Heat dissipation method and system for energy storage and boosting all-in-one machine, terminal and medium

Abstract

The invention belongs to the technical field of heat dissipation of energy storage and boosting all-in-one machines, and particularly discloses a heat dissipation method and system for an energy storage and boosting all-in-one machine, a terminal and a medium, and the method comprises the steps: dividing a heat management region according to the structure and thermal characteristics of the energy storage and boosting all-in-one machine; collecting temperature and load parameters of each area, and predicting a target temperature for air cooling control of each area; determining an air cooling demand grade according to the target temperature and the actual temperature, and constructing a thermal risk index for grade division; a temperature buffer area is set according to the temperature fluctuation condition, and stable adjustment of the state of the fan is achieved; and optimizing a fan action control strategy by minimizing the target function of the start-stop cost and the service life loss of the fan. And efficient and intelligent heat dissipation control can be realized. According to the method, the response precision of an air cooling strategy and the robustness of equipment heat management can be improved, energy consumption and fan loss are reduced, and the method has good practical application value and popularization prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat dissipation of an energy storage and boosting integrated machine, and in particular relates to a heat dissipation method, system, terminal and medium for an energy storage and boosting integrated machine. Background Art

[0002] Energy storage technology plays an increasingly important role in power system regulation, grid frequency regulation, load balancing, and new energy consumption. The energy storage and booster integrated unit, as a core device integrating energy storage conversion and boosting functions, realizes power conversion, transmission, and regulation. Its operational stability and safety are crucial to the performance of the entire energy storage system.

[0003] During operation, energy storage and boost systems generate significant heat in key components such as the power module, magnetic elements, and drive control components. Failure to dissipate heat promptly or properly can easily lead to thermal runaway, insulation degradation, electrical faults, and even thermal failure, seriously threatening the equipment's safe operation and service life. Therefore, the industry generally employs fixed-parameter air cooling or simple threshold-driven methods to manage the temperature of energy storage equipment.

[0004] However, existing control strategies often rely on statically set temperature limits or single wind speed adjustment mechanisms, making them incapable of addressing dynamic operating conditions, such as load fluctuations, ambient temperature variations, and localized component temperature rise. Furthermore, existing technologies often fail to manage heat load distribution in a granular manner and lack a regional thermal risk assessment mechanism. This can lead to insufficient cooling in some thermally managed areas and overcooling in others, resulting in inefficient fan resource scheduling, high energy consumption, and delayed cooling control response. Summary of the Invention

[0005] In response to the problems in the prior art, the present invention provides a heat dissipation method, system, terminal and medium for an energy storage and boosting integrated machine, which solves the problems of low fan resource scheduling efficiency, high energy consumption and delayed cooling control response in the prior art.

[0006] The technical solution adopted in the present invention is as follows: In a first aspect, the present application provides a heat dissipation method for an energy storage and boosting integrated device, the method comprising the following steps: Step S1: Divide the air-cooled object into several thermal management areas according to the structure and thermal characteristics of the energy storage and boost integrated device; Step S2: Obtain operating parameters of the energy storage and boost integrated device, including temperature parameters and load parameters of each thermal management area, calculate the actual temperature based on the temperature parameters, and calculate the load value based on the load parameters; Step S3: Calculate the target temperature for air cooling control in the corresponding thermal management area based on the operating parameters and historical temperature data; Step S4: Determine the air cooling requirement area and select the corresponding air cooling requirement level based on the target temperature and the actual temperature of the corresponding area; Step S5, calculating the temperature buffer zone for fan control according to the target temperature and the actual temperature; Step S6: Cool the heat management area with fans according to the air cooling demand level. When the temperature is within the buffer zone, maintain the current fan state unchanged.

[0007] Preferably, in step S3, the target temperature is obtained by predicting the target temperature prediction model based on the actual temperature of the thermal management area, the temperature rise trend and the load power; The warming trend is calculated by actual temperature changes; The target temperature prediction model is trained offline using historical temperature data; The target temperature prediction model uses incremental learning to update model parameters online.

[0008] Preferably, the target temperature prediction model dynamically adjusts the prediction time length in real time by setting a sliding prediction time window and a temperature rise gradient threshold.

