Intelligent window group cooperative control method and system based on building energy consumption simulation

Abstract

The application relates to the technical field of building intelligence, and discloses an intelligent window group cooperative control method and system based on building energy consumption simulation. The method collects temperature data through indoor and outdoor temperature sensors, obtains a window group space target temperature, and calculates a target difference value and an environment difference value, according to which a control mode is switched. When both the two types of difference values are greater than a preset adjustment difference value, the cooperative control mode is entered, and the window group opening degree and the control step length are adaptively adjusted according to the adjustment control value and the step length control value. When only the target difference value is greater than the adjustment difference value, the pulse control mode is entered, and the window group ventilation is first started and stopped for a short time with a matched opening degree and time length, and then the air conditioner is started to stabilize the indoor temperature. The system also realizes strong wind safety control in combination with outdoor wind power and temperature fluctuation, and adaptively adjusts the curtain shielding area according to the indoor light intensity and seasonal working conditions, so that the solar energy utilization and sun-shading refrigeration are optimized. According to the application, the window group opening degree is adaptively optimized according to the building dynamic load and energy consumption change, and the building energy efficiency and intelligent control level are improved.

Description

Technical Field

[0001] This application relates to the technical field of building intelligence, and in particular to a method and system for intelligent window group collaborative control based on building energy consumption simulation. Background Technology

[0002] With the rapid development of green building and intelligent building technologies, intelligent building control has become a core technological approach to improve building energy efficiency, optimize indoor environmental quality, and reduce energy consumption throughout the building's life cycle. Through intelligent control of the building envelope and electromechanical equipment, energy consumption for heating, cooling, and ventilation can be effectively reduced.

[0003] Currently, high-end intelligent buildings commonly employ large-aperture automatic window structures. These windows typically feature a layered design, with the upper window equipped with a vertical sliding drive mechanism. This allows for directional sliding along the vertical direction to open, close, or adjust the opening size, meeting the functional requirements of large-space buildings for natural ventilation and lighting. These windows offer controllable opening and closing speeds, excellent sealing performance, and adjustable opening areas, and are widely used in high-end intelligent building scenarios such as upscale residences, high-rise office buildings, and large public buildings.

[0004] Existing intelligent building automatic window control methods mostly rely on user voice commands to trigger execution, or perform fixed opening and closing actions based on preset scene modes. The control logic is not coupled with the building's real-time energy consumption simulation data and indoor and outdoor thermal environment, and fails to achieve adaptive and collaborative optimization of window group opening based on the building's dynamic load and energy consumption changes. Summary of the Invention

[0005] In order to enable the window group opening to operate in coordination with changes in building dynamic load and energy consumption, this application provides an intelligent window group collaborative control method and system based on building energy consumption simulation.

[0006] Firstly, this application provides a method for intelligent window group collaborative control based on building energy consumption simulation, employing the following technical solution: A method for collaborative control of intelligent window groups based on building energy consumption simulation includes the following steps: The outdoor temperature value is obtained based on the outdoor temperature sensor, and the indoor temperature value is obtained based on the temperature sensor set in the preset window group space; the target temperature value corresponding to the window group space is obtained. The absolute value of the difference between the target temperature value and the indoor temperature value is calculated as the target difference, and the absolute value of the difference between the indoor temperature value and the outdoor temperature value is calculated as the environmental difference. If both the target difference and the environmental difference are greater than the preset adjustment difference, the system enters the collaborative control mode. In the collaborative control mode: if the indoor temperature value is greater than or less than both the target temperature value and the outdoor temperature value, the ratio of the target difference to the preset first stable temperature value is calculated as the adjustment control value. Based on the adjustment control value, the opening of the window group is positively correlated with the preset control step size. The ratio of the environmental difference to the preset second stable temperature value is calculated as the step size control value. Based on the step size control value, the control step size of the window group is negatively correlated with the control step size control value. If the target difference is greater than the adjustment difference and the environmental difference is less than the adjustment difference, then the pulse control mode is entered. In pulse control mode: the opening degree of the window group is matched from the preset opening degree database according to the target difference as the pulse opening degree, and the opening degree maintenance duration is matched from the preset duration database according to the environmental difference as the pulse duration; the opening degree of the window group is controlled to the pulse opening degree, and after the pulse duration, the window group is closed, and then the air conditioner is started to run for the preset set duration.

[0007] Furthermore, the method also includes the following steps: After entering the collaborative control mode, the control space corresponding to the target temperature value is obtained, the volume of the control space is calculated as the control volume, the volume of the window group space is obtained as the window group volume, and the ratio of the control volume to the window group volume is calculated as the volume control value. If the volume control value is greater than the preset volume setting control value, all windows in the window group space are controlled simultaneously; otherwise, the number of windows in the window group space is adjusted according to the positive correlation of the volume control value, and the corresponding windows are controlled sequentially based on the preset set time interval.

[0008] By adopting the above technical solution, the number of windows to be opened and the control method can be intelligently determined by calculating the volume ratio of the control space to the window group space. Based on the actual space volume, it is possible to flexibly choose to open all windows simultaneously or sequentially at time intervals. This can accurately match the ventilation and heat exchange needs of different spaces, avoid problems such as excessive indoor temperature fluctuations and uneven heat exchange efficiency caused by improper number of windows, and reduce redundant operation of the window drive mechanism.

