Charging pile fan speed regulation control method, device and storage medium

Abstract

The application discloses a charging pile fan speed regulation control method and device and a storage medium, relates to the technical field of charging piles, and comprises the following steps: matching corresponding scene control parameter groups according to scene characteristic parameters; substituting thermal load working condition parameters and the scene control parameter groups of the charging pile into a thermal model to perform convergent operation according to a thermal safety boundary, and outputting a lowest fan safety rotating speed value; performing three-constraint joint decision based on the lowest fan safety rotating speed value, a target noise limit value and a power derating rule in the scene control parameter groups, to obtain a fan target rotating speed; and matching power parameters based on the scene control parameter groups to obtain a target control parameter group, so that the charging pile is controlled to realize temperature-noise-power collaborative optimization. Through scene parameter group matching, thermal safety boundary convergent operation and three-constraint joint decision of temperature-noise-power, the application solves the problem of low equipment operation efficiency, improves multi-scene adaptation capability, and realizes the effect of temperature control noise reduction and charging efficiency collaborative optimization.

Description

Technical Field

[0001] This application relates to the field of charging pile technology, and in particular to a control method, device and storage medium for adjusting the speed of a charging pile fan. Background Technology

[0002] In the scenario of intelligent control for noise reduction and energy saving of charging piles, the ability to dynamically balance and control the heat dissipation status, operating noise and charging conditions of the equipment is directly related to the operational safety of the charging pile equipment, the efficiency of charging services and the noise reduction effect of the surrounding environment.

[0003] In related technologies, noise reduction and control of charging piles are achieved by limiting the wind turbine speed during preset fixed time periods and uniformly adopting low-speed constant operation at night. This method relies on static time rules to lock the wind turbine operating parameters, which is difficult to adapt to complex operation and maintenance scenarios with large temperature fluctuations and different noise control standards, resulting in low equipment operating efficiency.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a control method, device and storage medium for speed regulation of charging pile fans, which aims to solve the technical problem of low equipment operating efficiency.

[0006] To achieve the above objectives, this application proposes a method for controlling the speed of a charging pile fan, the method comprising: Based on the scene characteristic parameters of the charging pile, and combined with the mapping relationship between the scene and the control parameters, the corresponding control parameters are matched and filtered to form the scene control parameter group corresponding to the charging pile. The thermal load parameters of the charging pile and the scenario control parameter group are substituted into the thermal model and converged according to the thermal safety boundary to output the minimum safe fan speed value of the charging pile. Based on the minimum safe wind turbine speed value, the target noise limit and power derating rule in the scenario control parameter group, a joint decision of three constraints is made to obtain the target wind turbine speed. Based on the scenario control parameter set, the power parameters of the target speed of the wind turbine are matched to obtain the target control parameter set of the charging pile, so as to control the charging pile to achieve temperature, noise and power coordinated optimization.

[0007] In one embodiment, based on the scene feature parameters uploaded by the charging pile, and combined with the mapping relationship between the scene and control parameters, candidate configuration parameters corresponding to the scene feature parameters are filtered in the parameter library in the cloud. The compliant parameter set is tested according to the parameter operation rules. Conflicting parameters are corrected and redundant parameters are removed to obtain the scene control parameter set of the charging pile.

[0008] In one embodiment, based on the thermal load condition parameters of the charging pile and the scene control parameter group, the corresponding scene thermal model benchmark coefficients, graded temperature safety thresholds and multi-dimensional environmental correction factors are matched in the parameter library, and the thermal model calculation benchmark dataset is obtained by association and integration. Substitute the thermal model calculation benchmark dataset into the cloud-based thermal model, and iteratively solve the problem based on the real-time temperature rise rate of key components and the heat exchange efficiency of the cabinet's air intake and exhaust, to calculate the initial minimum fan speed to ensure that the component temperature does not exceed the safety limit. Based on the regional altitude correction rules and the dust accumulation loss prediction coefficient of the duct built into the scenario control parameter group, the initial minimum fan speed is corrected by boundary compensation, and abnormal speed deviation values ​​under extreme conditions are eliminated to obtain the intermediate value of the safe fan speed. The intermediate safe speed of the fan is calculated by performing convergence calculations based on the thermal safety boundary. After confirming that the heat dissipation capacity corresponding to the intermediate safe speed of the fan can cover the temperature margin requirements under all operating conditions, the minimum safe speed value of the fan is obtained.

[0009] In one embodiment, a joint decision matrix is ​​constructed based on the target noise limit and power derating rules in the scenario control parameter group of the charging pile, combined with the scenario priority weight corresponding to the charging pile, which includes three constraints: temperature safety, noise control and power guarantee. Based on the joint decision matrix and the mapping relationship between wind turbine speed and noise, the maximum allowable wind turbine speed corresponding to satisfying the target noise limit is calculated. The maximum allowable wind turbine speed is matched and determined with the minimum safe wind turbine speed value, and the constraint conflict level result and feasible speed candidate range are output. Based on the scenario priority weight and the constraint conflict level, within the temperature safety hard constraint boundary, the system adapts according to the power derating triggering ladder and speed grade adjustment rules preset for the corresponding scenario, and outputs the initial wind turbine target speed and matching power coordination strategy. The initial target wind turbine speed and the matching power coordination strategy are subjected to triple boundary verification of temperature safety upper limit, noise control upper limit and minimum guaranteed power lower limit. After the verification is passed, the target wind turbine speed is obtained.

[0010] In one embodiment, a power coordination constraint set is constructed based on the power derating trigger step, minimum guaranteed power limit, and power recovery hysteresis parameter corresponding to the scenario control parameter set; Using the power coordination constraint set as the control boundary, and combining the wind turbine target speed and the thermal load parameters, the power adjustment range, execution step size and state recovery trigger condition are matched step by step to generate an initial power coordination instruction set; The initial power coordination instruction set is checked for minimum power baseline, temperature safety linkage and noise control adaptation. Abnormal adjustment items that exceed the scene control boundary are removed to obtain the power coordination intermediate instructions that pass the verification. The power coordination intermediate command is time-bound with the corresponding wind turbine target speed, and an automatic rollback rule for abnormal operating conditions and an operating status feedback identifier are embedded to obtain a target control parameter group that can be directly issued and executed.

[0011] In one embodiment, the basic information of the charging pile's site scene management is decomposed according to preset dimension labels to obtain a scene label dataset; Based on the scene label dataset, combined with the whole-machine thermal calibration data of the charging pile, the fan noise mapping data and the power safety boundary rules, an initial scene parameter set including temperature control threshold, fan speed curve, power derating rules and model correction coefficients is generated for each category. Perform parameter boundary logic verification, version number assignment, and integrity verification value calculation on the initial scene parameter group, and attach digital signature and effective time rules to output the scene parameter configuration package that has passed the verification. The mapping relationship between the project batch of the charging pile and the scene parameter configuration package is constructed to obtain the scene control parameter group associated with the charging pile and store it in the parameter library in the cloud.

[0012] In one embodiment, the operation data uploaded by the charging piles operating the target control parameter group is classified and archived according to equipment signal, site area and project dimensions to obtain an operation file; Based on the aforementioned operational data, the deviations between the temperature rise predicted by the thermal model and the actual temperature rise, as well as the deviations between the noise model's predicted value and the corresponding noise value, are compared. Combined with the equipment's operating time and environmental condition labels, the initial values ​​of the model correction coefficients are calculated. Based on the grouping rules of equipment of the same model, site in the same region, or project of the same customer, the initial values ​​of the model correction coefficients are aggregated by deviation mean and outlier is removed, and the model target correction parameters are obtained by convergence within the preset correction upper and lower limits. The model target correction parameters are updated to the model coefficient items of the corresponding scene control parameter group to generate a target model group with version identifier.

[0013] In one embodiment, the charging pile receives the target control parameter group sent by the cloud, and after the adaptation verification is passed, writes the target control parameter group into the local candidate parameter storage area to obtain a candidate parameter set; The candidate parameter set is subjected to logical conflict simulation verification, and the matching consistency between the temperature safety threshold, the fan speed range and the power derating rule is verified item by item. The set of control parameters to be implemented is then output. When the set of control parameters to be activated reaches the preset activation trigger condition, the charging pile switches the set of control parameters to be activated as the running activation parameters and detects the running status. Based on the aforementioned operational parameters, and combined with anti-shake constraints such as acceleration / deceleration hysteresis, minimum operating hold time, and speed change slope, a speed regulation execution command is output, and the operational data is uploaded to the cloud.

[0014] In addition, to achieve the above objectives, this application also proposes a charging pile fan speed control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the charging pile fan speed control method described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the speed control method for the charging pile fan described above.

[0016] This application provides a method for controlling the speed of a charging pile's fan turbine. The method includes: matching and filtering corresponding scene control parameter sets based on the scene characteristic parameters of the charging pile; substituting the thermal load parameters of the charging pile and the scene control parameter sets into a thermal model to perform thermal safety boundary convergence calculations to output a minimum safe fan speed value; then, based on the minimum safe fan speed value and the target noise limit and power derating rules within the scene control parameter set, performing a three-constraint joint decision to obtain the target fan speed; and finally, matching the corresponding power parameters to generate a target control parameter set to drive the charging pile to achieve temperature, noise, and power coordinated optimization. This method solves the problem of existing charging pile fan turbine speed regulation issues. The system addresses technical issues such as crude speed control logic, relying solely on temperature to adjust speed, inability to accurately adapt to various noise-sensitive scenarios, difficulty in dynamically balancing temperature control and noise reduction with charging power assurance, high parameter configuration and maintenance costs, and frequent speed fluctuations affecting equipment lifespan and user experience. It improves the charging pile's adaptability to noise control requirements in different regions, stations, and time periods, achieving multi-objective collaborative optimization of temperature safety, noise control, and charging efficiency. This reduces the operation and maintenance costs of on-site parameter debugging, while also minimizing noise fluctuations and equipment wear caused by frequent fan speed changes, balancing charging efficiency with low-noise environmental friendliness.

[0017] In summary, this application solves the technical problem of low equipment operating efficiency by matching scene parameter groups, converging thermal safety boundary calculation, and jointly deciding on the three constraints of temperature, noise, and power. It also improves the adaptability to multiple scenarios and achieves the effect of synergistic optimization of temperature control and noise reduction and charging efficiency. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the speed control method for charging pile fans in this application. Figure 2 This is a flowchart of the application process. Figure 3 This is a flowchart illustrating the sixth embodiment of the control method for adjusting the speed of the charging pile fan in this application. Figure 4 This is a flowchart illustrating the seventh embodiment of the control method for adjusting the speed of a charging pile fan in this application. Figure 5 This is a flowchart illustrating the eighth embodiment of the speed control method for charging pile fans in this application. Figure 6 This is a structural schematic diagram of the charging pile fan speed control device of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] In related technologies, noise reduction and control of charging piles are achieved by limiting the wind turbine speed during preset fixed time periods and uniformly adopting low-speed constant operation at night. This method relies on static time rules to lock the wind turbine operating parameters, which is difficult to adapt to complex operation and maintenance scenarios with large temperature fluctuations and different noise control standards, resulting in low equipment operating efficiency.

[0024] This application provides a solution: First, based on the scene characteristic parameters of the charging pile and the mapping relationship between the scene and control parameters, corresponding control parameters are matched and filtered to form a scene control parameter group corresponding to the charging pile. Then, the thermal load condition parameters of the charging pile and the scene control parameter group are substituted into the thermal model and converged according to the thermal safety boundary to output the minimum safe fan speed value of the charging pile. Then, based on the minimum safe fan speed value and the target noise limit and power derating rule in the scene control parameter group, a three-constraint joint decision is made to obtain the target fan speed. Finally, based on the scene control parameter group, the power parameters of the target fan speed are matched to obtain the target control parameter group of the charging pile, so as to control the charging pile to achieve temperature, noise and power coordinated optimization.

[0025] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a charging pile fan speed control device. The following description uses a charging pile fan speed control device as an example to illustrate this embodiment and the subsequent embodiments.

[0026] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0027] This application provides a method for controlling the speed of a charging pile fan, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the control method for adjusting the speed of the charging pile fan in this application.

[0028] In this embodiment, the method for controlling the speed of the charging pile fan includes steps S10 to S40: Step S10: Based on the scene feature parameters of the charging pile and the mapping relationship between the scene and the control parameters, match and filter the corresponding control parameters to form the scene control parameter group corresponding to the charging pile.

[0029] Scene characteristic parameters are multi-dimensional attribute indicators describing the operating environment and management requirements of charging piles. Examples include regional climate type, site function type, seasonal time classification, and noise control level. Scene control parameter sets are standardized sets of parameters adapted to specific operating scenarios, including full-scale rules for fan speed regulation and power control. Examples include temperature safety thresholds, fan speed reference curves, power derating step rules, model correction coefficients, and speed anti-vibration parameters.

[0030] In this embodiment, the above-mentioned scenario parameter group matching process can be triggered in four ways. First, it is triggered by site deployment: after the charging pile completes on-site installation and registration and reports basic site information, the system automatically starts the scenario parameter group matching process to match the initial operating parameter group for the charging pile. Second, it is triggered by time period switching: according to preset day / night time period division rules, the system automatically triggers parameter group matching switching at time nodes such as daytime, nighttime, and late night to adapt to noise control requirements at different times. Third, it is triggered by seasonal changes: according to local climate season divisions or ambient temperature range thresholds, the system automatically triggers parameter group matching switching when seasons change or temperatures cross ranges to adapt to heat dissipation needs in different seasons. Fourth, it is triggered by cloud configuration: when the cloud platform issues new scenario control requirements or parameter version updates, it actively triggers the parameter group matching verification process on the charging pile side to complete parameter iteration and upgrade.

[0031] Once the charging pile receives the scene parameter matching trigger signal, it begins to collect its own corresponding scene feature parameters.

[0032] For example, there are two methods for collecting scene feature parameters. The first is to collect all static registration information. After the charging pile site is deployed, the cloud platform uniformly enters static attribute information such as the site's regional type, site attributes, and noise control level. When the charging pile starts the registration process, all scene feature parameters are retrieved from the cloud at once and stored in the local parameter area as a long-term matching benchmark. This method has a stable and unified collection logic, and the parameter source is standardized and controllable. It can provide a consistent judgment basis for matching scene parameter groups and is suitable for batch deployment scenarios of newly built sites.