[0009] Preferably, in step S4, the air cooling demand level is divided by constructing a thermal risk index R for level judgment:

[0010] in, is the target temperature, is the actual temperature, P is the load parameter, is the heat capacity.

[0011] Preferably, when judging the air cooling level, a temperature prediction deviation factor is further introduced ,like If the set threshold ε is exceeded, the current air cooling level will be frozen in the current period until the next level judgment cycle is triggered.

[0012] Preferably, in step S5, the upper and lower limits of the temperature buffer zone are determined by the following formula:

[0013] in, is the upper limit of the buffer zone, is the lower limit of the buffer zone, is the temperature standard deviation, η is the stability adjustment coefficient, and ξ is the fixed compensation term; The temperature standard deviation Calculated by the following formula:

[0014] Among them, n is the number of sampling temperature points in the statistical period, is the actual temperature recorded at the i-th sampling moment, The sliding average temperature value is the average value of n temperatures within the statistical period.

[0015] Preferably, in step S6, the wind turbine scheduling strategy optimizes the wind turbine action decision based on the following objective function:

[0016] in, is the current control state of the i-th fan, is the state of the previous control cycle, 、 are the first cost weight and the second cost weight of the fan start and stop, is the current remaining life of the i-th wind turbine.

[0017] In a second aspect, the present application provides a heat dissipation system for an integrated energy storage and boosting device, the system comprising: The data acquisition module is used to collect the structural information, thermal characteristics and operating parameters of the energy storage and booster integrated unit. The operating parameters include the temperature parameters and load parameters of each thermal management area; Thermal zone division module, used to divide the air-cooled object into several thermal management zones based on structural information and thermal characteristics; The target temperature prediction module is used to predict the target temperature for air cooling control in each thermal management zone based on the operating parameters and historical temperature data of the zone. The module uses an incremental learning model and supports dynamic adjustment of the sliding prediction time window. The air cooling demand identification module is used to determine the air cooling demand area and classify the corresponding air cooling demand level based on the target temperature and the actual temperature of the corresponding area. The classification is based on the calculation of the thermal risk index; The fan buffer control module is used to calculate the temperature buffer range for fan control based on the target temperature and the actual temperature, and maintain the current fan state unchanged when the current temperature is within the buffer range. The upper and lower limits of the temperature buffer range are calculated based on the temperature standard deviation and the adjustment coefficient; The fan scheduling module is used to optimize the fan motion control based on the objective function. The objective function takes into account the fan's current control state, the state of the previous control cycle, and the current remaining life, and combines the fan start-up and shutdown costs for optimized control.

[0018] In a third aspect, the present application provides a terminal, including: A memory, used for storing a heat dissipation program of the energy storage and boosting integrated machine; A processor is used to implement the steps of the energy storage and boost integrated machine heat dissipation method as described in the first aspect when executing the energy storage and boost integrated machine heat dissipation system.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the heat dissipation method of the energy storage and boosting integrated machine as described in the first aspect.

[0020] It can be seen from the above technical solutions that the advantages of the present invention are: (1) By dividing the equipment thermal management object into multiple thermal management areas and combining the operating parameters and historical temperature data to predict the target temperature of each area, accurate perception and differentiated control of thermal distribution are achieved, avoiding the problems of delayed response to temperature changes and unreasonable allocation of air cooling resources in the existing technology, and significantly improving the system's heat dissipation efficiency and operational safety.

[0021] (2) A target temperature prediction model that integrates temperature, load, and heating trend is adopted, and an incremental learning mechanism is introduced to update the model online, so that the temperature prediction results can dynamically adapt to changes in the equipment operating status, improving the timeliness and robustness of the model, and effectively reducing the risk of overcooling or undercooling caused by prediction errors.

[0022] (3) The judgment mechanism of air cooling demand level introduces the thermal risk index R, which comprehensively considers multiple factors such as temperature deviation, load level and heat capacity, and realizes risk warning and level quantification in the early stage of air cooling control, avoiding the response delay or insufficient cooling caused by the traditional judgment method based on a single threshold, and enhancing the forward-looking nature of air cooling control.