[0009] Furthermore, the method also includes the following steps: Wind force data is acquired based on wind sensors installed outdoors; Calculate temperature fluctuation values ​​within a preset period based on an outdoor temperature sensor; If the wind force data value is greater than the preset wind force setting value, and the temperature fluctuation value is greater than the preset fluctuation threshold, then the instantaneous control mode will be entered in the pulse control mode. In instantaneous control mode: Obtain instantaneous opening and instantaneous duration. If the opening of the window group is less than the instantaneous opening, control the opening of the window group to the instantaneous opening. After the instantaneous duration, close the window group. Among them, the instantaneous opening is less than the pulse opening, and the instantaneous duration is shorter than the pulse duration.

[0010] Furthermore, the method also includes the following steps: Calculate the number of times the target temperature value is adjusted within a preset time period; If the number of adjustments exceeds the preset number of adjustments, the ratio of the number of adjustments to the preset number of adjustments is calculated as the number of adjustments control value, and the uniform setting value is adjusted in a positive correlation with the number of adjustments control value.

[0011] By adopting the above technical solution, and by statistically analyzing the number of times the target temperature is adjusted within a preset time period, the uniform set value is adaptively increased when the adjustment frequency is too high. This effectively avoids the problem of frequent switching of window group control mode and repeated opening and closing of windows caused by small temperature fluctuations, and reduces the ineffective actions and wear of the drive mechanism.

[0012] Furthermore, the method also includes the following steps: Environmental data values ​​are acquired based on environmental sensors installed within the window group space, and these environmental data values ​​are negatively correlated with the air quality within the window group space. If the environmental data value is greater than the preset quality warning value, then obtain the corresponding loop opening and loop period of the window group space; otherwise, set the loop opening and loop period to empty. If both the target difference and the environmental difference are less than the adjustment difference, the system enters the cyclic control mode. In the cyclic control mode, based on the non-empty cyclic opening and cyclic period, the opening of the window group is controlled to the cyclic opening by the cyclic period. The ratio of the environmental data value and the quality warning value is calculated as the quality control value. The duration for which the window group maintains the cyclic opening is adjusted according to the positive correlation of the quality control value.

[0013] By adopting the above technical solution, indoor environmental sensors monitor air quality in real time. When the indoor and outdoor temperature difference and the target temperature difference both meet the standards, the circulating control mode is intelligently activated based on the air quality status. The duration of the circulating window opening is adaptively adjusted according to the degree of air quality exceeding the standard. This can not only effectively improve indoor air quality and meet the health needs of living while ensuring the stability of the indoor thermal environment, but also avoid unnecessary continuous window opening leading to increased building energy consumption.

[0014] Furthermore, the method also includes the following steps: Multiple temperature sensors are installed outdoors, each generating temperature data. The outdoor temperature value is calculated based on the multiple temperature data. The fluctuation value of the temperature sensor is calculated. If the fluctuation value is greater than the preset fluctuation reference value, the area corresponding to the temperature sensor in the window group space is marked as a dynamic area; otherwise, the area corresponding to the temperature sensor in the window group space is marked as a static area. Cancel the control operation of the window group in the dynamic area and continue for multiple cycles.

[0015] By adopting the above technical solution, the outdoor multi-temperature sensor monitors temperature fluctuation values ​​and intelligently divides dynamic and static areas. The control operation of the corresponding window group is suspended for multiple cycles in the dynamic area where the temperature fluctuation exceeds the standard. This can effectively avoid the problem of window group misjudging opening and closing and frequent adjustment caused by drastic fluctuations in outdoor temperature, and reduce the ineffective action and mechanical wear of the window drive mechanism.

[0016] Furthermore, the method also includes the following steps: If the fluctuation value is less than the preset fluctuation reference value, the ratio of the fluctuation value to the fluctuation reference value is calculated as the fluctuation control value, and the opening of the window group in the static area is adjusted according to the negative correlation of the fluctuation control value.

[0017] By adopting the above technical solution, for static areas with small temperature fluctuations, by calculating the temperature fluctuation control value and negatively adjusting the window opening, the window size can be precisely matched according to the outdoor temperature fluctuation range, achieving accurate matching of indoor and outdoor heat exchange efficiency. This effectively maintains a stable and balanced indoor thermal environment and avoids energy waste caused by improper opening.

[0018] Furthermore, the method also includes the following steps: Indoor light intensity values ​​are obtained based on light intensity sensors installed within a pre-defined window group space; If the target temperature value > indoor temperature value > outdoor temperature value, and the target temperature value is greater than the preset first temperature value, then compare the indoor light intensity value with the preset light intensity value. If the indoor light intensity value is greater than the set light intensity value, the ratio of the indoor light intensity value to the set light intensity value is calculated as the light intensity control value. The shading area of ​​the curtains on the window group is adjusted according to the negative correlation with the light intensity control value, and the shading area is positively correlated with the light flux of the window group.

[0019] Furthermore, the method also includes the following steps: Indoor light intensity values ​​are obtained based on light intensity sensors installed within a pre-defined window group space; If the target temperature value is less than the indoor temperature value and less than the outdoor temperature value, and the target temperature value is greater than the preset second temperature value, then the indoor light intensity value is compared with the preset light intensity value; wherein, the second temperature value is greater than the first temperature value. If the indoor light intensity is greater than the set light intensity, the ratio of the indoor light intensity to the set light intensity is calculated as the light intensity control value. The shading area of ​​the curtains on the window group is adjusted according to the negative correlation of the light intensity control value, and the shading area is positively correlated with the light flux of the window group.

