-up time of the heat source, adjusting the heat source's spatial location, or redistributing energy consumption ratios, ultimately generating a conflict-free optimized thermal management solution.

[0063] The technical solution of the present invention is further described in detail below through six embodiments.

[0064] Example 1:

[0065] In practical applications, after generating an optimized thermal management plan, it needs to be synchronized with the device's real-time operating data. This data can be obtained through various sensors, such as temperature sensors that monitor the temperature of various parts of the device, and flow sensors that monitor the flow rate of the cooling medium. This real-time data is compared with the optimized thermal management plan. If any discrepancies are found, the parameters of the thermodynamic constraint model are dynamically adjusted. For example, if the actual temperature in a certain area is higher than the temperature predicted in the optimized plan, parameters such as the heat exchange threshold corresponding to that area may need to be adjusted.

[0066] Based on the revised thermodynamic constraint model, the final thermal management instruction set is generated. This instruction set includes the start-stop sequence of heat sources, which specifies when each heat source starts and stops. This is crucial for properly controlling the heat generation of the equipment. For example, for some heat sources that are not needed at the initial startup of the equipment, the start-up can be delayed to reduce unnecessary heat generation and energy consumption. Heat transfer path planning specifies the specific path for heat transfer from the heat source to the heat dissipation part, ensuring that the heat can be dissipated efficiently. Energy consumption allocation information accurately allocates energy to ensure that each heat source receives the appropriate energy supply, thereby improving energy utilization efficiency.

[0067] Pushing thermal management instruction sets to the equipment control terminal and heat source execution unit triggers the automated thermal management process. Upon receiving the instructions, the equipment control terminal automatically controls the equipment's operation, while the heat source execution unit operates the heat source according to the instructions, achieving automated and intelligent thermal management for the entire industrial equipment.

[0068] Example 2:

[0069] When constructing a multi-dimensional thermodynamic constraint model, the grid partitioning of the spatial axis is defined according to the equipment structure layout. Take a large industrial furnace as an example. Its internal structure is complex, with multiple heat-generating components and heat dissipation areas. The internal space of the furnace is divided into small grid areas. Each partition is associated with the capacity of the heat dissipation channel, which determines the upper limit of the heat that can be dissipated from the area. For example, the heat dissipation channel capacity of the partition close to the cooling water pipe is larger and can take away heat faster. The installation location of the heat source is also closely related to the partition. Knowing which partition the heat source is in helps to accurately analyze the generation and transfer of heat. The distribution of the cooling medium is also important. The flow rate and temperature of the cooling medium in different partitions may be different, which will affect the heat dissipation effect.

[0070] The timeline is divided into continuous or discrete thermal cycles. In continuous thermal cycle mode, the timeline is continuous and uninterrupted, as if the device is running continuously without obvious start and stop intervals. Each thermal cycle is associated with a priority weight for the start and stop of heat sources. For example, during the device startup phase, certain critical heat sources need to be started first to ensure normal operation of the device, so their priority weights are set higher. In discrete thermal cycle mode, the device has a clear start and stop process, and the duration of each thermal cycle and the start and stop status of the heat sources can be set according to actual needs.

[0071] The dynamic distribution of equipment energy consumption is defined based on the energy consumption axis, including power consumption, cooling energy consumption, and waste heat recovery efficiency. In actual operation, the distribution of these three energy consumptions is dynamically adjusted based on the equipment's operating status and environmental conditions. For example, when the ambient temperature is low, cooling energy consumption can be appropriately reduced while waste heat recovery efficiency can be improved. This recovered waste heat can be used in other processes requiring heat, thereby reducing overall power consumption.

[0072] Example 3:

[0073] In a thermal management system for industrial equipment with multiple coupled heat sources, conflict detection of the initial thermal management solution reveals conflicting heat source nodes. An improved branch-and-bound method, a mixed integer programming algorithm, is used to optimize these conflicting nodes.

[0074] In practice, the thermodynamic parameters of the conflicting nodes must first be encoded as decision variables. For example, a complex industrial equipment with multiple heat sources is used. Suppose one of the conflicting nodes involves the temperature of the heat source. , heat flow and the operating time of the heat source Thermodynamic parameters such as . These parameters are encoded as decision variables 、 、 .in, Represents temperature , Represents heat flow , Represents the running time By reasonably selecting and adjusting the values of these decision variables, we can find a better thermal management solution.

