Optimization method and device for waterproof cabinet

Abstract

The application provides an optimization method and device for a waterproof cabinet, and relates to the technical field of cabinet design. The optimization method for the waterproof cabinet comprises the following steps: obtaining to-be-optimized air inlet panel structure parameters of the waterproof cabinet and qualified reference data; and based on the qualified reference data, the to-be-optimized air inlet panel structure parameters are subjected to iterative simulation optimization through a preset waterproof cabinet simulation model to obtain corresponding optimization results. The application can improve the design efficiency of the waterproof cabinet.

Description

Technical Field

[0001] This invention relates to the field of server rack design technology, and more specifically, to an optimization method and apparatus for waterproof server racks. Background Technology

[0002] With the large-scale deployment of outdoor infrastructure, outdoor server racks, as the carriers of core electronic equipment, are rapidly expanding their deployment scenarios from traditional suburban base stations to densely populated urban areas, high-altitude mountainous regions, coastal areas with high humidity and salt spray, and windy and sandy desert areas. Outdoor server racks must possess efficient heat dissipation capabilities to ensure that device junction temperatures do not exceed limits, and must also meet dustproof and waterproof requirements to withstand harsh environments, ensuring equipment lifespan and operational reliability. Therefore, synergistically improving the protection level and heat dissipation performance of server racks within a limited volume has become a key technical problem that urgently needs to be solved in the field of outdoor server rack structural design and thermal management.

[0003] However, traditional cabinet design typically uses empirical trial-and-error or single-factor testing methods to determine the cabinet ventilation structure and protection parameters, resulting in long design cycles, high testing costs, and reduced cabinet design efficiency. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the efficiency of server rack design.

[0005] To address the above problems, the present invention provides an optimized method and apparatus for waterproof cabinets.

[0006] In a first aspect, the present invention provides an optimized method for waterproof server racks, comprising: Obtain the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; Based on qualified benchmark data, the corresponding optimization results are obtained by iterative simulation optimization using a preset waterproof cabinet simulation model according to the structural parameters of the air intake panel to be optimized.

[0007] Optionally, before obtaining the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet, the following steps are also included: Based on the obtained initial air intake panel combination, qualified air intake panel combinations are selected through preset cabinet operating conditions. Based on the structural parameters of a qualified air intake panel assembly, a qualified baseline data is obtained through simulation using a waterproof cabinet simulation model.

[0008] Optionally, the rack operating conditions include heat dissipation conditions and rain protection conditions; based on the obtained initial air intake panel combination, qualified air intake panel combinations are obtained through screening using preset rack operating conditions, including: Multiple initial air inlet panels are obtained by combining the initial air inlet panels; The results of the rain test were obtained by testing a waterproof cabinet with an initial air intake panel installed in a preset rain test environment. When the rain test results simultaneously meet the heat dissipation and rain protection conditions, the initial air inlet panel is determined to be a qualified air inlet panel. A qualified air intake panel assembly is obtained based on all qualified air intake panels.

[0009] Optionally, qualified baseline data can be obtained by simulating the structural parameters of a qualified air intake panel assembly using a waterproof cabinet simulation model, including: The structural parameters of the qualified air inlet panel assembly are input into the simulation model of the waterproof cabinet to obtain the simulation working point data set. The simulation working point data set includes the air inlet panel wind speed data set and the fan volume flow rate data set. The maximum wind speed in the air inlet panel wind speed data group is determined as the baseline air inlet panel wind speed. The minimum volumetric flow rate in the fan volumetric flow rate data set is determined as the baseline fan volumetric flow rate. Qualified baseline data is generated based on the baseline inlet panel wind speed and the baseline fan volume flow rate.

[0010] Optionally, based on the structural parameters of the air intake panel to be optimized, iterative simulation optimization is performed using a preset waterproof cabinet simulation model to obtain the corresponding optimization results, including: Obtain the structural influence order of the air intake panel to be optimized; Input the structural parameters of the air intake panel to be optimized into the simulation model of the waterproof cabinet to obtain the corresponding current working point data; Determine whether the qualification criteria are met based on the comparison between the current working point data and the qualification benchmark data. If so, the structural parameters of the air intake panel to be optimized shall be determined as the optimization result; Otherwise, adjust the structural parameters of the air intake panel to be optimized according to the order of structural influence, and input the adjusted structural parameters of the air intake panel to be optimized into the waterproof cabinet simulation model for iterative simulation optimization until the qualified benchmark conditions are met.

[0011] Optionally, the current operating point data includes the current inlet panel velocity and the current fan volumetric flow rate; the qualification benchmark data includes the benchmark inlet panel velocity and the benchmark fan volumetric flow rate; the qualification benchmark conditions are determined based on the comparison between the current operating point data and the qualification benchmark data, including: If the current air intake panel wind speed is less than or equal to the reference air intake panel wind speed, the waterproof condition is deemed to be met. If the current fan volume flow rate is greater than or equal to the reference fan volume flow rate, the heat dissipation condition is deemed met. If both waterproofing and heat dissipation conditions are met, the product is deemed to meet the qualification criteria.

[0012] Optionally, before obtaining the structural influence order of the air intake panel to be optimized, the following steps are also included: Obtain the temperature rise data of the initial air intake panel assembly during a rain test; Based on the temperature rise data, the parameter influence values ​​of each structure were obtained through orthogonal experimental variance analysis. The influence order of all structural parameters is obtained by sorting them from highest to lowest.