[0033] The second method combines dynamic sensing with cloud-based tag completion data collection. Charging piles use their own environmental sensors to collect real-time ambient temperature and day / night light intensity data, autonomously determining the current season and time of day. Simultaneously, they retrieve the functional type of their respective site and the local noise control level tag from the cloud. Combining these two data points yields complete scene characteristic parameters. This method can perceive environmental changes in real time, automatically adapting to seasonal changes and day / night cycles without requiring manual configuration modifications, making it suitable for unattended, distributed charging station scenarios.

[0034] Once the cloud-based management platform receives the scene matching trigger signal, it begins to retrieve all scene feature parameters bound to the target charging pile and loads the complete set of scene-control parameter mapping relationships stored in the cloud.

[0035] For example, there are two methods for retrieving scene feature parameters and loading mapping relationships. The first method involves static full mapping loading from a local parameter library. Before triggering the matching process, the cloud reads all scene tags, the complete mapping table, and all candidate control parameters for the current project from the solid-state parameter storage area at once. The binding relationships between the four categories of tags (region, site, time period, and noise level) and their corresponding parameters are fully loaded into memory, generating a static mapping index table. Subsequent parameter filtering and matching are completed only through the index table. This method has stable reading logic, does not require real-time cross-database queries, is suitable for batch parameter matching scenarios of small and medium-sized sites, and has low latency for single matching responses.

[0036] The second method uses distributed incremental streaming tag loading. The cloud employs a distributed database to store the mapping relationships between charging stations in different areas. When matching is triggered, only the tag subset and corresponding mapping segments of the target charging station's area are read. Simultaneously, real-time updated management tags are streamed and incrementally generated as a temporary mapping index. Redundant parameters from irrelevant areas are not loaded. After matching is complete, the temporary index memory is released. This method consumes less cloud memory resources, is compatible with large-scale charging station operation and maintenance platforms covering multiple areas nationwide, and supports simultaneous concurrent matching of tens of thousands of devices.

[0037] Once the complete scene feature parameters and the mapping relationship between the scene and control parameters are obtained, control parameters can be selected and scene control parameter groups can be assembled based on the following two differentiated schemes.

[0038] In one alternative approach, a hierarchical matching and assembly scheme is employed, using fixed-label levels. Pre-defined scene label matching priorities are established: noise control level > site location and climate > site function type > season > equipment model. Mapping relationships are retrieved sequentially from highest to lowest priority. Each level of label filtering narrows the range of candidate control parameters, resulting in a unique set of suitable control parameters after matching across all levels. The selected temperature threshold, fan curve, derating rules, and model correction coefficients are encapsulated as fixed fields to directly generate the scene control parameter set. This scheme features a fixed matching logic hierarchy, simple verification rules, and is suitable for batch deployment in standardized new sites, with minimal computational load for parameter assembly.

[0039] In another optional approach, a multi-label weighted fusion dynamic generation scheme is employed. Independent adaptation weights are configured for each type of scenario feature parameter; for example, residential area noise label weight is 0.5, regional climate label weight is 0.25, time period label weight is 0.2, and equipment model weight is 0.05. All candidate control parameters in the cloud are traversed, and the weighted matching score of each set of candidate parameters with all current scenario labels is calculated. Multiple sets of candidate parameters with matching scores higher than a preset qualified threshold are retained. Then, through virtual simulation operation, three typical operating conditions—high temperature and full load, low noise at night, and high altitude losses—are simulated. Candidate parameters with conflicting operating logic are eliminated, and the advantageous rules of multiple sets of parameters are integrated to generate customized scenario control parameter sets. This scheme is not limited to a single fixed mapping table and can generate compromise-optimized parameters for mixed and complex scenarios, adapting to composite stations jointly built in commercial districts, hospitals, and residential areas, with a higher degree of parameter adaptation refinement.

[0040] Once the scene feature parameters are obtained, a set of parameters that match them can be selected as the scene control parameter group based on the following methods.

[0041] In one alternative approach, the full set of scene control parameters pre-stored in the cloud is retrieved first. The adaptation label threshold for each parameter set is extracted. Then, the collected scene feature parameters are compared item by item with the adaptation labels of each parameter set. Scene control parameter sets that meet the adaptation requirements in all feature dimensions are selected, while parameter sets that do not meet the adaptation conditions are removed. The final retained parameter set is the scene control parameter set corresponding to the current charging pile. This method relies on matching pre-set parameter sets in the cloud with fixed labels to achieve filtering. It is logically intuitive, computationally efficient, and fast, and is suitable for standardized charging stations with mature adaptation parameter rules.

[0042] In another alternative approach, the scenario's characteristic parameters are first decomposed into multiple dimensions, extracting core features such as regional climate, site attributes, seasonal periods, and noise control levels. Temperature safety threshold correction rules are generated based on regional climate characteristics; target noise limits and power derating weights are generated based on site attributes and noise control levels; and speed stabilization and hold-up time rules are generated based on seasonal periods. These rules are combined to generate a draft of a customized scenario control parameter set. Subsequently, the draft parameter set undergoes feasibility verification. Verification includes checking whether the temperature threshold and power derating rules match device safety boundaries, whether the speed curve is compatible with the fan hardware specifications, and whether there are logical conflicts between noise reduction rules and power guarantee rules. Infeasible parameter combinations are eliminated, and the final compliant parameter set becomes the scenario control parameter set corresponding to the current charging pile. This method can flexibly generate personalized parameter sets tailored to the actual conditions of the site, achieving higher adaptation accuracy. It can fully consider the heat dissipation needs and noise control requirements of different sites, improving the refinement of scenario adaptation and enabling customized sites for special scenarios.

[0043] In an exemplary scheme for determining scenario control parameter groups, a set of scenario control parameter groups pre-stored in the cloud is first loaded. This set includes four basic parameter groups: low-noise residential areas, strict control hospitals, high-power industrial areas, and balanced highway service areas. Each parameter group is pre-configured with a corresponding adaptation label matrix and boundary rule list. Then, a preliminary screening of the basic labels is performed. The climate type of the charging pile's location is compared with a preset regional adaptation range; the site function type is compared with a preset site adaptation category; and the noise control level is compared with a preset level adaptation range. Candidate parameter groups that simultaneously meet the requirements of all three label categories are selected. Next, for the candidate parameter groups that pass the preliminary screening, their temperature safety threshold is verified to meet the heat dissipation requirements of local high-temperature operating conditions; their power derating rules are verified to meet the charging service requirements of the site; and their speed anti-vibration parameters are verified to be compatible with the fan hardware performance. Next, a time-segment adaptation check is performed to verify whether the day / night time-segment rules of the candidate parameter groups cover the current runtime. Parameter groups with mismatched time periods are eliminated. Then, redundant parameters are excluded. If multiple parameter groups of the same type exist but differ only in parameter weights, the group with the highest matching degree to the current scene features is retained, and the remaining redundant parameter groups are eliminated. Finally, all parameter groups that pass all the above checks are summarized to generate the final scene control parameter group, which is used for subsequent thermal model calculations and joint decision-making.

[0044] It should be noted that in some special cases, if only one set of parameters matches the scene characteristic parameters of the charging pile, the multi-set screening and verification process will be skipped, and it will be directly used as the currently effective scene control parameter set.

[0045] Step S20: Substitute the thermal load condition parameters of the charging pile and the scenario control parameter group into the thermal model and perform convergence calculation according to the thermal safety boundary to output the minimum safe fan speed value of the charging pile.

[0046] The thermal model is a pre-defined calculation model used to quantify the relationship between the heat dissipation requirements of charging piles and the fan speed. Thermal load parameters are operational indicators describing the real-time heat generation and dissipation status of the charging pile. Examples include the real-time temperature of the power module, the temperature at the cabinet inlet, the temperature at the cabinet outlet, the current charging power, and the module temperature rise rate. The thermal safety boundary convergence calculation uses the constraint that the temperature of key components does not exceed a safety threshold, and iteratively solves to obtain the minimum required fan speed. The minimum safe fan speed value is the minimum fan speed required to ensure that the temperature of key components in the charging pile remains within a safe range.

[0047] In this embodiment, the thermal load condition parameters and the scenario control parameter set are substituted into the thermal model as inputs to start the calculation process for the minimum safe speed of the fan.

[0048] For example, there are two methods for collecting thermal load parameters. The first is synchronous acquisition at all points. At the beginning of each control cycle, all temperature sensor interfaces and power acquisition interfaces inside the charging pile are synchronously called to collect all thermal load data at once, including power module temperature, inlet and outlet air temperature, ambient temperature, current charging power, and module load rate. After collection, the data is uniformly output to the thermal model calculation module. This method ensures consistent acquisition timing and high data timestamp alignment, providing accurate and synchronized input data for thermal model calculation, avoiding calculation errors caused by timing deviations, and adapting to the high-precision temperature control scenarios of high-power fast charging piles.

[0049] The second method involves tiered, region-specific data acquisition. First, temperature data is collected from the core area of ​​the power module. When the core temperature is in a low-load range, only ambient temperature and charging power are needed to characterize the thermal load. As the core temperature approaches the warning threshold, high-frequency acquisition of secondary parameters such as inlet and outlet temperatures and temperature rise rates is initiated, gradually refining the thermal load parameter set. This method dynamically adjusts the acquisition granularity based on the heating status, reducing resource consumption under low load and ensuring data accuracy under high load, balancing computational efficiency and acquisition accuracy. It is suitable for residential sites where low loads are common.

[0050] Once the thermal load parameters and scenario control parameter set are obtained, thermal safety boundary convergence calculations can be performed in the following manner.

[0051] In one alternative approach, a lookup-based interpolation convergence calculation is employed. A calibration table of minimum safe operating speeds under different ambient temperatures and charging powers is pre-stored in the thermal model. The collected thermal load parameters and temperature safety thresholds from the scenario control parameter group are substituted into the calibration table, and the initial safe operating speed is calculated through piecewise linear interpolation. A single correction is then performed based on the temperature rise rate parameter. If the corrected value meets the temperature margin requirement, it is directly output; otherwise, the speed is increased by a fixed step size for re-verification until the safety boundary requirements are met, ultimately yielding the minimum safe operating speed of the fan. This method relies on pre-calibrated data for rapid solution, with low computational load and fast response speed, making it suitable for low-end charging pile main controllers with limited computing resources.

[0052] In another alternative approach, an iterative thermal balance convergence calculation is employed. Based on the physical model of heat exchange in the charging pile cabinet, a real-time thermal balance equation is constructed using thermal load parameters. The convergence target is the temperature safety threshold in the scenario control parameter set. After setting the initial fan speed, the corresponding steady-state temperature of the components is calculated. If the calculated temperature is below the safety threshold, the speed is gradually reduced; if it is above the safety threshold, the speed is gradually increased. Through multiple iterations, the calculated temperature converges to near the safety threshold, and the final corresponding speed is the minimum safe fan speed value. This method is based on a physical model and does not rely on a large amount of pre-calibrated data, making it more adaptable and able to accurately handle scenarios of operational drift such as dust accumulation in the duct and fan aging, thus adapting to existing charging pile stations operating for extended periods.

[0053] In an exemplary scheme for determining the minimum safe operating speed of a wind turbine, the thermal model configuration parameters of the corresponding scenario control parameter group are first loaded. This configuration includes three basic parameters: temperature safety threshold matrix, heat exchange correction coefficient, and convergence margin. It also pre-configures two solution entry points: a calibration data table and a thermal balance equation. Next, parameter normalization is performed, standardizing the collected thermal load parameters to eliminate dimensional differences between parameters. Then, initial calculation is initiated, substituting the normalized parameters into the pre-calibration data table and calculating the initial safe operating speed through interpolation. Convergence verification is then performed, substituting the initial speed into the thermal balance equation to calculate the corresponding steady-state temperature and temperature rise rate of the device, determining whether the temperature margin meets the safety requirements of the scenario control parameter group. If the convergence condition is not met, the speed is adjusted by a preset step size and re-verified until the temperature margin is within the preset convergence range. Finally, boundary correction is performed, compensating and correcting the converged speed based on the regional altitude correction coefficient and duct loss prediction coefficient in the scenario control parameter group, eliminating abnormal speed deviations under extreme conditions. Finally, the validity of the execution result is verified to check whether the calculated speed is within the speed range allowed by the wind turbine hardware. Abnormal results that exceed the hardware boundary are eliminated, and the final minimum safe wind turbine speed value is generated for subsequent joint decision-making based on three constraints.

[0054] Step S30: Based on the minimum safe wind turbine speed value and the target noise limit and power derating rule in the scenario control parameter group, a three-constraint joint decision is made to obtain the target wind turbine speed.

[0055] The three-constraint joint decision-making is a decision-making logic that comprehensively considers three types of constraints—temperature safety, noise control, and power guarantee—to determine the optimal wind turbine speed. The target noise limit is the upper limit of the allowable operating noise of the charging pile in the corresponding scenario. The power derating rule is a tiered execution rule for adjusting the charging power when temperature control and noise reduction requirements conflict. The target wind turbine speed is the final commanded operating speed value determined after weighing multiple constraints.

[0056] In this embodiment, the minimum safe wind turbine speed is used as the temperature constraint input, the allowable speed corresponding to the target noise limit is used as the noise constraint input, and the power derating rule is used as the power constraint input to initiate a three-constraint joint decision-making process.

[0057] For example, there are two methods for converting the target noise limit to the allowable speed. The first is calibration curve mapping conversion. A pre-stored fan speed and noise calibration curve is used, combined with the target noise limit in the scenario control parameter group, to retrieve the corresponding maximum allowable fan speed from the curve. Simultaneously, minor corrections are made based on the number of fans and the cabinet sound field reflection coefficient to obtain the maximum allowable speed that meets the noise requirements. This method has a simple and direct conversion logic, relies on high accuracy based on measured calibration data, and is suitable for standardized sites with clearly defined noise control requirements.

