A method and system for intelligent temperature and humidity control of outdoor integrated power distribution boxes

Abstract

This invention provides an intelligent temperature and humidity control method and system for outdoor integrated distribution boxes. It acquires real-time temperature and humidity inside and outside the distribution box, the operating load of equipment inside the box, and environmental prediction data for a preset time period. It calculates the dew point temperature of the air inside the box, and, combined with the real-time temperature and humidity difference inside and outside the box, the rate of temperature change, and the difference between the dew point and the real-time temperature inside the box, defines a stable zone, a fine-tuning zone, and a coarse-tuning zone. Based on the dew point and the real-time temperature difference inside the box, it adjusts the humidity-related weights at the boundary of the fine-tuning zone. Based on environmental predictions and load data, it formulates a pre-adjustment strategy, and initiates a lead time for the strategy according to the reverse correction strategy within the stable zone range. It constructs a temperature and humidity deviation state vector; when the vector's end enters the corresponding region, the control device is activated. Based on the vector angle, it determines a temperature and humidity priority control decoupling strategy. When moving from the coarse-tuning zone to the fine-tuning zone, it calculates the thermal inertia compensation value based on the coarse-tuning device's operating history, corrects the initial output overshoot of the fine-tuning, and simultaneously uses a weighted average of the historical compensation values ​​as a feedforward signal to correct the boundary of the coarse-tuning zone for the next cycle.

Description

Technical Field

[0001] This application belongs to the field of control, and in particular relates to an intelligent temperature and humidity control method and system for an outdoor integrated power distribution box. Background Technology

[0002] The stable operation of equipment within outdoor integrated distribution boxes is highly sensitive to temperature and humidity conditions. Frequent start-ups and shutdowns of control devices lead to energy waste and exacerbate mechanical wear on the equipment. Furthermore, overshooting in temperature or humidity can cause fluctuations in the box's internal environment around the setpoint. Temperature control often overlooks the risk of condensation; forced cooling in hot and humid environments can rapidly drop the box's temperature below the dew point, exacerbating condensation. Existing control methods, such as PID control algorithms, cannot adapt to real-time changes in the external environment and fluctuations in equipment load. They lack the ability to predict future environmental trends. Moreover, temperature and humidity are interdependent; relying solely on threshold values ​​for each ignores their correlation. Additionally, current technologies fail to provide correction mechanisms for disturbances generated during the switching between coarse and fine adjustment devices, or for optimizing control boundaries during long-term operation. Therefore, developing an intelligent control method capable of predicting environmental changes, defining control strategies, collaboratively decoupling temperature and humidity control, and possessing learning and optimization capabilities has become an urgent need to improve the reliability and energy efficiency of outdoor integrated distribution boxes. Summary of the Invention

[0003] In response to the problems mentioned in the background art, in the first aspect, the present invention proposes an intelligent temperature and humidity control method for an outdoor integrated power distribution box, comprising: The system acquires real-time temperature and humidity inside and outside the distribution box, the operating load of the equipment inside the box, and environmental prediction data within a preset time period. Calculate the dew point temperature of the air inside the chamber; and combine the real-time temperature difference, humidity difference, and temperature change rate between the inside and outside of the chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, to calculate and define a stable zone, a fine-tuning zone around the stable zone, and a coarse-tuning zone on the periphery; wherein, the humidity-related weights in the calculation of defining the boundary of the fine-tuning zone are adjusted based on the difference between the dew point temperature and the real-time temperature inside the chamber. A pre-adjustment strategy is formulated based on the environmental prediction data and operating load data; and the start-up lead time of the strategy is reversed according to the size of the stable zone; a state vector with real-time temperature deviation and humidity deviation as components is constructed; when the end of the state vector enters the fine adjustment zone or coarse adjustment zone, the corresponding control device is activated; and a decoupling strategy for prioritizing temperature or humidity control is determined according to the angle of the vector. When the state transitions from the coarse adjustment zone to the fine adjustment zone, the thermal inertia power compensation value is calculated based on the recent operating history of the coarse adjustment device. This value is used to correct the initial output of the fine adjustment device to suppress overshoot when coarse adjustment is stopped. At the same time, the weighted average of the historical power compensation values ​​is used as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle.

