Air conditioner outdoor unit heat dissipation cooling processing system and method

Abstract

This invention relates to the field of air conditioning heat dissipation technology, and discloses a heat dissipation and cooling system and method for an air conditioner outdoor unit. The method includes acquiring multi-dimensional data streams such as the cooling fan speed sequence, casing temperature distribution map, and environmental parameter set during outdoor unit operation via a multi-source sensor array; processing these data streams using an adaptive performance evaluation algorithm to generate a dynamic heat dissipation performance score, comparing it with a benchmark range, and generating an performance anomaly marker when deviations occur. Responding to the anomaly marker, an internal temperature field scan is initiated to acquire spatiotemporal temperature data, and the heat load influence factor is calculated using a thermodynamic influence model; a high-temperature warning is activated when the threshold is exceeded. Based on the correlation analysis of the warning execution parameters, the temporal synergy and distribution matching degree of heat dissipation and temperature parameter groups are calculated. A unified correlation index is generated through a multi-dimensional fusion engine, based on which the fan speed control parameters are derived and closed-loop fan speed adjustment is implemented. This method achieves precise perception and dynamic control of the heat dissipation status, ensuring stable operation of the outdoor unit.

Description

Technical Field

[0001] This invention relates to the field of air conditioning heat dissipation technology, specifically to an air conditioning outdoor unit heat dissipation and cooling system and method. Background Technology

[0002] As an indispensable temperature control device in modern life and industrial production, the operational stability of air conditioners directly affects user experience and energy consumption. The heat dissipation efficiency of the outdoor unit is one of the core factors determining the overall operating status of the unit. With the continuous increase in air conditioner power and the growing frequency of extreme weather events, the heat dissipation system often faces severe challenges when the outdoor unit operates in complex environments with high temperature, high humidity, or high dust levels. Traditional air conditioner outdoor units rely heavily on fixed-speed cooling fans, which can only start, stop, or adjust their speed according to preset, simple logic. This passive cooling mode lacks precise perception and dynamic adaptation to actual operating conditions.

[0003] In practical applications, the heat dissipation efficiency of the outdoor unit is affected by a combination of factors, such as fluctuations in ambient temperature, wear and tear of the cooling fan, changes in the heat generated by internal components, and interference from external airflow. While some existing technologies have introduced single sensors to monitor temperature or speed, the limited data collection fails to comprehensively reflect the true operating status of the cooling system, leading to inaccurate assessments of heat dissipation efficiency. When the outdoor unit experiences poor heat dissipation, current technologies often struggle to quickly pinpoint the root cause, resorting only to triggering shutdown or frequency reduction via overheat protection devices. This delayed response not only affects the normal operation of the air conditioner but may also exacerbate component wear due to prolonged high-temperature operation, shortening the outdoor unit's lifespan and increasing unnecessary energy consumption.

[0004] Traditional heat dissipation control methods lack a scientific performance evaluation system and precise parameter adjustment mechanism. Even when abnormal temperatures are detected, they often employ a crude speed adjustment strategy, making it difficult to achieve optimal matching between the cooling fan and the actual heat load. This results in neither ideal heat dissipation nor adequate cooling, and may also generate additional noise and energy consumption due to excessive fan speed. Therefore, current air conditioner outdoor unit heat dissipation technology needs an integrated approach that enables multi-dimensional sensing, precise evaluation, rapid response, and intelligent control to meet the heat dissipation demands in complex environments and improve the stability and economy of the entire unit's operation. Summary of the Invention

[0005] The purpose of this invention is to provide a heat dissipation and cooling system and method for an air conditioner outdoor unit, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for heat dissipation and cooling of an air conditioner outdoor unit, the method comprising: Multidimensional data streams during the operation of the outdoor unit of the air conditioner are collected by a multi-source sensor array. The multidimensional data streams include cooling fan speed sequence, shell temperature distribution map and environmental parameter set. An adaptive performance evaluation algorithm is used to process the multidimensional data stream to generate a dynamic heat dissipation performance score. The dynamic heat dissipation performance score is compared with a benchmark performance range. When the dynamic heat dissipation performance score deviates from the benchmark performance range, a performance anomaly marker is generated. In response to the performance anomaly flag, the internal temperature field scanning program is initiated to acquire internal temperature spatiotemporal data. The thermodynamic influence model is applied to calculate the heat load influence factor. If the heat load influence factor exceeds a predetermined threshold, a high temperature warning flag is activated. Based on the high temperature warning sign, a parameter correlation analysis process is executed to calculate the temporal synergy and distribution matching degree between the heat dissipation parameter group and the temperature parameter group, and a unified correlation index is generated through a multi-dimensional fusion engine. Based on high temperature warning indicators and a unified correlation index, the speed control parameters are derived, and the closed-loop adjustment of the cooling fan speed is implemented to complete the heat dissipation and cooling operation.

[0007] Preferably, the method for processing the multidimensional data stream using an adaptive performance evaluation algorithm includes: dividing the heat dissipation surface of the air conditioner outdoor unit into honeycomb grid cells, with each grid cell deploying an infrared temperature measurement point and an airflow sensor; reading the temperature and wind speed readings of each grid cell in real time; calculating the instantaneous heat dissipation performance value of each grid cell, where the instantaneous heat dissipation performance value is the ratio of the wind speed reading to the temperature reading relative to the ambient temperature difference; and spatially weighting and aggregating the instantaneous heat dissipation performance values ​​of all grid cells to obtain a dynamic heat dissipation performance score.

[0008] Preferably, the method for calculating the heat load influence factor using the thermodynamic influence model includes: extracting the temporal fluctuation pattern and spatial heterogeneity pattern from the internal temperature spatiotemporal data; quantifying the temporal fluctuation pattern using the coefficient of variation of the temperature sequence; measuring the spatial heterogeneity pattern using the entropy value of the temperature distribution map; inputting the temporal fluctuation pattern and spatial heterogeneity pattern into the feature weighting module, and outputting the heat load influence factor.

[0009] Preferably, the method for extracting the time fluctuation pattern includes: segmenting the internal temperature spatiotemporal data into time windows, calculating the ratio of the temperature range to the average temperature within each time window as the fluctuation intensity within the window; summarizing the fluctuation intensities of all time windows, calculating their standard deviation, and using this as the time fluctuation pattern.

[0010] Preferably, the method for extracting the spatial heterogeneous mode includes: rasterizing the internal temperature distribution map into a pixel array, calculating the sum of the absolute values ​​of the temperature differences between each pixel and its neighboring pixels as the local heterogeneity; performing histogram statistics on the local heterogeneity of the entire pixel array, and taking the kurtosis of the histogram as the spatial heterogeneous mode.

[0011] Preferably, the method for calculating the time-series coherence includes: selecting the fan speed sequence from the heat dissipation parameter group and the core temperature sequence from the temperature parameter group, performing dynamic time warping and alignment, and calculating the Pearson correlation coefficient of the aligned sequence as the time-series coherence.

[0012] Preferably, the method for calculating the distribution matching degree includes: performing an overlay analysis on the wind speed distribution map of the heat dissipation parameter group and the hot spot distribution map of the temperature parameter group, and calculating the mutual information value of the two distribution maps in the overlapping area as the distribution matching degree.

[0013] Preferably, the method for generating a unified correlation index through a multi-dimensional fusion engine includes: using temporal synergy and distribution matching degree as input features to construct a feature vector, applying an attention mechanism to assign weights to the feature vector, and obtaining a unified correlation index by weighted summation.

[0014] Preferably, the method for deriving the speed control parameters and implementing closed-loop adjustment of the cooling fan speed includes: establishing a speed control rule base, which contains empirical values ​​for speed adjustment under various high-temperature scenarios; matching the optimal speed adjustment amount from the rule base as the speed control parameter based on the level of the high-temperature warning indicator and the magnitude of the unified correlation index. Using the speed control parameter as the set value, the actual speed of the cooling fan is monitored in real time, the deviation between the actual speed and the set value is calculated, and an adjustment signal is generated by a proportional-integral-derivative controller to drive the fan motor to adjust the speed. Multidimensional data streams are continuously collected for feedback optimization.

