A flushing and cooling method, system, and equipment with automatic route identification.

Abstract

This application provides a flushing and cooling method, system, and device with automatic route identification. The method includes: constructing a device model through three-dimensional laser scanning and obtaining the surface thermal conductivity using material identification technology; generating a temperature gradient and heat load distribution map using multi-sensor data fusion; analyzing heat dissipation requirements and determining flushing priorities based on a thermodynamic model; optimizing the flushing path and sequence using a dynamic programming algorithm; and adaptively adjusting the pressure, flow rate, and time parameters at each flushing point according to the heat load distribution to form a personalized flushing plan. During execution, temperature changes are monitored in real time, and parameters are dynamically adjusted to ensure cooling efficiency. This method achieves intelligent identification of the thermal state of the equipment surface, automatic planning of the flushing path, and precise control of flushing parameters, significantly improving the accuracy and efficiency of flushing and cooling, and is suitable for efficient heat dissipation maintenance of industrial equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a flushing cooling method, system and device with automatic route identification. BACKGROUND

[0002] Industrial equipment flushing cooling technology plays a crucial role in modern manufacturing, directly affecting equipment operation stability, production efficiency and product quality. With the continuous improvement of industrial automation, the intelligentization and precision of the flushing cooling system are increasingly strict, and this technology has become a regional support to ensure the safe operation of industrial equipment.

[0003] Traditional flushing cooling methods mainly rely on manual experience to develop fixed flushing routes and parameters, which cannot be dynamically adjusted according to different equipment characteristics. This static processing method leads to unsatisfactory flushing results, which may result in insufficient flushing in some areas or waste of resources, making it difficult to meet the diversified and personalized cooling needs of modern industrial equipment.

[0004] The current regional challenge in this field is due to the complexity of identifying equipment structural characteristics. Different industrial equipment has significant differences in geometry, internal structure, material properties, etc., and the system is difficult to accurately identify these structural features and develop appropriate flushing strategies. This identification difficulty further raises the technical problem of heat load distribution analysis, because the heat generation and heat dissipation requirements of each part of the equipment during operation are not uniform, and the system needs to accurately analyze the heat distribution law and trend. Inaccurate heat load distribution analysis directly leads to the blindness of flushing route planning, and the system cannot determine the optimal flushing sequence, key flushing areas, and the required flushing intensity and duration of each area, ultimately affecting the overall cooling effect.

[0005] Therefore, how to build a system that can automatically identify equipment structural characteristics, accurately analyze heat load distribution and intelligently plan flushing routes based on this, and realize the automatic customization of flushing cooling solutions, has become a key problem in the development of current industrial equipment flushing cooling technology. SUMMARY

[0006] To realize the automation customization of the device flushing cooling scheme, in the first aspect, the application provides a flushing cooling method with automatic identification route, mainly including: S101. The surface of the device is scanned comprehensively by using three-dimensional laser scanning technology to obtain a three-dimensional model of the device; S102. Based on the three-dimensional model, the material identification method is used to analyze the material attribute information of the device surface, and the thermal conductivity of the device surface is obtained based on the material attribute information; S103. The temperature distribution of each part in the device running process is monitored in real time, and the measurement results of multiple sensors are integrated by using temperature data fusion technology to obtain a temperature gradient distribution map of the device surface; S104. According to the temperature gradient distribution map and the thermal conductivity, the heat load calculation model is used to analyze the heat load data, heat load density distribution data and heat dissipation demand of each region, and the priority of flushing is determined based on the heat load distribution map and the heat dissipation demand; S105. The shortest path and flushing order between each flushing point are calculated based on the priority by using the dynamic programming method to obtain the flushing route; S106. According to the flushing route and the heat load density distribution data of each region, the parameter self-adaptive adjustment algorithm is used to determine the flushing pressure, flow and duration of each flushing point to obtain a personalized flushing parameter configuration table; S107. Based on the flushing route and the flushing parameter configuration table, the automatic flushing operation is executed, and the temperature change of each region is monitored in real time. If the temperature drop rate is lower than the expected value, the flushing parameters are dynamically adjusted.

[0007] Preferably, the step S101 comprises:

[0008] The surface of the device is comprehensively detected by using three-dimensional laser technology to collect original geometric shape information and spatial coordinate data to obtain an initial scanning data set;

[0009] The initial scanning data set is denoised by using a point cloud data processing algorithm to remove noise interference and obtain a cleaned point cloud data set;

[0010] The registration operation is performed on the cleaned point cloud data set to adjust the data consistency of different scanning angles and determine a unified point cloud registration result;

[0011] The three-dimensional model of the device is constructed according to the unified point cloud registration result, the key structural feature information is extracted, and preliminary three-dimensional model data is obtained;

[0012] The preliminary three-dimensional model data is optimized to correct the geometric deviation in the three-dimensional model to obtain refined three-dimensional model data set;

[0013] If the structural feature information of some regions in the refined three-dimensional model data set is incomplete, the interpolation algorithm is used to supplement the missing part to obtain the final three-dimensional model data.

[0014] Preferably, the step S102 comprises:

[0015] Based on the device's 3D model data, an initial model is generated using modeling tools. The surface of the initial model is then meshed to obtain surface geometric feature data.

[0016] Based on the surface geometric feature data, the material identification method is used to analyze the material properties of each grid region on the surface, obtain the material property information of each region, and output a material distribution map based on the material property information;

[0017] Based on the material distribution map and a pre-established database of material thermal conductivity, the thermal conductivity of each region is obtained.

[0018] Preferably, step S103 includes:

[0019] Real-time data collection of various parts of the equipment during operation is performed using infrared thermal imaging sensors to obtain a set of raw temperature data.

[0020] Based on the original temperature data set, data preprocessing techniques are used to denoise and calibrate the collected information to obtain the corrected temperature data set.

[0021] For the calibrated temperature data set, the measurement results of multiple sensors are fused to construct a unified temperature distribution matrix;

[0022] Based on the temperature distribution matrix, the temperature data from different sensors are weighted according to their location importance and sensor accuracy to obtain the overall reference temperature. Then, an interpolation algorithm is used to estimate the temperature of the uncovered area to calculate the complete temperature gradient distribution map of the device surface.