[0058] The second method is multi-source superposition estimation and conversion. First, the noise contribution of the wind turbine itself is calculated based on the wind turbine speed reference curve. Then, the estimated values ​​of power module operating noise and inductor vibration noise are superimposed. Combined with the spatial acoustic characteristic correction coefficient of the site, the maximum allowable wind turbine speed that meets the target noise limit is iteratively calculated. This method comprehensively considers the superposition effect of multiple noise sources and can more accurately adapt to special acoustic environments such as underground parking garages and enclosed sites, improving the accuracy of noise control.

[0059] Once the three types of constraint boundaries are obtained, joint decision-making based on the three constraints can be performed in the following manner.

[0060] In one alternative approach, a priority-based hierarchical decision-making process is employed. Based on the pre-defined constraint priorities of the scenario control parameter group, the temperature safety hard constraint is checked first. After confirming that the minimum safe fan speed meets the hardware boundary, the maximum allowable speed for noise constraints is then compared. If the minimum safe speed is not higher than the noise-allowable speed, the lowest possible speed is selected as the target fan speed, while still meeting temperature control requirements. If the minimum safe speed is higher than the noise-allowable speed, the charging power is gradually reduced according to a pre-defined power derating step, and the minimum safe speed at the corresponding lower power is recalculated simultaneously until the speed falls within the noise-allowable range, ultimately yielding the target fan speed. This approach features a clear, hierarchical decision-making logic, a stable and controllable execution process, and is suitable for the management and control needs of most conventional sites.

[0061] In another alternative approach, a multi-objective optimization decision-making method is employed. Temperature safety is used as a hard constraint boundary, with noise minimization and power maximization as dual optimization objectives. A multi-objective optimization function is constructed, and combined with weighting coefficients in the scenario control parameter set, iteratively searches for the optimal combination of speed and power within the feasible solution space to maximize the overall benefit value. The resulting speed is the target speed of the fan. This method achieves a fine balance between temperature control, noise reduction, and power preservation, avoiding excessive derating or noise reduction caused by a single priority. It is suitable for scenarios such as commercial complexes and office parks where both charging efficiency and noise levels are critical.

[0062] In an exemplary scheme for determining the target wind turbine speed, the three-constraint decision configuration in the scenario control parameter group is first loaded. This configuration includes four basic rules: temperature constraint boundary, noise constraint limit, power derating step, and scenario weight coefficient. It also pre-configures two decision modes: hierarchical decision and optimization decision. Next, constraint boundary conversion is performed, converting the target noise limit into the maximum allowable wind turbine speed and the power derating rule into a power-speed correspondence, thus determining the feasible solution range. Then, conflict determination is performed, comparing the minimum safe wind turbine speed with the maximum allowable wind turbine speed to determine if there is a conflict between the temperature constraint and the noise constraint. If there is no conflict, the speed that best matches the low-noise target within the speed range that meets the temperature control requirements is selected as the initial target speed. If a conflict exists, hierarchical coordination is initiated based on the scenario weight coefficient. A small power derating is first performed, and the corresponding minimum safe speed is recalculated to determine if the conflict is eliminated. If the conflict still exists, the speed is gradually increased step by step, while matching the corresponding power adjustment, until both the temperature safety baseline and noise control requirements are simultaneously met. Boundary checks are then performed to verify whether the initial target speed is within the allowable range of the wind turbine hardware and whether the corresponding power is not lower than the minimum guaranteed power. Finally, the results are confirmed, and the final wind turbine target speed and corresponding power coordination strategy are generated for subsequent target control parameter generation.

[0063] Step S40: Based on the scenario control parameter group, match the power parameters of the target speed of the wind turbine to obtain the target control parameter group of the charging pile, so as to control the charging pile to achieve temperature, noise and power coordinated optimization.

[0064] The target control parameter set is a complete set of execution parameters that includes wind turbine operation commands and power management commands. Temperature, noise, and power coordinated optimization refers to achieving a comprehensive operating state that simultaneously ensures safe and controllable temperature, compliant noise levels, and efficient charging power. Examples include wind turbine speed control commands, power derating commands, speed anti-vibration parameters, and status feedback rules.

[0065] In this embodiment, based on the wind turbine target speed and matching power coordination strategy, and combined with the execution rules of the scenario control parameter group, a target control parameter group that can be directly issued and executed is generated.

[0066] For example, there are two methods for matching power parameters. The first is stepped mapping matching, which matches the power adjustment range corresponding to the target speed of the wind turbine to a preset power level according to the power derating step rules in the scenario control parameter group, and directly outputs the power parameters of the corresponding level, while matching the corresponding power recovery hysteresis conditions. This method has simple matching logic, clear power adjustment levels, avoids frequent power fluctuations, and is suitable for sites with high requirements for charging stability.

[0067] The second method is continuous smooth matching. Based on the correspondence between the target fan speed and the thermal load conditions, the optimal charging power value is continuously calculated to generate power adjustment parameters for a smooth transition. Simultaneously, a power change rate limit is set to avoid power jumps. This method enables smooth power adjustment, reducing the impact of sudden power changes on user experience, and is suitable for scenarios such as residential areas and hospitals where charging experience is sensitive.

[0068] Once the matching power parameters are obtained, the target control parameter set can be generated in the following way.

[0069] In one alternative approach, parameter encapsulation is used to generate standardized target control parameter sets. This method encapsulates the wind turbine's target speed, matched power parameters, speed anti-jitter parameters from the scenario control parameter group, and fault rollback rules, adding parameter version and effective time identifiers. This results in a simple and standardized generation process with a unified parameter structure, facilitating parsing and execution by the charging pile side and adapting to standardized sites deployed in batches.

[0070] In another optional approach, a timing-bound generation method is used. This method binds and matches the execution timing of fan speed and power adjustments, sets the order and transition duration for speed and power adjustments, embeds automatic rollback rules for abnormal operating conditions and operational status feedback requirements, and generates a target control parameter set with timing control logic. This approach ensures coordinated execution of speed and power adjustments, avoids temperature or noise exceeding limits due to timing misalignment, and improves the stability and reliability of the control process.

[0071] In an exemplary scheme for generating a target control parameter group, the execution rule configuration in the scenario control parameter group is first loaded. This configuration includes four basic rules: power adjustment steps, speed anti-jitter rules, timing transition requirements, and anomaly rollback strategies. Then, power parameter matching is performed. Based on the power coordination strategy between the wind turbine target speed and the three-constraint decision output, the corresponding charging power target value, power adjustment step size, and power recovery trigger condition are matched. Next, anti-jitter parameter matching is performed. The minimum hold time, acceleration / deceleration hysteresis, and speed change slope parameters in the scenario control parameter group are adapted and bound to the wind turbine target speed to generate a speed smoothing execution rule. Then, timing binding is performed, setting the execution timing and transition period for wind turbine speed adjustment and power adjustment to ensure coordinated operation and avoid control timing misalignment. Finally, anomaly rule embedding is performed, adding anomaly trigger rules for forced acceleration due to temperature exceeding limits and automatic derating due to noise exceeding limits, as well as a rollback mechanism for parameter anomalies. Finally, the parameters are encapsulated and verified to ensure they meet the hardware boundaries and scenario requirements. Once verified, the final target control parameter set is generated and sent to the charging pile execution unit to drive the charging pile to achieve temperature, noise, and power coordinated optimization operation.

[0072] Further, please refer to Figure 2 , Figure 2 This is the process architecture diagram for this application. After the charging pile wind turbine speed control process starts at the start node, it first completes the configuration of the automatic wind turbine speed adjustment profile (FAP) on the cloud platform (CP). Then, it enters a judgment stage to determine whether time, region, and season rules are effective based on matching rules. If the judgment result is negative, control logic without limiting the wind turbine speed is executed and the process proceeds to the end node. If the judgment result is positive, the control action of limiting the wind turbine speed according to noise requirements is executed first, and then the process enters the judgment stage for exceeding the environmental noise (EN) standard. If the environmental noise exceeding the standard judgment result is positive, the process returns to the stage of limiting the wind turbine speed according to noise requirements to iteratively adjust the speed. If the environmental noise exceeding the standard judgment result is negative, the process proceeds to the end node.

[0073] Second Embodiment This embodiment provides an exemplary scheme for accurately matching and determining a scene control parameter set. In this example, firstly, based on the scene feature parameters uploaded by the charging pile, corresponding candidate configuration parameters are filtered in the cloud parameter library. Then, the parameter adaptability is verified to obtain a compliant parameter set. Finally, after trial operation correction and redundancy removal, the final scene control parameter set is obtained. Step S10 includes steps A11~A12: Step A11: Based on the scene feature parameters uploaded by the charging pile, and combined with the mapping relationship between the scene and control parameters, filter the candidate configuration parameters that match the scene feature parameters in the parameter library in the cloud. Step A12: Run the compliant parameter set according to the parameter operation rules, correct conflicting parameters, and remove redundant parameters to obtain the scene control parameter group of the charging pile.

[0074] Candidate configuration parameters are pre-stored wind turbine speed regulation and power control parameter units with corresponding scenario adaptation tags in the cloud parameter library. They are the initial candidates for scenario parameter group matching and screening. Examples include temperature threshold parameters, wind turbine speed curve parameters, power derating rule parameters, noise control limit parameters, and model correction coefficient parameters. Adaptability is a quantitative indicator characterizing the degree of matching between candidate configuration parameters and the current scenario characteristic parameters of the charging pile. It is the core criterion for screening compliant parameters. Examples include regional climate adaptability, site type adaptability, seasonal time adaptability, noise control level adaptability, and parameter boundary compatibility. Parameter operation rules are a set of preset verification rules used to verify the rationality of the actual operation logic of the parameter group. They are the execution benchmark for checking parameter conflicts and redundancies. Examples include temperature and speed linkage rules, power and noise constraint rules, parameter boundary mutual exclusion rules, and timing execution priority rules. The scenario control parameter group is a set of full control parameters that are finally determined after matching, verification, and correction and are adapted to the current operating scenario of the charging pile. It is the benchmark parameter basis for subsequent thermal model calculations and joint decision-making.

[0075] In this example, when filtering candidate configuration parameters based on the scene feature parameters uploaded by the charging pile, a multi-dimensional tag matching method can be used. By extracting four core tags from the scene feature parameters—region, station, time period, and control level—each tag is compared with the matching tags of each configuration parameter in the cloud parameter library to filter out candidate configuration parameters that match the tags, thus completing the initial selection of candidate parameters.

[0076] After the candidate configuration parameters are screened, the adaptability verification process is initiated. For each candidate configuration parameter, its individual adaptability in each scenario dimension is calculated, and then the comprehensive adaptability is obtained by weighted summation. Candidate configuration parameters whose comprehensive adaptability reaches the preset qualified threshold are determined to be compliant parameters. All compliant parameters are summarized to obtain a compliant parameter set. In this way, the accuracy of parameter matching is improved through multi-dimensional quantitative verification, avoiding parameter adaptation deviation caused by single tag matching.

[0077] After obtaining the set of compliant parameters, a virtual trial run is performed on the set of compliant parameters according to the preset parameter operation rules. This simulates the linkage execution logic of parameters under different working conditions, identifies and corrects parameter items with logical conflicts, and removes redundant parameter items with overlapping functions. The result is a set of scenario control parameters adapted to the current charging pile. This pre-trial verification ensures the feasibility of the parameter set and reduces logical anomalies in on-site operation.

[0078] For example, there are two ways to obtain a compliance parameter set and generate a scenario control parameter group through adaptability verification. The first is a sequential label-by-label adaptation verification. Based on the priority order of the dimensions bound to the scenario feature parameters, starting with the highest priority noise control level dimension, single-dimensional features are selected sequentially and their corresponding labels are compared with the candidate configuration parameters. After each dimension comparison is completed, it is simultaneously determined whether the parameter meets the adaptation requirements of the current dimension. If it meets the requirements, the parameter is retained and proceeds to the next dimension verification; if it does not meet the requirements, the parameter is directly removed, and the verification of the remaining dimensions is terminated. After all the sequential verifications of all dimensions are completed, all parameters that pass the full-dimensional verification are summarized to obtain the compliance parameter set. Subsequently, a sequential virtual trial run is performed on the compliance parameter set, simulating the parameter execution logic one by one according to the order of changes in operating conditions, checking for parameter conflicts and redundancies one by one, and correcting them to obtain the scenario control parameter group. This method employs a priority-locked, dimension-by-dimensional serial verification and sequential trial-run investigation processing logic. By verifying and investigating step by step in accordance with the control priority, it avoids invalid verification operations of low-priority parameters and ensures the matching accuracy of core control dimensions.

[0079] The second method is multi-dimensional clustering parallel adaptation and verification. Global label distribution parsing is performed on all configuration parameters in the cloud parameter library. Clustering is then performed based on four label features: region, site, time period, and control level. Parameters with similar label features are grouped into the same parameter cluster, and the core feature intervals of each cluster are marked, generating multiple parameter clusters that are computationally independent of each other. Then, matching and verification processing is performed synchronously on all the completed parameter clusters. Within each parameter cluster, the multi-dimensional adaptability of all parameters within the cluster to the current scene features is compared, and parameters that meet the adaptation requirements are selected. After all parallel matching and verification of all clusters is completed, all compliant parameters are summarized to obtain a compliant parameter set. Subsequently, parallel multi-condition virtual trial runs are performed on the compliant parameter set, simulating parameter execution logic under multiple typical working conditions, batch checking for parameter conflicts and redundancies, and correcting them to obtain the scene control parameter group. This method employs a processing logic of global label clustering partitioning and full-cluster parallel matching verification. By pre-emptively clustering global parameter features, it locks in the potential parameter range for scene adaptation in advance, reducing the number of invalid single-parameter matching attempts.