[0015] Preferably, the present invention also includes an air conditioner outdoor unit heat dissipation and cooling system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the air conditioner outdoor unit heat dissipation and cooling method described above.

[0016] Compared with the prior art, the beneficial effects of the present invention are: In the data acquisition stage, a multi-source sensor array is used to replace the traditional single-sensor monitoring mode. This allows for the simultaneous acquisition of multi-dimensional data streams, such as cooling fan speed sequences, casing temperature distribution maps, and environmental parameter sets. This comprehensive information acquisition method breaks through the limitations of single data dimensions in traditional technologies. It can fully capture various state characteristics of the air conditioner outdoor unit during operation, providing rich and reliable basic information for subsequent performance evaluation and control decisions, and avoiding judgment errors caused by incomplete data.

[0017] In terms of performance evaluation, the application of the adaptive performance evaluation algorithm enables dynamic and accurate judgment of heat dissipation status. Traditional technologies often rely on fixed thresholds for simple judgments, which cannot adapt to the dynamic changes in the outdoor unit's operating status. However, this algorithm can generate dynamic heat dissipation performance scores based on real-time changes in multi-dimensional data streams. By comparing with the baseline performance range, it can promptly detect performance anomalies and generate markers, transforming the status monitoring of the heat dissipation system from passive threshold triggering to proactive dynamic evaluation. This improves the sensitivity and accuracy of identifying heat dissipation anomalies, saving valuable time for subsequent response and processing.

[0018] For the response and handling of performance anomalies, this method uses spatiotemporal temperature data obtained through internal temperature field scanning, combined with a thermodynamic influence model to calculate the heat load impact factor. This allows for accurate determination of whether the anomaly is caused by excessive heat load, avoiding the blind judgment of anomaly causes found in traditional technologies. The activation of high-temperature warning indicators provides a clear basis for subsequent control measures, ensuring that control measures are only initiated when truly needed, reducing unnecessary control operations, and lowering energy consumption and equipment wear.

[0019] In the parameter adjustment phase, the combination of parameter correlation analysis and a multi-dimensional fusion engine enables the scientific derivation of control parameters. By calculating the temporal synergy and distribution matching degree of the heat dissipation parameter group and the temperature parameter group, the generated unified correlation index can comprehensively reflect the intrinsic relationship between the two types of parameters, making the derivation of speed control parameters no longer dependent on empirical presets, but based on objective data analysis. The closed-loop adjustment mechanism ensures real-time feedback and dynamic optimization of the control effect, and can adjust the control parameters in a timely manner according to changes in heat dissipation effect, achieving precise matching between the cooling fan speed and the actual heat load, avoiding the problems of insufficient or excessive heat dissipation caused by traditional coarse adjustment. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the working principle of the air conditioner outdoor unit heat dissipation and cooling treatment method described in this invention. Figure 2 The flowchart is for the adaptive performance evaluation algorithm; Figure 3 A flowchart for calculating the thermal load influence factor; Figure 4 A graph showing the trend of temporal synergy and distribution matching degree over time; Figure 5 A time-series correlation analysis diagram of the heat dissipation parameters of the outdoor unit of the air conditioner. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 This invention provides a method for heat dissipation and cooling of an air conditioner outdoor unit. The method includes: real-time acquisition of a multi-dimensional data stream during the operation of the air conditioner outdoor unit using a multi-source sensor array. This multi-dimensional data stream includes a cooling fan speed sequence, a casing temperature distribution map, and an environmental parameter set. The cooling fan speed sequence is acquired through an encoder or Hall sensor, recording the sequence data of fan speed changes over time. The casing temperature distribution map is acquired through an infrared thermal imager or a distributed temperature sensor array, generating a two-dimensional temperature field image of the outdoor unit casing surface. The environmental parameter set includes ambient temperature, humidity, wind speed, etc., and is monitored in real-time by an environmental sensor module. The acquired multi-dimensional data stream is transmitted to a central processing unit for subsequent analysis.

[0023] An adaptive performance evaluation algorithm is employed to process the multidimensional data stream. This algorithm calculates a dynamic heat dissipation performance score based on real-time data. The dynamic heat dissipation performance score reflects the overall performance level of the current heat dissipation system, and its calculation process involves data normalization, feature extraction, and weighted aggregation. The dynamic heat dissipation performance score is compared with a preset benchmark performance range, which is dynamically adjusted based on the air conditioner outdoor unit model, historical operating data, and environmental conditions. When the dynamic heat dissipation performance score is lower or higher than the benchmark performance range, the system generates a performance anomaly marker, indicating a deviation in heat dissipation performance. In response to the performance anomaly marker, the system automatically initiates an internal temperature field scanning program. This program acquires spatiotemporal temperature data of key internal components through temperature probes or scanning thermal sensors built into the air conditioner outdoor unit. The internal temperature spatiotemporal data includes the sequence and spatial distribution information of temperature values ​​over time. A thermodynamic influence model is applied to analyze the internal temperature spatiotemporal data and calculate the heat load influence factor. The thermodynamic influence model, based on the principles of heat conduction and heat convection, quantifies the degree of influence of internal heat accumulation on overall heat dissipation. If the heat load influence factor exceeds the predetermined threshold, it indicates that the internal heat load is too high, and the system will activate the high temperature warning indicator.

[0024] Based on the high-temperature warning sign, a parameter correlation analysis process is executed. This process analyzes the correlation between the heat dissipation parameter group and the temperature parameter group. The heat dissipation parameter group includes data such as fan speed and airflow distribution; the temperature parameter group includes data such as casing temperature and internal hot spot temperature. The temporal synergy and distribution matching degree of the heat dissipation parameter group and the temperature parameter group are calculated. The temporal synergy evaluates the synchronicity of parameter changes over time; the distribution matching degree evaluates the consistency of parameter spatial distribution. The temporal synergy and distribution matching degree are integrated through a multi-dimensional fusion engine to generate a unified correlation index. The unified correlation index comprehensively reflects the coupling strength between the heat dissipation system and the temperature field. Based on the high-temperature warning sign and the unified correlation index, the fan speed control parameter is derived. The fan speed control parameter determines the direction and magnitude of the fan speed adjustment. The system generates a fan speed setpoint based on the severity of the high-temperature warning and the magnitude of the unified correlation index, using a rule base or machine learning model. Closed-loop adjustment of the fan speed is implemented, using the fan speed control parameter as the setpoint, monitoring the actual fan speed in real time, and dynamically adjusting the motor drive signal through a feedback controller to ensure that the actual speed tracks the setpoint. Continuously collect multi-dimensional data streams for feedback optimization to ensure the effectiveness and stability of heat dissipation and cooling operations.

[0025] Example 1: See Figure 2In practical implementation, the heat dissipation surface of the air conditioner outdoor unit is divided using a honeycomb grid structure. The grid cells are regular hexagons, and the side length of each grid cell is determined based on the actual size of the outdoor unit and its heat dissipation requirements, ranging from 5 cm to 10 cm. This division method ensures coverage of the entire heat dissipation surface and provides a uniform distribution of monitoring points. The grid cell arrangement is completed using digital modeling software, and the model is imported into the control system to guide the physical installation of the sensors. In some embodiments, the size of the grid cells can be dynamically adjusted based on the outdoor unit's power; higher-power outdoor units use smaller grids to improve resolution. An infrared temperature measurement point is deployed at the center of each grid cell. The infrared temperature measurement point uses a non-contact infrared sensor, and the sensor model is selected to have high accuracy and fast response characteristics. During installation, it is ensured that the sensor lens is perpendicular to the heat dissipation surface to avoid measurement errors. The sampling frequency of the infrared temperature measurement point is set to 1 to 5 times per second to ensure the real-time and continuous temperature readings. Simultaneously, airflow sensors are deployed at the edge of each grid cell. These sensors detect wind speed and direction and can be either hot-wire or ultrasonic. Hot-wire sensors operate based on heat dissipation principles, with a measurement range covering 0 to 10 meters per second. Ultrasonic sensors calculate wind speed using the time difference of sound wave propagation. During installation, the airflow sensors are oriented towards the fan's outlet direction to accurately capture airflow dynamics. The system uses a data acquisition module to read the temperature and wind speed readings of each grid cell in real time. Temperature readings are recorded in degrees Celsius, and wind speed readings in meters per second. Parallel processing technology is used to ensure that all sensor data is acquired synchronously and timestamped. The data acquisition module integrates signal conditioning circuitry to amplify, filter, and convert the raw sensor signals to digital, eliminating noise interference.