[0020] By adopting the above technical solutions, for the working conditions where the outdoor temperature is higher than the indoor temperature and the indoor temperature exceeds the standard in summer, the curtain shading area can be adjusted according to the negative correlation of light intensity based on indoor light intensity monitoring data. This can effectively block solar radiation heat from entering the room, reduce the air conditioning cooling load, reduce building cooling energy consumption, and at the same time maintain the stability of the indoor thermal environment while ensuring basic lighting. This achieves synergistic optimization of shading, lighting and cooling energy saving, and improves indoor comfort and building energy efficiency in summer.

[0021] Secondly, this application provides an intelligent window group collaborative control system based on building energy consumption simulation, which adopts the following technical solution: A collaborative control system for intelligent window clusters based on building energy consumption simulation includes a processor, wherein the processor executes the steps of the collaborative control method for intelligent window clusters based on building energy consumption simulation as described in any of the preceding claims. Attached Figure Description

[0022] Figure 1 This is the overall flowchart of the intelligent window group collaborative control method of this application; Figure 2 This is a flowchart of the strong wind and temperature fluctuation protection control for this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0024] This application discloses an intelligent window group collaborative control method based on building energy consumption simulation, applicable to green and intelligent building scenarios such as high-end residential buildings, high-rise office buildings, and large public buildings. It relies on real-time building energy consumption simulation data, indoor and outdoor multi-source temperature sensing modules, window group electric drive actuators, and a central air conditioning linkage control system for collaborative operation. The following refers to... Figure 1 This paper provides a detailed explanation of the method.

[0025] Step 1: Multi-point temperature acquisition and reference temperature configuration Temperature sensors are arrayed in the exterior facade area of ​​the building, which is shaded, free from direct sunlight, and has uniform ventilation. The raw outdoor temperature data is continuously collected at a fixed acquisition cycle of 10 seconds. The controller performs outlier removal, sliding filtering, and multi-point mean correction on the raw data to eliminate interference from instantaneous strong winds, local heat sources, and short-term sunlight, and outputs a stable outdoor temperature value T_outer. Indoor temperature sensors were installed in the indoor spaces where people frequently reside and in office activity areas covered by windows. The indoor temperature value T_inner was obtained after strictly following the indoor environmental monitoring deployment standards and collecting and calibrating the data. The controller combines a building energy consumption simulation model, seasonal load parameters, the heat dissipation coefficient of the building envelope, and human comfort standards to dynamically configure the current target temperature value T_target of the space as the reference value for indoor thermal environment regulation.

[0026] Step 2: Quantitative calculation of temperature difference The controller calculates two groups of core deviation parameters in real time for the determination of the control mode threshold: Target difference: ΔT_target = |T_target - T_indoor|, which represents the deviation degree between the actual indoor temperature and the comfortable target temperature; Environmental difference: ΔT_environment = |T_indoor - T_outdoor|, which represents the heat exchange capacity and temperature difference level between the indoor and outdoor.

[0027] In this embodiment, the system presets a fixed threshold: adjustment difference ΔT_0 = 2°C, which is used as the switching determination criterion between the coordinated control mode and the pulse control mode.

[0028] Step 3: Quantitative adjustment of the coordinated control mode When ΔT_target > 2°C and ΔT_environment > 2°C, the system enters the coordinated control mode; If it is satisfied that T_indoor is greater than both T_target and T_outdoor, or T_indoor is less than both T_target and T_outdoor, it is determined that there is a need for forced heat dissipation or supplementary heating in the room, and the positive correlation adjustment of the window group opening degree and the negative correlation adjustment of the adjustment step size are respectively realized through the quantitative formula.

[0029] 3.1 Window group opening degree The system locally presets fixed reference parameters: the first stable temperature value T_1 = 5°C; Set the calculation formula for the adjustment control value: K_1 = ΔT_target / T_1; At the same time, preset the basic opening step size of the window group S_base = 5%, and the single - time opening adjustment amount of the window group satisfies the positive - correlation formula: ΔS = S_base × K_1; the overall adjustment range of the opening degree is limited to 0% - 100% to prevent over - range adjustment.

[0030] Positive - correlation adjustment logic: The larger the value of the adjustment control value K_1, the higher the indoor temperature deviation, the larger the single - time opening increment of the window group, and the faster the natural ventilation heat exchange efficiency is enhanced; the smaller the value of K_1, the smaller the temperature deviation, and the synchronous reduction of the fine - tuning amplitude of the opening degree realizes refined temperature control.

[0031] 3.2 Control step size The system locally presets fixed reference parameters: the second stable temperature value T_2 = 8°C; Set the calculation formula for the step - size control value: K_2 = ΔT_environment / T_2; The preset base adjustment time step L_base = 60s, and the window adjustment interval step satisfies the negative correlation formula: L = L_base / K_2; the adjustment step limit range is 30s to 180s to avoid excessive adjustment or response lag.

[0032] Negative correlation adjustment logic: The larger the environmental difference, the higher the step control value K_2, the shorter the time interval between two window group adjustments, and the smoother the rhythm of a single adjustment action, avoiding indoor thermal shock caused by large-scale window opening under large temperature differences; the smaller the environmental difference, the lower K_2, the longer the adjustment interval step size, reducing the loss of frequent motor start-stop.

[0033] Step 4: Pulse control mode determination and graded matching control When ΔT_m > 2℃ and ΔT_a < 2℃, the indoor temperature control demand is strong, but the indoor and outdoor natural heat exchange capacity is insufficient, so the system switches to pulse control mode.