[0023] (4) During the fan scheduling process, a multi-objective optimization function is constructed to quantify the fan start-stop frequency, fan life loss and state change cost, thereby improving the rationality and intelligence of the fan action strategy. While ensuring that the cooling capacity meets the thermal management requirements, the fan service life is extended and the maintenance frequency and energy consumption cost are reduced.

[0024] (5) The proposed temperature buffer zone control method adopts standard deviation modeling and introduces system adjustment parameters η and compensation terms ξ to ensure that the fan control maintains a stable state under the condition of slight temperature changes, effectively suppressing the mechanical shock and control oscillation caused by frequent start and stop of the fan, and further improving the smoothness and stability of the system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1A schematic flow chart of a heat dissipation method for an energy storage and boost integrated device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] See also Figure 1 As shown, the present invention provides a heat dissipation method for an energy storage and boosting integrated device, comprising the following steps: Step S1: Divide the air-cooled object into several thermal management areas according to the structure and thermal characteristics of the energy storage and boost integrated device; The internal structure of the energy storage and booster integrated unit is complex, containing multiple electrical components and power units, and its heat distribution has significant spatial non-uniformity. To improve the accuracy and responsiveness of air cooling control, the entire unit is divided into multiple thermal management zones based on functional distribution, structural layout, and thermal inertia characteristics, which facilitates the implementation of localized precise temperature control. Taking a typical all-in-one system with liquid cooling and supplementary air cooling as an example, zones can be divided according to the locations of power modules, inductor components, DC bus, battery management units, etc. Each thermal management zone is equipped with an independent temperature sensor and air cooling control inlet to facilitate regional temperature monitoring and local fan adjustment. Step S2: Obtain operating parameters of the energy storage and boost integrated device, including temperature parameters and load parameters of each thermal management area, calculate the actual temperature based on the temperature parameters, and calculate the load value based on the load parameters; Operating parameters reflect the operating status of the all-in-one machine under actual load and thermal environment. Temperature parameters are collected by real-time sensors, while load parameters usually refer to data related to heat intensity, such as power output and current changes per unit time, and are used to estimate thermal load.

[0029] The system periodically reads values ​​uploaded by embedded temperature sensors and obtains load current, voltage, and power values ​​from the power controller via CAN or Modbus protocols. Using a filtering algorithm, it calculates the real-time temperature T and load value P for each thermal management zone, providing input for subsequent control steps.

[0030] Step S3: Calculate the target temperature for air cooling control in the corresponding thermal management area based on the operating parameters and historical temperature data; The target temperature is obtained by predicting the target temperature prediction model based on the actual temperature of the thermal management area, the temperature rise trend, and the load power; The warming trend is calculated by actual temperature changes; The target temperature prediction model is trained offline using historical temperature data; The target temperature prediction model uses incremental learning to update model parameters online; The target temperature prediction model dynamically adjusts the prediction time length in real time by setting the sliding prediction time window and temperature rise gradient threshold; Target temperature prediction is a key step in intelligent temperature control. Based on current operating trends and historical behavior patterns, it uses machine learning methods to predict future thermal development trends and form an expected temperature control reference value, thereby improving the foresight and stability of regulation.

[0031] A regression model based on a support vector machine (SVM) or random forest is constructed as a predictor. It uses the current regional temperature, load, and the temperature rise rate over a period of time as feature inputs and outputs a target temperature. Training samples are derived from historical system operation data, and an incremental update mechanism ensures the model's adaptability to seasonal changes and environmental disturbances. The model dynamically sets the sliding window length (e.g., the last 5 minutes) and temperature rise threshold to control prediction response time and accuracy.

[0032] A target temperature prediction model that integrates temperature, load, and heating trend is adopted, and an incremental learning mechanism is introduced to update the model online. This enables the temperature prediction results to dynamically adapt to changes in the equipment's operating status, improves the timeliness and robustness of the model, and effectively reduces the risk of overcooling or undercooling caused by prediction errors.