[0023] Preferably, step S104 includes:

[0024] Temperature distribution data for each region is obtained by using temperature gradient distribution maps, and heat load distribution data and heat load density distribution data for each region are obtained by using a pre-established heat load calculation model.

[0025] Based on the heat load distribution data and the thermal conductivity in the material property database, the heat dissipation requirements of each area are obtained.

[0026] If the temperature of a region exceeds a preset threshold in the heat dissipation requirement, the region is determined to be a high heat load region, and its location information is recorded and a heat load distribution map is generated.

[0027] Based on the heat load distribution map and heat dissipation requirements, determine the priority of the rinsing areas.

[0028] Preferably, step S105 includes:

[0029] Based on the priority, the area with the highest heat load is determined as the starting flushing point to obtain initial flushing location data;

[0030] Based on the initial flushing location data, obtain the heat load distribution information of adjacent areas. If the heat load of an adjacent area is lower than that of the current area and meets the preset heat load threshold, mark it as the next flushing candidate point and determine the candidate flushing point set.

[0031] For the set of candidate flushing points, a dynamic programming method is used to calculate the path distance between each point, obtain all possible paths from the current flushing point to the candidate point, and determine the shortest path as the next flushing route;

[0032] Based on the shortest path data, obtain the specific location information of the next flushing point and determine whether all areas have been included in the flushing route;

[0033] If there are areas not included in the flushing route, the new area with the highest heat load is identified as the new flushing point, and a new flushing sequence is determined.

[0034] Based on the new flushing sequence, repeat the path optimization calculation to obtain the shortest path connection method for the remaining areas, and obtain the complete flushing route.

[0035] Preferably, step S106 includes:

[0036] Based on the preset flushing scheme and combined with the heat load density distribution data, if the heat load density of a certain area exceeds the preset area threshold, the flushing pressure and flushing flow rate are adjusted through an adaptive algorithm to obtain the optimized flushing parameters.

[0037] For the optimized flushing parameters, analyze the duration requirement. If the heat load density continues to be higher than the area threshold, extend the flushing time to determine the final flushing duration configuration.

[0038] By using the final flushing duration configuration and combining it with the flushing intensity requirements, the intensity adjustment value for each flushing point is obtained, and intensity correction data is generated.

[0039] By using intensity correction data and configuration parameters, and employing a unified formatting tool, a personalized flushing parameter configuration table is obtained.

[0040] Preferably, step S107 includes:

[0041] The intelligent control system obtains flushing parameter data from the flushing parameter configuration table, initializes the automated operation process based on the obtained parameters, and obtains a preliminary operation execution plan.

[0042] Based on the preliminary work plan, the automated flushing operation was initiated, and at the same time, the temperature change data of each area was monitored in real time through the sensor network to obtain real-time records of temperature changes.

[0043] The system records temperature changes in real time and calculates the rate of temperature decrease in each region. If the calculated rate of decrease is lower than the preset expected value, a parameter adjustment mechanism is triggered.

[0044] By dynamically updating the flushing parameter data through a parameter adjustment mechanism, the automated operation process is reconfigured using the updated parameters to obtain an adjusted operation execution plan.

[0045] According to the adjusted operation plan, the rinsing operation is continuously carried out, and the updated temperature change data is obtained through the sensor network to determine whether the temperature drop rate has reached the expected value.

[0046] If the updated rate of temperature decrease still does not reach the expected value, then the historical temperature change data and flushing parameter data are processed by a regression analysis model to determine the optimal adjustment parameters.

[0047] Secondly, this application provides a flushing and cooling system with automatic route identification. The system includes: a scanning module that uses three-dimensional laser scanning technology to perform an all-round scan of the equipment surface to obtain a three-dimensional model of the equipment; a material analysis module that, based on the three-dimensional model, uses material identification methods to analyze the material property information of the equipment surface and obtains the thermal conductivity of the equipment surface based on the material property information; and a real-time monitoring module that monitors the temperature distribution of various parts of the equipment in real time during operation and integrates the measurement results of multiple sensors using temperature data fusion technology to obtain a temperature gradient distribution map of the equipment surface.

[0048] The priority determination module analyzes the heat load data, heat load density distribution data and heat dissipation requirements of each area based on the temperature gradient distribution map and thermal conductivity using a heat load calculation model, and determines the priority of flushing.

[0049] The flushing route acquisition module calculates the shortest path and flushing sequence between each flushing point using dynamic programming based on priority, thus obtaining the flushing route. The flushing parameter acquisition module determines the flushing pressure, flow rate, and duration of each flushing point using an adaptive parameter adjustment algorithm based on the flushing route and the heat load density distribution data of each area, thus obtaining a personalized flushing parameter configuration table. The dynamic adjustment module executes automated flushing operations based on the flushing route and the flushing parameter configuration table, monitors the temperature changes in each area in real time, and dynamically adjusts the flushing parameters if the temperature drop rate is lower than expected.

[0050] Thirdly, this application provides a flushing and cooling device with automatic route identification, the device comprising: a memory, a processor, and a flushing and cooling program with automatic route identification stored in the memory and executable on the processor, the flushing and cooling program with automatic route identification configured to implement the flushing and cooling method with automatic route identification as described above.

[0051] The technical solutions provided by the embodiments of the invention may include the following beneficial effects:

[0052] This invention discloses a flushing and cooling method with automatic route identification. It acquires the geometric shape and temperature distribution data of the equipment using three-dimensional laser scanning and infrared thermal imaging technology. Combined with material identification algorithms, it analyzes the surface heat conduction characteristics, uses a heat load calculation model to identify high heat load areas, employs a path optimization algorithm to plan the optimal flushing route, and utilizes a parameter adaptive adjustment algorithm to determine personalized flushing parameter configurations. This invention achieves precise positioning and intelligent cooling of hot spots on the surface of industrial equipment. It can dynamically adjust the flushing strategy according to real-time temperature changes, effectively improving cooling efficiency and equipment heat dissipation performance, extending equipment lifespan, and reducing energy consumption. It provides a new technical solution for the intelligent operation and maintenance of industrial equipment. Attached Figure Description

[0053] Figure 1 This is a flowchart of a flushing and cooling method with automatic route identification according to the present invention.

