Heat dissipation management methods and systems for power tools
By receiving production task assignments, reverse-engineering target operating conditions, and constructing a distributed temperature prediction model, the system identifies over-temperature time zone sequences and combines a water-cooled heat dissipation architecture for coolant circulation control. This solves the problem of low heat dissipation efficiency in power tools and achieves precise heat dissipation management and efficiency improvement.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANTONG FUGEN MASCH MFG CO LTD
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-26
Smart Images
Figure CN122086147A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of heat dissipation management technology, and more specifically to a heat dissipation management method and system for power tools. Background Technology
[0002] With the rapid development of industrial production and technology, power tools are widely used in manufacturing and maintenance. However, with increasing usage intensity and more complex working environments, the heat dissipation problem of power tools under high loads has become increasingly prominent. Traditional heat dissipation methods, such as natural convection and fan-assisted cooling, are no longer sufficient to meet the heat dissipation requirements of modern power tools during prolonged high-intensity operation, especially in the continuous operation of high-power tools, where heat dissipation efficiency and control precision are inadequate. To improve heat dissipation, water cooling has been gradually introduced into existing technologies. Water cooling removes heat from the tool's interior through coolant circulation, offering better heat conduction performance compared to air cooling. However, current water cooling technology in power tools suffers from low precision. Due to the lack of precise monitoring and real-time control of dynamic temperature changes, water cooling systems often cannot accurately adjust according to the real-time temperature changes of various key components of the power tool, resulting in low heat dissipation efficiency. Summary of the Invention
[0003] This application provides a heat dissipation management method and system for power tools, which addresses the technical problem of low heat dissipation efficiency caused by the inability of existing technologies to accurately adjust the heat dissipation based on the real-time temperature changes of various key components of power tools.
[0004] In view of the above problems, this application provides a heat dissipation management method and system for power tools.
[0005] The first aspect of this application provides a heat dissipation management method for power tools, the method comprising: The system receives production task assignments for temperature-controlled power tools and reverse-engineers the tool operating conditions based on these assignments to obtain target operating conditions. It interactively obtains multiple distributed temperature thresholds for multiple temperature control zones within the temperature-controlled power tool. A distributed temperature prediction model is pre-built, and the target operating conditions are synchronized to this model to obtain multiple production temperature change sequences mapped to the multiple temperature control zones. The system uses the multiple distributed temperature thresholds to map and traverse the multiple production temperature change sequences to obtain multiple tool overheating time zone sequences. It interactively obtains the water-cooling heat dissipation architecture of the temperature-controlled power tool and performs coolant circulation analysis by fitting the multiple tool overheating time zone sequences to the water-cooling heat dissipation architecture, outputting heat dissipation timing control parameters. During the execution of the production task assignments by the temperature-controlled power tool, the heat dissipation timing control parameters are used to synchronously control the coolant circulation within the water-cooling heat dissipation architecture.
[0006] A second aspect of this application provides a thermal management system for power tools, the system comprising: The system comprises the following modules: a target operating condition acquisition module, which receives production task allocations for temperature-controlled power tools and reverse-engineers the tool operating conditions based on the allocations to obtain target operating conditions; an information interaction module, which interactively obtains multiple distributed temperature thresholds for multiple temperature control zones within the temperature-controlled power tool; a production temperature change sequence acquisition module, which pre-builds a distributed temperature prediction model and synchronizes the target operating conditions to the model to obtain multiple production temperature change sequences mapped to the multiple temperature control zones; a mapping traversal module, which uses the distributed temperature thresholds to map and traverse the multiple production temperature change sequences to obtain multiple tool overheating time zone sequences; a coolant circulation analysis module, which interactively obtains the water-cooling heat dissipation architecture of the temperature-controlled power tool and performs coolant circulation analysis by fitting the multiple tool overheating time zone sequences to the water-cooling heat dissipation architecture, outputting heat dissipation timing control parameters; and a circulation control module, which uses the heat dissipation timing control parameters to synchronously control the circulation of coolant in the water-cooling heat dissipation architecture during the execution of the production task allocations by the temperature-controlled power tool.
[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application receives production task assignments for temperature-controlled power tools and reverse-engineers the tool's operating conditions based on the assignments to obtain target operating conditions. It interactively obtains multiple distributed temperature thresholds for multiple temperature control zones within the power tool. A distributed temperature prediction model is pre-built, and by synchronizing the target operating conditions to this model, multiple production temperature change sequences mapped to multiple temperature control zones are obtained. Multiple distributed temperature thresholds are used to map and traverse these production temperature change sequences to obtain multiple tool overheating time zone sequences. The water-cooling heat dissipation architecture of the power tool is interactively obtained, and by fitting the multiple tool overheating time zone sequences to the water-cooling architecture, coolant circulation analysis is performed, outputting heat dissipation timing control parameters. During the execution of production task assignments by the power tool, the heat dissipation timing control parameters are used to synchronously control the coolant circulation within the water-cooling architecture. This invention solves the technical problem of low heat dissipation efficiency in existing technologies, which cannot accurately adjust based on real-time temperature changes of various key components of the power tool. By receiving production task assignments, reverse-engineering the target operating conditions of the power tool, and combining distributed temperature thresholds from multiple temperature control zones, a distributed temperature prediction model is pre-built. This model analyzes production temperature changes, identifies the overheating time sequence of tools, and combines it with a water-cooled heat dissipation architecture to control coolant circulation, thereby improving the heat dissipation efficiency of power tools. Attached Figure Description
[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0009] Figure 1 This is a schematic flowchart of a heat dissipation management method for power tools provided in an embodiment of this application; Figure 2 This is a schematic diagram of the thermal management system for power tools provided in an embodiment of this application.