[0004] In a second aspect, the present invention provides an intelligent temperature and humidity control system for an outdoor integrated power distribution box, comprising the following units: The acquisition unit is used to acquire real-time temperature and humidity inside and outside the distribution box, operating load of the equipment inside the box, and environmental prediction data within a preset time period. The adjustment unit is used to calculate the dew point temperature of the air inside the chamber; and to calculate and define a stable zone, a fine adjustment zone around the stable zone, and a coarse adjustment zone around the dew point temperature and the real-time temperature inside the chamber by taking into account the real-time temperature difference between the inside and outside of the chamber, the humidity difference, the temperature change rate, and the difference between the dew point temperature and the real-time temperature inside the chamber. Among them, the humidity-related weights in the calculation of the boundary of the fine adjustment zone are adjusted based on the difference between the dew point temperature and the real-time temperature inside the chamber. The determining unit is used to formulate a pre-adjustment strategy based on the environmental prediction data and operating load data; and to reverse the start-up lead time of the strategy according to the size of the stable zone; to construct a state vector with real-time temperature deviation and humidity deviation as components; to activate the corresponding control device when the end of the state vector enters the fine-tuning zone or coarse-tuning zone; and to determine the decoupling strategy that prioritizes temperature or humidity control according to the angle of the vector. The correction unit is used to calculate the thermal inertia power compensation value based on the recent operating history of the coarse adjustment device when the state enters the fine adjustment zone from the coarse adjustment zone. It is used to correct the initial output of the fine adjustment device to suppress overshoot when the coarse adjustment stops. At the same time, the weighted average of the historical compensation values ​​is used as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle.

[0005] This invention utilizes environmental prediction data to pre-adjust conditions before they occur, thus avoiding drastic fluctuations in the internal environment. The control range is defined based on multiple factors, including internal and external temperature, humidity, and dew point, ensuring the control strategy always aligns with actual operating conditions to reduce energy consumption. By constructing a state vector, the conflict between temperature and humidity controls is resolved, allowing for priority handling of deviations. Furthermore, by compensating for thermal inertia during switching between devices with different power levels, environmental stability is maintained. Simultaneously, the control boundaries are continuously refined based on historical operating data, resulting in sustained optimization of control performance over long-term operation. This achieves low energy consumption and a long equipment lifespan while ensuring equipment safety. Attached Figure Description

[0006] Figure 1 A flowchart of the first embodiment; Figure 2 This is a schematic diagram of multidimensional data acquisition. Figure 3 This is a schematic diagram illustrating thermal inertia compensation to suppress overshoot. Figure 4 This is a schematic diagram of the boundary correction for the coarse adjustment area. Detailed Implementation

[0007] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0008] It should be noted that, in this document, relational terms such as "first" and "second" are used merely 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0009] In the first embodiment, the present invention proposes an intelligent temperature and humidity control method for an outdoor integrated power distribution box, such as... Figure 1 ,include: S1, acquire the real-time temperature and humidity inside and outside the distribution box, the operating load of the equipment inside the box, and the environmental prediction data within a preset time period; Digital temperature and humidity sensors are deployed on the inner and outer walls of the distribution box and near the core heat-generating equipment, collecting data once per second or minute. Current transformers monitor the current values ​​of the main circuits within the box in real time and multiply them by the rated voltage to estimate the operating load power of the equipment. Simultaneously, the controller inside the box connects periodically to a public or proprietary meteorological service platform via a built-in IoT communication unit to obtain hourly environmental forecasts of temperature, humidity, and precipitation probability for the next 24 to 72 hours. All data is transmitted to a central controller for processing.

[0010] In an optional embodiment, acquiring real-time temperature and humidity inside and outside the distribution box, operating load of equipment inside the box, and environmental prediction data within a preset time period includes: The real-time temperature and humidity inside the box are obtained by using multiple sensors placed at different locations inside the box and taking the average value of the sensor readings. The operating load is calculated by monitoring the electrical parameters of the equipment; and environmental forecast data is obtained from external data sources.

[0011] To obtain the internal conditions of the chamber, a multi-point measurement and averaging method is used. For example, in a large test chamber, temperature and humidity sensors are installed at three different locations: top, middle, and bottom. At any given moment, the three sensor readings might be 25.2℃ and 51% relative humidity at the top, 25.0℃ and 50% relative humidity at the middle, and 24.8℃ and 49% relative humidity at the bottom. After receiving these three sets of data, the controller does not use any single reading, but instead calculates the average value, i.e., temperature 25.0℃ and humidity 50% relative humidity. This average value is then used as the true state of the entire chamber environment, avoiding single-point measurement errors caused by uneven local airflow.

[0012] To achieve deep awareness of equipment operating status, the electrical parameters of key components are monitored in real time. For example, for a refrigeration compressor, the built-in monitoring unit measures the real-time voltage and current at the input terminal. If the measured voltage is 220 volts, the current is 5 amps, and the power factor is 0.9, the actual operating load or power consumption of the compressor is calculated to be 990 watts. This data is not only used for energy consumption statistics but also serves as input for advanced control algorithms such as thermal inertia compensation, enabling calculations based on the actual work done by the equipment. Furthermore, a network interface connects to a meteorological service data source to periodically obtain future weather forecasts for the equipment's location, such as hourly ambient temperature and humidity forecasts for the next 24 hours. This forecast data forms the basis for implementing predictive pre-conditioning strategies, such as... Figure 2 .