[0054] Figure 2 This is a schematic diagram of the specific process of step S104 of the present invention.

[0055] Figure 3 This is a schematic diagram of the specific process of step S107 of the present invention. Detailed Implementation

[0056] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0057] like Figure 1 This embodiment of a flushing and cooling method with automatic route identification may specifically include:

[0058] S101. Use three-dimensional laser scanning technology to perform a full-range scan of the equipment surface to obtain a three-dimensional model of the equipment;

[0059] In this embodiment, a comprehensive detection of the device surface is performed using three-dimensional laser technology to collect the original geometric shape information and spatial coordinate data, resulting in an initial scan dataset. A point cloud data processing algorithm is then used to denoise the initial scan dataset, removing noise interference and obtaining a cleaned point cloud dataset.

[0060] A registration operation is performed on the cleaned point cloud dataset to adjust the data consistency of different scanning angles and determine a unified point cloud registration result. Based on the unified point cloud registration result, a 3D model of the device is constructed, key structural feature information is extracted, and preliminary 3D model data is obtained. The preliminary 3D model data is optimized to correct geometric deviations in the 3D model, resulting in a refined 3D model dataset. If the structural feature information of some areas in the refined 3D model dataset is incomplete, the missing parts are supplemented by an interpolation algorithm to obtain the final 3D model data.

[0061] For example, when using 3D laser scanning technology to perform omnidirectional scanning of the surface of industrial equipment, a high-precision laser scanner, such as the FARO Focus S350, can be used. Its scanning range can reach 350 meters, with an accuracy of ±1 mm and a scanning rate of up to 976,000 points per second. In actual operation, the equipment is placed in the center of the factory workshop, and the scanner is set with multiple scanning points around the equipment to ensure coverage of all surfaces. The scanning time is approximately 2 hours, generating approximately 500 million raw point cloud data points. Subsequently, noise reduction processing is performed using point cloud data processing algorithms. A statistical filtering method is used, setting the neighborhood radius to 0.05 meters, and outliers deviating from the average distance by more than 2 standard deviations are removed. After filtering, the point cloud data is reduced to approximately 480 million points, and the noise points are reduced by approximately 4%, improving data quality. Next, registration processing is performed using the iterative nearest-point algorithm. Based on the initially aligned point cloud data, the maximum number of iterations is set to 50, and the convergence threshold is 0.001 meters. This achieves accurate stitching of multi-view point cloud data, with the registration error controlled within 0.002 meters, forming a unified 3D model of the equipment. Building upon this, structural feature parameters are extracted, and the curvature distribution of the equipment surface is calculated using a curvature analysis algorithm. With a curvature threshold of 0.1, approximately 1000 key geometric feature points of the equipment are identified, and major dimensional parameters, such as a height of 2.5 meters and a width of 1.8 meters, are calculated with an error range within ±0.005 meters. Finally, a triangulation algorithm is used to construct a 3D mesh model, generating approximately 2 million triangular faces to ensure the smoothness of the model surface meets industrial design requirements, with a surface reconstruction error of less than 0.003 meters.

[0062] Through the above process, a complete technology chain is formed from scanning to modeling. At the same time, the model data can be connected with the factory's digital management system to realize intelligent applications of equipment maintenance and space planning, ensuring data consistency and business collaboration.

[0063] S102. Based on the three-dimensional model, the material property information of the equipment surface is analyzed using the material identification method, and the thermal conductivity of the equipment surface is obtained based on the material property information.

[0064] In this embodiment, based on the three-dimensional model data of the device, an initial model is generated using a modeling tool. The surface of the initial model is then divided into meshes to obtain surface geometric feature data. Based on the surface geometric feature data, a material identification method is used to analyze the material properties of each mesh region on the surface to obtain material property information for each region. A material distribution map is then output based on the material property information. Based on the material distribution map and combined with a pre-established material thermal conductivity database, the thermal conductivity of each region is obtained.

[0065] For example, based on the acquired 3D model of the device, the geometric features and texture information of the device surface are first extracted using high-precision scanning technology. It is assumed that the model data comes from a laser scanner with a resolution of 0.01 mm to ensure complete capture of surface details. Next, material identification methods are used, such as a deep learning-based convolutional neural network (CNN) model. The pre-trained dataset contains the spectral reflectance characteristics of metal alloys, ceramics, and composite materials, achieving an accuracy of 95%. By analyzing the reflectance spectrum data of the device surface, assuming that the reflectance peak of a certain area is at 600 nm, and combining it with database comparison, it is determined to be an aluminum alloy with a thermal conductivity of approximately 200 W / (m·K); another area has a reflectance peak at 800 nm, which is determined to be alumina ceramic with a thermal conductivity of only 30 W / (m·K).

[0066] S103. Monitor the temperature distribution of various parts of the equipment in real time during operation, and use temperature data fusion technology to integrate the measurement results of multiple sensors to obtain a temperature gradient distribution map of the equipment surface.

[0067] In this embodiment, infrared thermal imaging sensors are used to collect real-time data from various parts of the device during operation, resulting in a raw temperature data set. Based on the raw temperature data set, data preprocessing techniques are used to denoise and calibrate the collected information, resulting in a corrected temperature data set. For the corrected temperature data set, the measurement results of multiple sensors are fused to construct a unified temperature distribution matrix. The temperature data from different sensors are weighted according to their location importance and sensor accuracy to obtain the overall reference temperature. An interpolation algorithm is used to estimate the temperature of uncovered areas, and a complete temperature gradient distribution map of the device surface is calculated.