[0013] Optionally, the temperature rise data includes the experimental temperature rise value and the average temperature rise value of the structural parameters corresponding to each structure; based on the temperature rise data, the influence values ​​of the parameters corresponding to each structure are obtained through orthogonal experimental variance analysis, including: The average value of the temperature rise corresponding to all tests is determined as the total average temperature rise. The total sum of squares of deviations is obtained by using the preset first deviation relationship based on all test temperature rise values ​​and the total average temperature rise value. Based on the average temperature rise value and total average temperature rise value of the structural parameters, the corresponding sum of squared structural deviations is obtained through a preset second deviation relationship; Subtracting the total sum of squared deviations from the sum of squared deviations of all structures yields the sum of squared deviations of the error. The parameter influence values ​​corresponding to the structure are obtained by using the sum of squared deviations of the structure and the sum of squared deviations of the error through a preset variance ratio relationship.

[0014] Optionally, the first deviation relation satisfies: ; The second deviation relation satisfies: ; The variance ratio relationship satisfies: ; in, For the total sum of squares of deviations, Let be the temperature rise value of the i-th test. This represents the total average temperature rise. For the number of trials, Let S be the sum of squared structural deviations corresponding to the A-th structure. The number of tests for each structural parameter of the structure. The number of structural parameters, Let be the average temperature rise value of the structural parameter corresponding to the j-th structural parameter of the A-th structure. This represents the structural influence value corresponding to the A-th structure. For the sum of squared deviations of the error, The number of structures.

[0015] Secondly, the present invention provides an optimized device for a waterproof server rack, comprising: The acquisition module is used to acquire the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; The optimization module is used to perform iterative simulation optimization based on qualified benchmark data and the structural parameters of the air intake panel to be optimized through a preset waterproof cabinet simulation model to obtain the corresponding optimization results.

[0016] Thirdly, the present invention provides an electronic device, including a memory and a processor; Memory, used to store computer programs; A processor, used to implement the optimized method for waterproof cabinets, as described in the first aspect, when executing computer programs.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the optimized method for a waterproof cabinet as described in the first aspect.

[0018] The beneficial effects of the waterproof cabinet optimization method of the present invention are as follows: By acquiring the structural parameters and qualified benchmark data of the air intake panel to be optimized, clear input conditions and judgment criteria are provided for the subsequent air intake panel to be optimized. This allows designers to conduct scheme design driven by data instead of blindly trying based on experience, reducing the blindness and uncertainty of scheme design. Based on the qualified benchmark data, the air intake panel to be optimized is iteratively simulated and optimized using a preset waterproof cabinet simulation model. The waterproof and heat dissipation performance of different structural parameters can be quickly evaluated in a computer virtual environment. There is no need to repeatedly make physical prototypes and conduct rain tests and heat dissipation tests for each set of structural parameters. This avoids the time-consuming links such as prototype making, test device construction and environmental simulation waiting in physical testing, and greatly reduces the time cost of single-round scheme verification. At the same time, during the iterative optimization process, the merits of the scheme are automatically judged according to the clear qualified benchmark conditions, and the parameters can be adjusted sequentially according to the order of structural influence, ensuring the clarity and convergence of the optimization path and avoiding repeated trial and error caused by coupling effects in traditional single-factor experiments. In summary, by combining performance benchmark acquisition with simulation iterative optimization, and replacing a large number of physical tests with virtual simulation, the design process of the rack air intake panel is transformed from an experience-driven "trial and error mode" to a data-driven "intelligent optimization mode". This significantly shortens the R&D cycle and reduces testing investment while ensuring optimization accuracy, thereby comprehensively improving the efficiency of rack design. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an optimization method for a waterproof cabinet according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the waterproof cabinet according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an optimized device for a waterproof cabinet according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached figures: 1-Rack top panel; 2-Rack frame; 3-Side door panel; 4-Front door panel; 5-Air intake panel; 6-Support casters. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0022] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0024] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0025] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0026] In related technologies, traditional server rack design typically employs trial-and-error or single-factor testing methods to determine the rack's ventilation structure and protection parameters. Trial-and-error relies on the designer's personal experience to propose an initial solution, which is then repeatedly prototyped and tested. The design is gradually modified based on the test results. This process heavily depends on the designer's subjective judgment, lacks systematic theoretical guidance, and requires re-fabrication of the prototype and setup of the testing environment for each round of modifications, resulting in long design cycles and high R&D costs. Single-factor testing involves changing only one structural parameter at a time during the design process, keeping the remaining parameters constant for comparative testing to examine the impact of that parameter on performance. However, there is an inherent contradiction between the ventilation and heat dissipation performance and the waterproof and dustproof performance of a server rack, and the various structural parameters are strongly coupled. Adjusting a single parameter may have unexpected interactive effects on overall performance. Single-factor testing cannot effectively capture this coupling relationship, often resulting in unintended consequences. For example, increasing the ventilation area to improve heat dissipation may lead to a decrease in waterproof performance; increasing the density of dust filters to enhance protection may result in insufficient heat dissipation. The above design approach results in long R&D cycles, high trial-and-error costs, and makes it difficult to quickly find the optimal balance between heat dissipation and protection, which seriously affects the efficiency and quality of rack design.

[0027] To address the problems existing in the aforementioned related technologies, embodiments of the present invention provide an optimization method and apparatus for waterproof cabinets.

[0028] like Figure 1 As shown in the figure, an optimization method for a waterproof cabinet provided by an embodiment of the present invention includes: S110: Obtain the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet.

[0029] Specifically, obtaining the structural parameters and benchmark data of the air intake panel to be optimized for the waterproof cabinet is the prerequisite and foundation for subsequent simulation optimization analysis. The structural parameters of the air intake panel to be optimized refer to the physical geometric characteristics of the air intake panel to be evaluated, including but not limited to the number of louvers, the number of ventilation components, and the number of dustproof meshes. These parameters determine the ventilation area, airflow resistance, and waterproof and dustproof capabilities of the air intake panel. For example, a set of structural parameters to be optimized can be represented as "10 louvers, 3 sets of ventilation components, and 200 dustproof meshes," which represents the initial design scheme to be optimized. Benchmark data refers to the critical performance indicators extracted from qualified air intake panels that meet preset heat dissipation and waterproof conditions. These are used to measure whether the air intake panel to be optimized meets the standards, including critical wind speed benchmark values ​​and critical flow rate benchmark values. After obtaining the above two, the structural parameters of the air inlet panel to be optimized can be input into the simulation model. The wind speed output by the simulation model is compared with the critical wind speed benchmark value, and the volumetric flow rate is compared with the critical flow rate benchmark value. This will determine whether the optimization scheme meets the qualified benchmark conditions and provide a basis for subsequent iterative optimization.