[0010] Figure labeling: Target operating condition acquisition module 11, information interaction module 12, production temperature change sequence acquisition module 13, mapping traversal module 14, coolant circulation analysis module 15, circulation control module 16. Detailed Implementation
[0011] This application provides a heat dissipation management method and system for power tools, addressing the technical problem of low heat dissipation efficiency in existing technologies that cannot accurately adjust based on real-time temperature changes of key components. By receiving production task assignments, the method reverse-engineers the target operating conditions of the power tool and, combined with distributed temperature thresholds for multiple temperature control zones, pre-constructs a distributed temperature prediction model. This model analyzes production temperature changes, identifies the tool's over-temperature time sequence, and then integrates with a water-cooling architecture for coolant circulation control, thereby improving the heat dissipation efficiency of the power tool.
[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.
[0014] Example 1, as Figure 1 As shown, this application provides a heat dissipation management method for power tools, the method comprising: Step S100: Receive the production task allocation for temperature-controlled electric tools, and reverse-engineer the tool operating conditions based on the production task allocation to obtain the target operating conditions.
[0015] In this embodiment, the system first receives production task assignments for temperature-controlled power tools via an interface with the production management system. These assignments include specific production workload and work type information. The production task assignments are pre-set by the production scheduler. Upon receipt, data processing is performed based on these task assignments.
[0016] Next, the tool's model information is used to access network data and obtain sample operating conditions for various sample work types. These operating conditions are stored in an operating condition information database and linked through a knowledge graph. Subsequently, based on a specific production work type, the operating condition information database is traversed to deduce the individual production operating conditions.
[0017] Furthermore, the method provided in the application embodiments also includes: The production task allocation includes production workload and production work type.
[0018] In this embodiment, production task allocation includes production workload and production work type. Production workload refers to the amount of work that needs to be completed within a specific time period, such as the production quantity per unit of product or the working time. Production work type refers to the specific task category, such as cutting, grinding, or assembly.
[0019] Furthermore, in the method provided in the application embodiments, receiving the production task allocation for temperature-controlled power tools and reversing the tool operating conditions based on the production task allocation to obtain the target operating conditions further includes: Based on the model information of the temperature-controlled electric tool, network data is retrieved to obtain multiple sample operating conditions for various sample work types; the multiple sample operating conditions for various sample work types are associated and stored based on a knowledge graph to obtain an operating condition information database; by traversing the operating condition information database using the production work type, the tool operating conditions are reverse-engineered to obtain the individual production operating conditions; the individual production operating conditions are iterated according to the production workload to obtain the target operating conditions.
[0020] In this embodiment, the model information of the temperature-controlled electric tool is first received from the production management system. Then, the network data is called through API or database query technology to extract the sample operating conditions of the sample working type related to the tool model from the cloud database, thus obtaining multiple sample operating conditions for multiple sample working types.
[0021] Next, the acquired sample operating conditions will be associated and stored using knowledge graph technology. Specifically, a graph database, such as Neo4j, will be used to establish connections between sample operating conditions and corresponding job types, creating nodes (job types) and edges (relationships between operating conditions), thereby forming an operating condition information database.
[0022] Then, the tool's operating conditions are reverse-engineered by traversing the operating condition information database based on production job type. Specifically, a graph traversal algorithm is used for data analysis. Based on a specific production job type, such as cutting or grinding, the operating condition information database is traversed to find all operating conditions related to that job type. Through this traversal, suitable individual production operating conditions are identified.
[0023] Finally, the target operating conditions are obtained by iteratively applying the individual production operating conditions based on the production workload. Specifically, the individual production operating conditions are iteratively applied in conjunction with the production workload, i.e., the amount of production tasks to be completed within a certain time, such as the number of products or working hours, to simulate the performance of the tool under different production volumes. Ultimately, the target operating conditions are generated. The target operating conditions refer to the specific operating parameters that need to be followed in the actual production process.
[0024] Step S200: Interact to obtain multiple distributed temperature thresholds for multiple temperature control zones in the temperature-controlled power tool.
[0025] In this embodiment, the user interface interacts with the operator, who selects or inputs specific temperature-controlled power tool model information and enters specific settings for each temperature control zone on the interface. These temperature control zones refer to different parts inside the power tool, such as the motor, housing, and battery, each with its specific temperature control requirements. Through interaction with the operator, multiple distributed temperature thresholds for multiple temperature control zones within the temperature-controlled power tool are obtained.
[0026] Step S300: Pre-build a distributed temperature prediction model, and obtain multiple production temperature change sequences mapped to the multiple temperature control zones by synchronizing the target operating conditions to the distributed temperature prediction model.