[0013] S2, calculate the dew point temperature of the air inside the chamber; and combine the real-time temperature difference, humidity difference, temperature change rate inside and outside the chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, to calculate and define a stable zone, a fine-tuning zone around the stable zone, and a coarse-tuning zone on the periphery; wherein, the humidity-related weights in the calculation of defining the boundary of the fine-tuning zone are adjusted based on the difference between the dew point temperature and the real-time temperature inside the chamber. The dew point temperature of the air inside the chamber is calculated using the Magnus empirical formula based on the real-time temperature T and relative humidity RH inside the chamber, as follows: in, parameter , It is suitable for applications where the temperature is above 0℃.

[0014] The stable zone is defined as a small range centered on the target temperature and humidity values, for example, target temperature ±1℃ and target humidity ±5%. The boundaries between the fine-tuning zone and the coarse-tuning zone are determined by a comprehensive deviation index D, which is a weighted sum of the internal and external temperature difference, the internal and external humidity difference, the rate of change of temperature inside the chamber, and the difference between the dew point and the internal temperature. For example, when the dew point temperature is very close to the real-time internal temperature, the risk of condensation is high. In this case, increasing the weighting coefficient of the humidity difference term in the calculation of index D makes the control boundary sensitive to changes in humidity. Even if the temperature deviation is small, it may enter the control zone prematurely due to humidity issues.

[0015] In an optional embodiment, the real-time temperature difference, humidity difference, and temperature change rate inside and outside the integrated chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, are used to calculate and define a stable zone, a fine-tuning zone surrounding the stable zone, and a coarse-tuning zone on the periphery, including: Set the first temperature deviation threshold and the first humidity deviation threshold Define the stable region; Set a second temperature deviation threshold and the adjusted second humidity deviation threshold Define the fine-tuning area; When the temperature or humidity deviation exceeds the boundary of the fine-tuning zone, it is defined as the coarse-tuning zone. Wherein, the second humidity deviation threshold Based on the real-time temperature inside the box With dew point temperature The difference is calculated using the following formula: in The basic humidity deviation threshold is given by k, which is the sensitivity coefficient.

[0016] Specifically, assuming the target temperature and humidity are 25℃ and 50% relative humidity, if a first temperature deviation threshold is set... The first humidity deviation threshold is 0.2℃. With a relative humidity of 2%, the stable temperature range is 24.8 to 25.2°C and 48% to 52% relative humidity. The boundaries of the fine-tuning zone are set, and these temperature boundaries are fixed, for example, by setting a second temperature deviation threshold. The temperature is 1℃, while the humidity boundary is... It is variable to prevent condensation from occurring near the dew point.

[0017] Second humidity deviation threshold The calculation is based on the difference between the current chamber temperature and the dew point temperature. A baseline humidity deviation threshold is assumed. With a relative humidity of 5%, the sensitivity coefficient k is 0.5. Scenario 1: When the temperature inside the chamber... The dew point temperature is 25℃. When the temperature is 10℃, the difference between the two is 15℃. The calculated value at this point... The value is very close to 5%, far from the risk of condensation, and a wider humidity control range can be used for adjustment. Scenario 2: When the internal temperature... When the dew point temperature drops to 12℃, which is only 2℃ different from 10℃, the calculated value according to the formula is... The relative humidity will decrease, for example, to 3.15%. This reduced humidity boundary makes the control system more cautious when approaching the condensation point, prioritizing humidity control to prevent condensation inside the equipment. The area beyond the fine-tuning zone boundary is the coarse-tuning zone, where high-power equipment will be activated for rapid adjustment.

[0018] S3, formulate a pre-adjustment strategy based on the environmental prediction data and operating load data; and reverse the start-up lead time of the strategy according to the size of the stable zone; construct a state vector with real-time temperature deviation and humidity deviation as components; when the end of the state vector enters the fine adjustment zone or coarse adjustment zone, activate the corresponding control device; and determine the decoupling strategy that prioritizes temperature or humidity control according to the angle of the vector. The controller analyzes environmental forecast data for the next few hours. If it identifies an upcoming period of sustained high temperatures and, combined with load data, predicts that the equipment will operate under high load, it formulates a pre-cooling strategy. This strategy calculates a start-up lead time, such as 1.5 hours before the predicted temperature peak, to start the cooling system and slowly lower the internal temperature to the lower limit of the target range at a lower power, thus reserving sufficient cooling capacity for the upcoming thermal shock. If historical data shows that the controlled stable range remains small, indicating insufficient margin for handling disturbances, the lead time is extended from 1.5 hours to 2 hours to ensure sufficient time for smooth adjustment.