[0034] The system pre-establishes a hierarchical database and sets fixed matching intervals: Openness database matching range: ΔT_ is 2℃~4℃, with a matched pulse opening of 15%; ΔT_ is 4℃~6℃, and the matching pulse opening is 25%.

[0035] Duration database matching range: ΔT_loop is 0℃~1℃, and the matching pulse duration is 30s; The ΔT loop is 1℃~2℃, and the matching pulse duration is 60s.

[0036] Specific execution process: The system first controls the opening of the window group to the matching pulse opening, and the pulse duration is used to complete short-term ventilation; after the timer ends, the window group is immediately closed; then the air conditioning equipment is started and maintained for the preset duration, which is set to 5 minutes in this embodiment, to stabilize the indoor basic heat load through air conditioning.

[0037] By opening windows for ventilation first and then closing them for air conditioning control, the system avoids the impact of opening windows on the air conditioning temperature control effect. It takes into account the indoor ventilation needs under low heat exchange efficiency conditions, while avoiding the loss of heat and cold caused by indiscriminate window opening, thus matching the building's low energy consumption operation requirements.

[0038] In this embodiment, to address the issues of excessive local heat exchange, temperature imbalance in small spaces, and excessive load on simultaneous operation of multiple windows caused by unified global window opening, the following control steps are also included: After entering the collaborative control mode, the controller retrieves the building space layout parameters and volume data pre-stored by the system, accurately locates the temperature control area that uniquely corresponds to the current target temperature value, clarifies the spatial boundary, floor height and coverage of the area, and accurately calculates the enclosed space volume of the temperature control area through the volume calculation algorithm, defining it as the control volume V_control. At the same time, the controller reads the entire indoor space responsible for ventilation, heat exchange and environmental control of the entire window group, and calculates the total volume of the complete service space in a unified manner, which is defined as the window group volume V_window; The controller calculates the ratio of the control volume to the window group volume using a fixed calculation formula, generating the volume control value K_3. The specific calculation formula is: K_3 = V_control / V_window; The volume control value can intuitively reflect the proportion of the effective space that needs temperature regulation in the overall service space of the window group, providing a quantitative basis for judging the number of windows and the switching of control methods.

[0039] In this embodiment, the system pre-sets a fixed judgment threshold and defines a volume setting control value α=0.8, which is used as the critical boundary condition between global synchronous control and local time-sharing control.

[0040] When the real-time calculated volume control value K_3 > 0.8, it is determined that the control space requiring temperature optimization occupies most of the window group service space, and local window adjustment cannot meet the needs of large-scale thermal environment balance. At this time, the controller directly outputs synchronous control commands to centrally drive all motorized windows within the window group space to synchronously perform opening adjustment actions. All windows synchronously complete opening, closing, or fine-tuning of opening according to the adjustment range and optimized control step size determined above, realizing rapid ventilation and heat exchange in the large volume space and ensuring synchronous and balanced regulation of indoor temperature over a large area.

[0041] When the real-time calculated volume control value K_3 ≤ 0.8, it indicates that the temperature control target is only a small local area within the window group's coverage area, and not all windows need to participate in the adjustment. In this scheme, the number of controlled windows is strictly positively correlated with the volume control value. The preset total number of windows in the window group is N, and the formula for calculating the number of effective windows participating in the control is as follows: N_control = Int(N×K_3); where Int is the integer operation; The positive correlation regulation logic is as follows: the larger the volume control value, the higher the proportion of space requiring temperature control, and the more windows are needed to participate in heat exchange; the smaller the volume control value, the more limited the temperature control coverage, and the fewer controlled windows are needed to accurately match the ventilation and heat exchange load of local spaces, thus eliminating energy waste caused by ineffective window opening.

[0042] To achieve standardized control, this embodiment sets a fixed matching interval: 0.2 < K_3 ≤ 0.4, the number of controlled windows accounts for 20% to 40% of the total number of windows; 0.4 < K_3 ≤ 0.6, the number of controlled windows accounts for 40% to 60% of the total number of windows; 0.6 < K_3 ≤ 0.8, the number of controlled windows accounts for 60% to 80% of the total number of windows.

[0043] This will be illustrated with specific examples: For example, the total number of windows in a single scene group is set to N=10. Case 1: Localized, small-scale temperature control scenario, with a control volume V_control = 200m³ 3 The volume of the window group is V_window = 500m³ 3 , K_3=200 / 500=0.4, and the number of controlled windows is 4. Only some windows are activated to complete precise heat exchange. Case 2: Medium-range temperature control scenario, control volume V_control = 350m³ 3 The volume of the window group is V_window = 500m³ 3 , K_3=350 / 500=0.7, and the number of controlled windows is calculated to be 7, thus expanding the control coverage area simultaneously.

[0044] After determining the number and specific window numbers to be controlled, the controller abandons the synchronous drive mode and adopts a time-sharing sequential control strategy. The system presets a fixed time interval, preferably 15 seconds, and sends drive control signals sequentially every 15 seconds according to the spatial distribution order of the windows and the priority of the area division, controlling the corresponding windows to complete the opening adjustment action in batches and at different times.