[0030] S120, based on qualified benchmark data, obtains the corresponding optimization results by iterative simulation optimization through a preset waterproof cabinet simulation model according to the structural parameters of the air intake panel to be optimized.

[0031] Specifically, firstly, the structural parameters of the air inlet panel to be optimized (including the number of louvers, the number of ventilation components, and the number of dustproof meshes) are input into a pre-established and verified waterproof cabinet simulation model. The simulation model is built based on computational fluid dynamics (CFD) principles and can simulate the internal flow field, temperature field, and airflow velocity and volumetric flow rate at the air inlet panel under rated operating conditions. After running the simulation, the current operating point data corresponding to the air inlet panel to be optimized is extracted from the simulation results. The current operating point data includes at least the current simulated wind speed and the current simulated volumetric flow rate. Secondly, the current simulated wind speed is compared with the critical wind speed benchmark value in the qualified benchmark data, and the current simulated volumetric flow rate is compared with the critical flow rate benchmark value in the qualified benchmark data. If the current simulated wind speed is less than or equal to the critical wind speed benchmark value, and the current simulated volumetric flow rate is greater than or equal to the critical flow rate benchmark value, then the current air inlet panel to be optimized is determined to meet the qualified benchmark conditions, and the current structural parameters are output as the optimization result. If any of the above conditions are not met, then according to the pre-determined order of structural influence (i.e., the order of importance of each structural parameter's influence on heat dissipation performance, such as the influence of dustproof mesh count being greater than the number of louvers, and the influence of the number of louvers being greater than the number of ventilation components), the structural parameter with the most significant influence is adjusted first (e.g., the dustproof mesh count is adjusted from 200 meshes to 150 meshes), while keeping other parameters unchanged. The adjusted structural parameters are then re-input into the simulation model for the next round of simulation. If the conditions are still not met, the next structural parameter is adjusted sequentially according to the order of structural influence, and this process is iterated until the simulated output wind speed and volumetric flow rate simultaneously meet the qualified benchmark conditions. Through the above iterative simulation process, the optimal air inlet panel design that balances waterproofing and heat dissipation performance can be quickly obtained without repeatedly manufacturing physical prototypes.

[0032] In this embodiment, by acquiring the structural parameters and qualified benchmark data of the air intake panel to be optimized, clear input conditions and judgment criteria are provided for the subsequent optimization of the air intake panel. This allows designers to conduct scheme design driven by data instead of blindly trying based on experience, reducing the blindness and uncertainty of scheme design. Based on the qualified benchmark data, the air intake panel to be optimized is iteratively simulated and optimized using a preset waterproof cabinet simulation model. The waterproof and heat dissipation performance of different structural parameters can be quickly evaluated in a computer virtual environment. There is no need to repeatedly make physical prototypes and conduct rain tests and heat dissipation tests for each set of structural parameters. This avoids the time-consuming links such as prototype making, test device construction and environmental simulation waiting in physical testing, and significantly reduces the time cost of single-round scheme verification. At the same time, during the iterative optimization process, the merits of the scheme are automatically judged according to the clear qualified benchmark conditions, and the parameters can be adjusted sequentially according to the order of structural influence, ensuring the clarity and convergence of the optimization path and avoiding repeated trial and error caused by coupling effects in traditional single-factor experiments. In summary, by combining performance benchmark acquisition with simulation iterative optimization, and replacing a large number of physical tests with virtual simulation, the design process of the rack air intake panel is transformed from an experience-driven "trial and error mode" to a data-driven "intelligent optimization mode". This significantly shortens the R&D cycle and reduces testing investment while ensuring optimization accuracy, thereby comprehensively improving the efficiency of rack design.

[0033] Optionally, before obtaining the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet, the following steps are also included: Based on the obtained initial air intake panel combination, qualified air intake panel combinations are selected through preset cabinet operating conditions. Based on the structural parameters of a qualified air intake panel assembly, a qualified baseline data is obtained through simulation using a waterproof cabinet simulation model.

[0034] Specifically, the initial air intake panel assembly includes multiple sets of air intake panels with different combinations of structural parameters. Each set of structural parameters includes at least different values ​​for the number of louvers, the number of ventilation components, and the mesh size of the dustproof mesh. For example, the first set has 10 louvers, 3 sets of ventilation components, and 200 mesh dustproof mesh, while the second set has 8 louvers, 4 sets of ventilation components, and 150 mesh dustproof mesh. Each set of initial air intake panels is installed on a waterproof cabinet and tested under a preset rain test environment. The waterproof performance is judged by observing whether water seepage or water ingress occurs inside the cabinet. At the same time, thermocouples arranged at multiple temperature measuring points inside the cabinet record the temperature data at each key location, calculate the temperature rise value of the internal modules, and compare it with the preset allowable temperature rise value threshold to judge whether the heat dissipation performance meets the standard. The initial air intake panel assembly that simultaneously meets the two conditions of "no water seepage or water ingress inside" and "temperature rise value not exceeding the preset threshold" is selected and marked as a qualified air intake panel assembly. For example, if only the third, fifth, and seventh groups out of the ten initial panels simultaneously meet both of the above requirements, then these three groups are considered qualified air inlet panel combinations. Subsequently, the structural parameters of each air inlet panel combination are input into a pre-built and verified waterproof cabinet simulation model. The simulation model is based on computational fluid dynamics principles and can simulate the internal flow field characteristics of the cabinet under rated operating conditions. After running the simulation, the wind speed and volumetric flow rate data corresponding to each air inlet panel combination are extracted from the simulation results. For example, the wind speed for the third group is 2.5 m / s, and the volumetric flow rate is 0.12 m³ / s. 3 The wind speed in the fifth group is 3.0 m / s, and the volumetric flow rate is 0.10 m³ / s. 3 The wind speed in Group 7 is 2.8 m / s, and the volumetric flow rate is 0.11 m³ / s. 3 / s; Select the maximum value from all wind speed data as the critical wind speed reference value (e.g., 3.0 m / s in the example above), and select the minimum value from all volumetric flow rate data as the critical flow rate reference value (e.g., 0.10 m³ / s in the example above). 3 / s), and the critical wind speed benchmark value and the critical flow rate benchmark value are used together as qualified benchmark data.