[0019] In a two-dimensional state space with normalized temperature deviation as the horizontal axis and normalized humidity deviation as the vertical axis, a state vector is constructed from the origin to the current real-time state point. When the length of this vector exceeds the boundary radius of the stable region, regulation is triggered. If the end of the vector enters the fine-tuning region, a low-power fan is activated; if it enters the coarse-tuning region, a high-power air conditioner is activated. Simultaneously, the angle θ between this state vector and the horizontal axis is calculated using the following formula: .

[0020] Preset first angle threshold Second angle threshold ; like This indicates that the main problem is that the temperature is too high or too low, and all or most of the power will be allocated to the cooling or heating devices. like If the humidity is high, it indicates that humidity is the primary issue, and the dehumidifier will be activated first. for In other cases, the temperature and humidity control devices will be activated in a proportional manner.

[0021] In an optional embodiment, the step of formulating a pre-adjustment strategy based on the environmental prediction data and operating load data, and reversing the start-up lead time of the strategy according to the size of the stable region, includes: When it is predicted that the ambient temperature will continue to exceed the preset high temperature threshold for a preset duration within a future time window, a pre-cooling strategy will be implemented. The initiation lead time of the pre-adjustment strategy The calculation formula is: in This is the pre-adjustment proportional constant. The temperature range of the stable zone; and the temperature inside the chamber is pre-adjusted to the preset cooling target value according to the lead time.

[0022] For example, if the system obtains hourly temperature forecasts for the next 24 hours, it checks whether the forecast data meets preset conditions, such as a preset high-temperature threshold of 35℃ and a preset duration of 2 hours. If the weather forecast shows that the ambient temperature will remain above 35℃ from 2 PM to 4 PM, then the pre-cooling strategy is triggered.

[0023] Once triggered, the lead time for initiating pre-cooling will be calculated. This lead time is related to the equipment's stability requirements, which are determined by the temperature range of the stability region. This is expressed as follows. Assume a pre-adjustment proportional constant. The temperature is 30 degrees Celsius per minute. If an application requires a stable temperature range... If the temperature is 0.1℃, then the calculated lead time is... This is for 300 minutes, or 5 hours. Cooling needs to begin at 9 AM, before the heatwave arrives at 2 PM, to slowly lower the internal temperature to a lower preset target value, such as 24°C, to buffer against external heat shocks. Conversely, for a typical application, the permissible stable temperature range is... If the temperature is 0.5℃, then the calculated lead time is... The cooling time is 60 minutes, and you only need to start the pre-cooling process at 1 p.m.

[0024] In an optional embodiment, the state vector is constructed with real-time temperature deviation and humidity deviation as components; when the end of the state vector enters the fine-tuning zone or the coarse-tuning zone, the corresponding control device is activated, including: The real-time temperature and humidity deviations are normalized to obtain normalized temperature and humidity deviations. When the normalized temperature deviation is greater than the normalized humidity deviation, it is determined that the temperature deviation is dominant, and the temperature control device is activated first. Otherwise, if the humidity deviation is determined to be the dominant factor, the humidity control device should be activated first.

[0025] Specifically, assuming the target is set at 25°C and 50% relative humidity, and the current measurement is 26.2°C and 53% relative humidity, the original temperature deviation is... The temperature deviation is +1.2℃, and the humidity deviation is... The relative humidity is set to +3%. Deviations of different physical dimensions are converted to dimensionless relative deviations for comparison. A reference deviation value is set. It is 1.0℃. With a relative humidity of 5%, the two values ​​can correspond to the boundaries of the fine-tuning zone.

[0026] Based on the above data, calculate the normalized temperature deviation. The normalized humidity deviation is 1.2. The value is 0.6. Comparing the absolute values, 1.2 is greater than 0.6, therefore the primary issue is the excessive temperature deviation. The decision is to prioritize temperature control, allocating more power to the refrigeration compressor, and potentially reducing the power of the dehumidification unit to concentrate resources on addressing the dominant deviation. In another scenario, if the measured values ​​are 25.5℃ and 54.5% relative humidity, then... It is 0.5℃. The relative humidity is 4.5%. After calculation... It is 0.5. The value is 0.9, at which point the humidity deviation is dominant, and the dehumidifier will be activated first.

[0027] S4, When the state moves from the coarse adjustment zone to the fine adjustment zone, the thermal inertia compensation value is calculated based on the recent operating history of the coarse adjustment device. This value is used to correct the initial output of the fine adjustment device to suppress overshoot when coarse adjustment stops. At the same time, the weighted average of the historical compensation values ​​is used as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle.