[0068] For example, real-time monitoring of temperature distribution in various parts of an equipment during operation can be achieved using an infrared thermal imaging sensor. A high-resolution infrared thermal imaging camera, such as the FLIRT540, can be used, with a resolution of 464x348 pixels, a temperature measurement range of -20℃ to 1500℃, and an accuracy of ±2℃. During equipment operation, the sensor collects data every second, covering key areas of the equipment surface, such as motors, bearings, and transmission components, resulting in a temperature matrix data for multiple areas. For example, the average temperature for the motor area is 85.3℃, for the bearing area it is 72.8℃, and for the transmission components it is 65.1℃. Next, temperature data fusion technology is used to integrate the measurement results from multiple sensors. A weighted average algorithm can be used to weight the temperature data from different sensors according to their location importance and sensor accuracy. For example, the weight for the motor area is set to 0.5, for the bearing area to 0.3, and for the transmission components to 0.2. The fused temperature distribution data is: 85.3x0.5 + 72.8x0.3 + 65.1x0.2 = 78.34℃, which serves as the overall reference temperature. Subsequently, an interpolation algorithm (such as universal Kriging interpolation) is used to estimate the temperature in the uncovered area, resulting in a complete temperature gradient distribution map of the equipment surface. This map shows that the temperature gradually decreases from the motor area outwards, with the lowest point at the edge region at 60.2℃, forming the temperature gradient distribution map. This process is achieved through automated system integration; sensor data acquisition and algorithm processing are both completed by the background program, ensuring real-time performance and accuracy.

[0069] S104. Based on the temperature gradient distribution map and thermal conductivity, use the heat load calculation model to analyze the heat load data, heat load density distribution data and heat dissipation requirements of each area, and determine the priority of the rinsing area based on the heat load distribution map and heat dissipation requirements.

[0070] In this embodiment, see Appendix Figure 2 Temperature distribution data for each region is obtained through a temperature gradient distribution map. A pre-established heat load calculation model is used to obtain heat load distribution data and heat load density distribution data for each region. Based on the heat load distribution data and the thermal conductivity in the material property database, the heat dissipation requirements for each region are obtained. If the temperature of a region exceeds a preset threshold, that region is identified as a high heat load region, its location information is recorded, and a heat load distribution map is generated. Based on the heat load distribution map and heat dissipation requirements, the priority of the rinsing areas is determined.

[0071] For example, heat load refers to the amount of heat absorbed per unit area per unit time, reflecting the intensity of heat transfer in a region or device. It is often used to assess the concentration of heat dissipation or heating demands. Temperature values ​​for each region are extracted from temperature gradient distribution maps (such as infrared thermograms or analog outputs). Using a pre-established heat load calculation model (based on thermodynamic formulas, such as the heat conduction equation), the temperature data is converted into heat load (unit: W), representing the heat generation rate of each region. Combined with a material property database (such as thermal conductivity), the heat dissipation demand (unit: W) for each region is calculated, representing the heat that needs to be actively removed. If the region temperature exceeds a preset threshold, it is marked as a high heat load region, and its location is recorded. Based on the recorded location, a visual heat map is created, highlighting the high heat load regions. Priorities are assigned to flushing (such as coolant flow) according to the heat load distribution map, with high heat load regions treated first. Temperature gradient distribution maps (such as images or data tables) are acquired using a thermal imager. Preset parameters: Temperature threshold: 80℃ (exceeding this value is considered an overheating risk). Material property database: The equipment material is silicon with a thermal conductivity of 150 W / (m·K); this database is used to calculate heat dissipation requirements. Heat load calculation model: A pre-established simplified model: Heat load (W) = Temperature (°C) × Thermal conductivity (assuming a thermal conductivity of 2 W / °C, based on experimental calibration), heat load density calculation formula (heat load density = heat load / area). Taking a heat load of 20 kW and an area of ​​0.2 square meters in the motor area as an example, the heat load density is calculated to be 100 kW / m², reflecting the concentration of heat load; a higher value indicates greater heat concentration. The specific process for priority determination includes the following steps:

[0072] Step 1: For example, assuming the device has four zones, obtain the temperature distribution data for each zone using a temperature gradient distribution map. The temperature gradient distribution map is generated by scanning the device surface with an infrared thermal imager (e.g., a pseudo-color image showing temperature changes). Extract temperature data: Read the temperature values ​​for each zone from the temperature gradient distribution map: Zone 1: 75℃, Zone 2: 82℃, Zone 3: 88℃, Zone 4: 70℃;

[0073] Step 2: Obtain heat load distribution data for each region using a pre-established heat load calculation model. This example uses a simplified model: Heat Load = Temperature × Thermal Conductivity Factor (2W / ℃). This model is calibrated based on historical data and considers equipment power consumption and heat dissipation characteristics. Calculate the heat load: Region 1: 75℃ × 2W / ℃ = 150W, Region 2: 82℃ × 2W / ℃ = 164W, Region 3: 88℃ × 2W / ℃ = 176W, Region 4: 70℃ × 2W / ℃ = 140W, and output the heat load distribution data (Region 1: 150W, Region 2: 164W, Region 3: 176W, Region 4: 140W).

[0074] Step 3: Based on the heat load distribution data and the thermal conductivity in the material property database, obtain the heat dissipation requirement for each region; identify and record high heat load regions; heat dissipation requirement calculation: heat dissipation requirement represents the heat that needs to be actively cooled (e.g., liquid flushing) to remove in order to prevent the temperature from rising further. Formula: Heat dissipation requirement (W) = heat load × heat dissipation coefficient. The heat dissipation coefficient is calculated based on the thermal conductivity (simplified in this example: heat dissipation coefficient = 1.2). Region 1: 150W × 1.2 = 180W, Region 2: 164W × 1.2 = 196.8W ≈ 197W, Region 3: 176W × 1.2 = 211.2W ≈ 211W, Region 4: 140W × 1.2 = 168W;

[0075] Identify high heat load areas: Compare the temperature with the preset threshold (80℃):

[0076] Zone 1: 75℃ < 80℃ → Not exceeded, not a high heat load zone;

[0077] Zone 2: 82℃ > 80℃ → Exceeds the threshold and is marked as a high heat load zone;

[0078] Zone 3: 88℃ > 80℃ → Exceeds the threshold and is marked as a high heat load zone;

[0079] Zone 4: 70℃ < 80℃ → Not exceeded, not a high heat load zone;

[0080] Record location information: Region 2 (location coordinates: x=10mm, y=20mm) and Region 3 (location coordinates: x=30mm, y=20mm) are recorded as high heat load regions. Heat dissipation demand data and a list of high heat load regions are generated; a heat load distribution map is generated based on the heat dissipation demand.