[0027] In this embodiment, the distributed temperature prediction model is pre-built. This model aims to predict temperature changes in different regions using historical temperature data and operating conditions. The model includes multiple input nodes, corresponding to the temperature parameters and operating conditions of each temperature control zone.
[0028] Next, the target operating conditions are synchronized to the distributed temperature prediction model. The model calculates based on the input target operating conditions, generating multiple production temperature change sequences mapped to the various temperature control zones. The production temperature change sequence refers to the temperature change trend of each temperature control zone under specific operating conditions.
[0029] Step S400: Use the multiple distributed temperature threshold mappings to traverse the multiple production temperature change sequences to obtain multiple tool over-temperature time zone sequences.
[0030] In this embodiment, when traversing multiple production temperature change sequences using multiple distributed temperature threshold mappings, each distributed temperature threshold is compared with its corresponding production temperature change sequence. This process uses a traversal algorithm to check each data point in the temperature change sequence one by one to determine whether it exceeds a preset temperature threshold. When the temperature data exceeds the set safety range, the time period is marked as an over-temperature zone.
[0031] Finally, by mapping and traversing, multiple tool temperature zone sequences were obtained.
[0032] Step S500: Interact to obtain the water-cooled heat dissipation architecture of the temperature-controlled power tool, and perform coolant circulation analysis by fitting the multiple tool over-temperature time zone sequences to the water-cooled heat dissipation architecture, and output heat dissipation timing control parameters.
[0033] In this embodiment, the operator first interacts with the user interface to obtain the water-cooling architecture of the temperature-controlled power tool, including components such as the coolant circulation path, radiator, and pump. Next, multiple tool overheating time zone sequences are fitted to the water-cooling architecture. This fitting process identifies which water-cooling areas require focused cooling within different overheating time zones. Finally, based on the fitting results, heat dissipation timing control parameters are output, including coolant flow rate, temperature, and circulation frequency.
[0034] Furthermore, in the method provided in the application embodiment, the water-cooled heat dissipation architecture of the temperature-controlled power tool is obtained interactively, and the coolant circulation analysis is performed by fitting the multiple tool over-temperature time zone sequences to the water-cooled heat dissipation architecture to output heat dissipation timing control parameters. The method also includes: The structural design information of the temperature-controlled power tool is obtained interactively, and the tool structure is digitized based on the structural design information to obtain a target tool model. The water-cooled heat dissipation architecture is disassembled through the coolant circuit to obtain K heat dissipation circuits, and K water-cooled heat dissipation areas in contact with the K heat dissipation circuits are selected in the target tool model. After fitting the multiple temperature control areas to the target tool model, temperature control correlation analysis is performed on the multiple temperature control areas in the target tool model based on the intersection of the multiple temperature control areas and the K water-cooled heat dissipation areas to obtain K sets of correlated control areas. Data merging of the multiple tool over-temperature time zone sequences is performed using the K sets of correlated control areas as constraints to obtain K heat dissipation demand sequences. Heat dissipation timing control parameters are generated based on the K heat dissipation demand sequences.
[0035] In this embodiment, the operator first interacts with the user interface to collect structural design information of the temperature-controlled power tool, including component layout, material properties, and dimensional parameters. Then, based on the collected structural design information, computer-aided design software is used to digitize the tool structure. Specifically, a two-dimensional sketch is created, defining the dimensions and relative positions of each component. The sketch is then converted into a three-dimensional model to ensure all physical parameters are accurately reflected. Subsequently, fluid dynamics analysis software, such as ANSYS Fluent, is used to disassemble the coolant circuit of the water-cooled heat dissipation architecture. During coolant circuit disassembly, a flow simulation is performed by inputting the target tool model to determine the coolant flow path and velocity, obtaining thermal performance data for K heat dissipation circuits. Next, a geometric intersection algorithm is used in the target tool model to select K water-cooled heat dissipation areas that contact the K heat dissipation circuits.
[0036] After fitting multiple temperature control regions to the target tool model, the geometry of each temperature control region within the tool model is first analyzed to ensure the correct position and extent of these regions. Next, K water-cooling heat dissipation regions are identified, representing the portions covered by the water-cooling system to remove heat generated during tool operation. Then, intersection analysis is performed based on the overlap between the multiple temperature control regions and the K water-cooling heat dissipation regions. Specifically, geometric operations are used to determine the overlapping portion of each temperature control region and the water-cooling heat dissipation region. The intersection represents the portion of the temperature control region affected by the water-cooling system. Through intersection analysis, multiple temperature control regions are associated with the water-cooling heat dissipation regions. The intersection portion of each temperature control region is matched with its corresponding water-cooling heat dissipation region, forming temperature control associations. Finally, these intersection relationships are organized into K groups of associated control regions, each group representing the association between a temperature control region and its corresponding water-cooling heat dissipation region.
[0037] After obtaining K sets of associated control areas, data from multiple tool over-temperature time zone sequences are merged, using these K sets of associated control areas as constraints. Specifically, firstly, multiple tool over-temperature time zone sequences are matched with the K sets of associated control areas to determine which over-temperature time zones correspond to each set of associated areas. Then, the matched over-temperature time zone data are integrated to generate K heat dissipation demand sequences. Each heat dissipation demand sequence reflects the cooling demand of a specific associated area during the over-temperature period.