[0026] In practical implementation, the instantaneous heat dissipation efficiency value of each grid cell is calculated. This instantaneous efficiency value is defined as the ratio of wind speed reading to temperature reading relative to the ambient temperature difference. The ambient temperature difference is calculated by comparing the temperature reading of the current grid cell with the ambient temperature difference in the environmental parameter set. The environmental parameter set is provided by independent environmental sensors and includes ambient temperature, humidity, and wind speed data. The formula for calculating the instantaneous heat dissipation efficiency value is: efficiency value equals wind speed reading divided by the difference between temperature reading and ambient temperature. Both temperature readings and ambient temperatures are in Kelvin absolute temperature units to avoid mathematical errors caused by negative values. The calculation process is executed by an embedded processor, and the instantaneous heat dissipation efficiency value of each grid cell is updated in real time, with the update frequency consistent with the sensor sampling frequency. In some embodiments, the calculation of the ambient temperature difference may incorporate hysteresis filtering and use a moving average method to smooth ambient temperature fluctuations, ensuring the stability of the difference. The instantaneous heat dissipation efficiency values ​​of all grid cells are spatially weighted and aggregated. Weight allocation is based on the location and importance of the grid cells; for example, grid cells closer to heat sources such as compressors or condensers are assigned higher weights. Weight values ​​are determined through expert experience or historical data analysis, ranging from 0.5 to 2.0. The weighted aggregation uses a linear weighted average method, where the instantaneous heat dissipation efficiency value of each grid cell is multiplied by its weight, summed, and then divided by the total weight value to obtain a dynamic heat dissipation efficiency score. The dynamic heat dissipation efficiency score is a dimensionless value; its magnitude directly reflects the quality of heat dissipation efficiency, with higher values ​​indicating better heat dissipation. Optionally, a normalization step can be introduced into the weighted aggregation process to scale the instantaneous heat dissipation efficiency values ​​to a standard range before calculation, thus eliminating the influence of dimensions. The system compares the dynamic heat dissipation efficiency score with a baseline efficiency range. This range is derived from historical operating data of the outdoor unit, calculated using methods including the mean and standard deviation of the dynamic heat dissipation efficiency score during normal operation. The baseline efficiency range is typically set to the mean plus or minus 10%, i.e., 90% to 110%. When the dynamic heat dissipation efficiency score is lower or higher than the baseline efficiency range, the system generates an efficiency anomaly flag, indicated by a Boolean flag or numeric code, to trigger subsequent processing. The baseline efficiency range can be adaptively adjusted based on long-term operating data, using a sliding window algorithm to periodically recalculate the range boundaries.

[0027] In practical implementation, the deployment details of infrared temperature measurement points include sensor calibration procedures. Calibration is performed before installation, using a standard blackbody radiation source as a reference, adjusting the sensor output to match the actual temperature value, and storing calibration data in non-volatile memory. The layout of infrared temperature measurement points considers the geometric characteristics of the heat dissipation surface. For curved or uneven areas, the grid cell size is appropriately reduced to ensure measurement accuracy. The installation angle of the airflow sensor is adjustable and fixed by a mechanical bracket, allowing fine-tuning to optimize wind direction detection. In some embodiments, the airflow sensor array can be configured for multi-directional measurement, simultaneously capturing radial and tangential wind speed components. When reading data in real time, the system uses a circular buffer to store the latest readings. The buffer size is set according to the sampling frequency and the number of grids, typically retaining the data from the most recent 60 seconds for short-term analysis. When calculating the instantaneous heat dissipation efficiency value, for edge cases where the temperature reading is close to the ambient temperature, the system introduces a minimum threshold protection. When the temperature difference is less than 0.5 Kelvin, the efficiency value is forcibly set to zero to avoid division by zero errors or numerical overflow. The weight allocation of spatial weighted aggregation can be dynamically optimized based on thermal imaging analysis, learning hotspot distribution patterns through machine learning algorithms and automatically adjusting weight values. The weighted average calculation uses floating-point units to ensure accuracy, and the aggregated result is rounded to two decimal places. The establishment of the baseline performance range relies on data accumulation during the initial operation phase, which lasts from 24 to 72 hours, during which the outdoor unit operates under standard conditions. The logic for generating performance anomaly markers includes a continuous monitoring mechanism; a marker is only confirmed to be effective when the dynamic heat dissipation performance score deviates from the baseline performance range for multiple consecutive cycles (e.g., three consecutive sampling cycles), to prevent false alarms due to momentary interference. It can be understood that the entire processing flow is integrated into a real-time operating system, and task scheduling ensures the temporal consistency of data acquisition, calculation, and comparison.

[0028] In practical implementation, the division of the honeycomb grid cells is achieved using computer-aided design tools. The grid generation algorithm is based on the Delaunay triangulation principle, ensuring that the grid is uniform and non-overlapping. The grid data is stored in memory as a two-dimensional array. Each grid cell has a unique identifier including a row number and a column number, used for data indexing and mapping. The selection of infrared temperature measurement points considers environmental adaptability; the sensor packaging is waterproof and dustproof, adaptable to harsh outdoor conditions; and a low-voltage DC power supply is used, centrally managed through a bus system. Airflow sensor calibration, including wind speed calibration, is performed in a wind tunnel experiment, establishing a linear relationship table between output voltage and wind speed, which is embedded in the firmware for real-time lookup. The data reading interface supports multiple communication protocols, such as I2C or SPI, enabling high-speed data transmission. The code for calculating instantaneous heat dissipation efficiency is optimized to use a lookup table method or approximate calculation to improve processing speed and avoid complex floating-point operations. Environmental temperature difference is acquired by accessing the environmental parameter set through a shared memory area; environmental sensor data is updated every second. The weight allocation strategy is configurable; users can manually set weights or select automatic mode through the configuration interface. The linear weighted average method is implemented by calculating stepwise using accumulator variables, reducing memory usage. The dynamic heat dissipation performance score is output as a 32-bit floating-point number and transmitted to the main control module for subsequent decision-making. Statistical calculations for the baseline performance interval use a historical database, which stores data from the most recent 30 days in a circular structure. The interval update task is executed during low-load periods. The trigger conditions for performance anomaly marking can be set with sensitivity parameters, allowing users to adjust the deviation tolerance according to the application scenario. It can be understood that the system design supports modular expansion; adding new sensors or grid units is plug-and-play, without modifying the core algorithm.

[0029] In practical implementation, the grid cell size adjustment mechanism allows for dynamic reconfiguration. When local overheating is detected, the system can temporarily subdivide the grid cells to increase sensor density. The subdivision algorithm is based on the quadtree partitioning principle. The sampling frequency of the infrared temperature measurement points can be adaptively adjusted according to the temperature change rate. When the temperature gradient is large, the frequency is increased to 10 times per second to capture rapid changes. The measurement range of the airflow sensor can be configured by software, selecting the optimal range for different outdoor unit models. During real-time data reading, the system performs data integrity checks, using CRC checksums to verify error-free transmission. When calculating the instantaneous heat dissipation performance value, a temperature compensation factor is introduced to compensate for sensor drift errors over time. The calculation of ambient temperature difference considers the influence of humidity, using a wet-bulb temperature correction formula to improve accuracy. In weight allocation, importance assessment is based on a heat source distance model; the closer to the heat source, the higher the weight. The model parameters are fitted using experimental data. The weighted aggregation process can be parallelized, utilizing multi-core processors to calculate multiple grid cells simultaneously. The normalization of the dynamic heat dissipation performance score uses a Min-Max scaling method, mapping the score to the 0-1 range for easy comparison. The establishment of the baseline performance range includes outlier removal steps, using box plots to filter outliers. Performance anomaly markers are generated with timestamps and location information for logging and fault diagnosis. It is understood that the entire implementation emphasizes robustness and real-time performance, employing an interrupt-driven architecture to ensure timely responses to high-priority tasks.