[0081] Step 4: Generate a heat load distribution map. Based on heat dissipation demand and high heat load areas, create a heat load distribution map (e.g., using software such as MATLAB or Python matplotlib). The map uses color gradients to represent heat dissipation demand (e.g., blue = low demand, red = high demand). High heat load areas (areas 2 and 3) are marked with flashing or highlighted boundaries. Example map description: Area 3 (211W) is the hottest, followed by Area 2 (197W), while Areas 1 (180W) and 4 (168W) are relatively cool. Output a visual heatmap for intuitive decision-making.

[0082] Step 5: Determine the priority of the flushing areas based on the heat load distribution map. Priority determination: Flushing (e.g., coolant flow) priority is based on the heat load distribution map; rule: high heat load areas have the highest priority (due to the greater risk of exceeding temperature limits); secondly, areas with high heat dissipation requirements are prioritized. If multiple areas exceed the threshold, they are sorted according to the degree of temperature exceedance or heat dissipation requirements.

[0083] In this example, the priority order is as follows: Zone 3: Highest priority (temperature 88℃ > 80℃, highest heat dissipation requirement 211W, greatest risk). Zone 2: Second highest priority (temperature 82℃ > 80℃, heat dissipation requirement 197W). Zone 1: Medium priority (heat dissipation requirement 180W, but temperature not exceeded). Zone 4: Lowest priority (heat dissipation requirement 168W, lowest, temperature safe). Flushing operation: In the cooling system, prioritize increasing the coolant flow rate in Zone 3, then adjust the flow rate in Zone 2, and finally process the other zones. Output: A flushing priority list (e.g., JSON format or system control instructions) to guide real-time cooling scheduling.

[0084] In this example, the process begins with a temperature gradient map, calculates the heat load and cooling requirements using a model, identifies regions 2 and 3 as high-heat-load areas (due to temperatures exceeding 80°C), and generates a heat load distribution map. Finally, based on the heat load distribution map, the flushing priority is determined: Region 3 > Region 2 > Region 1 > Region 4. This ensures that cooling resources are preferentially allocated to the hottest areas, preventing equipment damage and improving energy efficiency.

[0085] S105. Based on priority, the shortest path and flushing sequence between each flushing point are calculated using dynamic programming to obtain the flushing route.

[0086] In this embodiment, the region with the highest heat load is determined as the starting flushing point based on the priority, thus obtaining initial flushing location data. Based on the initial flushing location data, heat load distribution information of adjacent regions is obtained. If the heat load of an adjacent region is lower than that of the current region and meets a preset heat load threshold, it is marked as the next flushing candidate point, thus determining a candidate flushing point set. For the candidate flushing point set, a dynamic programming method is used to calculate the path distance between each point, obtaining all possible paths from the current flushing point to the candidate point, and determining the shortest path as the next flushing route. Based on the shortest path data, the specific location information of the next flushing point is obtained, and it is determined whether all regions have been included in the flushing route. If there are regions not included in the flushing route, a new region with the highest heat load is obtained as a new flushing point, and a new flushing sequence is determined. Based on the new flushing sequence, the path optimization calculation is repeated to obtain the shortest path connection method for the remaining regions, resulting in a complete flushing route.

[0087] For example, suppose there is a system consisting of 5 regions: A, B, C, D, and E. The heat load values ​​(higher values ​​indicate greater heat load) and adjacency relationships of the regions are as follows: Heat load distribution: A: 100 (highest), B: 90, C: 85, D: 80, E: 75; Preset heat load threshold: 50 (i.e., only regions with a heat load ≥ 50 can be considered as candidate points). Region adjacency relationships (based on graph structure, regions can be directly connected): A is adjacent to B and C, B is adjacent to A, C, and D, C is adjacent to A, B, and E, D is adjacent to B and E, and E is adjacent to C and D; Path distances (used for dynamic programming calculations): The distance from A to B is 2, the distance from A to C is 3, the distance from B to C is 1, the distance from B to D is 4, the distance from C to E is 2, and the distance from D to E is 3.

[0088] Optimization objective: Starting from the area with the highest heat load, generate a complete flushing route, ensuring that each step prioritizes flushing areas with higher heat loads, and minimize the path distance through dynamic programming. The optimization process is as follows:

[0089] Step 1: Determine the starting flushing point. Based on priority, the area with the highest heat load is A (heat load 100). Initial flushing location data: A (starting point).

[0090] Step 2: Obtain information on adjacent regions and mark candidate points. Starting from the current point A, obtain adjacent regions B and C, and check the heat load of the adjacent regions: B: Heat load 90 < current A's 100, and 90 ≥ threshold 50 → mark as a candidate point. C: Heat load 85 < current A's 100, and 85 ≥ threshold 50 → mark as a candidate point. Candidate point set: {B, C}.

[0091] Step 3: Calculate the shortest path to candidate points using dynamic programming. For the candidate point set {B, C}, calculate all possible paths from the current point A to each candidate point: Path to B: A→B, distance 2 (unique path). Path to C: A→C, distance 3 (unique path). Among all possible paths, the shortest path is A→B (distance 2). Therefore, the next flushing point is B. Update flushing route: A→B. Current position: B.

[0092] Step 4: Determine if all areas have been included in the flushing route. Included areas: A, B; Unincluded areas: C, D, E. Since there are unincluded areas, proceed as follows: Select the area with the highest heat load among the remaining unincluded areas as the new flushing point. Remaining area heat loads: C(85), D(80), E(75), with C(85) being the highest. New flushing point: C. New flushing sequence: C will be the new starting point, and the optimization process will be repeated based on C.

[0093] Step 5: Move to the new flushing point and repeat path optimization. First, we need to move from the current location B to the new flushing point C (because the new starting point C is an independent point and needs to be connected to the existing route). The shortest path from B to C: B→C, distance 1. Update the flushing route: A→B→C (add the B→C segment). Current location: C. Now, starting from C, repeat steps 2-3: Obtain information on adjacent areas and mark candidate points. Starting from the current point C, obtain the adjacent areas: A (included), B (included), E (not included); check E: heat load 75 < current C's 85, and 75 ≥ threshold 50 → mark as a candidate point. Candidate point set: {E} (A and B are already included and will not be considered again). Dynamic programming calculates the shortest path to the candidate point. For the candidate point set {E}, calculate the path from the current point C to E. The path to E: C→E, distance 2 (unique path), the shortest path is C→E (distance 2). Therefore, the next flushing point is E. Update the flushing route: A→B→C→E. Current location: E.