[0033] Step S4: Determine the air cooling requirement area and select the corresponding air cooling requirement level based on the target temperature and the actual temperature of the corresponding area; The air cooling demand level is divided into different levels by constructing the thermal risk index R:

[0034] in, is the target temperature, is the actual temperature, P is the load parameter, is the heat capacity; When judging the air cooling level, the temperature prediction deviation factor is further introduced ,like If the set threshold ε is exceeded, the current air cooling level will be frozen in the current period until the next level judgment cycle is triggered; The judgment mechanism for the air cooling demand level introduces the thermal risk index R, which comprehensively considers multiple factors such as temperature deviation, load level and heat capacity, and realizes risk warning and level quantification in the early stage of air cooling control. It avoids the response delay or insufficient cooling caused by the traditional single-threshold judgment method, and enhances the forward-looking nature of air cooling control.

[0035] Step S5, calculating the temperature buffer zone for fan control according to the target temperature and the actual temperature; The upper and lower limits of the temperature buffer are determined by the following formula:

[0036] in, is the upper limit of the buffer zone, is the lower limit of the buffer zone, is the temperature standard deviation, η is the stability adjustment coefficient, and ξ is the fixed compensation term; The temperature standard deviation Calculated by the following formula:

[0037] Among them, n is the number of sampling temperature points in the statistical period, is the actual temperature recorded at the i-th sampling moment, The sliding average temperature is the average value of n temperatures within the statistical period; The buffer zone is used to filter out ineffective fan starts and stops caused by minor disturbances, improving the system's ability to resist interference. The temperature standard deviation measures the range of regional temperature fluctuations and is an important basis for setting the buffer zone boundary.

[0038] The proposed temperature buffer zone control method adopts standard deviation modeling and introduces system adjustment parameters η and compensation term ξ to ensure that the fan control maintains a stable state under conditions of slight temperature changes, effectively suppressing the mechanical shock and control oscillation caused by frequent start and stop of the fan, and further improving the smoothness and stability of the system operation.

[0039] The system collects data from n = 60 temperature sampling points every cycle (e.g., 1 minute) and calculates the sliding mean and standard deviation. After selecting empirical values ​​such as η = 1.5 and ξ = 0.8°C, upper and lower thresholds can be set to determine whether to trigger fan operation. When T falls within the range, the fan's current on / off state is maintained, preventing frequent on / off cycles caused by transient disturbances.

[0040] Step S6: Cooling the thermal management area with fans according to the air cooling demand level. When the temperature is within the buffer zone, maintaining the current fan state unchanged. The wind turbine scheduling strategy optimizes wind turbine action decisions based on the following objective function:

[0041] in, is the current control state of the i-th fan, is the state of the previous control cycle, 、 are the first cost weight and the second cost weight of the fan start and stop, is the current remaining life of the i-th wind turbine.

[0042] This scheduling model aims to minimize the loss of fan life and comprehensively considers the cost of switching frequency and continuous operation time, which helps to extend the life of the fan and reduce unnecessary operations.

[0043] During the fan scheduling process, a multi-objective optimization function is constructed to quantify the fan start-stop frequency, fan life loss and state change cost, thereby improving the rationality and intelligence of the fan action strategy. While ensuring that the cooling capacity meets the thermal management requirements, the fan service life is extended and the maintenance frequency and energy consumption cost are reduced.

[0044] The system establishes an independent life tracker for each fan, recording state changes whenever the fan operates. An empirical weighting factor is set to integrate the fan's behavior, dynamically updating its remaining life. Based on this, the system selects a switch combination that matches the cooling level to minimize overall operating cost.

[0045] By dividing the equipment thermal management objects into multiple thermal management areas and combining operating parameters and historical temperature data to predict the target temperature of each area, accurate perception and differentiated control of thermal distribution are achieved, avoiding the problems of delayed response to temperature changes and unreasonable allocation of air cooling resources in existing technologies, and significantly improving the system's heat dissipation efficiency and operational safety.

[0046] In some embodiments, the present application provides a heat dissipation system for an integrated energy storage and boosting device, the system comprising: The data acquisition module is used to collect the structural information, thermal characteristics and operating parameters of the energy storage and booster integrated unit. The operating parameters include the temperature parameters and load parameters of each thermal management area; The data acquisition module collects and initially processes basic operational information from the energy storage and booster system in real time. This information includes, but is not limited to, the device's structural layout, component material properties, thermal response parameters, operating load, current, voltage, and ambient temperature and humidity. This module supports the collection of both static structural information and operational data that changes dynamically over time.