[0030] In practical implementation, the honeycomb mesh is physically realized by bonding the sensor substrate with thermally conductive adhesive. The substrate material is aluminum alloy to promote heat conduction, and the mesh boundaries are marked by laser engraving for easy installation. The optical lenses of the infrared temperature measurement points are equipped with a cleaning mechanism, which automatically wipes them periodically to prevent dust from affecting the sensor. The installation position of the airflow sensor is optimized through computational fluid dynamics simulation to ensure representative wind speed capture. The data reading link adopts a redundant design, with primary and backup dual-channel switching to ensure reliability. Intermediate results of instantaneous heat dissipation performance value calculation are cached to reduce redundant calculation overhead. The acquisition of ambient temperature difference is achieved by fusing readings from multiple environmental sensors and using a weighted average to improve robustness. The dynamic optimization of weight allocation is based on a recursive least squares algorithm, with weight coefficients learned online. The numerical stability of the linear weighted average method is ensured by the Kahan summation algorithm to avoid cumulative errors. The output interface of the dynamic heat dissipation performance score supports network transmission for remote monitoring. The adaptive update of the baseline performance interval uses an exponentially weighted moving average model to smooth historical data fluctuations. The confirmation logic for performance anomaly marking includes a voting mechanism, with consensus among multiple independent judgment units taking effect. It is understood that this implementation method is compatible with various outdoor unit models, and the configuration parameters are loaded through an XML file to achieve flexible deployment.

[0031] Example 2: See Figure 3In practical implementation, the thermodynamic influence model for calculating the heat load influence factor requires extracting temporal fluctuation patterns and spatial heterogeneity patterns from the internal temperature spatiotemporal data. This data originates from an array of temperature sensors located inside the air conditioner's outdoor unit. These sensors collect temperature readings of key components such as the compressor and condenser surfaces at fixed time intervals, forming a data stream containing timestamps and three-dimensional spatial coordinates. Extracting the temporal fluctuation pattern is achieved by calculating the coefficient of variation of the temperature sequence. Specifically, the continuous internal temperature spatiotemporal data is segmented into preset time windows, each with a length of 5 minutes. A sliding window mechanism is used for dividing the time windows, allowing for partial overlap to ensure continuity. For each time window's temperature reading sequence, the difference between the maximum and minimum values ​​is calculated to obtain the range. Simultaneously, the arithmetic mean of all temperature readings within that time window is calculated. The range is then divided by the mean to obtain the fluctuation intensity within the window. This ratio, dimensionless, reflects the relative amplitude of temperature fluctuations during that time period. After calculating the in-window fluctuation intensity for all time windows, an in-window fluctuation intensity sequence is obtained. The standard deviation of this sequence is then calculated, and the resulting standard deviation value is the quantization value of the time fluctuation pattern. A larger standard deviation indicates that the temperature change over time is more unstable. It can be understood that the length of the time window and the sliding step size are configurable parameters that can be adjusted according to the time series resolution requirements of specific application scenarios.

[0032] In practical implementation, extracting spatial heterogeneity patterns involves processing the internal temperature distribution map. This map is a two-dimensional grayscale image generated by spatial interpolating readings from all internal temperature sensors at a specific time. Each pixel's grayscale value corresponds to a temperature value. Rasterizing the internal temperature distribution map into a pixel array involves discretizing it into a regular grid, with each pixel representing a small region. The pixel size is typically set to 1.5 cm square to balance detail and computational complexity. When calculating the local heterogeneity of each pixel, the neighborhood of that pixel is defined as a 3x3 pixel region centered on it. The absolute value of the temperature difference between the central pixel and its eight neighboring pixels is calculated, and these eight absolute values ​​are summed. The summation result is the local heterogeneity of that pixel; a high local heterogeneity value indicates a large temperature difference between that point and its surroundings. After calculating the local heterogeneity for all pixels in the entire pixel array, a local heterogeneity distribution map of the same size as the original temperature distribution map is obtained. Next, histogram statistics are performed on all values ​​in the local heterogeneity distribution map. The histogram is grouped into 15 groups, and the frequency of pixel occurrence within each group interval is counted to form a frequency distribution. The kurtosis of this frequency distribution is calculated. Kurtosis is a statistic describing the steepness of the distribution shape, calculated as the ratio of the fourth central moment to the fourth power of the standard deviation. The calculated kurtosis value is used as the quantization output of the spatial heterogeneity mode. High kurtosis in the spatial heterogeneity mode indicates the presence of sharp peaks in the temperature distribution map, i.e., a concentration of hot or cold spots. In some embodiments, the rasterization process can use a bilinear interpolation algorithm to ensure a smooth transition of pixel values.

[0033] In practical implementation, the extraction of temporal fluctuation patterns requires data preprocessing. The original temperature sequence may contain outliers caused by sensor noise or transient interference. During preprocessing, a median filter is used to smooth the temperature sequence within each time window. The median filter window size is 5 data points. When calculating the fluctuation intensity within a window, for the special case where the average temperature is close to zero, the system sets a minimum denominator protection. When the absolute value of the average temperature is less than a preset threshold, the fluctuation intensity within the window is directly assigned a zero value or a default value to avoid mathematical calculation errors. The standard deviation of the fluctuation intensity sequence within the window is calculated using an unbiased estimation formula, i.e., divided by N-1 instead of N, where N is the number of time windows, to improve statistical accuracy. It can be understood that the entire temporal fluctuation pattern extraction algorithm is encapsulated as an independent software module, with the input being a time-stamped temperature data stream and the output being a single temporal fluctuation pattern value. When extracting spatially heterogeneous patterns, the generation of the internal temperature distribution map relies on a spatial interpolation algorithm. The inverse distance weighting interpolation algorithm is chosen, estimating the temperature value of unknown pixels based on the known temperature values ​​of sensor points. Local heterogeneity calculations employ special handling for boundary pixels. For pixels at image edges, whose neighboring pixels may not exist, the approach is to calculate only the sum of the differences between the existing neighboring pixels and the center pixel. The histogram statistics process uses an equal-width binning method, and the histogram kurtosis calculation calls standard functions from numerical computation libraries to ensure accuracy. Optionally, local heterogeneity calculations can consider more complex neighborhood structures, such as 5x5 pixel regions, to capture a wider range of spatial variations.

[0034] In specific implementation, the feature weighting module receives temporal fluctuation patterns and spatial heterogeneity patterns as input features. The module is implemented as a two-layer feedforward neural network. The input layer has two neurons corresponding to the temporal fluctuation pattern and the spatial heterogeneity pattern respectively. The hidden layer has four neurons using the ReLU activation function, and the output layer has one neuron using a linear activation function to output the heat load influence factor. The weight parameters of the neural network are obtained through supervised training using historical data. The training data comes from the temporal fluctuation patterns, spatial heterogeneity patterns, and corresponding expert-annotated heat load state labels recorded during the long-term operation of the air conditioner's outdoor unit under normal and abnormal operating conditions. The training objective is to minimize the mean square error between the network output and the true label. In some embodiments, the feature weighting module can also be simplified to a linear weighted combination: the temporal fluctuation pattern is multiplied by a weight coefficient Wt, and the spatial heterogeneity pattern is multiplied by a weight coefficient Ws. The weight coefficients Wt and Ws are determined through principal component analysis or expert experience. For example, setting the weight coefficient Wt to ​​0.6 and the weight coefficient Ws to 0.4 indicates that the temporal fluctuation pattern is slightly heavier than the spatial heterogeneity pattern. The weighted sum may be scaled using a sigmoid function to limit the heat load impact factor to the range of 0 to 1, facilitating comparison with a predetermined threshold. The predetermined threshold is a configurable scalar value, for example, set to 0.7. When the heat load impact factor exceeds 0.7, a high-temperature warning is activated. It can be understood that the parameters of the feature weighting module can be updated online to adapt to the slow changes in air conditioning system performance.