[0094] Step 6: Determine if all areas have been included. Included areas: A, B, C, E. Unincluded area: D. Since there is an unincluded area, proceed as follows: Obtain the area with the highest heat load among the remaining unincluded areas as the new flushing point. Only area D (80) remains. New flushing point: D. New flushing sequence: D as the new starting point.

[0095] Step 7: Move to the new flushing point and repeat path optimization. Move from the current position E to the new flushing point D. Shortest path from E to D: E→D, distance 3 (direct connection). Update flushing route: A→B→C→E→D (add E→D segment). Current position: D. Now, repeat steps 2-3 starting from D: Step 2 (repeated): Obtain information on adjacent areas and mark candidate points. Starting from the current point D, obtain adjacent areas: B (already included), E (already included). There are no unincluded adjacent areas, so the candidate point set is empty. Since the candidate point set is empty and all areas have been included (D is the last one), the process ends.

[0096] Final flushing route and results: Complete flushing route: A→B→C→E→D, Total path distance: 2(A→B)+1(B→C)+2(C→E)+3(E→D)=8, Order of inclusion of all areas: Based on heat load priority and path optimization, the route covers all areas, and the heat load gradually decreases from high to low (100→90→85→75→80), but at each step, it is ensured that the heat load at the current point is higher than that at the next flushing point and meets the threshold.

[0097] Heat load threshold function: Since all areas have heat loads above the threshold of 50, the threshold does not filter any points. If an area has a heat load < 50 (e.g., suppose there is an area F with a heat load of 40), it will not be marked as a candidate point until its heat load is increased in some way or the threshold is adjusted. Application of dynamic programming: In step 3, when the candidate point set has multiple points (e.g., the initial {B, C}), dynamic programming calculates all possible paths and selects the shortest. When the set has only one point (e.g., {E}), the unique path is directly selected. New starting point handling: When there are areas not included, the one with the highest remaining heat load is used as the new starting point, and the path is moved to that point (the path is calculated via direct connection or dynamic programming). This ensures that the route always prioritizes high-load areas while optimizing connection paths. Route continuity: The final route is connected, but the introduction of a new starting point may cause "jumps" (e.g., directly from B to C), but path optimization ensures the overall distance is minimized.

[0098] This example demonstrates how the process balances heat load priority and path efficiency. In real-world applications, the number of regions, heat load distribution, and network structure will affect the optimization details, but the core logic remains the same.

[0099] S106. Based on the flushing route and the heat load density distribution data of each area, a parameter adaptive adjustment algorithm is used to determine the flushing pressure, flow rate, and duration at each flushing point, thereby obtaining a personalized flushing parameter configuration table;

[0100] In this embodiment, based on the preset flushing scheme and combined with the heat load density distribution data, if the heat load density of a certain area exceeds the preset area threshold, the flushing pressure and flushing flow rate are adjusted through an adaptive algorithm to obtain optimized flushing parameters; for the optimized flushing parameters, the duration requirement is analyzed, and if the heat load density continues to be higher than the area threshold, the flushing time is extended to determine the final flushing duration configuration.

[0101] Using the final flushing duration configuration and combined with the flushing intensity requirements, the intensity adjustment value for each flushing point is obtained, generating intensity correction data. Through the intensity correction data and the configuration parameters, a personalized flushing parameter configuration table is obtained using a unified formatting tool.

[0102] For example, for the personalized parameter configuration of the flushing route plan and the heat load density of each area, the system first collects the heat load density data of each area, such as the heat load density of area A. A threshold of 120 kW / m² is set. The initial flushing pressure was set at 100 kW / m². The system automatically determined that the area exceeded the threshold and required increased flushing intensity. Then, an adaptive parameter adjustment algorithm was used to adjust the initial flushing pressure. Set at 5 bar, flow rate of 10 liters / minute, and duration of 5 minutes, the algorithm is based on the formula... (The sentence is incomplete and requires more context to translate accurately.) The new pressure is calculated as 5*(1+(120-100) / 100*0.2)=5.2 bar, while the flow rate is adjusted proportionally to 10.4 liters / minute, and the duration is extended by 20% to 6 minutes. The system records these parameters as the personalized configuration for Zone A. Further analysis shows that if the heat load density of Zone B is 90 kW / m², which is below the threshold, the basic parameters remain unchanged: pressure 5 bar, flow rate 10 liters / minute, and time 5 minutes, ensuring reasonable resource allocation. Subsequently, the system integrates all zone data to generate a flushing parameter configuration table, including fields such as zone number, heat load density, adjusted pressure, flow rate, and time. For example, the record for Zone A is {Zone A, 120, 5.2 bar, 10.4 liters / minute, 6 minutes}, and for Zone B it is {Zone B, 90, 5 bar, 10 liters / minute, 5 minutes}. To ensure logical rigor, the system also incorporates a flushing route optimization algorithm, prioritizing high heat load areas and ensuring that the flushing equipment covers area A in sequence before entering area B. This reduces energy consumption from frequent parameter adjustments. Finally, data analysis verifies the effectiveness of the adjusted parameters in reducing heat load. For example, if the heat load density in area A drops to 95 kW / m² after flushing, it proves that the parameter adjustment is reasonable. The system then automatically updates the database to provide a reference for subsequent flushing.

[0103] S107. Automated flushing operations are performed based on the flushing route and flushing parameter configuration table, and the temperature changes in each area are monitored in real time. If the temperature drop rate is lower than the expected value, the flushing parameters are dynamically adjusted.