[0038] After generating the heat dissipation demand sequence, the required coolant flow rate and temperature setpoint are calculated. Control algorithms, such as fuzzy control or PID control, are used to transform these parameters into actual control strategies to obtain heat dissipation timing control parameters.
[0039] Furthermore, in the method provided in the application embodiments, the process of performing tool structure digitization based on the structural design information to obtain the target tool model further includes: By disassembling the structural design information, component assembly relationships and component application information are obtained. The component application information includes multiple model parameters and multiple demand information for various components. Based on the multiple model parameters, network data is called to obtain multiple component models. Using the component assembly relationships as assembly constraints, the multiple component models as assembly materials, and the multiple demand information as scheduling constraints, tool structure digitization is performed to obtain the target tool model.
[0040] In this embodiment, to dissect the structural design information, document analysis technology is employed to extract component assembly relationships and component application information from the design documents. Specifically, design drawings and instruction manuals are analyzed to identify the connection relationships between components. Simultaneously, component application information is obtained by parsing the design parameter list, which includes model parameters and quantity requirements for multiple components.
[0041] Next, based on multiple model parameters, the API interface is used to call network data and connect to external databases or supplier platforms to obtain 3D models of multiple components.
[0042] Subsequently, the component assembly relationships are used as assembly constraints to ensure the correct position and connection method of each component in the target tool model. Simultaneously, multiple acquired component models are used as assembly materials, combined with multiple demand information as scheduling constraints, to ensure that components are assembled on demand. Finally, the tool structure is digitized, integrating all components according to the assembly constraints to generate the target tool model.
[0043] Furthermore, in the method provided in the application embodiment, the data merging of the multiple tool over-temperature time zone sequences using the K groups of associated control regions as constraints to obtain K heat dissipation demand sequences further includes: Based on the mapping relationship between the multiple temperature control zones and K groups of associated control zones, the multiple tool over-temperature time zone sequences are divided into K groups of tool over-temperature time zone sequences. After data alignment of the K groups of tool over-temperature time zone sequences, overlapping time zone data is identified to obtain K groups of overlapping time zones and K fused time zone sequences. Production temperature maximum value extraction is performed on the K groups of overlapping time zones to obtain K updated time zone sequences. The K updated time zone sequences and K fused time zone sequences are mapped and spliced to obtain the K heat dissipation demand sequences.
[0044] In this embodiment, the over-temperature time zone of each tool is first traversed, and then classified based on its corresponding associated control area. Each over-temperature time zone is assigned to a corresponding group, and the multiple tool over-temperature time zone sequences are divided into K groups of tool over-temperature time zone sequences.
[0045] Next, the K sets of tool temperature time zone sequences are aligned using either Dynamic Time Warping (DTW) or interpolation. Specifically, the DTW algorithm calculates the distance between different time series to find the best matching path, thereby aligning the time series. Interpolation, on the other hand, smooths the time series by inserting additional data points, ensuring that the tool's temperature data are aligned on the time axis and eliminating the impact of time differences.
[0046] After data alignment, overlapping time zone data is identified. Set operations, such as intersection, are used to find overheating periods that overlap across multiple instruments, thus obtaining K sets of overlapping time zones and K fused time zone sequences. Overlapping time zones refer to multiple instruments experiencing overheating within the same time period. The fused time zones are obtained by comprehensively processing information from these overlapping time zones, calculating the average or maximum value, to provide representative temperature data.
[0047] Subsequently, for the K overlapping time zones, the maximum production temperature is extracted. In this step, statistical analysis methods, such as a maximum value function, are used to extract the highest temperature value from each overlapping time zone, forming K updated time zone sequences. These updated time zone sequences represent the maximum temperature of the tool within each overlapping time zone, providing a basis for subsequent heat dissipation requirements.
[0048] Finally, the K updated time zone sequences are mapped and concatenated with the K merged time zone sequences. Using a data merging algorithm, such as a simple join operation, the two sequences are combined to generate the final K heat dissipation requirement sequences.
[0049] Furthermore, in the method provided in the application embodiments, generating heat dissipation timing control parameters based on the K heat dissipation demand sequences further includes: A preset production temperature deviation scale and deviation vector are used, and the production temperature is updated for the K heat dissipation demand sequences using the deviation vector as a constraint to obtain K updated heat dissipation sequences. Multiple sample production temperatures and multiple sample water cooling control parameters are interactively obtained, and a water cooling heat dissipation analysis model is constructed based on the multiple sample production temperatures and multiple sample water cooling control parameters. The K updated heat dissipation sequences are synchronized to the water cooling heat dissipation analysis model to obtain K water cooling timing control parameters. The K heat dissipation loops and the K water cooling timing control parameters are associated and stored as the heat dissipation timing control parameters.
[0050] In this embodiment, technical experts first preset a production temperature deviation scale and a deviation vector. The deviation scale defines the range of temperature change, while the deviation vector represents the temperature change trend under specific conditions. Using this deviation vector as a constraint, the production temperature is updated for K heat dissipation demand sequences. This process includes adjusting the temperature value in each heat dissipation demand sequence according to the deviation vector, ultimately obtaining K updated heat dissipation sequences.