[0047] The data acquisition module consists of several sensor nodes deployed in key locations across the integrated circuit, such as the power module, inductor assembly, and cooling channel inlets and outlets. During operation, the system synchronizes collected data to the upper-level data management platform based on a fixed cycle or event triggering. To ensure data quality, the acquisition module preprocesses the raw data, including standardizing all data fields to a pre-set standard format, filling in missing values, removing values ​​that exceed limits or are not physically reasonable, and generating standardized data packets for subsequent module calls.

[0048] Thermal zone division module, used to divide the air-cooled object into several thermal management zones based on structural information and thermal characteristics; Based on the collected device structure information and thermal response characteristics, this module divides the objects to be cooled inside the entire all-in-one machine into multiple areas with relatively independent heat conduction paths or heat dissipation behaviors, so as to achieve more detailed and zoned air cooling control.

[0049] The partitioning process uses a 3D device model and combines thermal simulation analysis results to cluster areas within the device, such as those with high heat density, structural insulation, and restricted ventilation. Each thermal management zone contains at least one temperature collection point and air cooling control object. The partitioning results are stored in the system configuration for subsequent use by the prediction and control modules. The number and distribution of thermal management zones can be adjusted based on the specific device structure and operational requirements.

[0050] The target temperature prediction module is used to predict the target temperature for air cooling control in each thermal management zone based on the operating parameters and historical temperature data of the zone. The module uses an incremental learning model and supports dynamic adjustment of the sliding prediction time window. This module predicts the ideal temperature that each thermal management zone should maintain or approach over a specific period of time. This target temperature is determined by factors such as historical temperature trends, current temperature levels, and current load conditions. The predictions guide the active control of the cooling system.

[0051] The module uses an incremental machine learning model built on historical operating data to provide rolling target temperature forecasts. During system initialization, the model is trained offline using historical operating condition samples. During operation, the model continuously updates online based on the latest data to adapt to changes in equipment operating conditions. A sliding time prediction window is built into the module, automatically adjusting the prediction time span based on real-time temperature fluctuations, thereby improving prediction stability and responsiveness.

[0052] The air cooling demand identification module is used to determine the air cooling demand area and classify the corresponding air cooling demand level based on the target temperature and the actual temperature of the corresponding area. The classification is based on the calculation of the thermal risk index; This module identifies whether each thermal management zone currently requires air cooling, as well as the urgency or level of that need. This identification is based on multiple factors, including whether the actual temperature exceeds the predicted temperature, the magnitude and duration of the excess, and whether the load level has significantly increased.

[0053] The system establishes a risk assessment indicator system for each thermal management zone and, based on various data, automatically determines whether cooling needs exist. When the actual temperature in a zone is significantly higher than the predicted temperature and is accompanied by high load or rising temperature trends, the system identifies a high level of cooling need. Temperatures only slightly higher than the predicted value and low load are identified as medium or low levels of need. This identification results are used to drive the dynamic response of the fan control module and are visualized through a graphical interface.

[0054] The fan buffer control module is used to calculate the temperature buffer range for fan control based on the target temperature and the actual temperature, and maintain the current fan state unchanged when the current temperature is within the buffer range. The upper and lower limits of the temperature buffer range are calculated based on the temperature standard deviation and the adjustment coefficient; This module introduces a temperature buffer mechanism into air cooling control to prevent frequent fan starts and stops caused by short-term temperature fluctuations. By setting upper and lower limit buffers, the fan status is maintained when the current temperature is within this range, thereby improving system stability and fan lifespan.

[0055] The system dynamically calculates a buffer zone for the current area based on recent statistical characteristics of temperature fluctuations, such as the average temperature fluctuation range and the fluctuation range. The upper and lower limits are adjusted based on the actual fluctuation range. If the temperature fluctuation is stable, the buffer zone is appropriately expanded; if the temperature fluctuation is severe, the buffer zone is automatically narrowed. This mechanism effectively avoids repeated control cycles and reduces fan wear.