[0104] In this embodiment, see Appendix Figure 3 The system obtains flushing parameter data from the flushing parameter configuration table through an intelligent control system, initializes the automated operation process based on the obtained parameters, and obtains a preliminary operation execution plan. According to the preliminary operation execution plan, the automated flushing operation is initiated, while the temperature change data of each area is monitored in real time through a sensor network, and real-time temperature change records are obtained. For the obtained real-time temperature change records, the temperature drop rate of each area is calculated. If the calculated drop rate is lower than the preset expected value, a parameter adjustment mechanism is triggered. Through the parameter adjustment mechanism, the flushing parameter data is dynamically updated, and the automated operation process is reconfigured using the updated parameters to obtain an adjusted operation execution plan. According to the adjusted operation execution plan, the flushing operation continues to be executed, while the updated temperature change data is obtained through the sensor network to determine whether the temperature drop rate has reached the expected value. If the updated temperature drop rate still does not reach the expected value, a regression analysis model is used to process the historical temperature change data and flushing parameter data to determine the optimal adjustment parameters.

[0105] For example, an automated flushing operation can be achieved through an intelligent control system. First, initial parameters are set according to a flushing parameter configuration table, such as a flushing water flow rate of 5.0 liters / second, a flushing frequency of twice per minute, and a flushing duration of 10 minutes. The system automatically drives the flushing equipment to start based on these parameters, ensuring full-area cleaning of the target area. Next, real-time monitoring of temperature changes in each area is performed. Temperature sensors deployed within the area collect data every 5 seconds. Assuming an initial temperature of 50.0 degrees Celsius in a certain area, with a target temperature of 30.0 degrees Celsius and an expected cooling rate of 2.0 degrees Celsius per minute, the system calculates the actual cooling rate using an algorithm. For example, if the temperature drops from 50.0 to 42.0 degrees Celsius within 5 minutes, the calculated actual rate is only 1.6 degrees Celsius per minute, lower than the expected value. In response, the system automatically triggers a dynamic adjustment mechanism, increasing the water flow rate to 6.5 liters / second based on a preset algorithm, and simultaneously increasing the flushing frequency to 3 times per minute. Through simulation analysis, it predicts that the adjusted cooling rate can reach 2.2 degrees Celsius per minute, close to or exceeding the expectation. Subsequently, the system continuously monitors the effects of the adjustments. If the temperature drops to 31.0 degrees Celsius after 10 minutes, approaching the target value, the final cooling effect of 19.0 degrees Celsius is recorded. A heat map is generated using a temperature distribution analysis algorithm, displaying the temperature distribution in each area. For example, if the area temperature is 30.5 degrees Celsius and the edge area is 32.0 degrees Celsius, with a deviation of less than 2.0 degrees Celsius, it indicates that the equipment temperature distribution is uniform. The system automatically saves the data and generates optimization suggestions, such as fine-tuning the rinsing angle for the edge area to further balance the temperature distribution and ensure more efficient subsequent operations. If the updated temperature reduction rate still does not reach the expected value, a regression analysis model is used to process historical temperature change data and rinsing parameter data to determine the optimal adjustment parameters. Through this fully automated process, a complete closed-loop logic from parameter configuration to effect evaluation is formed.

[0106] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method of flushing hypothermia with automatic recognition of the route, characterized by, The method comprises the following steps: S101. The surface of the equipment is scanned comprehensively by using a three-dimensional laser scanning technology to obtain a three-dimensional model of the equipment; S102. Based on the three-dimensional model, the material attribute information of the surface of the equipment is analyzed by using a material identification method, and the thermal conductivity of the surface of the equipment is obtained based on the material attribute information; S103. The temperature distribution of each part during the operation of the equipment is monitored in real time, the measurement results of multiple sensors are integrated by using a temperature data fusion technology to obtain a temperature gradient distribution map of the surface of the equipment; S104. According to the temperature gradient distribution map and the thermal conductivity, the heat load distribution data, the heat load density distribution data and the heat dissipation demand of each region are analyzed by using a heat load calculation model, and the priority of flushing is determined based on the heat load distribution map and the heat dissipation demand. Specifically, the temperature distribution data of each region is obtained through the temperature gradient distribution map, the heat load distribution data and the heat load density distribution data of each region are obtained by using a pre-established heat load calculation model, wherein the heat load density distribution data is generated by dividing the heat load distribution data by the area of the region; the heat dissipation demand of each region is obtained according to the heat load distribution data in combination with the thermal conductivity in the material attribute database; the region temperature exceeding the preset threshold value is judged as a high heat load region, and the position information thereof is recorded and a heat load distribution map is generated; the priority of the flushing region is determined according to the heat load distribution map and the heat dissipation demand; S105. The shortest path and the flushing sequence between each flushing point are calculated based on the priority by using a dynamic programming method to obtain a flushing route; S106. According to the flushing route and the heat load density distribution data of each region, a parameter self-adaptive adjustment algorithm is used to determine the flushing pressure, flow and duration of each flushing point to obtain a personalized flushing parameter configuration table; S107. Based on the flushing route and the flushing parameter configuration table, an automatic flushing operation is performed, the temperature change of each region is monitored in real time, and if the temperature drop rate is lower than the expected value, the flushing parameters are dynamically adjusted.

2. The method of claim 1, wherein, The step S101 comprises: The surface of the equipment is comprehensively detected by using a three-dimensional laser technology to collect original geometric shape information and spatial coordinate data to obtain an initial scanning data set; The initial scanning data set is denoised by using a point cloud data processing algorithm to remove noise interference and obtain a cleaned point cloud data set; Registration is performed on the cleaned point cloud data set to adjust the consistency of data at different scanning angles and determine a unified point cloud registration result; Based on the unified point cloud registration result, a three-dimensional model of the equipment is constructed, key structural feature information is extracted, and preliminary three-dimensional model data is obtained; The preliminary three-dimensional model data is optimized to correct geometric deviations in the three-dimensional model to obtain refined three-dimensional model data set; If the structural feature information of some regions in the refined three-dimensional model data set is incomplete, the missing part is supplemented by using an interpolation algorithm to obtain final three-dimensional model data.

3. The method of claim 1, wherein, The step S102 comprises: Based on the device-based three-dimensional model data, an initial model is generated by a modeling tool, a mesh division is performed on the surface of the initial model, and surface geometric feature data is obtained; According to the surface geometric feature data, material attribute analysis is performed on each grid region of the surface by using a material identification method, material attribute information of each region is obtained, and a material distribution map is output based on the material attribute information; According to the material distribution map, in combination with a pre-established material thermal conductivity coefficient database, the thermal conductivity coefficient of each region is obtained.