[0051] Subsequently, the historical database was used to obtain multiple sample production temperatures and multiple sample water-cooling control parameters. The sample production temperatures were obtained from historical data, while the water-cooling control parameters included information such as coolant flow rate and temperature setting. Next, based on the acquired sample data, machine learning algorithms, such as support vector machines, were used for training to obtain a water-cooling heat dissipation analysis model.
[0052] K updated heat dissipation sequences are synchronized to the water-cooling heat dissipation analysis model. Using a data input method, the updated temperature data is input into the model, and K water-cooling timing control parameters, including flow rate and temperature settings, are calculated. Finally, the K heat dissipation loops and K water-cooling timing control parameters are associated and stored to obtain the heat dissipation timing control parameters.
[0053] Step S600: During the process of the temperature-controlled power tool performing the production task allocation, the circulation control of the coolant in the water-cooled heat dissipation architecture is synchronously performed using the heat dissipation timing control parameters.
[0054] In this embodiment, during the execution of the production task allocation by the temperature-controlled power tool, the circulation of coolant is controlled according to the obtained heat dissipation timing control parameters, such as flow rate and temperature settings. Specifically, during tool operation, the flow rate and temperature of the coolant in the water-cooled heat dissipation architecture are adjusted according to the heat dissipation timing control parameters. This adjustment ensures that the coolant can effectively remove the heat generated in each temperature-controlled area, keeping the power tool within a safe operating temperature range. Simultaneously, the coolant flows in different heat dissipation circuits, ensuring that all critical components are adequately cooled, thereby optimizing tool performance and extending its service life. Through this series of steps, effective heat dissipation management of the temperature-controlled power tool is ultimately achieved, preventing overheating problems.
[0055] Furthermore, in the method provided in the application embodiments, the pre-construction of the distributed temperature prediction model further includes: The temperature-controlled power tool is used to retrieve local data to obtain historical operating temperature data and historical operating condition data, wherein the historical operating temperature data and historical operating condition data are time-synchronized. Based on the historical operating temperature data, multiple historical regional temperature change sequence sets of the multiple temperature control zones are obtained. Based on the multiple historical regional temperature change sequence sets, multiple historical operating condition sequences are extracted from the historical operating condition data. Using the target tool model as a benchmark, a finite element analysis model is constructed based on the multiple historical regional temperature change sequence sets and multiple historical operating condition sequences to obtain the distributed temperature prediction model.
[0056] In this embodiment, data is first retrieved through a local database or data storage system, using query statements to extract historical operating temperature data and historical operating condition data. At this point, the historical operating temperature data and historical operating condition data are time-synchronized, meaning that the temperature data at each point in time corresponds one-to-one with its corresponding operating conditions.
[0057] Next, data processing techniques are used to segment the temperature changes across multiple temperature control zones from the acquired historical operating temperature data. Filtering or conditional selection methods are employed to organize the temperature changes from different zones into multiple sequences. For example, conditional statements are used to extract data from each zone, forming a historical temperature change sequence set. Then, based on this historical temperature change sequence set, data matching algorithms, such as timestamp-based join operations, are used to ensure that each temperature change sequence corresponds to the correct operating conditions, mapping and extracting multiple corresponding historical operating condition sequences from the historical operating condition data.
[0058] Next, using the target tool model as a benchmark, a finite element analysis model was constructed based on multiple historical temperature change sequence sets and multiple historical operating condition sequences. Specifically, a detailed model of the temperature-controlled electric tool was first constructed in a virtual three-dimensional space. This model included the tool's geometry, material properties, and the interrelationships of its various components. Then, relevant parameters were set, such as the material's thermal conductivity and specific heat capacity; these parameters form the basis for temperature analysis. Next, the constructed tool model was analyzed using the finite element analysis method. In this process, firstly, meshing was performed, dividing the target tool model into multiple small finite element elements to facilitate numerical calculations. This step ensured that the heat conduction characteristics of each part could be accurately simulated. Secondly, boundary conditions were applied. Based on the historical temperature change sequence sets and operating condition sequences, corresponding boundary conditions and loads were applied, such as temperature distribution and environmental influences. These conditions reflected the environmental factors in actual operation. Then, the simulation was run using finite element analysis software to simulate the engine's thermal behavior under different operating conditions. During this process, temperature change sequence data at specific locations were calculated based on the set parameters and boundary conditions. Finally, the calculation results are analyzed to obtain the temperature distribution and trend at specific locations. This data can be used to predict the temperature performance of tools under various working conditions. Through the above steps, a distributed temperature prediction model is finally constructed, which can effectively predict the temperature changes of temperature-controlled power tools under different operating conditions.
[0059] Furthermore, in the method provided in the application embodiments, by synchronizing the target operating conditions to the distributed temperature prediction model to obtain multiple production temperature change sequences mapped to the multiple temperature control zones, the method further includes: Multiple data acquisition units are configured in the distributed temperature prediction model with the multiple temperature control zones as constraints; during the operation of the distributed temperature prediction model under the target operating conditions, data is acquired based on the multiple data acquisition units to obtain the multiple production temperature change sequences mapped to the multiple temperature control zones.