[0080] In one implementation of the mapping selection, the cloud management platform receives complete scene feature parameters such as device model, site area, environmental conditions, noise control level, and season uploaded by the charging pile in real time. At the same time, it calls the standardized mapping relationship between the scene and control parameter pre-stored in the cloud parameter library. Based on the tag precise matching logic, it selects multiple sets of candidate configuration parameters that are highly adapted to the current charging pile scene features from the massive preset parameter resources to form an initial candidate parameter set. Subsequently, the cloud retrieves preset parameter operation rules and conducts full-condition virtual trial operation simulation on the initial candidate parameter set. This fully simulates the actual operating conditions of the charging pile, such as high temperature and full load, silent operation, power increase and decrease switching, and fan speed regulation linkage. The logical matching, boundary compatibility, and operating condition adaptability between each group of candidate configuration parameters are verified online. The system automatically identifies parameter conflicts such as temperature threshold and speed curve mismatch, power derating step and noise control rule conflict, model correction coefficient and operating condition adaptation deviation. Based on the optimal adaptation criterion for the scenario, conflicting parameters are adaptively corrected and optimized. At the same time, invalid candidate parameters with overlapping functions, low adaptability, and redundant operating conditions are eliminated. Finally, a standardized scenario control parameter set that adapts to the current real operating scenario of the charging pile, has no logical conflicts, and has no redundant deviations is selected and integrated. This provides accurate, compliant, and adaptable benchmark parameter support for subsequent cloud thermal model calculations, three-constraint joint decision-making, and target control parameter generation.

[0081] Furthermore, the cloud-based remote configuration, switching, and rollback processes are streamlined. The cloud platform stores various scenario parameter groups and can distribute configurations by single pile, site, city, customer project, or equipment batch. Configurations include parameter group number, applicable region, applicable site type, applicable season, applicable time period, noise control level, temperature threshold, noise target, turbine speed curve, power derating curve, model correction coefficient, version number, effective time, checksum, and signature information.

[0082] The cloud platform generates or selects target parameter groups based on site data, geographical location, customer noise requirements, and local control rules. The cloud distributes the configuration package to the charging pile, which verifies the version number, device compatibility, signature, and integrity check value. After successful verification, the charging pile writes the configuration to the candidate parameter area, without immediately overwriting the currently effective parameter group. The charging pile performs simulation verification in the candidate parameter area to check for conflicts between temperature thresholds, target noise, maximum speed, minimum speed, and power derating thresholds. After successful simulation verification, the parameter group is switched at the effective time or triggered by a cloud command, and the old parameter group before the switch is recorded as rollback parameters. After the switch, an observation window is opened, where the system monitors module temperature, fan speed, estimated noise, charging power, and alarm status. If temperature exceeds limits, fan control malfunctions, abnormal power derating occurs, or the noise target is clearly unmet, the old parameter group is automatically rolled back, and the cause is reported to the cloud. If the operation is stable within the observation window, the new parameter group is confirmed to be officially effective, and the operating data is used for subsequent model correction.

[0083] Third Embodiment This embodiment provides an exemplary scheme for convergent solution of the minimum safe wind turbine speed. In this example, a thermal model calculation benchmark dataset is first obtained by matching and integrating thermal load condition parameters and scenario control parameter groups. Then, the dataset is substituted into the thermal model to perform iterative solution to obtain the initial minimum wind turbine speed. After boundary compensation correction, an intermediate value of the safe wind turbine speed is obtained. Finally, the minimum safe wind turbine speed value is output through thermal safety boundary convergence verification. Step S20 includes steps B11 to B14: Step B11: Based on the thermal load operating parameters of the charging pile and the scene control parameter group, match the corresponding scene thermal model benchmark coefficients, graded temperature safety thresholds and multi-dimensional environmental correction factors in the parameter library, and integrate them to obtain the thermal model calculation benchmark dataset.

[0084] Step B12: Substitute the thermal model calculation benchmark dataset into the thermal model in the cloud, and perform iterative solutions based on the real-time temperature rise rate of key components and the heat exchange efficiency of the cabinet air intake and exhaust to calculate the initial minimum fan speed to ensure that the component temperature does not exceed the safety limit.

[0085] Step B13: Based on the regional altitude correction rules and the dust accumulation loss prediction coefficient of the duct built into the scene control parameter group, perform boundary compensation correction on the initial minimum fan speed, eliminate abnormal speed deviation values ​​under extreme conditions, and obtain the intermediate value of the safe fan speed.

[0086] Step B14: Perform convergence calculation on the intermediate safe speed value of the fan according to the thermal safety boundary. After confirming that the heat dissipation capacity corresponding to the intermediate safe speed value of the fan can cover the temperature margin requirements of the whole operating conditions, the minimum safe speed value of the fan is obtained.

[0087] The thermal model computational baseline dataset is a standardized data set containing all the input parameters and correction coefficients required for thermal model calculations, serving as the fundamental input unit for initiating thermal model computations. Examples include scenario thermal model baseline coefficients, graded temperature safety thresholds, multi-dimensional environmental correction factors, real-time thermal load parameters, and device thermal resistance calibration values. The initial minimum fan speed is the initial fan speed value obtained from the first iteration of the thermal model, meeting the basic temperature safety requirements, and is the object of subsequent boundary correction and convergence verification. Examples include the basic cooling speed under rated conditions, the minimum cooling speed corresponding to full load at room temperature, and the matching speed corresponding to a typical temperature rise rate. The intermediate safe fan speed is the intermediate result of the speed after correction for scenario factors such as altitude and duct losses, and is the object of preliminary processing for the final convergence verification. Examples include the compensated speed after altitude correction, the adjusted speed after dust accumulation loss correction, and the compliant speed after eliminating extreme operating conditions.

[0088] In this example, when matching and integrating the thermal model calculation benchmark dataset, it can be done by linking and retrieving the scene tags. By using the scene identifier corresponding to the scene control parameter group, the matching thermal model benchmark coefficients, temperature safety thresholds and environmental correction factors are accurately retrieved from the cloud parameter library. The fields are aligned and associated with the thermal load condition parameters reported in real time by the charging pile, and the thermal model calculation benchmark dataset is accumulated to complete the standardized integration of thermal model input data.

[0089] After completing the construction of the benchmark dataset for thermal model calculation, the iterative solution process of the thermal model is initiated. The benchmark dataset is substituted into the cloud thermal model. The real-time temperature rise rate of key components and the heat exchange efficiency of the cabinet air intake and exhaust are the core calculation dimensions. The constraint target is that the component temperature does not exceed the safety limit. Multiple rounds of iterative calculations are performed to obtain the initial minimum fan speed. In this way, the basic accuracy of the speed solution is ensured through physical model iteration.

[0090] After obtaining the initial minimum fan speed, the initial speed is compensated and corrected according to the regional altitude correction rules and the dust loss prediction coefficient of the duct built into the scenario control parameter group. At the same time, abnormal speed deviation values ​​caused by extreme operating conditions are identified and eliminated, and the intermediate value of the safe fan speed is output. In this way, the adaptability of the speed to different site environments is improved through scenario factor correction.

[0091] After obtaining the intermediate value of the safe speed of the fan, a thermal safety boundary convergence calculation is performed to simulate the temperature change of the device corresponding to the speed under full operating conditions. The calculation verifies whether the heat dissipation capacity can cover the preset temperature margin requirement. After the verification is passed, the final minimum safe speed value of the fan is output. In this way, the temperature safety and reliability of the speed under full operating conditions is guaranteed through convergence verification.

[0092] For example, there are two ways to obtain the minimum safe fan speed value through iterative solution and convergence verification of the thermal model. The first is a serial step-by-step iterative solution driven by the temperature rise rate. According to the priority order of the operating conditions bound to the thermal model calculation benchmark dataset, starting from the temperature dimension of the highest priority key components, single-dimensional operating condition parameters are selected sequentially to perform initial speed matching and temperature rise verification. After each level of operating condition verification is completed, it is simultaneously determined whether the current speed meets the temperature safety requirements of that dimension. If the requirements are met, the current speed is retained and the verification of the next level of operating condition is carried out. If the requirements are not met, the speed is increased by a fixed step size and the verification is repeated until the operating condition of that dimension meets the safety requirements. After all serial verifications of all operating condition dimensions are completed, the initial minimum fan speed that can cover all basic operating conditions is obtained. Subsequently, altitude correction, dust accumulation loss correction, and outlier removal are performed sequentially to obtain the intermediate value of the safe fan speed. Then, a full-condition margin serial convergence verification is performed to verify the temperature safety under operating condition fluctuations. After the verification is passed, the minimum safe fan speed value is obtained. This method employs a priority-locked, step-by-step serial iteration and sequential convergence verification processing logic. By verifying and progressively adjusting the safety priority of the device, it avoids the invalid computational overhead of non-critical operating conditions and ensures the accuracy of the solution for the temperature safety of the core device.

[0093] The second method involves parallel convergence solution for heat exchange zones. A global operating condition dimension analysis is performed on the baseline dataset for the thermal model calculation. Based on the heat exchange zone characteristics of the charging pile cabinet's air inlet, module heating zone, and air outlet, region division is performed. Temperature parameters, thermal resistance parameters, and wind speed parameters corresponding to each zone are assigned to the same heat exchange calculation zone, and the heat exchange boundary conditions of each zone are marked, generating multiple loosely coupled heat exchange calculation zones. Parallel solution processing is then initiated simultaneously for all divided heat exchange calculation zones. Within each heat exchange calculation zone, the relationship between wind speed and temperature rise is calculated one by one to determine the minimum fan speed value that meets the zone's temperature safety requirements. After all parallel solutions for all calculation zones are completed, the maximum value from the results of each zone is taken as the initial minimum fan speed. Subsequently, multi-scenario factor compensation and correction are performed in parallel, simultaneously completing multi-dimensional corrections and outlier removal for altitude, dust accumulation, etc., to obtain the median safe fan speed value. Then, a full-zone margin parallel convergence verification is performed to batch verify the temperature safety under full operating condition fluctuations. After successful verification, the minimum safe fan speed value is obtained. This method employs a processing logic of global hot-swapping partitioning and parallel iterative solution across all partitions. By pre-disassembling the rack hot-swapping partitions, coupled hot-computing links are decoupled in advance, reducing the cumulative time consumption of serial iterations and improving the speed calculation efficiency in high-computing scenarios.

[0094] Furthermore, the invention employs a dual-model joint calculation logic, using a thermal model and a noise model to jointly calculate the target speed of the wind turbine. The thermal model calculates the minimum safe speed R_safe required to ensure that the temperature of critical components does not exceed the safety threshold under current ambient temperature, module temperature, charging power, module load rate, inlet / outlet air temperature difference, and historical temperature rise rate. The thermal model can be implemented using lookup tables, piecewise functions, or empirical formulas. For example, the minimum safe speed can be pre-calibrated in different ambient temperature and power ranges and corrected based on the real-time temperature rise slope. When the module temperature rises rapidly, R_safe is increased. When the temperature steadily decreases, R_safe is allowed to decrease. The noise model calculates the maximum permissible speed R_noise that meets noise limits under the current number of wind turbines, wind turbine speed, power module operating status, site noise level, and target noise value. The noise model can be obtained from measured calibration tables, wind turbine speed-noise curves, or closed-loop correction using on-site noise sensors. When no noise sensor is configured, the system estimates noise based on fan speed, number of fans, module load, and scene parameters. When a noise sensor is configured, the system corrects the model based on measured noise. The joint constraint decision module makes judgments based on R_safe, R_noise, current charging power P_now, target charging power P_req, minimum guaranteed power P_min, temperature safety threshold T_safe, and target noise N_target: If R_safe is less than or equal to R_noise, it means that both temperature safety and noise targets can be met at the current power, and the system selects a fan speed that is not lower than R_safe and as close as possible to the low-noise target. If R_safe is greater than R_noise, it means that temperature safety and noise targets conflict at the current power. The system first judges the temperature margin from the safety threshold and the temperature rise rate: if the temperature margin is sufficient and the temperature rise rate is low, the current speed can be maintained for a short time and observation can continue. If the temperature rise rate increases or the temperature approaches the safety threshold, a phased speed increase or power derating is performed according to the scene parameter group. For high-noise-sensitive scenarios such as residential areas, hospitals, and schools, when R_safe is greater than R_noise and the charging power is higher than the minimum guaranteed power, the charging power is first slightly reduced to bring the new R_safe down to near R_noise. If temperature safety is still not met after derating, the fan speed is further increased, prioritizing temperature safety. For low-noise-sensitive scenarios such as industrial areas and highway service areas, when R_safe is greater than R_noise, the fan speed is first increased to ensure charging power, and power derating is only implemented when the temperature approaches the safety boundary or the fan has reached its upper limit. If the temperature of critical components reaches the mandatory protection threshold and is no longer limited by the target noise, the system forcibly increases the fan speed to the safe speed or the maximum allowable speed and reduces the charging power according to the protection strategy.

[0095] Fourth embodiment This embodiment provides an exemplary scheme for joint decision-making based on temperature, noise, and power constraints. In this example, a joint decision-making matrix is ​​first constructed based on the target noise limit and power derating rules within the scene control parameter group, combined with scene priority weights. Then, the maximum allowable wind turbine speed that meets the noise requirements is calculated, and the constraint conflict level and feasible speed range are determined. After hierarchical adaptation, a preliminary target wind turbine speed and corresponding power coordination strategy are output. Finally, the target wind turbine speed is obtained through triple boundary verification. Step S30 includes steps C11~C15: Step C11: Based on the target noise limit and power derating rules in the scenario control parameter group of the charging pile, and combined with the scenario priority weight corresponding to the charging pile, construct a joint decision matrix including three constraints: temperature safety, noise control, and power guarantee.

[0096] Step C12: Based on the joint decision matrix and the mapping relationship between wind turbine speed and noise, calculate the maximum allowable wind turbine speed that satisfies the target noise limit.

[0097] Step C13: Match the maximum allowable wind turbine speed with the minimum safe wind turbine speed, and output the constraint conflict level result and the feasible speed candidate range.

[0098] Step C14: Based on the scenario priority weight and the constraint conflict level result, adapt the system within the temperature safety hard constraint boundary according to the power derating triggering ladder and speed grade adjustment rules preset for the corresponding scenario, and output the preliminary wind turbine target speed and matching power coordination strategy.