[0028] When the internal temperature returns from the coarse-adjustment zone to the fine-adjustment zone due to cooling by a high-power air conditioner, the controller calculates a thermal inertia compensation value based on the air conditioner's average power over the past 5 minutes and the cabinet's thermal capacity model, just before shutting down the air conditioner. This value represents the expected temperature drop inside the cabinet after shutdown. The controller uses this compensation value to correct the fine-adjustment device's actions. For example, instead of directly shutting down the air conditioner, it may shut it down earlier while simultaneously starting the heater at a low power for a short period, or allowing the fan to continue running at a specific speed to accelerate heat exchange between the inside and outside, thereby offsetting residual cooling and preventing the temperature from falling below the target lower limit. Simultaneously, the compensation value is recorded each time, and the most recent 100 compensation values ​​are weighted and averaged. If the average compensation value remains consistently high, it indicates that the boundary of the coarse-adjustment zone is set too conservatively. In the next boundary definition, the average value is used as a feedforward signal to appropriately expand the boundary of the coarse-adjustment zone, causing the coarse-adjustment device to stop earlier, thus achieving long-term operational optimization.

[0029] To address the issue of temperature and humidity overshoot caused by residual heat or cold after high-power equipment stops, in one optional embodiment, the thermal inertia power compensation value calculated based on the recent operating history of the coarse adjustment device is used to correct the initial output of the fine adjustment device and suppress overshoot when coarse adjustment stops. The calculation formula is as follows: in, The average operating power during the preset time period before the coarse adjustment device stops. The cumulative running time of the coarse adjustment device during the specified time period. The inherent thermal inertia time constant of the equipment; thermal inertia time constant The temperature change time constant of the chamber after the coarse adjustment device is started and stopped can be obtained through experimental calibration or system identification modeling. Specifically, it can be determined by step response experiment.

[0030] The power compensation value is used to adjust the initial output power of the fine adjustment device by an equal magnitude but opposite direction when the coarse adjustment device stops.

[0031] During the coarse adjustment phase, monitoring is performed. A high-power compressor cools the internal temperature from a large deviation (e.g., 30°C) to near the fine adjustment zone boundary (e.g., 26.1°C). Data is analyzed during the final preset time period before stopping. The average operating power of the compressor during these 60 seconds is then calculated. It is 1000 watts, and the cumulative operating time is... It lasts 45 seconds.

[0032] Utilizing the inherent thermal inertia time constant of the equipment This constant is calibrated experimentally, for example, to be 180 seconds. According to the formula, the thermal inertia power compensation value... The calculation is 250 watts. This value represents the equivalent cooling power that the evaporator will continue to release due to its own stored cold after the compressor stops. When the temperature enters the fine-tuning range, for example, dropping to 26.0°C, the compressor stops working, and the fine-tuning device takes over. At this time, a 250-watt adjustment is performed in the opposite direction, i.e., a 250-watt heater is briefly activated to counteract the impending inertial undercooling, thereby stabilizing the temperature near the target value and preventing overshoot. Figure 3 .

[0033] In an optional embodiment, the step of using a weighted average of historical compensation values ​​as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle includes: Historical thermal inertia power compensation value A weighted average is performed to obtain a historical average power compensation value. ; The historical average power compensation value is converted into an adjustment amount for the corresponding boundary using the following formula. : in The thermal characteristic conversion coefficient; and the adjustment amount is used to determine the coarse adjustment zone boundary for the next control cycle. Make corrections to obtain a new boundary.

[0034] The method for determining the thermal property conversion coefficient is as follows: Initial settings: Based on the enclosure's heat capacity, equipment power, and sensor sensitivity, set initial values ​​(e.g., ...). ) Online self-tuning: During system operation, the actual overshoot after each thermal inertia compensation is recorded. If the overshoot persists, it is increased proportionally. If the adjustment is excessive, then reduce it. This enables dynamic convergence.

[0035] Formula estimation (optional): In the ideal model, ,in The equivalent heat capacity of the box is... This is the sensitivity coefficient of power to temperature changes.

[0036] Specifically, the thermal inertia power compensation value calculated each time the adjustment is switched from coarse to fine is continuously recorded. For example, the database stores the compensation values ​​for the ten most recent times: 250 watts, 260 watts, and 240 watts. A weighted average of these historical data yields an average power compensation value that reflects recent inertial performance. Using an exponentially weighted moving average algorithm, if the previous average was 250 watts and the newly calculated value is 270 watts, the new average may be updated to 90% of the original average plus 10% of the new value, which is 252 watts.