[0060] In this embodiment, multiple data acquisition units are first configured in a distributed temperature prediction model, constrained by multiple temperature control zones. This step involves technical experts setting parameters for each data acquisition unit in the model, such as sampling frequency and response time, to ensure they can effectively collect data related to temperature changes.
[0061] Next, when running the distributed temperature prediction model, the target operating conditions, such as workload and ambient temperature, are input to initiate the data acquisition process. During this process, the configured data acquisition unit will monitor and record the temperature changes in each temperature-controlled zone in real time. The data acquisition unit will periodically generate temperature data according to a preset sampling frequency, and add timestamps to ensure the time synchronization of each data point. This data not only reflects the temperature changes in each temperature-controlled zone, but also takes into account the influence of material properties and the external environment.
[0062] Ultimately, the collected temperature data will be mapped to the corresponding temperature control areas, forming multiple production temperature change sequences.
[0063] In summary, the embodiments of this application have at least the following technical effects: This application receives production task assignments for temperature-controlled power tools and reverse-engineers the tool's operating conditions based on the assignments to obtain target operating conditions. It interactively obtains multiple distributed temperature thresholds for multiple temperature control zones within the power tool. A distributed temperature prediction model is pre-built, and by synchronizing the target operating conditions to this model, multiple production temperature change sequences mapped to multiple temperature control zones are obtained. Multiple distributed temperature thresholds are used to map and traverse these production temperature change sequences to obtain multiple tool overheating time zone sequences. The water-cooling heat dissipation architecture of the power tool is interactively obtained, and by fitting the multiple tool overheating time zone sequences to the water-cooling architecture, coolant circulation analysis is performed, outputting heat dissipation timing control parameters. During the execution of production task assignments by the power tool, the heat dissipation timing control parameters are used to synchronously control the coolant circulation within the water-cooling architecture. This invention solves the technical problem of low heat dissipation efficiency in existing technologies, which cannot accurately adjust based on real-time temperature changes of various key components of the power tool. By receiving production task assignments, reverse-engineering the target operating conditions of the power tool, and combining distributed temperature thresholds from multiple temperature control zones, a distributed temperature prediction model is pre-built. This model analyzes production temperature changes, identifies the overheating time sequence of tools, and combines it with a water-cooled heat dissipation architecture to control coolant circulation, thereby improving the heat dissipation efficiency of power tools.
[0064] Example 2, based on the same inventive concept as the heat dissipation management method for power tools in the foregoing examples, such as... Figure 2 As shown, this application provides a thermal management system for power tools. The system and method embodiments in this application are based on the same inventive concept. The system includes: The system includes: a target operating condition acquisition module 11, which receives production task allocations for temperature-controlled power tools and performs reverse deduction of tool operating conditions based on the allocations to obtain target operating conditions; an information interaction module 12, which interactively obtains multiple distributed temperature thresholds for multiple temperature control zones in the temperature-controlled power tool; and a production temperature change sequence acquisition module 13, which pre-builds a distributed temperature prediction model and obtains multiple production temperature change sequences mapped to the multiple temperature control zones by synchronizing the target operating conditions to the distributed temperature prediction model; and a mapping traversal module. Module 14, the mapping traversal module 14 uses the multiple distributed temperature threshold mappings to traverse the multiple production temperature change sequences to obtain multiple tool over-temperature time zone sequences; Coolant circulation analysis module 15, the coolant circulation analysis module 15 interactively obtains the water-cooled heat dissipation architecture of the temperature-controlled power tool, and performs coolant circulation analysis by fitting the multiple tool over-temperature time zone sequences to the water-cooled heat dissipation architecture, outputting heat dissipation timing control parameters; Circulation control module 16, the circulation control module 16 uses the heat dissipation timing control parameters to synchronously control the circulation of coolant in the water-cooled heat dissipation architecture during the execution of the production task allocation by the temperature-controlled power tool.
[0065] Furthermore, the system is also used to implement the following functions: The structural design information of the temperature-controlled power tool is obtained interactively, and the tool structure is digitized based on the structural design information to obtain a target tool model. The water-cooled heat dissipation architecture is disassembled through the coolant circuit to obtain K heat dissipation circuits, and K water-cooled heat dissipation areas in contact with the K heat dissipation circuits are selected in the target tool model. After fitting the multiple temperature control areas to the target tool model, temperature control correlation analysis is performed on the multiple temperature control areas in the target tool model based on the intersection of the multiple temperature control areas and the K water-cooled heat dissipation areas to obtain K sets of correlated control areas. Data merging of the multiple tool over-temperature time zone sequences is performed using the K sets of correlated control areas as constraints to obtain K heat dissipation demand sequences. Heat dissipation timing control parameters are generated based on the K heat dissipation demand sequences.
[0066] Furthermore, the system is also used to implement the following functions: By disassembling the structural design information, component assembly relationships and component application information are obtained. The component application information includes multiple model parameters and multiple demand information for various components. Based on the multiple model parameters, network data is called to obtain multiple component models. Using the component assembly relationships as assembly constraints, the multiple component models as assembly materials, and the multiple demand information as scheduling constraints, tool structure digitization is performed to obtain the target tool model.