[0056] The fan scheduling module is used to optimize the fan motion control based on the objective function. The objective function takes into account the fan's current control state, the state of the previous control cycle, and the current remaining life, and combines the fan start-up and shutdown costs for optimized control.

[0057] The fan scheduling module is used to reasonably arrange the opening, closing, speed adjustment and other operations of multiple fans after identifying the air cooling demand, so as to meet the cooling demand while taking into account the fan life, system energy consumption and start-up and shutdown costs.

[0058] The system incorporates a multi-factor optimization strategy, comprehensively evaluating each round of wind turbine scheduling decisions by incorporating factors such as the turbine's historical control status, current operating status, remaining lifespan, and weighted start / stop costs. The control logic prioritizes turbines with low start / stop costs and long lifespans, while avoiding frequent switching of turbine states within a short period of time. Furthermore, the system accumulates statistics on each turbine's operating time and response frequency, which serve as a basis for lifespan estimation and subsequent O&M decisions. The scheduling results are used to drive actual control commands and are simultaneously recorded in the device log.

[0059] To ensure the lifespan of the fans, the system incorporates a dust removal function for the cooling fans. A power phase reversal device is incorporated into each fan group. This allows the fans to rotate in the opposite direction without affecting the normal operation of the energy storage and booster. This blows away dust, debris, and other foreign matter adsorbed on the cooling fans, keeping the cooling fan blades clean and ensuring the fans' heat dissipation efficiency. Furthermore, to prevent damage to the fans caused by sudden changes in the fan power phase, a one-minute delay has been added to the process of changing the fan power phase. This prevents unsafe conditions in the system caused by sudden changes in the cooling fan's direction.

[0060] When the internal temperature of the all-in-one reaches the fan start-up temperature, the air outlet system starts to start, the blower starts working, and the self-hanging ventilation louvers are blown by the internal blower. The self-hanging louvers open and the internal heat is blown out horizontally through the louvers. When the air intake system is working, external cold air enters through the lower air intake external maintenance ventilation louvers, and the dust filter filters out dust and other foreign matter entering from the air. When the filter needs to be cleaned, open the external maintenance louvers and remove the filter for maintenance without powering off. When the air outlet system stops working, the self-hanging louvers automatically close due to gravity, forming an enclosed space with the ventilation duct to prevent external foreign matter and dust from entering.

[0061] The energy storage and booster integrated unit's air outlet system consists of a blower, air duct, and self-hanging louvers. The blower is bolted to the end of the duct, which is primarily constructed from bent and welded steel plates. The self-hanging louvers, consisting of an external louver frame and movable louvers, are riveted to the upper portion of the duct, forming a complete air outlet system.

[0062] The air intake system further consists of externally accessible ventilation louvers, a protective door panel, and a removable dust screen. The protective door panel is primarily made of bent steel and primarily supports the externally accessible ventilation louvers. The externally accessible ventilation louvers consist of a frame, louvers, and a door lock. The removable dust screen consists of a dust screen and a frame.

[0063] In some embodiments, the present application provides a terminal, including: A memory, used for storing a heat dissipation program of the energy storage and boosting integrated machine; A processor is used to implement the steps of the energy storage and boost integrated machine heat dissipation method when executing the energy storage and boost integrated machine heat dissipation system.

[0064] In some embodiments, the present application provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the energy storage and boost integrated device heat dissipation method.

[0065] It is understood that the systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or physical devices, or by products having certain functions. A typical implementation device is a computer, which may be a personal computer, a laptop computer, a personal digital assistant, a tablet computer, a wearable device, or a combination of any of these devices.

[0066] In a typical configuration, a computer includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0067] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0068] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0070] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0071] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "an," "the," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0073] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when..." or "when..." or "in response to determining."

[0074] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A heat dissipation method for an energy storage and boosting integrated device, characterized in that: The following steps are involved: Step S1: Divide the air-cooled object into several thermal management areas according to the structure and thermal characteristics of the energy storage and boost integrated device; Step S2: Obtain operating parameters of the energy storage and boost integrated device, including temperature parameters and load parameters of each thermal management area, calculate the actual temperature based on the temperature parameters, and calculate the load value based on the load parameters; Step S3: Calculate the target temperature for air cooling control in the corresponding thermal management area based on the operating parameters and historical temperature data; Step S4: Determine the air cooling requirement area and select the corresponding air cooling requirement level based on the target temperature and the actual temperature of the corresponding area; Step S5, calculating the temperature buffer zone for fan control according to the target temperature and the actual temperature; Step S6: Cool the heat management area with fans according to the air cooling demand level. When the temperature is within the buffer zone, maintain the current fan state unchanged.