[0035] In practical implementation, the acquisition of internal temperature spatiotemporal data relies on a high-frequency sampling temperature sensor network. This network employs a distributed architecture, with each sensor node connected to the main controller via a digital bus. The sampling frequency is uniformly set to 1 Hz. Time window segmentation tasks are triggered by timer interrupt service routines in the real-time operating system. Each time window contains 300 consecutive sampling points, with a sliding step size of 60 sampling points, meaning that 80% of the window content is updated every minute. The calculation of intra-window fluctuation intensity is performed immediately after each time window data is ready, using fixed-point arithmetic to improve the efficiency of the embedded processor. The intra-window fluctuation intensity sequence is stored in a circular buffer with a capacity of 100 values, sufficient to cover 100 minutes of runtime history. The standard deviation of the sequence is recursively updated after each new intra-window fluctuation intensity value is added, avoiding redundant calculations of the entire sequence. The calculation frequency of spatial heterogeneity modes is lower than that of temporal fluctuation modes, typically triggered every 30 seconds, because spatial changes in the temperature field are usually slower than temporal changes. Pixel array generation is accelerated using a graphics processing library, and local heterogeneity calculations utilize parallel processing capabilities, dividing the image into blocks for simultaneous processing. Histogram statistics use a lookup table method to quickly determine the interval to which each local heterogeneity value belongs, while kurtosis calculation calls optimized mathematical library functions. The feature weighting module's neural network loads a pre-trained weight matrix from flash memory at system startup, and the forward inference process is efficiently completed using matrix multiplication instructions. It can be understood that the entire implementation design fully considers the constraints of computing resources, and the algorithm complexity has been optimized to meet the real-time requirements of embedded environments.

[0036] In practical implementation, time-series synchronization of the data stream is crucial. Readings from all internal temperature sensors are synchronized via a global clock signal, with timestamp accuracy down to the millisecond level, ensuring the fundamental reliability of time-fluctuation pattern analysis. When generating the internal temperature distribution map required for spatial heterogeneity pattern analysis, missing sensor data is marked, and neighboring sensors are given higher weights during interpolation. The interpolated image resolution is set to 256x256 pixels to accommodate common display and processing requirements. The local heterogeneity calculation kernel (3x3 region convolution) is implemented using pre-compiled convolution code, and histogram statistics utilize a hardware accelerator for frequency counting. The linearly weighted version of the feature weighting module allows for dynamic adjustment of weight coefficients Wt and Ws at runtime, based on factors such as the current ambient temperature or outdoor unit operating load. For example, under high ambient temperatures, the weight of spatial heterogeneity patterns can be appropriately increased. The output of the thermal load influence factor is updated every 10 seconds, with an update frequency that is the least common multiple of the update frequencies of time-fluctuation patterns and spatial heterogeneity patterns, ensuring the freshness of the input features. The activation settings for the high-temperature warning indicator include a de-jittering logic. The indicator is only activated after the heat load influence factor exceeds a predetermined threshold three times consecutively, preventing false alarms caused by momentary fluctuations. It is understood that the system has comprehensive logging capabilities, recording all intermediate variables, including temporal fluctuation patterns, spatial heterogeneity patterns, and historical values ​​of the heat load influence factor, for subsequent fault analysis and model optimization.

[0037] Example 3: In specific implementation, the extraction of spatial heterogeneity patterns begins with the rasterization of the internal temperature distribution map. The internal temperature distribution map is a two-dimensional matrix generated by spatial interpolation of data collected by an array of temperature sensors placed inside the air conditioner's outdoor unit. Each element in the matrix represents the temperature value at a specific location. The rasterization process resamples this two-dimensional matrix into a regular pixel array, with the pixel size set to 2 cm to ensure sufficient preservation of spatial details while controlling computational complexity. The rasterization algorithm uses nearest-neighbor interpolation, directly assigning the value of each grid point to the pixel covering the area. For each pixel in the pixel array, its neighborhood is defined as a 3x3 region of 9 pixels centered on that pixel. If the pixel is located at the image boundary, only existing neighboring pixels are considered. When calculating local heterogeneity, for the central pixel, the absolute value of the temperature difference between it and its eight neighboring pixels is calculated one by one. Then, these eight absolute values ​​are added together, and the sum is the local heterogeneity of the central pixel. A high local heterogeneity value indicates that the temperature at that point is significantly different from the surrounding environment. After calculating the local heterogeneity of all pixels in the entire pixel array, a local heterogeneity distribution map with the same size as the original temperature distribution map is obtained. Next, histograms are plotted on all values ​​in the local heterogeneity distribution map. The histogram is grouped into 12 groups, with group boundaries divided at equal intervals based on the minimum and maximum local heterogeneity values. The number of pixels falling into each group interval is counted, forming a frequency distribution histogram. The kurtosis of this frequency distribution histogram is then calculated. Kurtosis is a statistic describing the steepness of the distribution shape, and its calculation formula is: ;

[0038] Where: symbol Represents kurtosis value, symbol Represents a random variable with local heterogeneity, symbol The average value representing local heterogeneity, with the sign... Represents the expectation operator. The calculated kurtosis value. As the final quantization output of spatially heterogeneous patterns, a higher positive kurtosis value indicates that the local heterogeneity distribution is steeper than the normal distribution, meaning that there are concentrated high-heterogeneity regions. It can be understood that the raster pixel size and the number of histogram groups are configurable parameters that can be adjusted based on the specific sensor's accuracy and computational resources.

[0039] In practical implementation, the calculation of temporal coherence requires selecting the fan speed sequence from the heat dissipation parameter group and the core temperature sequence from the temperature parameter group as input data. The fan speed sequence is obtained from an encoder installed on the cooling fan motor, with a sampling frequency of once per second, recording the value of fan speed change over time. The core temperature sequence is obtained from a platinum resistance temperature sensor installed on the surface of the compressor cylinder, also with a sampling frequency of once per second, recording the value of core temperature change over time. Both sequences are accompanied by precise timestamps during acquisition, but slight time asynchrony may exist due to sensor response characteristics and data transmission delays. Therefore, before performing correlation analysis, the fan speed sequence and core temperature sequence must be dynamically time-warped and aligned. The dynamic time warping algorithm is a method for measuring the similarity between two time sequences that may have different lengths. It uses dynamic programming to find an optimal curved path, causing the two sequences to be non-linearly aligned on the time axis, thereby eliminating phase differences. The alignment process first normalizes the two sequences to a mean of 0 and a standard deviation of 1 to eliminate the influence of dimensions. Then, a cumulative distance matrix is ​​constructed, and the path with the minimum cumulative distance from the lower left to the upper right corner of the matrix is ​​iteratively searched; this path is the optimal alignment path. Finally, one sequence is stretched or compressed along the time axis according to the alignment path to make it correspond to the other sequence at a given time point. After dynamic time warping alignment, two time-synchronized fan speed aligned sequences and core temperature aligned sequences are obtained. The Pearson correlation coefficient of these two aligned sequences is calculated. The Pearson correlation coefficient measures the degree of linear correlation between two variables and is calculated by dividing the covariance of the two sequences by the product of their standard deviations. The calculated Pearson correlation coefficient ranges from -1 to 1, and this value is defined as the temporal coherence. The closer the temporal coherence is to 1, the stronger the positive correlation between changes in fan speed and changes in core temperature over time. In some embodiments, the alignment window size of dynamic time warping can be constrained to limit the maximum amplitude of time warping and avoid excessive distortion.