[0067] Furthermore, the system is also used to implement the following functions: Based on the mapping relationship between the multiple temperature control zones and K groups of associated control zones, the multiple tool over-temperature time zone sequences are divided into K groups of tool over-temperature time zone sequences. After data alignment of the K groups of tool over-temperature time zone sequences, overlapping time zone data is identified to obtain K groups of overlapping time zones and K fused time zone sequences. Production temperature maximum value extraction is performed on the K groups of overlapping time zones to obtain K updated time zone sequences. The K updated time zone sequences and K fused time zone sequences are mapped and spliced to obtain the K heat dissipation demand sequences.
[0068] Furthermore, the system is also used to implement the following functions: A preset production temperature deviation scale and deviation vector are used, and the production temperature is updated for the K heat dissipation demand sequences using the deviation vector as a constraint to obtain K updated heat dissipation sequences. Multiple sample production temperatures and multiple sample water cooling control parameters are interactively obtained, and a water cooling heat dissipation analysis model is constructed based on the multiple sample production temperatures and multiple sample water cooling control parameters. The K updated heat dissipation sequences are synchronized to the water cooling heat dissipation analysis model to obtain K water cooling timing control parameters. The K heat dissipation loops and the K water cooling timing control parameters are associated and stored as the heat dissipation timing control parameters.
[0069] Furthermore, the system is also used to implement the following functions: The production task allocation includes production workload and production work type.
[0070] Furthermore, the system is also used to implement the following functions: Based on the model information of the temperature-controlled electric tool, network data is retrieved to obtain multiple sample operating conditions for various sample work types; the multiple sample operating conditions for various sample work types are associated and stored based on a knowledge graph to obtain an operating condition information database; by traversing the operating condition information database using the production work type, the tool operating conditions are reverse-engineered to obtain the individual production operating conditions; the individual production operating conditions are iterated according to the production workload to obtain the target operating conditions.
[0071] Furthermore, the system is also used to implement the following functions: The temperature-controlled power tool is used to retrieve local data to obtain historical operating temperature data and historical operating condition data, wherein the historical operating temperature data and historical operating condition data are time-synchronized. Based on the historical operating temperature data, multiple historical regional temperature change sequence sets of the multiple temperature control zones are obtained. Based on the multiple historical regional temperature change sequence sets, multiple historical operating condition sequences are extracted from the historical operating condition data. Using the target tool model as a benchmark, a finite element analysis model is constructed based on the multiple historical regional temperature change sequence sets and multiple historical operating condition sequences to obtain the distributed temperature prediction model.
[0072] Furthermore, the system is also used to implement the following functions: Multiple data acquisition units are configured in the distributed temperature prediction model with the multiple temperature control zones as constraints; during the operation of the distributed temperature prediction model under the target operating conditions, data is acquired based on the multiple data acquisition units to obtain the multiple production temperature change sequences mapped to the multiple temperature control zones.
[0073] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0074] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0075] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A heat dissipation management method for power tools, characterized in that, The method includes: Receive the production task allocation for temperature-controlled power tools, and reverse-engineer the tool operating conditions based on the production task allocation to obtain the target operating conditions; Multiple distributed temperature thresholds for multiple temperature control zones in the temperature-controlled power tool are obtained interactively. A distributed temperature prediction model is pre-built, and multiple production temperature change sequences mapped to the multiple temperature control zones are obtained by synchronizing the target operating conditions to the distributed temperature prediction model. By using the multiple distributed temperature threshold mappings to traverse the multiple production temperature change sequences, multiple tool over-temperature time zone sequences are obtained; The water-cooled heat dissipation architecture of the temperature-controlled power tool is obtained interactively, and the coolant circulation is analyzed by fitting the multiple tool over-temperature time zone sequences to the water-cooled heat dissipation architecture, and the heat dissipation timing control parameters are output. During the process of the temperature-controlled power tool performing the production task allocation, the circulation control of the coolant in the water-cooled heat dissipation architecture is synchronously performed using the heat dissipation timing control parameters.
2. The heat dissipation management method for power tools as described in claim 1, characterized in that, The method involves interactively obtaining the water-cooled heat dissipation architecture of the temperature-controlled power tool, and performing coolant circulation analysis by fitting the multiple tool over-temperature time zone sequences to the water-cooled heat dissipation architecture to output heat dissipation timing control parameters. The structural design information of the temperature-controlled power tool is obtained interactively, and the tool structure is digitized based on the structural design information to obtain the target tool model; K heat dissipation circuits are obtained by disassembling the water-cooled heat dissipation architecture through the coolant circuit, and K water-cooled heat dissipation areas that are in contact with the K heat dissipation circuits are selected in the target tool model; After fitting the multiple temperature control regions to the target tool model, temperature control correlation analysis is performed on the multiple temperature control regions in the target tool model based on the intersection of the multiple temperature control regions and K water cooling heat dissipation regions to obtain K sets of correlated control regions; Using the K sets of associated control regions as constraints, the data of the multiple tool over-temperature time zone sequences are merged to obtain K heat dissipation demand sequences; Heat dissipation timing control parameters are generated based on the K heat dissipation demand sequences.