[0045] Reference Figure 2 In this embodiment, an additional mechanism for multi-area outdoor environmental monitoring, temperature fluctuation assessment, and secondary control mode switching under strong wind conditions is added. Multiple sets of outdoor temperature and wind sensors enable comprehensive outdoor microclimate perception, effectively addressing issues such as excessively large local outdoor temperature differences, indoor turbulence caused by pulsed window opening in strong winds, overload of doors and windows, and sudden changes in indoor thermal environment. An instantaneous control mode is derived from the pulse control mode, and the specific implementation steps are as follows: This solution deploys multiple sets of outdoor temperature sensors in different orientations and heights on the building facade. Each sensor independently collects real-time raw temperature data and generates independent temperature monitoring data for its corresponding location. The controller aggregates all outdoor temperature data, removes abnormal and sudden data, and uses an arithmetic average algorithm to perform unified calculations, accurately corrects and calculates the calibrated outdoor average temperature value, replacing data collected by a single sensor and avoiding control misjudgments caused by single-point monitoring errors.

[0046] Meanwhile, the controller calculates the temperature fluctuation value for each group of outdoor temperature sensors within a fixed statistical period. The fluctuation value is calculated using the temperature standard deviation or range within the period to evaluate the stability of the corresponding outdoor temperature. This calculation method is a conventional and mature algorithm in this field and can be directly deployed and implemented.

[0047] At the same time, high-precision wind sensors are installed on the windward side of the building to continuously collect real-time outdoor wind speed and wind pressure parameters, and integrate and calculate to output standardized wind force data value F. The system has preset fixed critical judgment parameters. The wind force setting value F_0 = 7.5m / s is set as the threshold for judging strong wind conditions. At the same time, a fluctuation threshold is preset as the benchmark for judging outdoor temperature stability.

[0048] The controller performs real-time operational condition linkage judgment: When the outdoor wind force data value F > F_0 is detected and the temperature fluctuation value is greater than the preset fluctuation threshold, it indicates that the current outdoor environment is both strong wind and drastic temperature fluctuation, which are both severe conditions. Conventional pulse opening ventilation will cause hidden dangers such as strong wind backflow, local cold and hot air impact, and excessive window load. The system immediately terminates the original pulse control logic and automatically switches to instantaneous control mode.

[0049] The system internally stores hierarchical fixed parameters, strictly limiting the relationship between the parameters of the two modes: Set the instantaneous opening S_c, instantaneous duration t_c, pulse opening S_m, and pulse duration t_m; The hard constraints are: S_c < S_m, t_c < t_m; Example fixed parameter configuration: The normal pulse opening is 15% to 25%, and the pulse duration is 30s to 60s; the instantaneous opening is uniformly limited to 5% to 10%, and the instantaneous duration is uniformly limited to 10s to 20s.

[0050] During the execution of the instantaneous control mode, the controller collects and compares the current actual opening degree of the window group with the preset instantaneous opening degree in real time: If the real-time opening of the window group is detected to be less than the preset instantaneous opening, the entire window group will be uniformly and precisely adjusted to the preset instantaneous opening at the rated adjustment speed of the motor. The system maintains this small ventilation opening for a fixed instantaneous duration. After the timing cycle ends, the controller immediately issues a full shutdown command, directly closing all smart windows and quickly cutting off the intrusion of strong outdoor winds and turbulent temperature airflow.

[0051] In this embodiment, the method adds a target temperature adjustment frequency feedback calibration mechanism. By dynamically monitoring the adjustment frequency of the target temperature within a preset time period, it adaptively optimizes the outdoor temperature fluctuation threshold, solving problems such as frequent switching of control modes, ineffective opening and closing of window groups, and fluctuations in the indoor thermal environment caused by overly strict or lenient initial thresholds. This achieves dynamic matching between control parameters and actual operating conditions. The specific implementation steps are as follows: 1. Statistics on the number of adjustments and definition of core parameters The system pre-configures key statistical parameters, clearly setting the time period T_statistics=30min, which can be customized according to the building usage scenario, such as setting it to 30min for office buildings and 60min for residential buildings, to limit the statistical cycle of the number of times the target temperature is adjusted; at the same time, it presets the number of times N_set=5 times as a critical threshold for judging whether the target temperature adjustment is too frequent.

[0052] The controller has a built-in counting module that, within a set time period T_system, counts in real time the number of times the target temperature value T_target is actively adjusted due to factors such as changes in indoor and outdoor environment and adjustments in building load. This number is defined as the actual number of adjustments N_actual. Only the target temperature correction initiated by the system is counted, excluding cases where the user manually forces a change.

[0053] 2. Calculation and Adjustment Judgment of Frequency Control Values After the set time period ends, the controller compares the actual number of adjustments N_actual with the preset number of adjustments N_set: If N_real ≤ N_set, the current target temperature adjustment frequency is determined to be within a reasonable range, and there is no need to adjust the fluctuation threshold; the original parameters remain unchanged. If N_actual > N_set, it indicates that the target temperature adjustment is too frequent, meaning that the current fluctuation threshold judgment standard is too strict, causing frequent mode switching under strong wind conditions. The fluctuation threshold needs to be optimized through feedback adjustment.

[0054] At this point, the number of control values ​​K_4 is calculated to quantify the degree to which the adjustment frequency exceeds the reasonable range. The calculation formula is: K_4 = N_actual / N_setup. The larger the value of the number of control values ​​K_4, the more frequently the target temperature is adjusted, and the optimization range of the fluctuation threshold needs to be increased accordingly.

[0055] 3. Quantitative adjustment of uniform setpoint with positive correlation In this scheme, the fluctuation threshold and the number of control values ​​K_4 are strictly positively correlated. That is, the larger the number of control values, the larger the value of the fluctuation threshold after adjustment, and the more lenient the judgment standard for outdoor temperature stability, thereby reducing the frequency of switching to the instantaneous control mode under strong wind conditions.