[0040] In practical implementation, the calculation of local heterogeneity requires special handling for pixels at image boundaries. For pixels located at the four corners of the pixel array, they have only three effective neighboring pixels; for pixels located on the four sides but not at the corners, they have five effective neighboring pixels. During the system's programming implementation, the position of each pixel is first determined, and then the set of neighboring pixels to be included in the calculation is dynamically determined to ensure accuracy. During histogram statistics, pixels with a local heterogeneity value of zero, i.e., temperature-uniform regions, are normally included in the first grouping interval of the histogram. Kurtosis calculation uses an unbiased estimator, employing sample correction coefficients for the calculation of the fourth and second central moments to reduce small sample bias. It can be understood that the entire spatial heterogeneity pattern extraction process is designed as a batch processing task, executed every 30 seconds, because the spatial distribution of the temperature field changes relatively slowly. The calculation of temporal coherence is more frequent, executed every 10 seconds, to quickly respond to the dynamic relationship between the fan and temperature. When the dynamic time warping algorithm is implemented in an embedded system, a sliding window mechanism is used to perform alignment calculations only on sequence data within the most recent time period. The window length is typically set to 5 minutes to balance computational load and the integrity of time-series information. The Pearson correlation coefficient is calculated using a numerically stable algorithm to avoid numerical overflow when the sequence standard deviation is very small. In some embodiments, the calculation of time-series coherence can rely not only on the Pearson correlation coefficient but also combine other correlation metrics, such as the Spearman rank correlation coefficient, to capture non-linear dependencies.

[0041] In practice, the generation of the pixel array relies on the spatial density of the internal temperature sensor network. Sensor nodes are arranged in a grid with a spacing of approximately 10 cm. For the regions between nodes, a bilinear interpolation algorithm is used to estimate temperature values, thereby generating a high-resolution internal temperature distribution map. Local heterogeneity calculation is essentially a spatial convolution operation. The convolution kernel is a 3x3 matrix, with the center element being 0 and the surrounding eight elements being 1. The convolution result is the local heterogeneity distribution map. Histogram statistics are performed by a dedicated hardware histogram counter, which can quickly perform bin counting on the image data. Kurtosis calculation utilizes the statistical function library in the mathematical coprocessor to ensure both speed and accuracy. For temporal coherence calculation, the dynamic time warping algorithm allocates a buffer in memory to store the fan speed sequence and core temperature sequence for the most recent 5 minutes, totaling 300 pairs of data points. The alignment calculation process uses dynamic programming, which requires a large memory space to store the cumulative distance matrix; therefore, a sparse matrix storage technique is employed to save memory. The Pearson correlation coefficient is calculated using an online algorithm, updating the coefficient as soon as a new data point is received to avoid repeatedly traversing the entire sequence. Understandably, the system continuously monitors the data quality of the fan speed and core temperature sequences. If a sensor malfunction causes data loss or anomalies, the calculation of the timing coherence is temporarily suspended, and the data is marked as invalid.

[0042] In practice, the spatial heterogeneous pattern extraction and temporal synergy calculation processes are relatively independent. They share the internal temperature distribution map as a data source, but process spatial and temporal information separately. The output of the spatial heterogeneous pattern extraction module is a scalar value, namely the kurtosis value. This value is passed to the subsequent feature weighting module. The output of the temporal coherence calculation module is also a scalar value, namely the Pearson correlation coefficient, which is passed to the multi-dimensional fusion engine. The two modules have different running cycles: the spatial heterogeneous pattern extraction module runs for 30 seconds, while the temporal coherence calculation module runs for 10 seconds. This means that the temporal coherence is updated three times more frequently than the spatial heterogeneous pattern. To coordinate asynchronous data, the system adopts the latest valid value principle, meaning that the multi-dimensional fusion engine always uses the most recently calculated spatial heterogeneous pattern and temporal coherence values ​​when needed. In some embodiments, if computing resources are limited, the frequency of spatial heterogeneous pattern extraction can be reduced to once per minute, while the temporal coherence calculation, due to its importance for real-time control, usually maintains a higher update frequency. Optionally, the system can set an importance threshold for the temporal coherence calculation. Only when the absolute value of the calculated Pearson correlation coefficient is greater than a certain threshold is the calculation considered valid; otherwise, the previous valid value is used to avoid introducing noise when the correlation is weak.

[0043] In practical implementation, the local heterogeneity distribution map is stored in 16-bit unsigned integer format to save storage space. The grouping interval boundaries for histogram statistics are pre-calculated and stored in a lookup table during system initialization to improve statistical speed. The calculation of higher-order moments involved in kurtosis calculation uses a recursive formula to avoid numerical instability caused by directly calculating higher powers. The path search of the dynamic time warping algorithm uses constraints to limit the slope of curved paths, ensuring the rationality of alignment. The covariance and variance terms of the Pearson correlation coefficient are calculated separately, and then the ratio is calculated. It can be understood that the software implementation of the entire embodiment adopts a modular design; spatial heterogeneity pattern extraction and temporal synergy calculation are encapsulated into independent function libraries with clear input / output interfaces, facilitating code maintenance and functional testing. All intermediate calculation results, such as the local heterogeneity distribution map and aligned time series, are output to a log file in debug mode for offline analysis and algorithm verification.

[0044] Example 4: In the specific implementation, referring to Table 1, the distribution matching degree is calculated based on the overlay analysis of the wind speed distribution map of the heat dissipation parameter group and the hotspot distribution map of the temperature parameter group. The wind speed distribution map is generated by an array of airflow sensors arranged around the outdoor unit of the air conditioner, and is a two-dimensional grid data. Each grid point contains a wind speed measurement value. The hotspot distribution map is extracted from the internal temperature spatiotemporal data, and high-temperature areas are identified by setting a temperature threshold. It is also represented in two-dimensional grid form. The two distribution maps have been registered in coordinate system to ensure spatial alignment during acquisition. The overlay analysis projects the wind speed distribution map and the hotspot distribution map onto the same spatial reference frame and calculates the mutual information value of the two in the overlapping area. The overlapping area refers to the physical space area jointly covered by the two distribution maps. The mutual information value is an information-theoretic metric used to measure the statistical dependence between two random variables, that is, the degree to which information about one variable can be obtained from information about another variable. The first step in calculating the mutual information value is to divide the overlapping area into several regular small blocks, each typically 5 cm by 5 cm in size, and then statistically analyze the distribution of wind speed and temperature values ​​within each small block. Specifically, it is necessary to calculate the joint probability distribution of the wind speed distribution and the hotspot distribution, as well as their respective marginal probability distributions. The joint probability distribution represents the probability that a small patch simultaneously possesses a specific wind speed value and a specific temperature state, while the marginal probability distribution represents the probability distribution of a single variable without considering the other variable. The mutual information value is calculated by summing the ratios of the products of the joint probability distribution and the marginal probability distribution; its mathematical expression reflects the amount of shared information between the two distributions. The calculated mutual information value serves as a quantification of the distribution matching degree; a higher mutual information value indicates better spatial consistency between the wind speed distribution and the hotspot distribution.

[0045] Table 1: Parameters for Calculating Distribution Matching Degree ; In practical implementation, the unified correlation index is generated through a multi-dimensional fusion engine. The input features of this engine are temporal synergy and distribution matching degree. Temporal synergy is the Pearson correlation coefficient, representing the temporal correlation between fan speed and core temperature, while distribution matching degree is the mutual information value, representing the spatial consistency between wind speed and hotspots. The process of constructing the feature vector involves combining temporal synergy and distribution matching degree as two elements into a two-dimensional vector: feature vector = [temporal synergy, distribution matching degree]. An attention mechanism is applied to weight the feature vector. This attention mechanism is a resource allocation scheme whose core idea is to dynamically adjust the contribution of each input feature based on its importance. In the multi-dimensional fusion engine, the attention mechanism first calculates the attention score for each feature. This score is obtained through a small neural network or linear transformation. This neural network takes the feature vector itself as input and outputs two values ​​corresponding to the attention score for temporal synergy and the attention score for distribution matching degree, respectively. Then, the Softmax function is used to normalize these attention scores, converting them into weight values ​​so that the sum of the weights of the two features is 1. The application of the Softmax function ensures that the weight values ​​are distributed between 0 and 1 and sum to 1, thus reflecting the probabilistic interpretation of the attention mechanism. Finally, the temporal coherence degree is multiplied by its corresponding weight, and the distribution matching degree is multiplied by its corresponding weight. The two weighted results are then summed, and the resulting value is the unified correlation index. The unified correlation index is a comprehensive scalar indicator that integrates correlation information from both temporal and spatial dimensions. A larger value indicates a stronger overall coupling relationship between the heat dissipation system and the temperature field. It can be understood that the weight calculation parameters in the attention mechanism can be trained using historical data to make the weight allocation more aligned with the actual needs of heat dissipation performance optimization.