[0035] In this optional embodiment, the initial air intake panel assembly is physically screened by pre-setting cabinet operating conditions, ensuring the real reliability of qualified air intake panel assemblies under actual rain conditions, providing a reliable benchmark for subsequent simulations. Furthermore, the qualified panels that have passed physical verification are simulated and analyzed using simulation models. The simulation extreme values ​​(maximum wind speed and minimum flow rate) of the qualified panels are used as the judgment thresholds. This not only preserves the authenticity of physical testing, but also quantifies and extrapolates the qualification judgment criteria through simulation, enabling subsequent optimizations to quickly determine the merits of new solutions without repeatedly relying on physical testing. This significantly reduces the dependence on physical testing while ensuring the reliability of the judgment, reduces the number of tests and prototypes, and significantly shortens the acquisition cycle of qualified benchmark data, laying a data foundation for efficient subsequent iterative optimization.

[0036] It should be noted that, as Figure 2As shown, the waterproof cabinet mainly includes the following structure: Cabinet frame 2, constructed from welded or assembled profiles, serves as the supporting skeleton for the entire cabinet, supporting and fixing the door panels and internal electrical equipment modules. Its shape and size can be adjusted according to actual needs and are not limited to a specific configuration; Cabinet top panel 1, installed on top of cabinet frame 2, seals the opening at the top of the cabinet to prevent rainwater from seeping in from the top. A drainage structure is provided around the top panel 1 to guide rainwater falling onto it to the sides or rear of the cabinet for drainage; Side door panels 3, installed on the left and right sides of cabinet frame 2, together with the top panel 1 and front door panel 4, form a closed cabinet space. A sealing strip is provided between the side door panel 3 and cabinet frame 2, achieving a waterproof seal through interference fit; Front door panel 4, installed on the front of cabinet frame 2, seals the front of the cabinet. A water guide groove is provided on the upper part of the front door panel 4, forming a concave structure with the concave side below the top panel 1, working in conjunction with the sealing strip and the cabinet... Frame 2 achieves an interference fit, and rainwater falling on the front door panel 4 is guided out through the drainage channel to prevent rainwater from seeping into the cabinet through the door seams. The air intake panel 5, installed at the ventilation opening of the front door panel 4, is a modular component that can be independently disassembled and installed. It is used to allow external cold air to enter the cabinet and provide cooling airflow for the equipment. The air intake panel 5 includes a first layer of louvered structure, a second layer of waterproof structure, and a third layer of dustproof structure arranged sequentially from the outside to the inside. The first layer of louvered structure is used to initially block falling rainwater and isolate most of the rainwater from the outside of the cabinet. The second layer of waterproof structure is used to intercept residual rainwater sucked in by the airflow and guide the condensate to the water tank below. The third layer of dustproof structure is used to filter dust particles in the incoming airflow and absorb some moisture. The overall shape, size, and number of components in each layer of the air intake panel 5 can be flexibly adjusted according to heat dissipation and protection requirements. Support casters 6 are installed at the bottom of the cabinet frame 2 to support the entire cabinet and facilitate the movement and on-site placement of the cabinet. Each of the aforementioned door panels and the cabinet frame 2 is equipped with a sealing strip to ensure that the cabinet as a whole meets the protection level requirements. Multiple electrical equipment modules are installed inside the cabinet, and the modules are arranged in parallel. Each module is equipped with an independent fan. The fan is installed on the side of the module near the air inlet panel 5 to draw in external cold air into the cabinet and force it to flow through the surface of each module to achieve forced air cooling.

[0037] Optionally, the rack operating conditions include heat dissipation conditions and rain protection conditions; based on the obtained initial air intake panel combination, qualified air intake panel combinations are obtained through screening using preset rack operating conditions, including: Multiple initial air inlet panels are obtained by combining the initial air inlet panels; A rain test was conducted on a waterproof cabinet with an initial air intake panel installed under a pre-defined rain test environment to obtain the rain test results, which include... When the rain test results simultaneously meet the heat dissipation and rain protection conditions, the initial air inlet panel is determined to be a qualified air inlet panel. A qualified air intake panel assembly is obtained based on all qualified air intake panels.