[0056] In this embodiment, the method adds a coordinated control mechanism for indoor air quality and thermal environment. By monitoring the air quality parameters in the window group space in real time, when the indoor and outdoor temperature difference and temperature control deviation are both within a stable range, the circulating ventilation control mode is activated to achieve a dynamic balance between air quality optimization and building energy consumption management. The specific implementation steps are as follows: 1. Real-time acquisition of air quality data and parameter definition Within the interior functional spaces covered by window clusters, based on 10m² per person... 2 Multiple environmental sensors are evenly distributed at a uniform density. The sensor types are adapted to the monitoring needs of key indoor air quality indicators, and can collect key parameters such as PM2.5 concentration, CO2 concentration, and volatile organic compound (VOC) concentration in real time. After signal amplification and data calibration, a standardized environmental data value D is output. This solution clearly states that the environmental data value D has a strict negative correlation with indoor air quality: that is, the larger the D value, the higher the concentration of pollutants such as PM2.5 and CO2, and the worse the air quality; the smaller the D value, the cleaner the indoor air and the lower the pollutant concentration.

[0057] The system has a pre-set quality warning value D0 based on national indoor air quality standards, which serves as a critical threshold for determining whether air quality requires intervention. In this embodiment, D0 is set to 1000 ppm for CO2 concentration monitoring and 50 μg / m³ for PM2.5 concentration monitoring. 3 (Custom configurations can be made based on building usage scenarios and pollutant types).

[0058] 2. Pre-configuration and determination of circulating ventilation parameters The controller compares the environmental data value D with the quality warning value D0 in real time and dynamically matches the circulating ventilation parameters. If D > D0, the indoor air quality is determined to be substandard, and circulation ventilation needs to be activated to improve it. The controller retrieves the circulation opening S_cycle and circulation period T_cycle corresponding to the current window group space from the system's preset parameter database. If D≤D0, the indoor air quality is deemed to meet the standard, and no additional ventilation is required. The circulation opening S_circulation and circulation period T_circulation are set to null values, and the circulation ventilation logic is not activated.

[0059] 3. Determination of entering the loop control mode The system continues with the core threshold parameters set earlier (adjustment difference ΔT0 = 2℃). When the following dual conditions are simultaneously detected in real time, it indicates that the indoor thermal environment has reached a stable range (small temperature control deviation, low indoor-outdoor heat exchange demand), and there may be a need to optimize air quality. The system then automatically switches to cyclic control mode: Target difference ΔT_target < ΔT0 (indoor temperature is close to the target comfort temperature); The environmental difference ΔT_ring < ΔT0 (small indoor-outdoor temperature difference, natural heat exchange has no risk of energy waste).

[0060] 4. Quantitative control of circulating ventilation After entering the cyclic control mode, the controller first checks whether the cyclic opening S_cycle and the cyclic period T_cycle are non-empty values: only when both are valid parameters will the cyclic ventilation operation be executed; if they are empty, the current state of the window group will be maintained and cyclic control will not be started.

[0061] During the recirculation ventilation process, the controller performs fine-tuning according to the following logic: Basic cycle control: The window group opening is periodically adjusted to the cycle opening S_cycle with a preset cycle period T_cycle as the cycle period to complete the "open-maintain-close" cycle ventilation action; Dynamic optimization of maintenance duration: Calculate the quality control value K_5 to quantify the degree of air quality exceeding the standard. The calculation formula is: K_5=D / D0; In this scheme, the duration t_ of the window group maintaining the circulating opening is positively correlated with the quality control value K_5: that is, the larger K_5 is, the worse the air quality, the longer the ventilation time is maintained, and the stronger the pollutant removal effect; the smaller K_5 is, the air quality is close to meeting the standard, the shorter the maintenance time is, and the less unnecessary energy consumption is reduced.

[0062] In this embodiment, a dynamic zoning control mechanism for the outdoor temperature field is added. By monitoring the real-time fluctuation status of multiple outdoor temperature sensors, the dynamic fluctuation area and the static stable area are accurately divided. The control operation of the window group corresponding to the area with drastic temperature fluctuation is suspended. The specific implementation steps are as follows: 1. Outdoor temperature data acquisition and mean calibration Continuing with the previous outdoor multi-sensor deployment scheme, n sets of high-precision temperature sensors are deployed in different orientations (east, south, west, north) and different height levels (low zone, middle zone, high zone) on the building facade. Each sensor independently collects raw temperature data at its location at a collection frequency of 5 seconds per acquisition.

[0063] The controller aggregates the raw temperature data from all n sets of sensors, performs outlier removal and sliding filter noise reduction in sequence, and then calculates the calibrated outdoor average temperature value using an arithmetic average algorithm. This value is used for parameter calculations in the subsequent overall temperature control logic to ensure the stability and representativeness of the outdoor temperature data.

[0064] 2. Quantitative Calculation and Parameter Definition of Temperature Fluctuation Values To accurately capture the dynamic temperature changes in the area where a single sensor is located, the controller sets a fluctuation statistics period for each group of outdoor temperature sensors and calculates the temperature fluctuation value of each sensor within that period, which is used to quantify the degree of temperature instability in a single area.

[0065] This embodiment uses the "temperature standard deviation within the period" as the basis for calculating the fluctuation value, which can reflect the dispersion of temperature fluctuations and avoid the interference of instantaneous extreme values. This algorithm is a conventional and mature algorithm in this field and is easy to implement.