[0046] In practice, the overlay analysis is performed within the digital image processing unit. The wind speed distribution map and the hotspot distribution map are treated as two registered grayscale images with identical size and resolution. The segmentation into smaller blocks is achieved using a sliding window with a size of 5 cm x 5 cm and a step size of 2 cm. This means there is some overlap between adjacent blocks, resulting in smoother statistical results. For each block, the wind speed values ​​are discretized, dividing continuous wind speed values ​​into 10 predefined intervals. Temperature states are directly divided into two states based on whether they exceed the 75°C threshold. The probability distribution is calculated based on frequency statistics. For example, the joint probability P(wind speed interval i, hotspot state j) equals the number of pixels within the block that simultaneously fall into wind speed interval i and hotspot state j, divided by the total number of pixels within the block. The marginal probability is obtained by summing the joint probabilities by rows or columns. The mutual information value is calculated strictly according to information theory formulas, using double-precision floating-point numbers to ensure accuracy. It is understandable that the calculation cycle of the entire distribution matching degree is synchronized with the distribution map update cycle, and is usually executed once every 30 seconds.

[0047] In its implementation, the attention mechanism of the multi-dimensional fusion engine is implemented as a miniature fully connected neural network with one input layer, one hidden layer, and one output layer. The input layer has two neurons, receiving values ​​for temporal coherence and distribution matching, respectively. The hidden layer has four neurons, using the ReLU activation function. The output layer has two neurons, corresponding to the attention scores for temporal coherence and distribution matching, respectively. The weight matrix of this network is pre-trained using training data derived from numerous examples of air conditioner outdoor unit operation. The training objective is to enable the unified correlation index to optimally predict heat dissipation efficiency. The elements of the feature vector are normalized before being input into the attention mechanism network, scaling them to the 0-1 range to eliminate the influence of dimensional differences on weight allocation. The calculation of the Softmax function is approximated in the embedded system using a lookup table to reduce computational complexity. The weighted summation operation is executed quickly by the arithmetic logic unit. The output value of the unified correlation index is updated every 10 seconds, with the update frequency consistent with that of the temporal coherence. Optionally, the system can set a valid range for a uniform correlation index, such as 0 to 1, and limit values ​​that exceed the range.

[0048] In practice, the memory space required for calculating the distribution matching degree is pre-allocated to store intermediate probability distribution tables. The network parameters for the attention mechanism are stored in non-volatile memory and loaded into memory upon system startup. Emphasis is placed on computational reliability and real-time performance; all algorithms are optimized to adapt to the resource constraints of embedded platforms. Temporal coherence and distribution matching degree, as fundamental features, directly impact the accuracy of the unified association index. Therefore, the system continuously monitors the data integrity of these two input features. If data anomalies are detected, the update of the unified association index is paused, and the previous valid value is retained.

[0049] See Figure 4 The graph quantifies the dynamic correlation characteristics of the temporal synergy (blue curve) and distribution matching degree (red curve) between the heat dissipation parameter group and the temperature parameter group in the air conditioner outdoor unit heat dissipation system over a time dimension of 0-100 seconds. The temporal synergy is obtained by calculating the Pearson correlation coefficient after dynamically aligning the fan speed sequence and the core temperature sequence over time, reflecting the strength of their correlation over time. The distribution matching degree is obtained by calculating the mutual information value of the overlapping area based on the overlay analysis of the wind speed distribution map and the hotspot distribution map, characterizing the consistency of their spatial distribution. As shown in the graph, the temporal synergy shows an upward trend in the early stage (approximately 0-50 seconds) and reaches a peak in the middle stage, while the distribution matching degree also remains at a high level, indicating a strong coupling between the heat dissipation parameters and temperature parameters in terms of both temporal correlation and spatial distribution during this stage. In the later stage (approximately after 60 seconds), the distribution matching degree drops sharply to near 0, and the temporal synergy also gradually decreases, reflecting a significant weakening of the overall coupling relationship between the heat dissipation system and the temperature field. This suggests that the speed control parameters should be derived based on this dynamic change to implement closed-loop regulation and ensure heat dissipation efficiency.

[0050] Example 5: In practical implementation, the derivation of the speed control parameter is based on the high-temperature warning indicator and the unified correlation index. The speed control parameter is a specific value representing the target amount of adjustment needed to the cooling fan speed. Establishing a speed control rule base is the first step. This rule base is a structured database or lookup table that stores various preset high-temperature scenarios and their corresponding speed adjustment experience values. High-temperature scenarios are classified according to the level of the high-temperature warning indicator, which is typically divided into three levels: low, medium, and high, corresponding to different degrees of temperature exceedance. The unified correlation index is also divided into three intervals: low, medium, and high. The boundary values ​​of these intervals are determined through statistical analysis of historical operating data. Each record in the speed control rule base contains four fields: high-temperature warning indicator level, unified correlation index interval, speed adjustment type, and specific speed adjustment value. The speed adjustment type can be an absolute increment or a relative percentage, and the speed adjustment value is an optimized value summarized from extensive experiments and field experience. For example, a record might be: High Temperature Warning Level = Medium, Unified Correlation Index Range = High Range, Speed ​​Adjustment Type = Percentage, Speed ​​Adjustment Value = +15%. This means that when the system determines that there is a medium high temperature warning and the unified correlation index is high, it will increase the fan speed by 15%.

[0051] In practice, based on the real-time high-temperature warning level and the calculated unified correlation index, the system matches the optimal engine speed adjustment from the engine speed control rule base. The matching process can be viewed as a query operation; the input variables are the high-temperature warning level and the interval to which the unified correlation index belongs, and the output variable is the engine speed adjustment. The matching algorithm employs an exact matching strategy, searching the engine speed control rule base for records that perfectly match the current input conditions. If multiple matching records exist, the first one is selected; if no perfectly matching record exists, the nearest neighbor algorithm is used to find the record with the closest conditions. For example, if the current high-temperature warning level is high and the unified correlation index is 0.92 (belonging to the high interval), the system will query the engine speed control rule base for records corresponding to the combination of high level and high interval, and extract the engine speed adjustment, such as +800 rpm or +25%. This successfully matched engine speed adjustment is then determined as the current engine speed control parameter. It's important to understand that the contents of the engine speed control rule base are not static; they can be updated and maintained by technicians using configuration tools to adapt to different models of air conditioner outdoor units or changing operating environments.

[0052] In practice, the closed-loop regulation of the cooling fan speed is a dynamic control process. The matched speed control parameters are used as the new setpoint for the fan speed. The system monitors the actual speed of the cooling fan in real time through a speed sensor installed on the fan motor. The speed sensor typically uses a Hall effect sensor or photoelectric encoder, which can acquire speed pulse signals at high frequency and convert them into digital values. The deviation between the actual speed and the setpoint is calculated; the deviation equals the setpoint minus the actual speed. This deviation value is sent to a proportional-integral-derivative (PID) controller. The PID controller is a widely used feedback controller that contains three independent control elements: a proportional element, an integral element, and a derivative element. The output of the proportional element is proportional to the current deviation, enabling a rapid response to the deviation; the output of the integral element is proportional to the integral of the deviation, used to eliminate steady-state errors; the output of the derivative element is proportional to the rate of change of the deviation, providing predictive capabilities and suppressing overshoot. The PID controller adds the outputs of the three elements to generate a comprehensive regulation signal.

[0053] In practical implementation, the regulating signal is used to drive the fan motor to adjust its speed. The regulating signal is typically a pulse-width modulation (PWM) signal or an analog voltage signal. For a brushless DC motor, the regulating signal controls the duty cycle of the switching transistors in the motor drive circuit, thereby changing the average voltage applied to the motor windings and achieving speed regulation. For an AC motor, the regulating signal may control the output frequency of the inverter. The fan motor changes its rotational speed according to the received regulating signal, bringing the actual speed closer to the set value. This closed-loop regulation process is continuous: the actual speed is monitored in real time, the deviation is calculated, a regulating signal is generated through a proportional-integral-derivative (PID) controller to drive the motor to adjust, and then monitoring is repeated, forming a negative feedback loop. In some embodiments, the parameters of the PID controller need to be tuned according to the specific dynamic characteristics of the fan motor. The tuning method can be the Ziegler-Nichols method or other engineering tuning methods to ensure the stability, speed, and accuracy of the control system.