[0099] Step C15: Perform triple boundary verification on the preliminary target speed of the wind turbine and the matching power coordination strategy, including the upper limit of temperature safety, the upper limit of noise control, and the lower limit of minimum guaranteed power. After the verification is passed, the target speed of the wind turbine is obtained.

[0100] The joint decision matrix is ​​a standardized decision data structure that integrates three types of constraint rules and weighting coefficients: temperature safety, noise control, and power assurance. It is the core computational carrier for joint decision-making based on these three constraints. Examples include temperature safety constraint thresholds, noise control constraint limits, power assurance constraint rules, scenario-level priority weights, and constraint conflict determination rules. The maximum permissible turbine speed is the maximum operating speed of the turbine while meeting the target noise limit requirements; it is the speed boundary indicator for noise control constraints. Examples include permissible speeds corresponding to daytime noise limits, nighttime noise limits, strict control in sensitive areas, and relaxed control in normal areas. The constraint conflict level is a graded determination of the degree of contradiction between the minimum speed of temperature safety constraints and the maximum speed of noise control constraints; it is the core basis for selecting decision-making adaptation strategies. Examples include no conflict level, slight conflict level, moderate conflict level, and severe conflict level.

[0101] The feasible speed candidate range is the range of speed values ​​that simultaneously meet the temperature safety baseline and noise control upper limit, representing the feasible solution space for speed decisions. Examples include a wide feasible range in conflict-free scenarios, a narrow feasible range under mild conflict, and a convergent feasible range after conflict resolution. The initial target wind turbine speed is the preliminary selection result of the wind turbine speed obtained through hierarchical adaptation and has not undergone final boundary verification; it is a pre-processing object for final verification. Examples include a low-noise preferred speed in conflict-free scenarios, a compromise balance speed under mild conflict, and a derating adaptation speed under moderate conflict. The supporting power coordination strategy is a set of charging power adjustment rules that are linked and matched with the initial target wind turbine speed, used to ensure the coordinated implementation of control objectives when constraints conflict. Examples include a first-level power derating strategy, a second-level power derating strategy, a power recovery triggering strategy, and derating hold duration rules.

[0102] In this example, when constructing the three-constraint joint decision matrix, it can be done by layering and encapsulating the constraints according to their dimensions. The temperature safety threshold, target noise limit, and power derating rule in the scenario control parameter group are extracted as the core boundaries of the three types of constraints. The priority weights corresponding to the scenario are combined for weighted configuration, and the fields are aligned and associated according to the matrix structure to construct a standardized joint decision matrix, thereby completing the structured integration of the decision-making basic data.

[0103] After completing the construction of the joint decision matrix, the noise constraint speed conversion process is initiated. The wind turbine speed and noise mapping curve is retrieved, and the maximum allowable wind turbine speed under the corresponding noise limit is calculated in combination with the target noise limit in the joint decision matrix. This clarifies the upper limit boundary of the speed in the noise control dimension.

[0104] After obtaining the maximum permissible wind turbine speed, it is matched with the minimum safe wind turbine speed value to determine the range of conflict. The numerical relationship between the two is compared to determine the level of constraint conflict. At the same time, the speed range covered by both is output as the candidate range of feasible speeds, thereby clarifying the feasible solution space and the degree of conflict for decision-making.

[0105] After determining the constraint conflict level and feasible speed candidate range, based on the scenario priority weight and conflict level, with temperature safety as an insurmountable hard constraint boundary, the corresponding level of power derating triggering ladder and speed grade adjustment rules are matched, and the coordinated adaptation of speed and power is executed. The initial wind turbine target speed and matching power coordination strategy are output to achieve graded balance of the three types of constraints.

[0106] After obtaining the initial target wind turbine speed and matching power coordination strategy, a triple boundary check is performed to verify whether it meets the upper limit requirements for temperature safety, the upper limit requirements for noise control, and the lower limit requirements for minimum guaranteed power. After all checks pass, the final target wind turbine speed is output to ensure the boundary compliance of the decision results.

[0107] For example, there are two ways to obtain the target wind turbine speed through joint decision-making of three constraints. The first is priority-driven serial hierarchical decision-making. According to the constraint priority order bound by the joint decision matrix, starting from the highest priority temperature safety hard constraint, the boundary conditions of each constraint dimension are checked in turn. First, the minimum safe wind turbine speed is locked as the lower limit benchmark of the speed. Then, the noise control constraint is substituted to check the maximum allowable speed, and the constraint conflict level is determined simultaneously. After the determination of each level of constraint is completed, the corresponding level of adaptation strategy is matched synchronously. If there is no conflict, the optimal speed with low noise is directly selected within the feasible range. If there is a slight conflict, a slight compromise adjustment of the speed is performed. If there is a moderate or higher conflict, power derating is triggered step by step and the lower limit of the speed is updated synchronously until a preliminary result that simultaneously satisfies all three types of constraints is obtained. After the serial adaptation of all constraint dimensions is completed, the preliminary wind turbine target speed and matching power coordination strategy are obtained. Then, the serial boundary checks of temperature, noise, and power are performed in turn. After all checks pass, the wind turbine target speed is obtained. This method employs priority-locked, dimension-by-dimensional serial judgment and step-by-step hierarchical adaptation processing logic. By verifying and progressively adjusting the priority of scenario-based management, it avoids the ineffective computational overhead of low-priority dimensions, ensures the priority implementation of core management objectives, and makes the decision-making logic stable and controllable.

[0108] The second approach is multi-constraint parallel optimization decision-making. Global constraint dimension analysis is performed on the joint decision matrix. Based on the boundary conditions and weighting coefficients of three types of constraints—temperature safety, noise control, and power assurance—a multi-objective optimization objective function is constructed, using speed and power as linked variables to divide the space into multiple independent optimization subspaces. Parallel optimization calculations are then initiated simultaneously for all optimization subspaces. Within each subspace, combinations of speed and power that satisfy all hard constraints are traversed and searched, and the comprehensive benefit value of each combination is calculated. After all parallel optimizations in all subspaces are completed, the combination with the highest comprehensive benefit value is selected as the preliminary result, and the preliminary wind turbine target speed and matching power coordination strategy are output. Subsequently, batch verification of the three types of boundaries—temperature, noise, and power—is performed in parallel, simultaneously completing compliance verification across all dimensions. Once all verifications pass, the wind turbine target speed is obtained. This method employs global multi-objective modeling and full-space parallel optimization processing logic. Through pre-constraint space decomposition and parallel search, it fully traverses all combinations within the feasible solution range, obtaining the optimal decision result with the best balance of the three types of constraints. This approach is suitable for complex scenarios with high requirements for both charging efficiency and noise experience.

[0109] Furthermore, regarding the constraints on temperature safety threshold, target noise, and charging power, the system treats temperature safety as a hard constraint and target noise and charging power as soft constraints that can be adjusted according to scenario weights. Temperature constraints include a warning threshold, a derating threshold, and a protection threshold. Noise constraints include a target noise value, a short-term permissible over-limit value, and a maximum over-limit duration. Power constraints include a target charging power, a minimum guaranteed power, a maximum derating range, and derating recovery conditions. When the temperature is below the warning threshold, the system prioritizes meeting the noise target and maintains a low fan speed. When the temperature reaches the warning threshold but not the derating threshold, the system prioritizes controlling the temperature rise through smooth speed increases. When the temperature reaches the derating threshold and the noise target limits further fan speed increases, the system performs power derating according to the scenario level. When the temperature reaches the protection threshold, the system forces a speed increase and derating, stopping charging if necessary.

[0110] Model updates and parameter corrections are performed. The model correction module records ambient temperature, charging power, fan speed, module temperature, temperature rise slope, estimated or measured noise levels, power derating, and alarm results for each charging process. The system corrects the thermal model coefficients based on the deviation between the actual temperature rise and the temperature rise predicted by the thermal model. It also corrects the noise model coefficients based on the deviation between the measured noise and the noise predicted by the noise model. Model updates can be divided into local minor corrections and cloud-based batch updates. Local corrections are used for differences in single-pile ductwork, fan aging, dust blockage, and installation environment differences. Cloud-based batch updates are used for parameter optimization of the same model of equipment, the same site in the same region, or the same customer project. Upper and lower limits are set for model corrections to prevent significant model deviations caused by abnormal sensor data.

[0111] Furthermore, the speed maintenance and anti-frequent speed change logic includes a speed anti-jitter module with minimum hold time, acceleration hysteresis, deceleration hysteresis, minimum step size for speed change, maximum slope for speed change, and temperature rise trend judgment conditions. The system does not immediately change the fan speed in every control cycle, but adjusts only when one of the following conditions is met: the module temperature reaches the warning threshold or the temperature rise rate exceeds the trend threshold, requiring acceleration; the difference between the R_safe calculated by the thermal model and the current speed exceeds the acceleration step size and continues to exceed the acceleration confirmation time; the temperature is continuously and stably below the deceleration threshold, and the temperature rise slope is zero or negative; and the current speed has been maintained for more than the minimum hold time before deceleration is allowed; after switching the target noise parameter group, the system does not immediately jump to the new speed, but gradually transitions according to the maximum slope; if the short-term fluctuation of charging power does not exceed the power change threshold, the fan speed adjustment is not triggered; the target speed is only recalculated when the power continuously changes or the change amplitude exceeds the threshold.

[0112] Fifth embodiment This embodiment provides an exemplary scheme for the collaborative generation of target control parameter groups for charging piles. In this example, a power coordination constraint set is first constructed based on the power management rules corresponding to the scenario control parameter group. Then, the power adjustment rules are matched step by step with the wind turbine target speed and thermal load parameters to generate an initial power coordination instruction set. After triple boundary verification to remove abnormal adjustment items, intermediate power coordination instructions are obtained. Finally, a target control parameter group that can be directly issued and executed is obtained through timing binding and abnormal rule embedding. Step S40 includes steps D11~D14: Step D11: Construct a power coordination constraint set based on the power derating trigger ladder, minimum guaranteed power limit and power recovery hysteresis parameter corresponding to the scenario control parameter group.

[0113] Step D12: Using the power coordination constraint set as the control boundary, and combining the wind turbine target speed and the thermal load parameters, the power adjustment range, execution step size and state recovery trigger condition are matched step by step to generate the initial power coordination instruction set.

[0114] Step D13: Perform minimum power baseline verification, temperature safety linkage verification, and noise control adaptation verification on the initial power coordination instruction set, eliminate abnormal adjustment items that exceed the scenario control boundary, and obtain the verified intermediate power coordination instructions.

[0115] Step D14: Bind the power coordination intermediate command to the corresponding wind turbine target speed in a time sequence, embed the abnormal operating condition automatic rollback rule and the operating status feedback identifier to obtain a target control parameter group that can be directly issued and executed.

[0116] The power coordination constraint set is a standardized set of constraint data that integrates power derating trigger conditions, power baseline limits, and power recovery rules. It serves as the control boundary benchmark for generating power coordination commands. Examples include multi-level power derating trigger steps, minimum guaranteed power limits, power recovery hysteresis thresholds, power adjustment step size rules, and derating hold duration rules. The initial power coordination command set is a set of power adjustment commands obtained by matching constraint boundaries with current operating conditions and has not undergone compliance verification. It serves as a pre-processing object for subsequent boundary verification. Examples include single-level derating adjustment commands, multi-level derating combination commands, power recovery trigger commands, power smooth transition parameters, and adjustment execution step size parameters. The intermediate power coordination commands are compliant power adjustment commands that have undergone triple boundary verification and have eliminated anomalies. They form the basis for timing binding and rule embedding. Examples include compliant derating execution commands, compliant power recovery commands, power smooth transition parameters, and boundary trigger protection parameters. The target control parameter set is a complete set of execution parameters that integrates turbine speed commands and power coordination commands, along with execution timing and anomaly handling rules. It serves as the final control basis that can be directly parsed and executed by the charging pile side. For example, wind turbine target speed command, power coordination execution command, timing linkage rules, abnormal rollback rules, and operation status feedback rules.

[0117] In this example, when constructing the power coordination constraint set, it can be done by extracting and encapsulating the control rules in a hierarchical manner. The power derating trigger ladder, minimum guaranteed power limit and power recovery hysteresis parameters are extracted one by one from the scenario control parameter group. They are then classified, organized and logically associated according to three dimensions: derating level, boundary limit and recovery rule, and encapsulated to form a standardized power coordination constraint set, thereby completing the structured integration of the power control boundary.

[0118] After the power coordination constraint set is constructed, the power command matching and generation process is initiated. Using the constraint set as the control boundary, and combining the current target wind turbine speed and thermal load parameters, the corresponding power adjustment range, execution step size and state recovery trigger conditions are matched step by step according to the derating trigger level. The initial power coordination command set is then generated to achieve the linkage matching between power commands and speed and operating conditions.

[0119] After obtaining the initial power coordination instruction set, a triple boundary verification process is initiated, sequentially executing minimum power baseline verification, temperature safety linkage verification, and noise control adaptation verification. Each adjustment item in the instruction set is checked one by one, abnormal adjustment items that exceed the scenario control boundary are eliminated, and all compliant instruction items are retained to obtain the verified intermediate power coordination instructions, thereby ensuring the full-dimensional compliance of power instructions.

[0120] After receiving the intermediate power coordination command, the timing binding and rule embedding process is performed to associate the intermediate power coordination command with the corresponding wind turbine target speed in the execution timing sequence, ensuring that the speed adjustment and power adjustment actions are coordinated and orderly. At the same time, the automatic rollback rules for abnormal operating conditions and the operation status feedback flags are embedded, and finally a target control parameter group that can be directly issued to the charging pile for execution is generated, thereby improving the reliability and traceability of the control command execution.