2. The heat dissipation method for an energy storage and boosting integrated device according to claim 1, characterized in that: In step S3, the target temperature is obtained by predicting the target temperature prediction model based on the actual temperature of the thermal management area, the temperature rise trend, and the load power; The warming trend is calculated by actual temperature changes; The target temperature prediction model is trained offline using historical temperature data; The target temperature prediction model uses incremental learning to update model parameters online.

3. The heat dissipation method for an energy storage and boosting integrated device according to claim 2, characterized in that: The target temperature prediction model dynamically adjusts the prediction time length in real time by setting the sliding prediction time window and temperature rise gradient threshold.

4. The heat dissipation method for an energy storage and boosting integrated device according to claim 1, characterized in that: In step S4, the air cooling demand level is divided by constructing a thermal risk index R to make a level judgment: in, is the target temperature, is the actual temperature, P is the load parameter, is the heat capacity.

5. The heat dissipation method for an energy storage and boosting integrated device according to claim 4, characterized in that: When judging the air cooling level, the temperature prediction deviation factor is further introduced ,like If the set threshold ε is exceeded, the current air cooling level will be frozen in the current period until the next level judgment cycle is triggered.

6. The heat dissipation method for an energy storage and boosting integrated device according to claim 1, characterized in that: In step S5, the upper and lower limits of the temperature buffer are determined by the following formula: in, is the upper limit of the buffer zone, is the lower limit of the buffer zone, is the temperature standard deviation, η is the stability adjustment coefficient, and ξ is the fixed compensation term; The temperature standard deviation Calculated by the following formula: Among them, n is the number of sampling temperature points in the statistical period, is the actual temperature recorded at the i-th sampling moment, The sliding average temperature value is the average value of n temperatures within the statistical period.

7. The heat dissipation method for an energy storage and boosting integrated device according to claim 1, characterized in that: In step S6, the wind turbine dispatching strategy optimizes the wind turbine action decision based on the following objective function: in, is the current control state of the i-th fan, is the state of the previous control cycle, 、 are the first cost weight and the second cost weight of the fan start and stop, is the current remaining life of the i-th wind turbine.

8. A heat dissipation system for an energy storage and boosting integrated machine, characterized in that: The system includes: The data acquisition module is used to collect the structural information, thermal characteristics and operating parameters of the energy storage and booster integrated unit. The operating parameters include the temperature parameters and load parameters of each thermal management area; Thermal zone division module, used to divide the air-cooled object into several thermal management zones based on structural information and thermal characteristics; The target temperature prediction module is used to predict the target temperature for air cooling control in each thermal management zone based on the operating parameters and historical temperature data of the zone. The module uses an incremental learning model and supports dynamic adjustment of the sliding prediction time window. The air cooling demand identification module is used to determine the air cooling demand area and classify the corresponding air cooling demand level based on the target temperature and the actual temperature of the corresponding area. The classification is based on the calculation of the thermal risk index; The fan buffer control module is used to calculate the temperature buffer range for fan control based on the target temperature and the actual temperature, and maintain the current fan state unchanged when the current temperature is within the buffer range. The upper and lower limits of the temperature buffer range are calculated based on the temperature standard deviation and the adjustment coefficient; The fan scheduling module is used to optimize the fan motion control based on the objective function. The objective function takes into account the fan's current control state, the state of the previous control cycle, and the current remaining life, and combines the fan start-up and shutdown costs for optimized control.

9. A terminal, characterized in that: include: A memory, used for storing a heat dissipation program of the energy storage and boosting integrated machine; A processor is used to implement the steps of the energy storage and boost integrated machine heat dissipation method as claimed in claim 1 when executing the energy storage and boost integrated machine heat dissipation system.

10. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the heat dissipation method for the energy storage and boosting integrated device as claimed in claim 1.

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