[0054] In practical implementation, the system continuously collects multi-dimensional data streams for feedback optimization while implementing closed-loop speed regulation. These continuously collected multi-dimensional data streams include updated cooling fan speed sequences, new casing temperature distribution maps, and changed environmental parameter sets. This new data is fed back to the system's front-end processing module for recalculating key indicators such as dynamic heat dissipation efficiency scores, thermal load impact factors, and unified correlation indices. The purpose of feedback optimization is to enable the control system to adapt to changes in the external environment and the slow drift of the system's own characteristics. For example, if, after a round of speed increases, newly collected temperature data indicates that the heat dissipation effect is not significantly improved, the system may match a larger speed adjustment in the next round of decision-making. Conversely, if the temperature has dropped rapidly, the system may reduce the speed in advance to save energy. This feedback optimization mechanism makes the entire heat dissipation and cooling process adaptive, enabling it to maintain an optimized operating state over a long period. Optionally, machine learning algorithms can be introduced into the feedback optimization process to automatically fine-tune the empirical values ​​in the speed regulation rule base or the parameters of the proportional-integral-derivative controller based on long-term operating data.

[0055] In practical implementation, the physical storage of the speed control rule base can be located in non-volatile memory, such as electrically erasable programmable read-only memory or flash memory, to ensure data integrity after power failure. Matching query operations are executed by a software algorithm in the central processing unit, and the query speed must meet real-time requirements. The proportional-integral-derivative (PID) controller can be implemented in software, calculating the control quantity through timed interrupt service routines in the microcontroller; or in hardware, using a dedicated PID control chip. The speed sensor signal needs to be filtered to eliminate noise interference. The drive circuit design must ensure sufficient power output to drive the fan motor. Continuous data acquisition is accomplished through multiplexers and analog-to-digital converters. Feedback optimization logic can run as a low-priority background task, periodically evaluating the control effect and updating internal parameters.

[0056] See Figure 5 This study presents the dynamic relationship between fan speed (rpm), casing temperature (°C), and ambient temperature (°C) over time. From a professional perspective, fan speed and casing temperature exhibit significant temporal correlation; as casing temperature increases, fan speed tends to rise, reflecting the dynamic response mechanism of the cooling system. Ambient temperature, on the other hand, remains relatively stable, serving as a benchmark parameter for evaluating cooling performance. This multi-parameter temporal correlation analysis is a direct representation of the parameter correlation analysis process in the air conditioner outdoor unit cooling method. By dynamically tracking the fan speed sequence and temperature parameter group, the temporal correlation degree and distribution matching degree can be further calculated, providing data support for deriving speed control parameters and achieving closed-loop adjustment of the cooling fan speed. Its core logic is based on the dynamic correlation of multi-source data to ensure the adaptive optimization operation of the cooling system.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for heat dissipation and cooling of an air conditioner outdoor unit, characterized in that, Includes the following steps: Multidimensional data streams during the operation of the outdoor unit of the air conditioner are collected by a multi-source sensor array. The multidimensional data streams include cooling fan speed sequence, shell temperature distribution map and environmental parameter set. An adaptive performance evaluation algorithm is used to process the multidimensional data stream to generate a dynamic heat dissipation performance score. The dynamic heat dissipation performance score is compared with a benchmark performance range. When the dynamic heat dissipation performance score deviates from the benchmark performance range, a performance anomaly marker is generated. In response to the performance anomaly flag, the internal temperature field scanning program is initiated to acquire internal temperature spatiotemporal data. The thermodynamic influence model is applied to calculate the heat load influence factor. If the heat load influence factor exceeds a predetermined threshold, a high temperature warning flag is activated. Based on the high temperature warning sign, a parameter correlation analysis process is executed to calculate the temporal synergy and distribution matching degree between the heat dissipation parameter group and the temperature parameter group, and a unified correlation index is generated through a multi-dimensional fusion engine. Based on high temperature warning indicators and a unified correlation index, the speed control parameters are derived, and the closed-loop adjustment of the cooling fan speed is implemented to complete the heat dissipation and cooling operation.

2. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 1, characterized in that, The method for processing the multidimensional data stream using an adaptive performance evaluation algorithm includes: dividing the heat dissipation surface of the air conditioner outdoor unit into honeycomb grid cells, with each grid cell deploying an infrared temperature measurement point and an airflow sensor; reading the temperature and wind speed readings of each grid cell in real time; calculating the instantaneous heat dissipation performance value of each grid cell, where the instantaneous heat dissipation performance value is the ratio of the wind speed reading to the temperature reading relative to the ambient temperature difference; and spatially weighting and aggregating the instantaneous heat dissipation performance values ​​of all grid cells to obtain a dynamic heat dissipation performance score.

3. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 2, characterized in that, The method for calculating the heat load influence factor using the thermodynamic influence model includes: extracting the temporal fluctuation pattern and spatial heterogeneity pattern from the internal temperature spatiotemporal data; quantifying the temporal fluctuation pattern using the coefficient of variation of the temperature sequence; measuring the spatial heterogeneity pattern using the entropy value of the temperature distribution map; inputting the temporal fluctuation pattern and spatial heterogeneity pattern into the feature weighting module, and outputting the heat load influence factor.

4. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 3, characterized in that, The method for extracting the time fluctuation pattern includes: segmenting the internal temperature spatiotemporal data into time windows, calculating the ratio of the temperature range to the average temperature within each time window as the fluctuation intensity within the window; summarizing the fluctuation intensity of all time windows, calculating their standard deviation, and using this as the time fluctuation pattern.

5. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 4, characterized in that, The method for extracting the spatial heterogeneous mode includes: rasterizing the internal temperature distribution map into a pixel array, calculating the sum of the absolute values ​​of the temperature difference between each pixel and its neighboring pixels as the local heterogeneity; performing histogram statistics on the local heterogeneity of the entire pixel array, and taking the kurtosis of the histogram as the spatial heterogeneous mode.

6. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 5, characterized in that, The method for calculating the time-series coherence includes: selecting the fan speed sequence from the heat dissipation parameter group and the core temperature sequence from the temperature parameter group, performing dynamic time warping and alignment, and calculating the Pearson correlation coefficient of the aligned sequence as the time-series coherence.

7. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 6, characterized in that, The method for calculating the distribution matching degree includes: overlaying the wind speed distribution map of the heat dissipation parameter group with the hot spot distribution map of the temperature parameter group, and calculating the mutual information value of the two distribution maps in the overlapping area as the distribution matching degree.

8. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 7, characterized in that, The method for generating a unified association index through a multi-dimensional fusion engine includes: using temporal synergy and distribution matching degree as input features, constructing a feature vector, applying an attention mechanism to assign weights to the feature vector, and obtaining the unified association index by weighted summation.

9. The method for heat dissipation and cooling of an air conditioner outdoor unit according to claim 8, characterized in that, The method for deriving speed control parameters and implementing closed-loop adjustment of cooling fan speed includes: establishing a speed control rule base containing empirical values ​​for speed adjustment under various high-temperature scenarios; matching the optimal speed adjustment amount from the rule base as the speed control parameter based on the level of the high-temperature warning indicator and the magnitude of the unified correlation index. Using the speed control parameter as the set value, the actual speed of the cooling fan is monitored in real time, the deviation between the actual speed and the set value is calculated, and an adjustment signal is generated by a proportional-integral-derivative controller to drive the fan motor to adjust the speed. Multidimensional data streams are continuously collected for feedback optimization.

10. A heat dissipation and cooling system for an outdoor unit of an air conditioner, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the air conditioner outdoor unit heat dissipation and cooling processing method according to any one of claims 1 to 9.

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