[0121] For example, there are two ways to generate the target control parameter set through constraint matching and verification. The first is a step-by-step serial generation and verification. Based on the priority order of the derating steps bound to the power coordination constraint set, starting from the lowest derating step, single-level derating rules are selected sequentially and matched with the current wind turbine target speed and thermal load conditions to generate corresponding level power adjustment instructions. After each step matching is completed, the instruction at that level is simultaneously verified to meet the boundary requirements. If it meets the requirements, the instruction is retained and the matching of the next step is initiated; otherwise, the instruction is discarded and the matching of higher steps is terminated. After all serial matching and verification of the derating steps are completed, all compliant instructions are summarized to obtain the initial power coordination instruction set. Subsequently, serial boundary verifications for minimum power, temperature safety, and noise control are performed sequentially. After filtering out abnormal adjustment items, intermediate power coordination instructions are obtained, followed by serial timing binding and rule embedding to finally obtain the target control parameter set. This method employs a step-by-step serial matching and sequential boundary verification processing logic with tiered priority locking. By matching and verifying progressively according to the derating level, it avoids the invalid computational overhead of high-order derating, ensures the tiered order of power adjustment, and makes the instruction generation logic stable and controllable.

[0122] The second method is a tiered, decoupled, parallel generation and verification. A global derating ladder analysis is performed on the power coordination constraint set. Based on the triggering conditions and adjustment range of each derating ladder, decoupling partitioning is performed, dividing derating ladders with independent triggering conditions and no logical dependency into the same parallel computation group. The boundary constraints of each group are marked, generating multiple derating ladder groups that are computationally independent of each other. Then, matching processing is performed synchronously on all partitioned derating ladder groups. Within each derating ladder group, power adjustment instructions are generated by matching the target fan speed and thermal load conditions. After all parallel matching of all groups is completed, all generated instructions are aggregated to obtain the initial power coordination instruction set. Subsequently, batch verification of three types of boundaries—minimum power floor, temperature safety linkage, and noise control adaptation—is performed in parallel, synchronously completing compliance verification of all instruction items. After removing abnormal adjustment items, intermediate power coordination instructions are obtained, followed by parallel time-series association and rule batch embedding, finally obtaining the target control parameter set. This method employs a global decoupling partitioning and parallel matching verification logic for grouping. By pre-emptively decoupling and splitting the ladder, the computational dependency between ladders is eliminated in advance, reducing the cumulative time consumption of serial step-by-step matching and improving the instruction generation efficiency in complex multi-ladder scenarios.

[0123] Furthermore, in the scenario of a 120kW DC charging pile operating at night in an underground parking garage in a residential area, the local nighttime noise control limit is 55 decibels. The corresponding scenario control parameter group is preset with three levels of power derating, a minimum guaranteed power of 30kW, and a power recovery hysteresis of 5℃. After obtaining the target speed of the charging pile's fan and the real-time thermal load conditions, the cloud first extracts the power derating trigger steps of Level 1 (derating to 90kW), Level 2 (derating to 60kW), and Level 3 (derating to 30kW) from the scenario control parameter group, along with the minimum guaranteed power limit of 30kW and the 5℃ power recovery hysteresis parameter, and integrates them to form a power coordination constraint set. Subsequently, using this set of constraints as the control boundary, and combining the current target wind turbine speed with the thermal load parameters of 75°C module temperature, 28°C ambient temperature, and 100kW charging power, the system matches step by step according to the derating level. This results in the corresponding rules for a first-level power derating adjustment of 30kW, an execution step of 5kW every 10 seconds, and power recovery triggered when the module temperature drops to 70°C. This generates an initial power coordination instruction set containing derating execution instructions and recovery trigger conditions. Next, the initial power coordination instruction set was sequentially checked for minimum power baseline, temperature safety linkage, and noise control adaptation. This confirmed that the power after derating was not lower than the 30kW baseline, the module temperature could converge to a safe range, and the operating noise at the corresponding fan speed did not exceed the 55dB limit. After removing abnormal adjustment items, the verified intermediate power coordination instructions were obtained. Finally, the intermediate power coordination instructions were bound to the corresponding fan target speed for execution timing. A linkage timing was set where the fan first accelerates to the target speed, and then the power is gradually reduced after 2 seconds. An automatic rollback rule was embedded for abnormal operating conditions where the module temperature exceeds 85℃, automatically de-derating and cooling at full speed. A feedback flag for reporting the operating status every 30 seconds was also included. A target control parameter set that can be directly sent to the charging pile for execution was generated, realizing the coordinated management of temperature control, noise reduction, and charging power in nighttime scenarios.

[0124] Sixth Embodiment This embodiment provides an exemplary scheme for constructing and mapping a cloud-based scene control parameter system. In this example, the basic information of charging pile site scene management is first decomposed according to preset dimension labels to obtain a scene label dataset. Then, combined with whole-machine thermal calibration data, wind turbine noise mapping data, and power safety boundary rules, initial scene parameter groups are generated category by category. After boundary logic verification, version assignment, and signature encapsulation, a scene parameter configuration package is output. Finally, a mapping association between project batches and parameter packages is constructed and stored in the cloud parameter library, forming a scene control parameter group system that can be accurately scheduled. Please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the sixth embodiment of the charging pile fan speed control method of this application. Before step S10, steps E11 to E14 are also included: Step E11: Decompose the basic information of the charging pile site scene management according to the preset dimension labels to obtain the scene label dataset.

[0125] Step E12: Based on the scene label dataset, combined with the whole-machine thermal calibration data of the charging pile, the fan noise mapping data and the power safety boundary rules, generate an initial scene parameter set including temperature control threshold, fan speed curve, power derating rules and model correction coefficients for each category.

[0126] Step E13: Perform parameter boundary logic verification, version number assignment, and integrity verification value calculation on the initial scene parameter group, and attach digital signature and effective time rules, and output the scene parameter configuration package that has passed the verification.

[0127] Step E14: Construct a mapping relationship between the project batch of the charging pile and the scene parameter configuration package, obtain the scene control parameter group associated with the charging pile, and store it in the parameter library in the cloud.

[0128] The basic information for charging station scenario management is a collection of raw information containing the geographical location, attribute characteristics, and management requirements of the charging station. It serves as the original input data source for scenario tag decomposition. Examples include the station's geographical location and altitude, station function type, customer noise control requirements, local noise control rules, and project batch and equipment model information. The scenario tag dataset is a tagged data set formed by standardizing and decomposing the raw management information according to preset dimensions. It serves as the classification and matching basis for generating the initial scenario parameter set. Examples include regional climate tags, station attribute tags, seasonal time tags, noise control level tags, and equipment model tags. The initial scenario parameter set is a draft set of parameters generated based on scenario tags and equipment calibration data, which has not undergone compliance verification. It is a pre-processing object for subsequent verification and encapsulation. Examples include graded temperature control thresholds, reference fan speed curves, multi-level power derating rules, thermal model correction coefficients, and noise model correction coefficients. The scenario parameter configuration package is a standardized parameter file that has undergone compliance verification, version identification, and secure encapsulation. It serves as the formal parameter carrier for distribution and associated mapping. For example, parameter group number, full control parameters, version number, effective time, integrity verification value, and digital signature.

[0129] In this example, when disassembling the basic information of the site scene management to obtain the scene label dataset, it can be carried out by mapping and disassembling item by item according to the preset dimensions. The five core attributes of region, site, time period, management level and equipment model in the original information are extracted in turn and mapped to the standardized label system respectively. The structured scene label dataset is obtained by accumulating the data, thereby completing the standardized label conversion of the original management information.

[0130] After completing the construction of the scene label dataset, the initial scene parameter group generation process is initiated. Using the scene label as the classification index, the corresponding device's whole-machine thermal calibration data, fan noise mapping data, and power safety boundary rules are retrieved. The parameters are generated one by one according to the four categories of temperature control, speed, derating, and correction coefficient. The initial scene parameter group is then integrated to achieve accurate adaptation and generation of parameters and scenes.

[0131] After obtaining the initial scenario parameter set, the parameter compliance verification and encapsulation process is initiated. First, parameter boundary logic verification is performed to check for logical conflicts and out-of-bounds items between parameters. Then, a unique version number is assigned to the parameter set that passes the verification, the integrity verification value is calculated, digital signature and effective time rules are attached, and a standardized scenario parameter configuration package is encapsulated and output to ensure the compliance, traceability and security of the parameter package.

[0132] After obtaining the scene parameter configuration package, the mapping association and storage process is initiated to establish a multi-dimensional mapping association relationship between a single charging pile, a charging station, a project batch and the scene parameter configuration package. The mapping relationship and the parameter configuration package are written into the cloud parameter library to form a scene control parameter group system that can be called on demand, thereby supporting the accurate parameter matching and calling of subsequent charging piles.

[0133] For example, there are two ways to implement the generation of a scenario control parameter group system through tag decomposition, verification, and encapsulation. The first is a single-site, dimension-by-dimensional serial construction and verification. Following the deployment order of site project batches, starting with the basic scenario management information of a single site, single-dimensional tag decomposition, single-class parameter generation, single-item compliance verification, and single-site mapping association are executed sequentially. After completing the entire process for each site, the corresponding parameter package and mapping relationship are simultaneously written to the cloud parameter library before proceeding to the next site's construction process. Once the serial construction of all sites is completed, a complete cloud-based scenario control parameter group system is formed. This method employs a single-site, step-by-step serial advancement and sequential database storage processing logic. Through refined processing on a site-by-site and dimension-by-dimensional basis, it ensures the accuracy and adaptability of parameter configuration for each site. The processing is stable and controllable, adaptable to parameter construction scenarios for small-scale customized sites.

[0134] The second method involves multi-site clustering and parallel construction verification. Global feature analysis is performed on the basic scene control information of all sites. Clustering is then performed based on the similarity of features according to geographical area, site type, and control level. Sites with similar features are grouped into the same parameter construction cluster, and common label features of each cluster are marked, generating multiple parameter construction clusters that are computationally independent of each other. Parallel construction processing is then initiated simultaneously for all the completed parameter construction clusters. Within each parameter construction cluster, scene label decomposition, initial parameter group generation, compliance verification encapsulation, and batch mapping association are completed in batches. After the parallel construction of all construction clusters is completed, the full parameter configuration package and mapping relationships are batch-written into the cloud parameter library, forming a complete cloud-based scene control parameter group system. This method employs a global site feature clustering and full-cluster parallel construction verification processing logic. By pre-clustering site features, parameter construction tasks for similar scenarios are merged in advance, reducing repetitive parameter generation and verification calculations, and significantly improving the efficiency of parameter system construction for large-scale batch site projects.

[0135] Furthermore, the multi-scenario control configuration scheme pre-sets multiple independent scenario parameter groups, each corresponding to one or more application scenarios. Parameter groups include at least the following: geographical type, site type, seasonal type, day / night time, noise control level, target noise value, temperature safety threshold, fan speed curve, power derating rules, speed hysteresis, minimum hold time, model correction coefficient, and alarm strategy. Geographical types can be categorized as high-temperature and high-humidity areas, cold areas, general temperate areas, high-altitude areas, and coastal salt spray areas. High-temperature and high-humidity areas have increased temperature safety margins, cold areas allow for lower initial fan speeds, and high-altitude areas adjust fan speed curves based on reduced heat dissipation capacity. Site types can be categorized as residential areas, hospitals, schools, office parks, commercial complexes, underground parking lots, industrial areas, highway service areas, and independent outdoor sites. Residential areas, hospitals, and schools have lower target noise levels and stricter nighttime control rules; industrial areas and highway service areas can have higher target noise levels and prioritize charging power. Seasonal types can be categorized as spring, summer, autumn, and winter, or by monthly / ambient temperature ranges. The summer parameter group prioritizes heat dissipation and improves temperature warning threshold response speed, while the winter parameter group reduces low-load fan speed and extends speed hold-up time. Day and night periods can be divided into daytime, midday break, nighttime, and late night. The nighttime and late night parameter groups reduce target noise, increase fan speed hysteresis and minimum hold-up time, and avoid frequent speed changes. The daytime parameter group increases permissible noise and prioritizes charging efficiency. Noise control levels are divided into L1 (normal), L2 (low noise), L3 (quiet), and L4 (strict control). L1 is suitable for industrial areas or highway stations. L2 is suitable for commercial areas or office parks. L3 is suitable for residential areas and schools. L4 is suitable for hospitals, nursing homes, or areas with strict nighttime control. Different levels correspond to different target noise limits, power derating trigger points, and maximum permissible speeds.

[0136] Example parameter groups are as follows: Residential Area Nighttime Low Noise Parameter Group: Target noise level not exceeding the preset low noise threshold; extended low-speed range for fans; phased speed increase prioritized before temperature approaches the safety threshold; power derating only implemented when temperature safety risks increase. Hospital Strict Control Parameter Group: Target noise level lowest; limited fan speed change slope; prohibiting frequent large-amplitude fan speed changes; prioritizing stable low noise; when temperature safety conflicts with noise targets, initial small power derating followed by gradual speed increase. Industrial Area High Power Parameter Group: Target noise upper limit relatively high; prioritizing charging power and temperature safety; derating only when temperature thresholds or equipment protection boundaries are exceeded. Underground Parking Lot Parameter Group: Considering environmental echo and ventilation conditions, appropriately reducing fan speed change amplitude; and adjusting the heat dissipation model based on the temperature difference between inlet and outlet air.

[0137] Seventh Embodiment This embodiment provides an exemplary scheme for closed-loop iterative correction of thermal and noise models. In this example, the operational data uploaded by charging piles to the target control parameter group are first archived according to multiple dimensions such as equipment, site, and project to obtain operational files. Then, based on the operational files, the prediction and actual deviations of the thermal and noise models are calculated respectively, and initial values ​​of model correction coefficients are generated by combining equipment runtime and operating condition labels. After grouping, aggregation, and anomaly removal, the model target correction parameters converge within the correction upper and lower limits. Finally, the parameters are updated to the scene control parameter group, generating a target model group with version identification. Please refer to... Figure 4 , Figure 4 This is a flowchart illustrating the seventh embodiment of the control method for adjusting the speed of the charging pile fan in this application. Following step S40, steps F11-F14 are also included: Step F11: Based on the operation data uploaded by the charging piles running the target control parameter group, classify and archive the data according to equipment signal, site area and project dimensions to obtain the operation file.

[0138] Step F12: Based on the running file, compare the deviation between the temperature rise predicted by the thermal model and the actual temperature rise, as well as the deviation between the noise model prediction value and the corresponding noise value, and calculate the initial value of the model correction coefficient by combining the equipment running time and environmental condition label.