[0038] Specifically, the initial air inlet panel assembly includes multiple sets of air inlet panels with different structural parameters. For example, the number of louvers, the number of ventilation components, and the number of dustproof meshes are all set to different horizontal values. Multiple combinations are formed according to orthogonal experimental design, with each combination corresponding to an independent initial air inlet panel, thus obtaining multiple initial air inlet panels. The panels differ only in structural parameters; other aspects such as external dimensions and installation interfaces remain consistent to ensure the comparability of test results. Rain test results are obtained by conducting rain test tests on waterproof cabinets equipped with the initial air inlet panels based on a preset rain test environment. Specifically, each initial air intake panel is sequentially installed at the air intake of the same waterproof cabinet and tested in a preset rain test environment. The rain test environment simulates natural rainstorm conditions, including preset parameters such as rainfall intensity, rainfall duration, and wind direction. During the test, humidity sensors placed in multiple locations inside the cabinet or visual observation are used to record whether water seepage or water ingress occurs inside the cabinet. At the same time, thermocouples placed on the surface of each electrical equipment module monitor temperature data in real time and calculate the temperature rise value of each measuring point as part of the rain test results. When the rain test results simultaneously meet both heat dissipation and rainproof conditions, the initial air intake panel is determined to be a qualified air intake panel. The heat dissipation condition is that the temperature rise at all temperature measurement points inside the cabinet does not exceed the preset allowable temperature rise threshold. The rainproof condition is that there is no water seepage or ingress inside the cabinet. For example, if, after the rain test, the temperature rise at all temperature measurement points inside a certain initial air intake panel is below the allowable value and the interior is dry with no water ingress, then the panel is determined to be a qualified air intake panel. Conversely, if water ingress occurs or any temperature measurement point exceeds the allowed temperature, the panel is determined to be an unqualified air intake panel. The above rain test and determination are performed on each initial air intake panel in the initial air intake panel assembly. The qualified air intake panel assembly is obtained by summing all the qualified initial air intake panels.

[0039] In this optional embodiment, by conducting standardized tests on multiple initial air intake panels with different structural parameters under the same preset rain test environment, the consistency of test conditions is ensured, so that the screening results truly reflect the actual protection and heat dissipation performance of each panel under the same harsh environment. By using both heat dissipation conditions and rainproof conditions as dual judgment criteria for screening, only panels that meet the requirements of both performance are retained as qualified air intake panels, avoiding the one-sidedness of focusing on only one performance while ignoring the other. At the same time, all the selected qualified air intake panels are summarized into a qualified air intake panel combination, providing a real and reliable sample source for obtaining qualified benchmark data based on simulation, ensuring the credibility and accuracy of subsequent benchmark optimization.

[0040] Optionally, qualified baseline data can be obtained by simulating the structural parameters of a qualified air intake panel assembly using a waterproof cabinet simulation model, including: The structural parameters of the qualified air inlet panel assembly are input into the simulation model of the waterproof cabinet to obtain the simulation working point data set. The simulation working point data set includes the air inlet panel wind speed data set and the fan volume flow rate data set. The maximum wind speed in the air inlet panel wind speed data group is determined as the baseline air inlet panel wind speed. The minimum volumetric flow rate in the fan volumetric flow rate data set is determined as the baseline fan volumetric flow rate. Qualified baseline data is generated based on the baseline inlet panel wind speed and the baseline fan volumetric flow rate.

[0041] Specifically, a qualified air inlet panel assembly includes multiple sets of air inlet panels that have been verified by rain tests to simultaneously meet heat dissipation and rainproof conditions. Each set of air inlet panels corresponds to a unique combination of structural parameters, such as specific values ​​for the number of louvers, the number of ventilation components, and the number of dustproof meshes. Each set of structural parameter combinations is input into a pre-established and verified waterproof cabinet simulation model. The simulation model is built based on the principles of computational fluid dynamics and can simulate the internal flow field distribution and fan operating state of the cabinet under rated operating conditions. After running the simulation, the simulation working point data corresponding to each set of structural parameters is extracted from the simulation results to obtain the simulation working point data set. The simulation working point data set includes the air inlet panel wind speed data set and the fan volume flow rate data set. The air inlet panel wind speed data set consists of the panel surface wind speed values ​​corresponding to each set of air inlet panels, and the fan volume flow rate data set consists of the fan volume flow rate values ​​corresponding to each set of air inlet panels. The maximum wind speed in the inlet panel wind speed data set is determined as the benchmark inlet panel wind speed, that is, the largest value among all wind speed values ​​is selected as the upper limit benchmark for subsequent judgment. The minimum volumetric flow rate in the fan volumetric flow rate data set is determined as the benchmark fan volumetric flow rate, that is, the smallest value among all volumetric flow rate values ​​is selected as the lower limit benchmark for subsequent judgment. Finally, qualified benchmark data is generated based on the benchmark inlet panel wind speed and the benchmark fan volumetric flow rate, that is, the benchmark inlet panel wind speed is used as the critical wind speed benchmark value and the benchmark fan volumetric flow rate is used as the critical flow rate benchmark value. The two together constitute the qualified benchmark data for subsequent evaluation of the simulation results of the inlet panel to be optimized.

[0042] In this optional embodiment, by inputting the structural parameters of qualified air inlet panels that have passed rain testing into the simulation model, and using the extreme values ​​(maximum wind speed and minimum flow rate) in the simulation results as the judgment benchmark, the generation of qualified benchmark data no longer depends on the data of a single group of panels, but integrates the extreme performance of multiple groups of panels that have passed physical verification, thus improving the representativeness and reliability of the benchmark data. At the same time, the wind speed of the benchmark air inlet panel reflects the critical upper limit of the qualified panel in terms of waterproof performance, and the volumetric flow rate of the benchmark fan reflects the critical lower limit of the qualified panel in terms of heat dissipation performance. The combination of the two defines the boundary conditions that simultaneously meet both waterproof and heat dissipation performance. In addition, the qualified benchmark data acquisition method based on the combination of physical testing and simulation verification not only retains the credibility of the physical rain test results, but also obtains the working point data of each group of panels in batches through simulation, avoiding the repetitive work of performing complex physical tests on each group of qualified panels one by one, greatly reducing the data acquisition cycle, and providing an accurate and reliable evaluation basis for quickly determining whether the panel to be optimized meets the standards using simulation.