[0066] The system has a preset fluctuation reference value V0 based on the building energy conservation control needs, which serves as the critical threshold for distinguishing between dynamic and static areas.

[0067] 3. Dynamic / Static Region Division Logic The controller compares the fluctuation values ​​of each sensor group with the preset fluctuation reference values ​​one by one, and combines this with the mapping relationship between the sensors and the indoor window area to complete the area attribute labeling: If the fluctuation value is greater than V0: it is determined that the outdoor area where the sensor is located has a drastic temperature fluctuation, and the corresponding indoor window area is marked as a dynamic area. If the fluctuation value is ≤V0: the outdoor area where the sensor is located is determined to be in a stable temperature, and the corresponding indoor window area is marked as a static area.

[0068] 4. Dynamic area window group control pause execution rules To avoid the unstable temperature in dynamic areas from interfering with the indoor thermal environment, the system is set to immediately cancel all active control operations for indoor window groups marked as dynamic areas, including opening adjustment commands in collaborative control mode and cyclic control mode, and maintain this paused state for multiple cycles.

[0069] Window group status during the pause: Maintain the current opening degree when the pause command was issued, do not respond to any adjustment commands based on outdoor temperature fluctuations, until the pause duration expires, then restart the fluctuation monitoring and normal control logic for this area.

[0070] In this embodiment, a temperature fluctuation-adaptive opening control mechanism is added for window groups marked as static areas. By quantifying the intensity of outdoor temperature fluctuations corresponding to static areas, the negative correlation dynamic optimization of window group opening is achieved using fluctuation control values. This solves the problem that static areas cannot be adapted to slight temperature fluctuations when only controlled at a fixed opening. The specific implementation steps are as follows: 1. Quantitative calculation of fluctuation control value Continuing with the parameter definition above, the outdoor temperature sensor fluctuation value corresponding to the static area is V_i (V_i≤V0). For each static area, the controller calculates the fluctuation control value K_6 to quantify the relative intensity of the outdoor temperature fluctuation in that area. The calculation formula is: K_6=V_i / V0. 2. Static area window group opening negative correlation adjustment logic In this scheme, the opening degree of the window group in the static area has a strict negative correlation with the fluctuation control value K_6: that is, the larger the fluctuation control value (the weaker the temperature stability), the smaller the window group opening, reducing the indoor thermal environment disturbance caused by slight temperature fluctuations; the smaller the fluctuation control value (the stronger the temperature stability), the larger the window group opening, making full use of the stable outdoor environment to enhance ventilation and heat exchange, and balancing comfort and energy-saving needs.

[0071] In this embodiment, a coordinated control mechanism for "temperature control-lighting-shading" under winter conditions is added. By monitoring indoor light intensity data in real time and combining it with typical winter temperature conditions (target temperature higher than indoor temperature, indoor temperature higher than outdoor temperature), the adaptive quantitative adjustment of the curtain shading area is achieved. The specific implementation steps are as follows: 1. Indoor light intensity data acquisition and parameter definition Within the interior functional spaces covered by window clusters, according to "every 20m 2 High-precision light intensity sensors are installed in a uniform density, with one set positioned 1.2m above the ground, avoiding direct light from lamps and areas obstructed by furniture, to ensure that the collected data accurately reflects the indoor natural light intensity. The sensors continuously collect data at a fixed frequency, and after filtering and calibration, output standardized indoor light intensity values.

[0072] The system has pre-set key decision parameters locally to adapt to winter heating conditions: The first temperature value T1 = 18℃: This is set as the lower limit of indoor comfort temperature in winter. When the target temperature is higher than this value, it is determined that indoor comfort needs to be maintained through heating (including natural lighting and heating). The light intensity value L0 is set at 5000 lux: based on the "Standard for Daylighting Design of Buildings", it is set as the critical value to avoid glare from natural light and ensure visual comfort.

[0073] At the same time, the core rule for adjusting curtains is clearly defined: the area covered by curtains is positively correlated with the light flux through the window group, that is, the larger the area covered, the smaller the natural light flux through the window group.

[0074] 2. Winter operating condition assessment The controller first verifies whether the current temperature parameters meet the winter coordinated control conditions, which requires the following three conditions to be met simultaneously: ① Target temperature T_target > Indoor temperature T_indoor (Indoor temperature has not reached a comfortable level and additional heating is required); ② Indoor temperature T_indoor > Outdoor temperature T_outdoor (Outdoor temperature is lower, natural ventilation can make up for the temperature). ③ The target temperature value T_target > the first temperature value T1 = 18℃ (within the winter heating demand range).

[0075] The curtain coverage area adjustment logic will only be activated when all three conditions are met simultaneously.

[0076] 3. Light intensity contrast and curtain adjustment If the indoor light intensity is greater than the set light intensity, the light intensity control value is calculated; the shading area of ​​the curtains on the window group is adjusted according to the negative correlation of the light intensity control value.

[0077] In winter, the stronger the indoor light intensity, the smaller the area covered by curtains should be, in order to maximize the use of solar thermal energy for auxiliary heating and reduce the energy consumption of air conditioning heating, which is in line with the basic principles of building HVAC energy conservation.