[0139] Step F13: Based on the grouping rules of the same model equipment, the same regional site, or the same customer project, the initial values ​​of the model correction coefficients are aggregated by deviation mean and outlier removal, and converged within the preset correction upper and lower limits to obtain the model target correction parameters.

[0140] Step F14: Update the model target correction parameters to the model coefficient items of the corresponding scene control parameter group to generate a target model group with version identifier.

[0141] Operational archives are standardized data collections formed by classifying and organizing all operational data of charging piles according to multiple dimensions. They serve as the fundamental data source for model deviation calculation and correction coefficient derivation. Examples include equipment-level temperature and speed operation data, site-level noise and power statistics, project-level derating and alarm summary data, equipment runtime ledgers, and environmental condition label data. Initial values ​​of model correction coefficients are initial calculation results based on single-device operational data, without grouping aggregation and boundary constraints. They are the pre-processing objects for subsequent aggregation and convergence. Examples include initial values ​​of thermal model temperature rise correction coefficient, thermal model thermal resistance correction coefficient, noise model sound pressure level correction coefficient, and duct loss correction coefficient. Model target correction parameters are standardized model correction parameters obtained after grouping aggregation, anomaly removal, and boundary convergence. They can be used for batch updates and are the direct basis for updating the model coefficients of the scenario control parameter group. Examples include unified correction coefficients for thermal models of the same type of equipment, unified correction coefficients for noise models of sites in the same region, and duct loss compensation correction coefficients for the same project. The target model set is an iterative version of the scene control parameter set that has completed model coefficient updates and includes a version identifier. It serves as a carrier for updating model parameters that will take effect in the next step. Examples include versioned hot model parameter sets, versioned noise model parameter sets, and updated full scene control parameter configuration packages.

[0142] In this example, when classifying and archiving the operational data uploaded by charging piles to obtain operational archives, a three-level hierarchical mapping and archiving method can be used. First, the full operational signal data of a single pile is collected according to the device number dimension. Then, the operational statistics data of all equipment in the site are summarized according to the site area dimension. Finally, the operational characteristic data of the entire project is integrated according to the project dimension, and the equipment runtime and environmental condition tags are simultaneously attached to form a structured operational archive, thereby completing the standardized archiving and governance of the full operational data.

[0143] After the operation file is constructed, the process of model deviation calculation and initial value generation of correction coefficients is started. The predicted temperature rise data and actual temperature rise data of the thermal model and the predicted noise value and actual noise value of the noise model are extracted from the operation file. The deviation rate of the two is calculated for each group. Then, the aging factor corresponding to the equipment running time and the environmental correction factor corresponding to the environmental conditions are combined for weighted correction to calculate the initial value of the model correction coefficient for a single device. In this way, the deviation data and the operating condition factors are linked for correction.

[0144] After obtaining the initial values ​​of the model correction coefficients, the grouping and aggregation convergence process is initiated. The initial values ​​of the correction coefficients are grouped according to the grouping rules of the same model of equipment, the same regional site, or the same customer project. Deviation mean aggregation is performed on the data within the group, and outliers that deviate from the statistical interval are removed. The aggregation results are then constrained within the preset correction upper and lower limits to converge the target correction parameters of the model. In this way, the universality and stability of the correction parameters are ensured through grouping and aggregation and boundary constraints, avoiding large deviations in the model caused by abnormal sensor data.

[0145] After obtaining the model target correction parameters, the parameter update and version generation process is initiated. The model target correction parameters are synchronously written into the model coefficient items of the corresponding scenario control parameter group, replacing the original old coefficients. At the same time, a new version identifier is assigned to the updated parameter group, the update log and the scope of effect are recorded, and finally a target model group with version identifier is generated, thereby completing the closed-loop iteration and traceable management of model parameters.

[0146] For example, there are two ways to generate target model groups by running data archiving and grouping aggregation. The first is single-group serial iterative correction. According to the priority order of the grouping dimensions, starting from the highest priority group of the same model of equipment, the initial values ​​of the correction coefficients of each group are selected sequentially to perform aggregation calculation, anomaly removal, and boundary convergence. After the convergence processing of each group is completed, the obtained model target correction parameters are updated to the corresponding scene control parameter group, generating the target model group for the corresponding group, and then the processing flow of the next group is entered. After the serial correction of all groups is completed, a target model group system covering all dimensions is formed. This method adopts a processing logic of group priority locking for sequential processing and sequential parameter update. Through fine correction of each group and step, the correction accuracy and stability of the model parameters of each group are ensured. The processing process is controllable and traceable, and it is suitable for model iteration scenarios of small-batch customized projects.

[0147] The second method is multi-group parallel aggregation correction. Global grouping feature analysis is performed on the initial values ​​of the full model correction coefficients. Clustering is performed based on grouping features such as equipment model, site area, and customer project. Initial correction coefficient values ​​belonging to the same group dimension and without data coupling are divided into the same parallel correction cluster, and the correction upper and lower bound rules of each cluster are marked, generating multiple parallel correction clusters that are computationally independent of each other. Then, parallel aggregation processing is simultaneously initiated for all the divided parallel correction clusters. Within each parallel correction cluster, the deviation mean aggregation, outlier removal, and boundary convergence calculations are performed in batches to obtain the model target correction parameters for the corresponding cluster. After all parallel calculations for all correction clusters are completed, all model target correction parameters are updated to the corresponding scenario control parameter group in batches, and version identifiers are uniformly assigned, generating a complete target model group system. This method uses global grouping feature clustering and full-cluster parallel aggregation convergence processing logic. Through pre-group decoupling and splitting, computational dependencies between groups are eliminated in advance, reducing the cumulative time consumption of serial group-by-group processing and improving the efficiency of batch model iteration for large-scale multi-site projects.

[0148] Furthermore, taking a batch operation and maintenance scenario of 20 identical 120kW DC charging piles at a residential area charging station as an example, this batch of charging piles belongs to the same customer project and has been running continuously for 3 months. The cloud first receives all the operational data uploaded by the charging piles, such as module temperature, fan speed, charging power, ambient temperature, noise estimation, and derating records. It then classifies and collects the data according to three dimensions: single device signal, station area, and customer project. Simultaneously, it attaches environmental condition tags such as the cumulative running time of each device, summer high temperature conditions, and nighttime low noise conditions, forming a complete standardized operation file. Subsequently, based on this operation file, it compares the deviation between the steady-state temperature rise of the module predicted by the thermal model and the actual collected temperature rise, and the deviation between the noise predicted by the noise model and the actual estimated noise, and combines the duct dust accumulation and fan aging factors corresponding to the device's running time, as well as the environmental correction coefficient for local summer high temperatures, to perform weighted calculations, obtaining the initial values ​​of the correction coefficients for the thermal model and noise model for each pile. Next, following the grouping rules of the same model of equipment, the same regional site, and the same customer project, the 20 charging piles in this batch were grouped into the same correction group. Deviation mean aggregation was performed on the initial values ​​of all correction coefficients within the group, eliminating outliers caused by sensor malfunctions that deviated from the statistical range. Simultaneously, the aggregation results were constrained within a preset ±15% correction upper and lower limit, converging to obtain the uniformly applicable model target correction parameters for this batch. Finally, the model target correction parameters were synchronously updated to the thermal and noise model coefficients of the corresponding residential area scenario control parameter group. New parameter version numbers were assigned, and iteration logs and effective ranges were recorded, generating a target model group with version identifiers, completing the closed-loop batch iterative optimization of the thermal and noise models.

[0149] Eighth embodiment This embodiment provides an exemplary scheme for charging pile side parameter safety verification and speed regulation anti-jitter execution, applied to charging piles. In this example, the charging pile first receives the target control parameter group sent from the cloud. After adaptation verification, it is written into the local candidate parameter storage area to obtain a candidate parameter set. Then, logical conflict simulation verification is performed on the candidate parameter set to output a set of control parameters to be effective. When the preset effective trigger condition is met, it switches to the running effective parameters and monitors the running status. Finally, after superimposing multi-dimensional anti-jitter constraints, the speed regulation execution command is output and the running data is uploaded synchronously. Please refer to Figure 5 , Figure 5 This is a flowchart illustrating the eighth embodiment of the charging pile fan speed control method of this application. The charging pile fan speed control method includes steps G11~G14: In step G11, the charging pile receives the target control parameter group sent from the cloud, and after the adaptation verification is passed, writes the target control parameter group into the local candidate parameter storage area to obtain the candidate parameter set.

[0150] Step G12: Perform logical conflict simulation verification on the candidate parameter set, verify the consistency of temperature safety threshold, fan speed range and power derating rule item by item, and output the set of control parameters to be implemented.

[0151] Step G13: When the set of control parameters to be activated reaches the preset activation trigger condition, the charging pile switches the set of control parameters to be activated as the running activation parameters and detects the running status.

[0152] Step G14: Based on the operating parameters, combined with anti-shake constraints such as acceleration / deceleration hysteresis, minimum operating hold time, and speed change slope, output speed regulation execution command and upload operating data to the cloud.

[0153] The candidate parameter set is a collection of parameters to be verified that the charging pile receives from the cloud and undergoes basic adaptation verification, then temporarily stores in the local candidate storage area. It serves as the input for local logic conflict simulation verification. Examples include candidate wind turbine target speed parameters, candidate power derating rule parameters, candidate temperature safety threshold parameters, candidate activation trigger condition parameters, and candidate anomaly rollback rule parameters. Logic conflict simulation verification is a pre-verification mechanism that simulates full-condition operation of the charging pile in a local offline state to verify the logical consistency between parameters. It is a core step in selecting compliant parameters to be activated. Examples include temperature threshold and speed range matching verification, power derating rule and speed rule linkage verification, parameter hardware boundary compatibility verification, and anomaly rollback logic integrity verification.

[0154] The set of control parameters to be activated is a set of parameters that have passed logical conflict simulation verification and have no logical anomalies. These are the prerequisites for parameter switching to take effect. Examples include verified fan speed control parameters, verified power coordination parameters, verified temperature protection parameters, and verified anomaly handling parameters. The set of parameters that take effect during operation is the set of valid parameters that, after meeting the activation trigger conditions, formally switch to the current operation of the charging pile. These are the direct reference for speed control command output. Examples include currently effective fan speed rules, currently effective power control rules, currently effective temperature protection thresholds, and currently effective anomaly handling rules.

[0155] Anti-jitter constraints are a set of multi-dimensional speed control limit rules used to suppress frequent fluctuations in wind turbine speed and ensure stable operation. They serve as the control basis for the smooth output of speed control execution commands. Examples include speed increase / decrease hysteresis thresholds, minimum operating hold time, upper limit of speed change slope, and speed adjustment trigger dead zone. Speed ​​control execution commands, processed by anti-jitter constraints, are the final execution commands sent to the wind turbine drive unit and are the direct control signals for wind turbine speed control actions. Examples include wind turbine target speed commands, speed increase / decrease rate commands, speed hold time commands, and abnormal shutdown commands.

[0156] In this example, when receiving the target control parameter group from the cloud and obtaining the candidate parameter set, the charging pile can perform multi-dimensional adaptation verification and writing. The charging pile first verifies the version compatibility, data integrity, and signature validity of the target control parameter group. After all verifications pass, the parameters are written to an independent local candidate parameter storage area, which is physically isolated from the current running parameters, thus obtaining the candidate parameter set. This ensures the security of the parameter receiving process and avoids abnormal parameters directly affecting the operation.

[0157] After obtaining the set of candidate parameters, the logic conflict simulation verification process is initiated. In the local offline simulation environment, the consistency of the temperature safety threshold, the fan speed range and the power derating rule is verified item by item. The system checks for conflicts such as parameter out-of-bounds, logic mutual exclusion, and linkage failure. After confirming that there are no conflicts, the set of control parameters to be implemented is output. In this way, the parameter operation risks are avoided in advance through pre-simulation verification, and the stability of on-site operation is ensured.

[0158] After obtaining the set of control parameters to be effective, the system continuously monitors the activation trigger conditions. When the preset time period switching, temperature threshold trigger, or cloud command trigger is reached, the charging pile performs a parameter switching action, replacing the set of control parameters to be effective with the currently active parameters. At the same time, real-time monitoring of the operating status is initiated to ensure that the timing of parameter switching is accurate and the process is controllable.

[0159] After switching to the effective operating parameters, the speed control command output process is initiated. Based on the target speed of the wind turbine in the effective operating parameters, three types of anti-shaking constraints are superimposed: acceleration / deceleration hysteresis, minimum operating hold time, and speed change slope. The speed command is smoothed and then the speed control execution command is output. At the same time, operating data is periodically collected and uploaded to the cloud to reduce wear and noise fluctuations caused by frequent start-stop and speed change of the wind turbine, thereby improving the stability of operation.

[0160] For example, there are two ways to obtain the speed adjustment execution command through parameter verification, simulation verification, and anti-jitter execution. The first is a serial step-by-step verification execution. Following the timing sequence of parameter processing, starting from the receiving verification stage, parameter adaptation verification and writing, full-item logic conflict simulation verification, effective condition determination and parameter switching, anti-jitter constraint loading, and command output are executed sequentially. After each stage is completed, it is simultaneously determined whether the verification of that stage has passed. If it passes, the process proceeds to the next stage; if it fails, the process terminates, the currently effective parameters are retained, and the exception is reported to the cloud. After the entire serial execution process is completed, the final speed adjustment execution command is output and the running data is uploaded. This method adopts a timing-locked, step-by-step serial advancement and single-node verification interception processing logic. By using step-by-step verification and sequential execution that aligns with the parameter processing flow, it avoids the spread of parameter anomalies across stages, ensuring the security and stability of parameter execution. The logic is simple and reliable, and it is suitable for low- to mid-range charging pile main controllers with limited computing resources.