[0075] Then define the objective function to evaluate the thermal imbalance intensity, energy utilization and thermal management delay cost. Can be constructed as: In this formula, 、 、 These are the weight coefficients corresponding to thermal imbalance intensity, energy utilization rate and thermal management delay cost. These coefficients are set according to the actual operation requirements of industrial equipment and the emphasis on different indicators. For example, for equipment with extremely high requirements for temperature stability, the The value of . Indicates the number of heat sources involved in calculating the thermal imbalance intensity, It is The actual temperature of the heat source, It is the average temperature of all heat sources. The intensity of thermal imbalance is measured by calculating the sum of the absolute values of the differences between the temperature of each heat source and the average temperature and then taking the average. is the actual amount of energy consumed by the device, It is the total energy that the equipment can theoretically consume during the operating period. The ratio of the two is used to evaluate energy utilization. Indicates the number of tasks that generate delays during thermal management. It is The delay time of each delayed task is the delay time of each delayed task, and the sum of the delay time of all delayed tasks represents the thermal management delay cost.

[0076] Generate a new set of thermal management solutions by relaxing constraints. Under the originally strict constraints, some restrictions are appropriately relaxed, such as relaxing the restrictions on the heat source power adjustment range. Originally, the heat source power was required to be selected only from a few specific discrete values, but now it is allowed to take values within a wider range. In this way, more possible thermal management solutions can be obtained. Then, the feasible domain pruning strategy is used to retain the optimal solution set. By calculating the objective function value of each new solution, the advantages and disadvantages of different solutions are compared. For those solutions with poor objective function values, that is, those with high thermal imbalance intensity, low energy utilization or high thermal management delay cost, they are removed from the solution set, and only solutions with better objective function values are retained, thereby improving the efficiency and quality of optimization.

[0077] Example 4:

[0078] In the operation of the thermal management system of industrial equipment with multiple heat sources coupled, the heat flow simulation plays a vital role.

[0079] A real-time temperature monitoring module has been introduced. This module collects data by installing various sensors at key locations on industrial equipment. For example, a high-precision temperature sensor is installed on the heat source surface to obtain real-time surface temperature data; a flow sensor is installed on the cooling pipe to monitor the flow rate of the cooling medium; and a power sensor is used to monitor heat source power fluctuations. For example, during the injection molding process, the injection mold, as the primary heat source, experiences temperature fluctuations throughout the injection cycle. A cooling medium (such as cooling water) flows through the pipes inside the mold to remove heat, and the power of the machine's heating device is adjusted according to the injection molding process requirements.

[0080] Adjust the boundary conditions of the simulation model based on the real-time data. Suppose that the temperature of a certain area of the injection mold suddenly rises in real time. This may be due to the adjustment of the injection molding process parameters, which has accelerated the filling speed of the plastic melt in this area and increased heat generation. In this case, the heat transfer coefficient of this area needs to be adjusted accordingly in the simulation model. The original value may be based on the normal injection molding process setting, now according to the temperature change, it is adjusted to , The specific value of is calculated by the relevant thermal formula and the actual temperature change. For example, according to Newton's law of cooling (in is the heat transfer rate, is the heat transfer area, is the object temperature, is the ambient temperature), combined with the currently monitored temperature changes, Correction is obtained By continuously adjusting boundary conditions based on real-time data, a fault-tolerant thermal management solution is generated. Even if anomalies occur during device operation, such as a temporary sensor failure causing data fluctuations, the thermal management solution, through its fault tolerance and comprehensive analysis of other relevant data and model adjustments, can continue to provide an effective thermal management strategy for the device, ensuring stable operation.

[0081] Example 5:

[0082] In the thermal management system of industrial equipment with multiple heat sources coupled, thermal conflict detection is a key step to ensure the effectiveness of the thermal management solution.