3. The heat dissipation management method for power tools as described in claim 2, characterized in that, Based on the structural design information, the tool structure is digitized to obtain a target tool model. The method includes: By disassembling the structural design information, the component assembly relationship and component application information are obtained. The component application information includes multiple model parameters and multiple demand information for various components. Based on the aforementioned multiple model parameters, network data is retrieved to obtain multiple component models; Using the assembly relationship of the components as assembly constraints, the multiple component models as assembly materials, and multiple demand information as scheduling constraints, the tool structure is digitized to obtain the target tool model.
4. The heat dissipation management method for power tools as described in claim 2, characterized in that, Using the K groups of associated control regions as constraints, the data of the multiple tool over-temperature time zone sequences are merged to obtain K heat dissipation demand sequences. The method includes: Based on the mapping relationship between the multiple temperature control zones and K groups of associated control zones, the multiple tool over-temperature time zone sequences are divided into K groups of tool over-temperature time zone sequences; After aligning the K sets of tool over-temperature time zone sequences, overlapping time zone data identification is performed to obtain K sets of overlapping time zones and K fused time zone sequences; The maximum production temperature values are extracted from the K overlapping time zones to obtain K updated time zone sequences; The K updated time zone sequences and the K merged time zone sequences are mapped and concatenated to obtain the K heat dissipation requirement sequences.
5. The heat dissipation management method for power tools as described in claim 2, characterized in that, The method for generating heat dissipation timing control parameters based on the K heat dissipation demand sequences includes: A production temperature deviation scale and deviation vector are preset, and the production temperature is updated for the K heat dissipation demand sequences with the deviation vector as a constraint to obtain K updated heat dissipation sequences. Multiple sample production temperatures and multiple sample water cooling control parameters are obtained interactively, and a water cooling heat dissipation analysis model is constructed based on the multiple sample production temperatures and multiple sample water cooling control parameters. The K updated heat dissipation sequences are synchronized to the water cooling heat dissipation analysis model to obtain K water cooling timing control parameters; The K heat dissipation loops and K water cooling timing control parameters are associated and stored as the heat dissipation timing control parameters.
6. The heat dissipation management method for power tools as described in claim 1, characterized in that, The production task allocation includes production workload and production work type.
7. The heat dissipation management method for power tools as described in claim 6, characterized in that, The method includes receiving production task assignments for temperature-controlled power tools and, based on these assignments, performing reverse calculations to determine the target operating conditions. Based on the model information of the temperature-controlled electric tool, network data is retrieved to obtain multiple sample operating conditions for various sample working types; Based on the knowledge graph, multiple sample operation conditions of various sample work types are associated and stored to obtain an operation condition information database; By traversing the operating condition information database using the production work type, the tool operating conditions are reversed to obtain the individual production operating conditions. The target operating conditions are obtained by cycling the individual production operating conditions based on the production workload.
8. The heat dissipation management method for power tools as described in claim 3, characterized in that, The method for pre-constructing a distributed temperature prediction model includes: The temperature-controlled power tool is used to retrieve local data to obtain historical operating temperature data and historical operating condition data, wherein the historical operating temperature data and historical operating condition data are time-synchronized. Based on the historical operating temperature data, multiple historical temperature change sequence sets of the multiple temperature control zones are obtained. Based on the multiple historical regional temperature change sequence sets, multiple historical operating condition sequences are extracted from the historical operating condition data mapping; Based on the target tool model, a finite element analysis model is constructed using the multiple historical regional temperature change sequence sets and multiple historical operating condition sequences to obtain the distributed temperature prediction model.
9. The heat dissipation management method for power tools as described in claim 8, characterized in that, The method includes synchronizing the target operating conditions to the distributed temperature prediction model to obtain multiple production temperature change sequences mapped to the multiple temperature control zones. Multiple data acquisition units are configured in the distributed temperature prediction model, constrained by the multiple temperature control zones. During the operation of the distributed temperature prediction model under the target operating conditions, data is collected based on the multiple data acquisition units to obtain the multiple production temperature change sequences mapped to the multiple temperature control areas.
10. A heat dissipation management system for power tools, characterized in that, The system includes: The target operating condition acquisition module receives the production task allocation for temperature-controlled power tools and reverse-engineers the tool operating conditions based on the production task allocation to obtain the target operating conditions. Information interaction module, which interactively obtains multiple distributed temperature thresholds for multiple temperature control zones in the temperature-controlled power tool; The production temperature change sequence acquisition module pre-builds a distributed temperature prediction model and obtains multiple production temperature change sequences mapped to the multiple temperature control areas by synchronizing the target operating conditions to the distributed temperature prediction model. The mapping traversal module uses the multiple distributed temperature thresholds to map and traverse the multiple production temperature change sequences to obtain multiple tool over-temperature time zone sequences; The coolant circulation analysis module interactively obtains the water-cooled heat dissipation architecture of the temperature-controlled power tool, and performs coolant circulation analysis by fitting the multiple tool over-temperature time zone sequences to the water-cooled heat dissipation architecture, and outputs heat dissipation timing control parameters. A circulation control module, wherein during the execution of the production task allocation by the temperature-controlled power tool, the circulation control module uses the heat dissipation timing control parameters to synchronously control the circulation of coolant in the water-cooled heat dissipation architecture.