[0037] Through thermal property conversion coefficient The coefficient represents the temperature and humidity change corresponding to each watt of compensation power, for example, a value of 0.002 degrees Celsius per watt. The boundary adjustment amount is calculated from this. The value is 0.504℃. Based on recent historical data, average thermal inertia can cause an overshoot of approximately 0.5℃. This is because the temperature boundary between the original coarse and fine adjustment zones... Set at a deviation of 1.0℃, and correct to =1.504℃. In future control systems, high-power coarse-adjustment equipment will stop prematurely when the temperature deviates from the target value by 1.504℃, allowing sufficient buffer space for thermal inertia and intelligently adapting to changes in itself and the environment, such as... Figure 4 .

[0038] In the second embodiment, the present invention also proposes an intelligent temperature and humidity control system for an outdoor integrated power distribution box, comprising the following units: The acquisition unit is used to acquire real-time temperature and humidity inside and outside the distribution box, operating load of the equipment inside the box, and environmental prediction data within a preset time period. The adjustment unit is used to calculate the dew point temperature of the air inside the chamber; and to calculate and define a stable zone, a fine adjustment zone around the stable zone, and a coarse adjustment zone around the dew point temperature and the real-time temperature inside the chamber by taking into account the real-time temperature difference between the inside and outside of the chamber, the humidity difference, the temperature change rate, and the difference between the dew point temperature and the real-time temperature inside the chamber. Among them, the humidity-related weights in the calculation of the boundary of the fine adjustment zone are adjusted based on the difference between the dew point temperature and the real-time temperature inside the chamber. The determining unit is used to formulate a pre-adjustment strategy based on the environmental prediction data and operating load data; and to reverse the start-up lead time of the strategy according to the size of the stable zone; to construct a state vector with real-time temperature deviation and humidity deviation as components; to activate the corresponding control device when the end of the state vector enters the fine-tuning zone or coarse-tuning zone; and to determine the decoupling strategy that prioritizes temperature or humidity control according to the angle of the vector. The correction unit is used to calculate the thermal inertia power compensation value based on the recent operating history of the coarse adjustment device when the state enters the fine adjustment zone from the coarse adjustment zone. It is used to correct the initial output of the fine adjustment device to suppress overshoot when the coarse adjustment stops. At the same time, the weighted average of the historical compensation values ​​is used as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle.

[0039] In an optional embodiment, the real-time temperature difference, humidity difference, and temperature change rate inside and outside the integrated chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, are used to calculate and define a stable zone, a fine-tuning zone surrounding the stable zone, and a coarse-tuning zone on the periphery, including: Set the first temperature deviation threshold and the first humidity deviation threshold Define the stable region; Set a second temperature deviation threshold and the adjusted second humidity deviation threshold Define the fine-tuning area; When the temperature or humidity deviation exceeds the boundary of the fine-tuning zone, it is defined as the coarse-tuning zone. Wherein, the second humidity deviation threshold Based on the real-time temperature inside the box With dew point temperature The difference is calculated using the following formula: in The basic humidity deviation threshold is given by k, which is the sensitivity coefficient.

[0040] In an optional embodiment, the step of formulating a pre-adjustment strategy based on the environmental prediction data and operating load data, and reversing the start-up lead time of the strategy according to the size of the stable region, includes: When it is predicted that the ambient temperature will continue to exceed the preset high temperature threshold for a preset duration within a future time window, a pre-cooling strategy will be implemented. The initiation lead time of the pre-adjustment strategy The calculation formula is: in This is the pre-adjustment proportional constant. The temperature range of the stable zone; and the temperature inside the chamber is pre-adjusted to the preset cooling target value according to the lead time.

[0041] In an optional embodiment, the state vector is constructed with real-time temperature deviation and humidity deviation as components; when the end of the state vector enters the fine-tuning zone or the coarse-tuning zone, the corresponding control device is activated, including: The real-time temperature and humidity deviations are normalized to obtain normalized temperature and humidity deviations. When the normalized temperature deviation is greater than the normalized humidity deviation, it is determined that the temperature deviation is dominant, and the temperature control device is activated first. Otherwise, if the humidity deviation is determined to be the dominant factor, the humidity control device should be activated first.

[0042] In an optional embodiment, the thermal inertia power compensation value calculated based on the recent operating history of the coarse adjustment device is used to correct the initial output of the fine adjustment device to suppress overshoot when coarse adjustment is stopped. The calculation formula is as follows: in, The average operating power during the preset time period before the coarse adjustment device stops. The cumulative running time of the coarse adjustment device during the specified time period. This is the inherent thermal inertia time constant of the equipment; The power compensation value is used to adjust the initial output power of the fine adjustment device by an equal magnitude but opposite direction when the coarse adjustment device stops.