[0083] Establish a heat flow path occupancy matrix. Assume that there are 5 heat source nodes in an industrial equipment, which are divided into 4 thermal cycles according to the operation process, and the space is divided into 3 partitions according to the structural characteristics of the equipment. Heat flow path occupancy matrix , where the elements Representative The heat source node is Thermal cycle, When the heat source node occupies the heat flow path in the corresponding thermal cycle and spatial partition, If not occupied, .For example, Indicates that the second heat source node occupies the heat flow path of the first spatial partition in the third thermal cycle. Matrix operations are used to detect the overlapping areas of heat source nodes in thermal cycles and spatial partitions, such as calculating the sum of matrix elements of all heat source nodes in the same thermal cycle and spatial partition. If in the second thermal cycle and the second spatial partition, , which means that there is an overlap of heat flow paths in this thermal cycle and spatial partition.

[0084] Based on the rule engine matching preset conflict judgment logic, it identifies conflict events that violate heat transfer priority or safe temperature difference. The preset conflict judgment logic is formulated according to the operating characteristics and safety requirements of industrial equipment. For example, setting the heat source The heat transfer priority is higher than the heat source , at a certain moment, according to the normal heat transfer priority, the heat source should be The heat is first transferred to a specific area. If the heat source is found through monitoring and calculation The heat comes before the heat source If the heat is transferred to this area, it is determined to be a conflict event that violates the heat transfer priority. For example, according to the material characteristics and operation safety standards of the equipment, the safety temperature difference between different heat sources is set to When the actual temperature difference between the two heat sources is monitored In this way, thermal conflict events can be accurately identified, providing a basis for subsequent optimization and adjustment.

[0085] Example 6:

[0086] When using the ant colony optimization algorithm for heat transfer path planning, the topology of the device's heat dissipation channels is modeled as a graph network. Each node in the heat dissipation channel is considered a vertex in the graph, and the connections between channels are considered edges. Heat flow diffusion actions are defined as state transitions. Ants move from one vertex to another in the graph network, simulating the transfer of heat flow in the heat dissipation channel.

[0087] A pheromone update strategy guides path selection to minimize thermal resistance and avoid local overheating. After ants traverse a path, they leave pheromones along the path. The concentration of the pheromone changes over time and the number of times the ants pass through it. The lower the thermal resistance on a path, the greater the probability that the ants will choose that path, thus directing heat flow toward the path with lower thermal resistance. Furthermore, to prevent local overheating, when the temperature in a certain area is too high, the pheromone concentration on the paths near that area is reduced, reducing the amount of heat transferred to that area.

[0088] When defining the energy consumption axis, integrate ambient temperature and humidity factors into the energy allocation strategy. Ambient temperature and humidity factors, such as seasonal variations, diurnal temperature differences, and air flow, significantly impact energy allocation. In summer, when ambient temperatures are higher, cooling energy consumption increases accordingly. In this case, the power of some non-critical heat sources can be appropriately reduced to balance overall energy consumption. Dynamically adjust the cooling medium circulation rate and heat source power adjustment threshold based on ambient temperature and humidity factors. For example, when air flow is good, the cooling medium circulation rate can be appropriately reduced to save energy.

[0089] The energy allocation strategy uses a dynamic weighted queue to manage heat source tasks. The queue order is adjusted based on device load and heat source efficiency. When device load is high, energy is prioritized for heat sources with high efficiency and a significant impact on device operation. Redundant capacity is reserved for critical cooling channels to ensure that high-priority heat sources receive priority in the event of conflicts. For example, a certain amount of cooling capacity is reserved in the cooling channels of core device components. This ensures that when conflicts arise with the corresponding heat sources, heat can be dissipated promptly, ensuring normal device operation.

[0090] In addition, a visual thermal management monitoring interface is constructed, mapping the thermodynamic constraint model, conflict detection results, and optimized thermal management solution into a three-dimensional thermal field distribution map. Operators can intuitively view information such as the temperature distribution within the equipment, the heat flow transfer path, and the location of conflicts. Manual control commands are received through the interactive operation interface, and the thermal management instruction set is updated in real time. Operators can manually adjust parameters such as the start and stop time of the heat source and the heat transfer path based on actual conditions. The system will promptly update the thermal management instruction set based on these commands, achieving more flexible thermal management.