[0043] Optionally, based on the structural parameters of the air intake panel to be optimized, iterative simulation optimization is performed using a preset waterproof cabinet simulation model to obtain the corresponding optimization results, including: Obtain the structural influence order of the air intake panel to be optimized; Input the structural parameters of the air intake panel to be optimized into the simulation model of the waterproof cabinet to obtain the corresponding current working point data; Determine whether the qualification criteria are met based on the comparison between the current working point data and the qualification benchmark data. If so, the structural parameters of the air intake panel to be optimized shall be determined as the optimization result; Otherwise, adjust the structural parameters of the air intake panel to be optimized according to the order of structural influence, and input the adjusted structural parameters of the air intake panel to be optimized into the waterproof cabinet simulation model for iterative simulation optimization until the qualified benchmark conditions are met.

[0044] Specifically, the structural influence order refers to the ranking of the degree of influence of the structural parameters of each structure of the air intake panel on the heat dissipation performance of the cabinet. This order is determined through orthogonal experiments and variance analysis. The structural parameters of the air intake panel to be optimized are input into the waterproof cabinet simulation model to obtain the corresponding current operating point data. That is, the initial structural parameters of the air intake panel to be optimized are set, such as 10 louvers, 3 sets of ventilation components, and 200 mesh dustproof mesh. The above parameters are input into the pre-built waterproof cabinet simulation model and the simulation is run. The current operating point data corresponding to the current air intake panel is extracted from the simulation results. The current operating point data includes at least the current air intake velocity and the current fan volumetric flow rate. The qualification criteria are determined by comparing the current operating point data with the qualification benchmark data. Specifically, the current inlet air velocity is compared with the benchmark inlet panel velocity (i.e., the critical velocity benchmark value) in the qualification benchmark data, and the current fan volumetric flow rate is compared with the benchmark fan volumetric flow rate (i.e., the critical flow rate benchmark value) in the qualification benchmark data. If the current inlet air velocity is less than or equal to the benchmark inlet panel velocity, and the current fan volumetric flow rate is greater than or equal to the benchmark fan volumetric flow rate, then the qualification criteria are met, indicating that the current inlet panel to be optimized meets the requirements in both waterproofing and heat dissipation. If the current inlet air velocity is greater than the benchmark inlet panel velocity, it indicates that the inlet air velocity is too high and may draw rainwater into the cabinet, failing to meet waterproofing standards. If the current fan volumetric flow rate is less than the benchmark fan volumetric flow rate, it indicates that the airflow is insufficient to remove enough heat, failing to meet heat dissipation standards. If neither of these criteria is met, or if either is not met, then the qualification criteria are not met. If so, meaning the current working point data simultaneously meets the dual conditions of wind speed not exceeding the reference wind speed and flow rate not less than the reference flow rate, then the structural parameters of the air inlet panel to be optimized are determined as the optimization result. This set of structural parameters is the optimal air inlet panel solution that takes into account both waterproofing and heat dissipation performance, and can be directly used as the final design for production application. Otherwise, if the current operating point data fails to simultaneously meet the above dual conditions, the structural parameters of the air intake panel to be optimized are adjusted according to the order of structural influence. That is, the structural parameters that are ranked first in the order of structural influence, i.e., the structural parameters that have the most significant impact on heat dissipation performance, are adjusted first. For example, if the number of dust filter meshes has the greatest impact on the temperature rise, the number of dust filter meshes is adjusted from 200 meshes to 150 meshes, while keeping other parameters unchanged. The adjusted structural parameters of the air intake panel to be optimized are then re-input into the waterproof cabinet simulation model for iterative simulation optimization. That is, the simulation is run again on the new scheme, a new round of current operating point data is extracted, and it is compared and judged again with the qualified benchmark data. If the conditions are still not met, the next structural parameter is adjusted according to the order of structural influence (such as adjusting the number of louvers). This process is repeated iteratively. Each iteration follows the above steps to simulate, compare, and judge until the qualified benchmark conditions are met. The final output structural parameters are the optimal air intake panel design scheme obtained through systematic optimization.

[0045] In this optional embodiment, by pre-obtaining the structural influence order, the iterative optimization process has a clear direction and priority, avoiding inefficient trial and error by blindly adjusting parameters, reducing invalid iterations, and accelerating the optimization convergence speed. By inputting the structural parameters of the air inlet panel to be optimized into the simulation model and automatically judging them in conjunction with qualified benchmark data, high-throughput rapid evaluation of the air inlet panel design scheme is achieved. There is no need to make physical prototypes and build rain test environments for each set of structural parameters to be tested, fundamentally eliminating the dependence on repeated physical tests and significantly reducing material costs, processing costs, and test manpower costs in the R&D process. At the same time, during the iterative optimization process, only structural parameters need to be adjusted and the simulation needs to be rerun. The single-round evaluation cycle can be shortened from several days in traditional physical tests to several hours or even tens of minutes, significantly shortening the design optimization cycle of the cabinet air inlet panel, greatly improving the overall R&D efficiency, and providing efficient and reliable technical support for the rapid iterative design of outdoor cabinets.

[0046] Optionally, the temperature rise data includes the experimental temperature rise value and the average temperature rise value of the structural parameters corresponding to each structure; based on the temperature rise data, the influence values ​​of the parameters corresponding to each structure are obtained through orthogonal experimental variance analysis, including: The average value of the temperature rise corresponding to all tests is determined as the total average temperature rise. The total sum of squares of deviations is obtained by using the preset first deviation relationship based on all test temperature rise values ​​and the total average temperature rise value. Based on the average temperature rise value and total average temperature rise value of the structural parameters, the corresponding sum of squared structural deviations is obtained through a preset second deviation relationship; Subtracting the total sum of squared deviations from the sum of squared deviations of all structures yields the sum of squared deviations of the error. The parameter influence values ​​corresponding to the structure are obtained by using the sum of squared deviations of the structure and the sum of squared deviations of the error through a preset variance ratio relationship.