[0043] In an optional embodiment, the step of using a weighted average of historical compensation values ​​as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle includes: Historical thermal inertia power compensation value A weighted average is performed to obtain a historical average power compensation value. ; The weighted average can be calculated using an exponentially weighted moving average method, as shown in the following formula: in The forgetting factor, typically set to 0.8 to 0.9, is used to adjust the rate at which the weights of historical data decay, enabling the system to both remember long-term trends and respond quickly to recent changes.

[0044] The historical average power compensation value is converted into an adjustment amount for the corresponding boundary using the following formula. : in The thermal characteristic conversion coefficient; and the adjustment amount is used to determine the coarse adjustment zone boundary for the next control cycle. Make corrections to obtain a new boundary.

[0045] In an optional embodiment, acquiring real-time temperature and humidity inside and outside the distribution box, operating load of equipment inside the box, and environmental prediction data within a preset time period includes: The real-time temperature and humidity inside the box are obtained by using multiple sensors placed at different locations inside the box and taking the average value of the sensor readings. The operating load is calculated by monitoring the electrical parameters of the equipment; and environmental forecast data is obtained from external data sources.

[0046] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0047] The functional units shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0048] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0049] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0050] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for intelligent temperature and humidity control of an outdoor integrated power distribution box, characterized in that, Includes the following steps: The system acquires real-time temperature and humidity inside and outside the distribution box, the operating load of the equipment inside the box, and environmental prediction data within a preset time period. Calculate the dew point temperature of the air inside the chamber; The system integrates the real-time temperature difference, humidity difference, and temperature change rate inside and outside the chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, to calculate and define a stable zone, a fine-tuning zone surrounding the stable zone, and a coarse-tuning zone on the periphery. The humidity-related weights in the calculation of defining the boundary of the fine-tuning zone are adjusted based on the difference between the dew point temperature and the real-time temperature inside the chamber. A pre-adjustment strategy is formulated based on the environmental prediction data and operating load data; and the start-up lead time of the strategy is reversed according to the size of the stable zone; a state vector with real-time temperature deviation and humidity deviation as components is constructed; when the end of the state vector enters the fine adjustment zone or coarse adjustment zone, the corresponding control device is activated; and a decoupling strategy for prioritizing temperature or humidity control is determined according to the angle of the vector. When the state transitions from the coarse adjustment zone to the fine adjustment zone, the thermal inertia power compensation value is calculated based on the recent operating history of the coarse adjustment device. This value is used to correct the initial output of the fine adjustment device to suppress overshoot when coarse adjustment is stopped. At the same time, the weighted average of the historical power compensation values ​​is used as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle.

2. The method according to claim 1, characterized in that, The real-time temperature difference, humidity difference, and temperature change rate inside and outside the integrated chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, are used to calculate and define a stable zone, a fine-tuning zone surrounding the stable zone, and a coarse-tuning zone on the periphery, including: Set the first temperature deviation threshold and the first humidity deviation threshold Define the stable region; Set a second temperature deviation threshold and the adjusted second humidity deviation threshold Define the fine-tuning area; When the temperature or humidity deviation exceeds the boundary of the fine-tuning zone, it is defined as the coarse-tuning zone. Wherein, the second humidity deviation threshold Based on the real-time temperature inside the box With dew point temperature The difference is calculated using the following formula: in The basic humidity deviation threshold is given by k, which is the sensitivity coefficient.

3. The method according to claim 1, characterized in that, The process of formulating a pre-adjustment strategy based on the environmental prediction data and operational load data, and reversing the start-up lead time of the strategy according to the size of the stable region, includes: When it is predicted that the ambient temperature will continue to exceed the preset high temperature threshold for a preset duration within a future time window, a pre-cooling strategy will be implemented. The initiation lead time of the pre-adjustment strategy The calculation formula is: in For pre-adjustment proportional constant, The temperature range of the stable zone; and the temperature inside the chamber is pre-adjusted to the preset cooling target value according to the lead time.

4. The method according to claim 1, characterized in that, The process involves constructing a state vector with real-time temperature and humidity deviations as components. When the end of the state vector enters the fine-tuning or coarse-tuning zone, the corresponding control device is activated, and a decoupling strategy for prioritizing temperature or humidity control is determined based on the angle of the vector, including: The real-time temperature deviation and humidity deviation are normalized to obtain normalized temperature deviation and normalized humidity deviation. The angle of the state vector is calculated based on the normalized temperature deviation and normalized humidity deviation. Based on the comparison between the absolute value of the angle and a preset angle threshold, a decoupling strategy is determined: When the absolute value of the angle is less than or equal to the first angle threshold, it is determined that the temperature deviation is dominant, and the temperature control device is activated first. When the absolute value of the angle is greater than or equal to the second angle threshold, it is determined that the humidity deviation is dominant, and the humidity control device is activated first. Wherein, the second angle threshold is greater than the first angle threshold.