[0091] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0092] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled, characterized in that: include: Obtaining heat source parameters and environmental data of industrial equipment, and generating structured heat source data by classifying the heat source parameters according to preset dimensions, including heat source type, temperature gradient, power range, and coupling mode; Construct a multidimensional thermodynamic constraint model and define a thermodynamic coordinate system based on the classification dimension of heat source data. The thermodynamic coordinate system includes a time axis, a space axis, a heat flow axis, and an energy consumption axis. Determining initial thermal constraints based on the heat source type and temperature gradient, wherein the thermal constraints include heat exchange thresholds, heat transfer path conflict rules, and energy consumption allocation priorities; Perform heat flow simulation on the heat source scheduling task based on the thermodynamic constraint model, simulate the dynamic distribution of heat sources within the equipment using the Monte Carlo method, and generate a preliminary thermal management plan; Perform thermal conflict detection on the preliminary thermal management plan, identify heat source nodes with overlapping heat flow paths or conflicting rules, and mark the conflict type and thermal imbalance intensity; A mixed integer programming algorithm is used to iteratively optimize the conflicting nodes, adjust the spatiotemporal parameters or energy consumption allocation strategy of the heat source nodes, and generate a conflict-free optimized thermal management solution.

2. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 1, characterized in that: The method further comprises: Synchronize the optimized thermal management plan with the real-time operation data of the equipment to dynamically modify the parameters of the thermodynamic constraint model; Generate a final thermal management instruction set based on the revised thermodynamic constraint model, the instruction set including heat source start and stop timing, heat transfer path planning, and energy consumption allocation information; The thermal management instruction set is pushed to the equipment control terminal and the heat source execution unit to trigger the automated thermal management process.

3. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 1, characterized in that: The multidimensional thermodynamic constraint model is constructed as follows: Define grid partitions of the spatial axis based on the equipment structure layout, with each partition associated with the heat dissipation channel capacity, heat source installation location, and cooling medium distribution; Divide the time axis into continuous or discrete thermal cycles, and associate each thermal cycle with a priority weight for starting and stopping the heat source; Define the dynamic distribution status of equipment energy consumption based on the energy consumption axis, including power consumption, cooling energy consumption and waste heat recovery efficiency.

4. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 1, characterized in that: The mixed integer programming algorithm adopts an improved branch and bound method, including: The thermodynamic parameters of conflicting nodes are encoded as decision variables, and an objective function is defined to evaluate the thermal imbalance intensity, energy utilization, and thermal management delay cost. A new set of thermal management solutions is generated by relaxing constraints, and the feasible region pruning strategy is used to retain the optimal solution set.

5. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 1, characterized in that: The thermal flow simulation includes: Introducing a real-time temperature monitoring module to dynamically receive equipment surface temperature data, cooling medium flow rate, and heat source power fluctuations; Adjust the boundary conditions of the simulation model based on real-time data to generate fault-tolerant thermal management solutions.

6. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 1, characterized in that: The thermal conflict detection includes: Establish a heat flow path occupancy matrix and detect the overlapping areas of heat source nodes in thermal cycles and spatial partitions through matrix operations; Based on the rule engine matching the preset conflict judgment logic, conflict events that violate heat transfer priority or safe temperature difference are identified.

7. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 2, characterized in that: The heat transfer path planning adopts the ant colony optimization algorithm, including: Model the topology of the device's heat dissipation channel as a graph network, and define heat flow diffusion actions as state transitions; Path selection is guided by a pheromone renewal strategy to minimize thermal resistance and avoid local overheating.

8. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 3, characterized in that: The definition of the energy consumption axis includes: Integrate environmental temperature and humidity factors into energy allocation strategies, including seasonal changes, diurnal temperature differences, and air mobility; Dynamically adjust the cooling medium circulation rate and heat source power adjustment threshold according to environmental temperature and humidity factors.

9. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 8, characterized in that: The energy consumption allocation strategy includes: Use dynamic weight queue to manage heat source tasks and adjust the queue order according to equipment load rate and heat source efficiency; Reserve redundant capacity for critical heat dissipation channels to ensure that high-priority heat sources take priority in resource conflicts.

10. The method for optimizing a thermal management system for industrial equipment with multiple heat sources coupled according to claim 1, characterized in that: The method further comprises: Build a visual thermal management monitoring interface to map the thermodynamic constraint model, conflict detection results, and optimized thermal management solutions into a three-dimensional thermal field distribution map; Receive manual control instructions through the interactive operation interface and update the thermal management instruction set in real time.

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