[0047] Optionally, the first deviation relation satisfies: ; The second deviation relation satisfies: ; The variance ratio relationship satisfies: ; in, For the total sum of squares of deviations, Let be the temperature rise value of the i-th test. This represents the total average temperature rise. For the number of trials, Let S be the sum of squared structural deviations corresponding to the A-th structure. The number of tests for each structural parameter of the structure. The number of structural parameters, Let be the average temperature rise value of the structural parameter corresponding to the j-th structural parameter of the A-th structure. This represents the structural influence value corresponding to the A-th structure. For the sum of squared deviations of the error, The number of structures.

[0048] Specifically, the temperature rise data includes the test temperature rise value (i.e., the specific value of the internal temperature rise value of the cabinet measured in each test in the orthogonal test) and the average temperature rise value of the structural parameters corresponding to each structure (i.e., the arithmetic mean of the test temperature rise values ​​of a certain structural parameter at the same level, such as the average of the three test temperature rise values ​​when the number of louvers is at level I).

[0049] For example, the orthogonal experimental combinations are shown in the table below:

[0050] Furthermore, based on the temperature rise data, orthogonal experimental variance analysis is used to obtain the parameter influence values ​​corresponding to each structure. This step is used to quantify the influence of each air intake panel structural parameter on the heat dissipation performance of the cabinet. First, the arithmetic mean of the temperature rise values ​​corresponding to all tests is determined as the total average temperature rise value, serving as a benchmark reference value for measuring the influence of each factor. Next, based on the sum of squares of the differences between all test temperature rise values ​​and the total average temperature rise value, the total sum of squares of deviations is calculated through a preset first deviation relationship. This value reflects the total fluctuation of all test temperature rise values ​​relative to the total average temperature rise value, i.e., the sum of temperature rise value fluctuations caused by all structural parameters and random errors. Then, based on the sum of squares of the differences between the average temperature rise value of each structure's structural parameters and the total average temperature rise value, multiplied by the number of repetitions at that level, the structural sum of squares of deviations corresponding to the structural parameters of that structure is calculated through a preset second deviation relationship. This value reflects the temperature rise value fluctuation caused by changes in the values ​​of a structural parameter at different levels. The larger the value, the more significant the impact of the change in the structural parameter on the heat dissipation performance of the cabinet. It should be noted that each structure corresponds to one tested... Structural parameters, such as the number of louvers corresponding to louvers (structural parameters of louvers) and the mesh count of dustproof nets corresponding to dustproof nets (structural parameters of dustproof nets), are used to determine the sum of squares of deviations for each structure. The sum of squares of deviations for each structure is then subtracted from the sum of squares of deviations for all structures to obtain the sum of squares of deviations for errors. This value reflects the residual fluctuations caused by random errors and unexamined factors, providing a quantitative assessment of uncontrollable factors in the experimental results and a benchmark for subsequent significance determination. Finally, based on the sum of squares of deviations for structures and the sum of squares of deviations for errors, the parameter influence value corresponding to each structural parameter is calculated using a preset variance ratio. This parameter influence value reflects the ratio of the average fluctuation caused by the structural parameter to the average fluctuation of the random error. A larger value indicates a more significant impact of the structural parameter on the heat dissipation performance of the cabinet. By comparing the magnitudes of the parameter influence values ​​corresponding to each structure, the order of influence of each structural parameter on heat dissipation performance can be determined. This allows for prioritizing the adjustment of structural parameters corresponding to structures with larger influence values, achieving the optimal air intake panel optimization scheme with the fewest design iterations.

[0051] In this optional embodiment, the influence of multiple structural parameters of the air intake panel on heat dissipation performance is quantified, decomposed, and ranked by significance through orthogonal experimental variance analysis. This transforms the originally vague qualitative understanding that "structure affects performance" into precise and comparable parameter influence values, providing a clear basis for priority in subsequent iterative optimization. It avoids ineffective adjustments caused by blind trial and error or subjective judgment in traditional design, and significantly improves the scientific nature and efficiency of the optimization process.

[0052] like Figure 3 As shown, an embodiment of the present invention provides an optimization device 300 for a waterproof cabinet, comprising: The acquisition module 310 is used to acquire the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; The optimization module 320 is used to perform iterative simulation optimization based on qualified benchmark data and according to the structural parameters of the air intake panel to be optimized through a preset waterproof cabinet simulation model to obtain the corresponding optimization results.

[0053] The waterproof cabinet optimization device in this embodiment is used to implement the above-mentioned waterproof cabinet optimization method. Its advantages over the prior art are the same as the advantages of the above-mentioned waterproof cabinet optimization method over the prior art, and will not be repeated here.

[0054] like Figure 4 As shown, an electronic device 400 provided in this embodiment of the invention includes a memory 410 and a processor 420; the memory 410 is used to store a computer program; the processor 420 is used to implement the above-mentioned optimization method for waterproof cabinet when the computer program is executed.

[0055] Alternatively, an electronic device 400 includes a memory 410 and a processor 420 coupled to the memory 410; the memory 410 is configured to store a computer program; the processor 420 is configured to perform the following operations when the computer program is executed: Obtain the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; Based on qualified benchmark data, the corresponding optimization results are obtained by iterative simulation optimization using a preset waterproof cabinet simulation model according to the structural parameters of the air intake panel to be optimized.

[0056] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described method for optimizing a waterproof cabinet.

[0057] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations: Obtain the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; Based on qualified benchmark data, the corresponding optimization results are obtained by iterative simulation optimization using a preset waterproof cabinet simulation model according to the structural parameters of the air intake panel to be optimized.

[0058] Electronic device 400, which can serve as a server or client of the present invention, is described below as an example of a hardware device applicable to various aspects of the present invention. Electronic device 400 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0059] Electronic device 400 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0060] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0061] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. An optimized method for a waterproof server rack, characterized in that, include: Obtain the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; Based on the qualified benchmark data, the corresponding optimization results are obtained by iterative simulation optimization using a preset waterproof cabinet simulation model according to the structural parameters of the air intake panel to be optimized.