4. The method of claim 1, wherein, The step S103 comprises: Real-time data acquisition of each part in the device running state is performed by an infrared thermal imaging sensor, and an original temperature data set is obtained; According to the original temperature data set, data preprocessing technology is used to denoise and calibrate the collected information, and a corrected temperature data set is obtained; For the corrected temperature data set, the measurement results of multiple sensors are fused to construct a unified temperature distribution matrix; Based on the temperature distribution matrix, the temperature data of different sensors is weighted according to its position importance and sensor accuracy to obtain an overall reference temperature, and an interpolation algorithm is used to estimate the temperature of the uncovered area, and a complete temperature gradient distribution map of the device surface is calculated.

5. The method of claim 1, wherein, The step S105 comprises: Based on the priority, the area with the highest heat load is determined as the starting flushing point, and initial flushing position data is obtained; According to the initial flushing position data, the heat load distribution information of the adjacent region is obtained, if the heat load of the adjacent region is lower than the current region and meets the preset heat load threshold, it is marked as the next flushing candidate point, and a candidate flushing point set is determined; For the candidate flushing point set, a dynamic programming method is used to calculate the path distance between each point, all possible paths from the current flushing point to the candidate point are obtained, and the shortest path is determined as the next flushing route; Based on the shortest path data, the specific position information of the next flushing point is obtained, and it is judged whether all regions have been included in the flushing route; If there is a region not included in the flushing route, a new area with the highest heat load is obtained as a new flushing point, and a new flushing order is determined; According to the new flushing order, the path optimization calculation is repeated to obtain the shortest path connection mode of the remaining regions, and a complete flushing route is obtained.

6. The method of claim 1, wherein, The step S106 comprises: Based on the preset flushing scheme, in combination with the heat load density distribution data, if the heat load density of a certain region exceeds the preset regional threshold, the flushing pressure and flow are adjusted by an adaptive algorithm, and the optimized flushing parameters are obtained; For the optimized flushing parameters, the duration requirement is analyzed, if the heat load density is continuously higher than the regional threshold, the flushing time is prolonged, and the final flushing time configuration is determined; Using the final flushing time configuration, in combination with the flushing intensity requirement, the intensity adjustment value of each flushing point is obtained, and intensity correction data is generated; Through the intensity correction data, in combination with the configuration parameters, a unified formatting processing tool is used to obtain a personalized flushing parameter configuration table.

7. The method of claim 1, wherein, The step S107 comprises: The flushing parameter data is obtained from the flushing parameter configuration table by the intelligent control system, the automatic operation process is initialized according to the obtained parameters, and a preliminary operation execution scheme is obtained. According to the preliminary operation execution scheme, the automatic flushing operation is started, and the temperature change data of each area is monitored in real time through the sensor network to obtain real-time records of temperature changes; According to the obtained real-time records of temperature changes, the temperature drop rate of each area is calculated, and if the calculated drop rate is lower than the preset expected value, a parameter adjustment mechanism is triggered; Through the parameter adjustment mechanism, the flushing parameter data is dynamically updated, the automatic operation process is reconfigured using the updated parameters, and an adjusted operation execution scheme is obtained; According to the adjusted operation execution scheme, the flushing operation is continuously performed, and the updated temperature change data is obtained through the sensor network to determine whether the temperature drop rate reaches the expected value; If the updated temperature drop rate still does not reach the expected value, the historical temperature change data and flushing parameter data are processed through a regression analysis model to determine the optimal adjustment parameters.

8. A flush cooling system with automatic recognition of the route, characterized in that The system comprises: a scanning module that uses three-dimensional laser scanning technology to perform omnidirectional scanning on the surface of the equipment to obtain a three-dimensional model of the equipment; a material analysis module that, based on the three-dimensional model, analyzes the material attribute information of the surface of the equipment using a material identification method, and obtains the thermal conductivity of the surface of the equipment based on the material attribute information; a real-time monitoring module that monitors the temperature distribution of each part of the equipment during operation in real time, integrates the measurement results of multiple sensors using temperature data fusion technology, and obtains a temperature gradient distribution map of the surface of the equipment; a priority determination module that, based on the temperature gradient distribution map and the thermal conductivity, analyzes the thermal load distribution data, thermal load density distribution data, and heat dissipation demand of each area using a thermal load calculation model, and determines the priority of flushing based on the thermal load distribution map and the heat dissipation demand, specifically including: obtaining the temperature distribution data of each area through the temperature gradient distribution map, obtaining the thermal load distribution data and thermal load density distribution data of each area using a pre-established thermal load calculation model, wherein the thermal load density distribution data is generated by dividing the thermal load distribution data by the area; obtaining the heat dissipation demand of each area according to the thermal load distribution data in combination with the thermal conductivity in the material attribute database; if the temperature in the heat dissipation demand exceeds a preset threshold, the area is determined as a high thermal load area, and its position information is recorded and a thermal load distribution map is generated; determining the priority of the flushing area according to the thermal load distribution map and the heat dissipation demand; a flushing route acquisition module that calculates the shortest path and flushing order between each flushing point based on the priority through a dynamic programming method to obtain a flushing route; a flushing parameter acquisition module that, according to the flushing route and the thermal load density distribution data of each area, determines the flushing pressure, flow rate, and duration of each flushing point using a parameter self-adaptive adjustment algorithm to obtain a personalized flushing parameter configuration table; a dynamic adjustment module that performs automatic flushing operation based on the flushing route and the flushing parameter configuration table, monitors the temperature change of each area in real time, and dynamically adjusts the flushing parameters if the temperature drop rate is lower than the expected value.

9. A flushing cooling device with automatic recognition of the route, characterized in that The device comprises a memory, a processor, and a flushing cooling program with automatic identification of the route stored on the memory and executable on the processor, the flushing cooling program with automatic identification of the route being configured to implement the flushing cooling method with automatic identification of the route according to any one of claims 1 to 7.

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