5. The method according to claim 1, characterized in that, The thermal inertia power compensation value calculated based on the recent operating history of the coarse adjustment device is used to correct the initial output of the fine adjustment device and suppress overshoot when coarse adjustment is stopped. The calculation formula is as follows: in, The average operating power during the preset time period before the coarse adjustment device stops. The cumulative running time of the coarse adjustment device during the specified time period. This is the inherent thermal inertia time constant of the equipment; The power compensation value is used to adjust the initial output power of the fine adjustment device by an equal magnitude but opposite direction when the coarse adjustment device stops.

6. The method according to claim 1, characterized in that, The step of using a weighted average of historical thermal inertia power compensation values ​​as a feedforward signal to correct the boundary of the coarse adjustment zone for the next cycle includes: Historical thermal inertia power compensation value A weighted average is performed to obtain a historical average power compensation value. ; The historical average power compensation value is converted into an adjustment amount for the corresponding boundary using the following formula. : in The thermal characteristic conversion coefficient; and the adjustment amount is used to determine the coarse adjustment zone boundary for the next control cycle. Make corrections to obtain a new boundary.

7. The method according to claim 1, characterized in that, The acquisition of real-time temperature and humidity inside and outside the distribution box, operating load of equipment inside the box, and environmental prediction data within a preset time period includes: The real-time temperature and humidity inside the box are obtained by using multiple sensors placed at different locations inside the box and taking the average value of the sensor readings. The operating load is calculated by monitoring the electrical parameters of the equipment; and environmental forecast data is obtained from external data sources.

8. An intelligent temperature and humidity control system for an outdoor integrated power distribution box, characterized in that, Includes the following units: The acquisition unit is used to acquire real-time temperature and humidity inside and outside the distribution box, operating load of the equipment inside the box, and environmental prediction data within a preset time period. Adjustment unit, used to calculate the dew point temperature of the air inside the chamber; The system integrates the real-time temperature difference, humidity difference, and temperature change rate inside and outside the chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, to calculate and define a stable zone, a fine-tuning zone surrounding the stable zone, and a coarse-tuning zone on the periphery. The humidity-related weights in the calculation of defining the boundary of the fine-tuning zone are adjusted based on the difference between the dew point temperature and the real-time temperature inside the chamber. The determining unit is used to formulate a pre-adjustment strategy based on the environmental prediction data and operating load data; and to reverse the start-up lead time of the strategy according to the size of the stable zone; to construct a state vector with real-time temperature deviation and humidity deviation as components; to activate the corresponding control device when the end of the state vector enters the fine-tuning zone or coarse-tuning zone; and to determine the decoupling strategy that prioritizes temperature or humidity control according to the angle of the vector. The correction unit is used to calculate the thermal inertia power compensation value based on the recent operating history of the coarse adjustment device when the state enters the fine adjustment zone from the coarse adjustment zone. It is used to correct the initial output of the fine adjustment device to suppress overshoot when coarse adjustment stops. At the same time, the weighted average of the historical compensation values ​​is used as a feedforward signal to correct the boundary of the coarse adjustment zone in the next cycle.

9. The system according to claim 8, characterized in that, The real-time temperature difference, humidity difference, and temperature change rate inside and outside the integrated chamber, as well as the difference between the dew point temperature and the real-time temperature inside the chamber, are used to calculate and define a stable zone, a fine-tuning zone surrounding the stable zone, and a coarse-tuning zone on the periphery, including: Set the first temperature deviation threshold and the first humidity deviation threshold Define the stable region; Set a second temperature deviation threshold and the adjusted second humidity deviation threshold Define the fine-tuning area; When the temperature or humidity deviation exceeds the boundary of the fine-tuning zone, it is defined as the coarse-tuning zone. Wherein, the second humidity deviation threshold Based on the real-time temperature inside the box With dew point temperature The difference is calculated using the following formula: in The basic humidity deviation threshold is given by k, which is the sensitivity coefficient.

10. The system according to claim 8, characterized in that, The process of formulating a pre-adjustment strategy based on the environmental prediction data and operational load data, and reversing the start-up lead time of the strategy according to the size of the stable region, includes: When it is predicted that the ambient temperature will continue to exceed the preset high temperature threshold for a preset duration within a future time window, a pre-cooling strategy will be implemented. The initiation lead time of the pre-adjustment strategy The calculation formula is: in For pre-adjustment proportional constant, The temperature range of the stable zone; and the temperature inside the chamber is pre-adjusted to the preset cooling target value according to the lead time.

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