2. The optimized method for waterproof cabinets according to claim 1, characterized in that, Before obtaining the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet, the process also includes: Based on the obtained initial air intake panel combination, qualified air intake panel combinations are selected through preset cabinet operating conditions. The qualified baseline data is obtained by simulating the waterproof cabinet simulation model based on the structural parameters of the qualified air intake panel assembly.

3. The optimized method for waterproof cabinets according to claim 2, characterized in that, The rack operating conditions include heat dissipation and rain protection; the process of selecting qualified air intake panel combinations based on the initial air intake panel combinations and pre-set rack operating conditions includes: Multiple initial air inlet panels are obtained by combining the initial air inlet panels; The waterproof cabinet with the initial air intake panel installed was tested based on a preset rain test environment to obtain the rain test results; When the rain test results simultaneously meet the heat dissipation conditions and the rainproof conditions, the initial air inlet panel is determined to be a qualified air inlet panel. The qualified air intake panel assembly is obtained based on all the qualified air intake panels.

4. The optimized method for waterproof cabinets according to claim 2, characterized in that, The process of obtaining the qualified benchmark data through simulation using the waterproof cabinet simulation model based on the structural parameters of the qualified air intake panel assembly includes: The structural parameters of the qualified air inlet panel assembly are input into the simulation model of the waterproof cabinet to obtain the simulation working point data set. The simulation working point data set includes the air inlet panel wind speed data set and the fan volume flow rate data set. The maximum wind speed in the air inlet panel wind speed data group is determined as the reference air inlet panel wind speed. The minimum volumetric flow rate in the aforementioned fan volumetric flow rate data set is determined as the baseline fan volumetric flow rate; The qualified benchmark data is generated based on the benchmark air inlet panel wind speed and the benchmark fan volume flow rate.

5. The optimized method for a waterproof cabinet according to claim 1, characterized in that, The step of iteratively simulating and optimizing the air intake panel structure parameters to be optimized using a preset waterproof cabinet simulation model to obtain the corresponding optimization results includes: Obtain the structural influence order of the air inlet panel to be optimized; The structural parameters of the air intake panel to be optimized are input into the simulation model of the waterproof cabinet to obtain the corresponding current working point data. Determine whether the qualification criteria are met based on the comparison result between the current working point data and the qualification benchmark data. If so, the structural parameters of the air intake panel to be optimized shall be determined as the optimization result; Otherwise, adjust the structural parameters of the air intake panel to be optimized according to the order of structural influence, and input the adjusted structural parameters of the air intake panel to be optimized into the waterproof cabinet simulation model for iterative simulation optimization until the qualified benchmark conditions are met.

6. The optimized method for a waterproof cabinet according to claim 5, characterized in that, The current operating point data includes the current air intake panel wind speed and the current fan volumetric flow rate; The qualified benchmark data includes the benchmark inlet panel wind speed and the benchmark fan volume flow rate; The step of determining whether the qualification criteria are met based on the comparison result between the current working point data and the qualification benchmark data includes: When the current air intake panel wind speed is less than or equal to the reference air intake panel wind speed, the waterproof condition is determined to be met. When the current fan volume flow rate is greater than or equal to the reference fan volume flow rate, the heat dissipation condition is determined to be met. When both the waterproofing condition and the heat dissipation condition are met simultaneously, the qualified benchmark condition is determined to be met.

7. The optimized method for a waterproof cabinet according to claim 5, characterized in that, Before obtaining the structural influence order of the air inlet panel to be optimized, the method further includes: Obtain the temperature rise data of the initial air intake panel assembly during a rain test; Based on the temperature rise data, the parameter influence values ​​corresponding to each structure were obtained through orthogonal experimental variance analysis. The influence order of the parameters corresponding to all the structures is obtained by sorting them from high to low.

8. The optimized method for a waterproof cabinet according to claim 7, characterized in that, The temperature rise data includes the experimental temperature rise value and the average temperature rise value of the structural parameters corresponding to each structure. The step of obtaining the parameter influence values ​​corresponding to each structure through orthogonal experimental variance analysis based on the temperature rise data includes: The average value of the temperature rise corresponding to all the tests is determined as the total average temperature rise. The total sum of squares of deviations is obtained by using all the test temperature rise values ​​and the total average temperature rise value through a preset first deviation relationship; Based on the average temperature rise value of the structural parameters and the total average temperature rise value of the structure, the corresponding structural deviation sum of squares is obtained through a preset second deviation relationship; Subtracting all the structural deviations from the total deviations sums of squares yields the error deviations sums of squares. The parameter influence value corresponding to the structure is obtained by using the sum of squared deviations of the structure and the sum of squared deviations of the error through a preset variance ratio relationship.

9. The optimized method for a waterproof cabinet according to claim 8, characterized in that, The first deviation relationship satisfies: ； The second deviation relation satisfies: ； The variance ratio relationship satisfies: ； in, The total sum of squares of deviations, Let be the temperature rise value of the i-th test. The total average temperature rise value is... For the number of trials, Let S be the sum of squared deviations of the structure corresponding to the Ath structure. The number of tests for each structural parameter of the structure. The number of the structural parameters. The average temperature rise value of the structural parameter corresponding to the j-th structural parameter of the A-th structure. Let A be the structural influence value corresponding to the Ath structure. The sum of squared errors is the sum of squared deviations. The number of the structures.

10. An optimization device for a waterproof server rack, characterized in that, include: The acquisition module is used to acquire the structural parameters of the air intake panel to be optimized and the qualified benchmark data of the waterproof cabinet; The optimization module is used to perform iterative simulation optimization based on the qualified benchmark data and the structural parameters of the air intake panel to be optimized through a preset waterproof cabinet simulation model to obtain the corresponding optimization results.

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