[0161] The second approach is a partitioned, decoupled, parallel verification with smooth execution. A full parameter dimension analysis is performed on the candidate parameter set. Based on the functional attributes of three types of parameters—temperature safety, fan speed, and power derating—decoupling partitioning is performed, mapping each type of parameter to three independent simulation verification partitions. Verification rules and boundary conditions for each partition are marked, generating multiple loosely coupled verification partitions. Simulation verification is then performed synchronously on all partitioned verification areas. Within each partition, boundary verification and logic verification of the corresponding parameters are performed independently. After all parallel verifications of all partitions are completed, the verification results are summarized to obtain the set of control parameters to be implemented. When parameters are switched on, a smooth transition mode is used, gradually loading new parameter rules while simultaneously loading multi-dimensional anti-jitter constraints in parallel. Speed ​​commands are smoothly interpolated before outputting speed control execution commands, and operational data is collected and uploaded to the cloud in parallel. This method employs a processing logic of functional partition decoupling and full partition parallel verification. By decoupling and splitting the parameters and functions in advance, the independent verification links are separated in advance, reducing the cumulative time consumption of serial item-by-item verification. At the same time, combined with smooth transition and anti-jitter processing, the running stability and execution efficiency of the parameter switching process are improved, making it suitable for high-power fast charging piles with high reliability requirements.

[0162] Furthermore, the charging pile fan speed regulation and collaborative control system is composed of a cloud-based management and control platform and the charging pile terminal. The cloud pre-collects basic information on site scene management and completes tag decomposition. Combining equipment thermal calibration, noise mapping, and power boundary rules, it generates an initial scene parameter set. After verification and encapsulation, a multi-dimensional mapping and association scene control parameter set system is constructed and stored in the cloud parameter library. After the charging pile is installed on-site, it reports equipment and site information to the cloud to complete registration and obtains and stores the corresponding initial scene control parameter set. During formal operation, the charging pile collects thermal load parameters such as power module temperature, inlet and outlet air temperature, ambient temperature, charging power, and temperature rise rate, as well as operating data such as fan operation, noise estimation, and derating records at fixed intervals, and packages and reports them to the cloud. After receiving data, the cloud retrieves the corresponding scenario control parameter set. First, it substitutes this into the thermal model to perform thermal safety boundary convergence calculations to obtain the minimum safe wind turbine speed. Then, it combines the target noise limit and power derating rules to conduct a joint decision-making process based on temperature, noise, and power constraints to obtain the target wind turbine speed. Subsequently, it matches power adjustment rules, binds execution timing, and embeds exception rollback and feedback rules to generate the target control parameter set, which is then sent to the charging pile. Upon receiving the target control parameter set, the charging pile sequentially performs version, integrity, and signature adaptation checks. After writing it into its local candidate parameter area, it performs offline logic conflict simulation checks. Once the checks pass, it switches to the effective parameters when the preset activation trigger conditions are met. Then, it overlays anti-jitter constraints such as acceleration / deceleration hysteresis, minimum operating hold time, and speed change slope before outputting the final speed adjustment execution command and synchronously adjusting the charging power. Simultaneously, it continuously transmits real-time operating data back to the cloud. The cloud continuously categorizes and archives the returned operational data by equipment, site, and project to form operational archives. It compares the deviations between the predicted and actual values ​​of the thermal and noise models group by group, calculates the initial values ​​of the model correction coefficients by combining the equipment runtime and environmental condition labels, and then completes mean aggregation, anomaly removal, and boundary convergence by grouping according to the same model, region, and project rules. The model target correction parameters are obtained and updated to the model coefficient items of the corresponding scenario control parameter group, generating the iterative target model group with version identification. This forms a complete collaborative control system of cloud-based intelligent decision-making, terminal safe execution, and data closed-loop iteration, realizing the full-link collaborative optimization of charging pile temperature safety, noise control, and charging power.

[0163] This application provides a charging pile fan speed control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the charging pile fan speed control method in the above embodiment 1.

[0164] The following is for reference. Figure 6The diagram illustrates a structural schematic suitable for implementing the charging pile fan speed control device in the embodiments of this application. The charging pile fan speed control device in the embodiments of this application may include, but is not limited to, mobile terminals such as charging pile cooling axial flow fans, DC fan speed controllers, and speed control units integrated into the main control unit of the charging pile, as well as fixed terminals such as fan speed controllers and temperature control speed controllers. Figure 6 The charging pile fan speed control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0165] like Figure 6 As shown, the charging pile wind turbine speed control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the charging pile wind turbine speed control device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the charging pile fan speed control equipment to communicate wirelessly or wiredly with other devices to exchange data. Although charging pile fan speed control equipment with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0166] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0167] The charging pile fan speed control device provided in this application adopts the charging pile fan speed control method in the above embodiments, which can solve the technical problem of low equipment operating efficiency. Compared with the prior art, the beneficial effects of the charging pile fan speed control device provided in this application are the same as the beneficial effects of the charging pile fan speed control method provided in the above embodiments, and other technical features of the charging pile fan speed control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0168] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0169] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0170] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the speed control method for regulating the charging pile fan in the above embodiments.

[0171] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0172] The aforementioned computer-readable storage medium may be included in the charging pile fan speed control device; or it may exist independently and not be assembled into the charging pile fan speed control device.

[0173] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the charging pile fan speed control device, the charging pile fan speed control device performs the following actions: Based on the scene characteristic parameters of the charging pile and the mapping relationship between the scene and control parameters, it matches and filters corresponding control parameters to form a scene control parameter group corresponding to the charging pile; it substitutes the thermal load condition parameters of the charging pile and the scene control parameter group into a thermal model and performs convergence calculations according to the thermal safety boundary to output the minimum safe fan speed value of the charging pile; based on the minimum safe fan speed value and the target noise limit and power derating rules in the scene control parameter group, it performs a three-constraint joint decision to obtain the target fan speed; based on the scene control parameter group, it matches the power parameters of the target fan speed to obtain the target control parameter group of the charging pile, thereby controlling the charging pile to achieve temperature, noise, and power coordinated optimization.

[0174] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0175] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0176] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0177] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described control method for regulating the speed of the charging pile fan, thereby solving the technical problem of low equipment operating efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the control method for regulating the speed of the charging pile fan provided in the above embodiments, and will not be repeated here.

[0178] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A control method for speed regulation of a charging pile fan, characterized in that, Applied to the cloud, the method includes: Based on the scene characteristic parameters of the charging pile, and combined with the mapping relationship between the scene and the control parameters, the corresponding control parameters are matched and filtered to form the scene control parameter group corresponding to the charging pile. The thermal load parameters of the charging pile and the scenario control parameter group are substituted into the thermal model and converged according to the thermal safety boundary to output the minimum safe fan speed value of the charging pile. Based on the minimum safe wind turbine speed value, the target noise limit and power derating rule in the scenario control parameter group, a joint decision of three constraints is made to obtain the target wind turbine speed. Based on the scenario control parameter set, the power parameters of the target speed of the wind turbine are matched to obtain the target control parameter set of the charging pile, so as to control the charging pile to achieve temperature, noise and power coordinated optimization.

2. The control method of claim 1, wherein, The step of matching and filtering corresponding control parameters based on the scene feature parameters of the charging pile, combined with the mapping relationship between the scene and control parameters, to form the scene control parameter group corresponding to the charging pile includes: Based on the scene feature parameters uploaded by the charging pile, and combined with the mapping relationship between the scene and control parameters, candidate configuration parameters corresponding to the scene feature parameters are filtered in the parameter library in the cloud. The compliant parameter set is tested according to the parameter operation rules. Conflicting parameters are corrected and redundant parameters are removed to obtain the scene control parameter set of the charging pile.

3. The control method of claim 1, wherein, The step of substituting the thermal load parameters of the charging pile and the scenario control parameter group into the thermal model and performing convergence calculation according to the thermal safety boundary to output the minimum safe wind turbine speed value of the charging pile includes: Based on the thermal load operating parameters of the charging pile and the scene control parameter group, the corresponding scene thermal model benchmark coefficients, graded temperature safety thresholds and multi-dimensional environmental correction factors are matched in the parameter library and integrated to obtain the thermal model calculation benchmark dataset. Substitute the thermal model calculation benchmark dataset into the cloud-based thermal model, and iteratively solve the problem based on the real-time temperature rise rate of key components and the heat exchange efficiency of the cabinet's air intake and exhaust, to calculate the initial minimum fan speed to ensure that the component temperature does not exceed the safety limit. Based on the regional altitude correction rules and the dust accumulation loss prediction coefficient of the duct built into the scenario control parameter group, the initial minimum fan speed is corrected by boundary compensation, and abnormal speed deviation values ​​under extreme conditions are eliminated to obtain the intermediate value of the safe fan speed. The intermediate safe speed of the fan is calculated by performing convergence calculations based on the thermal safety boundary. After confirming that the heat dissipation capacity corresponding to the intermediate safe speed of the fan can cover the temperature margin requirements under all operating conditions, the minimum safe speed value of the fan is obtained.

4. The control method of claim 1, wherein, The step of obtaining the target wind turbine speed by performing a three-constraint joint decision based on the minimum safe wind turbine speed value, the target noise limit within the scenario control parameter group, and the power derating rule includes: Based on the target noise limit and power derating rules in the scenario control parameter group of the charging pile, and combined with the scenario priority weight corresponding to the charging pile, a joint decision matrix including three constraints of temperature safety, noise control and power guarantee is constructed. Based on the joint decision matrix and the mapping relationship between wind turbine speed and noise, the maximum allowable wind turbine speed corresponding to satisfying the target noise limit is calculated. The maximum allowable wind turbine speed is matched and determined with the minimum safe wind turbine speed value, and the constraint conflict level result and feasible speed candidate range are output. Based on the scenario priority weight and the constraint conflict level, within the temperature safety hard constraint boundary, the system adapts according to the power derating triggering ladder and speed grade adjustment rules preset for the corresponding scenario, and outputs the initial wind turbine target speed and matching power coordination strategy. The initial target wind turbine speed and the matching power coordination strategy are subjected to triple boundary verification of temperature safety upper limit, noise control upper limit and minimum guaranteed power lower limit. After the verification is passed, the target wind turbine speed is obtained.

5. The control method of claim 1, wherein the control method comprises: The step of matching the power parameters of the wind turbine's target speed with the scene control parameter set to obtain the target control parameter set of the charging pile, in order to control the charging pile to achieve temperature-noise-power coordinated optimization, includes: Based on the power derating trigger step, minimum guaranteed power limit and power recovery hysteresis parameter corresponding to the scenario control parameter group, a power coordination constraint set is constructed. Using the power coordination constraint set as the control boundary, and combining the wind turbine target speed and the thermal load parameters, the power adjustment range, execution step size and state recovery trigger condition are matched step by step to generate an initial power coordination instruction set; The initial power coordination instruction set is checked for minimum power baseline, temperature safety linkage and noise control adaptation. Abnormal adjustment items that exceed the scene control boundary are removed to obtain the power coordination intermediate instructions that pass the verification. The power coordination intermediate command is time-bound with the corresponding wind turbine target speed, and an automatic rollback rule for abnormal operating conditions and an operating status feedback identifier are embedded to obtain a target control parameter group that can be directly issued and executed.

6. The control method of claim 1, wherein, Before the step of matching and filtering corresponding control parameters based on the scene characteristic parameters of the charging pile and the mapping relationship between the scene and control parameters to form the scene control parameter group corresponding to the charging pile, the control method for adjusting the speed of the charging pile fan further includes: The basic information on the site management of the charging piles is decomposed according to preset dimension labels to obtain a scene label dataset; Based on the scene label dataset, combined with the whole-machine thermal calibration data of the charging pile, the fan noise mapping data and the power safety boundary rules, an initial scene parameter set including temperature control threshold, fan speed curve, power derating rules and model correction coefficients is generated for each category. Perform parameter boundary logic verification, version number assignment, and integrity verification value calculation on the initial scene parameter group, and attach digital signature and effective time rules to output the scene parameter configuration package that has passed the verification. The mapping relationship between the project batch of the charging pile and the scene parameter configuration package is constructed to obtain the scene control parameter group associated with the charging pile and store it in the parameter library in the cloud.

7. The control method of claim 1, wherein the control method comprises: After the step of matching the power parameters of the target speed of the wind turbine with the scenario control parameter set to obtain the target control parameter set of the charging pile, and controlling the charging pile to achieve temperature, noise, and power coordinated optimization, the control method for adjusting the speed of the charging pile wind turbine further includes: Based on the operational data uploaded by the charging piles operating the target control parameter group, the data is classified and archived according to equipment signals, site area and project dimensions to obtain operational files; Based on the aforementioned operational data, the deviations between the temperature rise predicted by the thermal model and the actual temperature rise, as well as the deviations between the noise model's predicted value and the corresponding noise value, are compared. Combined with the equipment's operating time and environmental condition labels, the initial values ​​of the model correction coefficients are calculated. Based on the grouping rules of equipment of the same model, site in the same region, or project of the same customer, the initial values ​​of the model correction coefficients are aggregated by deviation mean and outlier is removed, and the model target correction parameters are obtained by convergence within the preset correction upper and lower limits. The model target correction parameters are updated to the model coefficient items of the corresponding scene control parameter group to generate a target model group with version identifier.

8. The control method of claim 1, wherein, Applied to charging piles, the method includes: The charging pile receives the target control parameter group sent by the cloud. After the adaptation verification is passed, the target control parameter group is written into the local candidate parameter storage area to obtain a candidate parameter set. The candidate parameter set is subjected to logical conflict simulation verification, and the matching consistency between the temperature safety threshold, the fan speed range and the power derating rule is verified item by item. The set of control parameters to be implemented is then output. When the set of control parameters to be activated reaches the preset activation trigger condition, the charging pile switches the set of control parameters to be activated as the running activation parameters and detects the running status. Based on the aforementioned operational parameters, and combined with anti-shake constraints such as acceleration / deceleration hysteresis, minimum operating hold time, and speed change slope, a speed regulation execution command is output, and the operational data is uploaded to the cloud.

9. A charging pile fan speed regulation device, characterized in that, The charging pile fan speed control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for regulating the speed of the charging pile fan as described in any one of claims 1 to 8.

10. A storage medium, characterized by The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the control method for adjusting the speed of the charging pile fan as described in any one of claims 1 to 8.

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