[0078] In this embodiment, a coordinated control mechanism of "temperature control-shading-radiation protection" is added for summer operation. Based on typical summer temperature distribution (target temperature lower than indoor temperature, indoor temperature lower than outdoor temperature), and combined with indoor light intensity monitoring data, a negative correlation quantitative adjustment of the curtain shading area is achieved. The goal is to block solar radiation heat from entering the room, reduce the air conditioning cooling load, and at the same time avoid excessive shading leading to a deterioration of the indoor light environment. The specific implementation steps are as follows: 1. Light intensity data acquisition and parameter definition Continuing with the previous light intensity sensor deployment scheme, high-precision light intensity sensors are deployed in the indoor functional spaces covered by the window clusters to ensure that the collected data accurately reflects the intensity of natural lighting. After filtering, noise reduction, and standardized calibration, the indoor light intensity value is output.

[0079] The system has local presets for key parameters adapted to summer operating conditions: The second temperature value T2 = 25℃: This is set as the lower limit of the comfortable indoor temperature in summer, which is greater than the first temperature value T1 = 18℃ mentioned above for winter. Set light intensity value L0=5000 lux: Use the critical value for anti-glare. When the light intensity exceeds the limit, it is easy to cause glare and the solar radiation heat will increase significantly.

[0080] 2. Summer operating condition assessment The controller first checks whether the current temperature parameter meets the summer coordinated control triggering conditions, which must simultaneously satisfy the following three logic requirements: ① Target temperature T_target < Indoor temperature T_indoor (Indoor temperature exceeds the standard, cooling is required); ② Indoor temperature T_indoor < outdoor temperature T_outdoor (outdoor temperature is higher, solar radiation is the main heat source); ③ The target temperature value T_target > the second temperature value T2 = 25℃ (within the summer cooling demand range).

[0081] The curtain coverage area adjustment logic is activated only when all three conditions are met simultaneously.

[0082] 3. Light intensity contrast and curtain adjustment If the indoor light intensity is greater than the set light intensity, the light intensity control value is calculated. Based on the negative correlation of the light intensity control value, the shading area of ​​the curtains on the window group is adjusted to quickly increase the shading area to block radiant heat, reduce the air conditioning cooling load, and at the same time retain basic lighting to avoid increasing the energy consumption of artificial lighting.

[0083] Hardware feasibility description: The technical solution in this application can be implemented using conventional and mature hardware in this field: The indoor and outdoor temperature sensors use NTC thermistor sensors; The wind sensor uses a hot-wire anemometer. The light intensity sensor uses a photodiode module; The window group drive uses an electric window opener and a stepper motor; The controller uses an STM32 / ARM embedded processor, which can directly acquire sensor data and output drive signals; All of the above-mentioned devices are commercially available and mature components, which can be directly selected, deployed and implemented by personnel in the relevant technical fields, and there are no technical obstacles that prevent implementation.

[0084] This application also discloses an intelligent window group collaborative control system based on building energy consumption simulation, including a processor, wherein the processor executes the steps of the intelligent window group collaborative control method based on building energy consumption simulation as described in any of the above embodiments.

[0085] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for collaborative control of intelligent window groups based on building energy consumption simulation, characterized in that, Includes the following steps: Outdoor temperature values ​​are obtained based on outdoor temperature sensors, and indoor temperature values ​​are obtained based on temperature sensors installed in a preset window group space. Obtain the target temperature value corresponding to the window group space; The absolute value of the difference between the target temperature value and the indoor temperature value is calculated as the target difference, and the absolute value of the difference between the indoor temperature value and the outdoor temperature value is calculated as the environmental difference. If both the target difference and the environmental difference are greater than the preset adjustment difference, the system enters the collaborative control mode. In the collaborative control mode: if the indoor temperature value is greater than or less than both the target temperature value and the outdoor temperature value, the ratio of the target difference to the preset first stable temperature value is calculated as the adjustment control value. Based on the adjustment control value, the opening of the window group is positively correlated with the preset control step size. The ratio of the environmental difference to the preset second stable temperature value is calculated as the step size control value. Based on the step size control value, the control step size of the window group is negatively correlated with the control step size control value. If the target difference is greater than the adjustment difference and the environmental difference is less than the adjustment difference, then the pulse control mode is entered. In pulse control mode: the opening of the window group is matched from the preset opening database according to the target difference as the pulse opening, and the duration of the opening is matched from the preset duration database according to the environmental difference as the pulse duration. Control the opening of the window group to the pulse opening level, close the window group after the pulse duration, and then start the air conditioner to run for the preset set time.

2. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 1, characterized in that, The method also includes the following steps: After entering the collaborative control mode, the control space corresponding to the target temperature value is obtained, the volume of the control space is calculated as the control volume, the volume of the window group space is obtained as the window group volume, and the ratio of the control volume to the window group volume is calculated as the volume control value. If the volume control value is greater than the preset volume setting control value, all windows in the window group space are controlled simultaneously; otherwise, the number of windows in the window group space is adjusted according to the positive correlation of the volume control value, and the corresponding windows are controlled sequentially based on the preset set time interval.

3. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 1, characterized in that, The method also includes the following steps: Wind force data is acquired based on wind sensors installed outdoors; Calculate temperature fluctuation values ​​within a preset period based on an outdoor temperature sensor; If the wind force data value is greater than the preset wind force setting value, and the temperature fluctuation value is greater than the preset fluctuation threshold, then the instantaneous control mode will be entered in the pulse control mode. In instantaneous control mode: Obtain instantaneous opening and instantaneous duration. If the opening of the window group is less than the instantaneous opening, control the opening of the window group to the instantaneous opening. After the instantaneous duration, close the window group. Among them, the instantaneous opening is less than the pulse opening, and the instantaneous duration is shorter than the pulse duration.

4. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 3, characterized in that, The method also includes the following steps: Calculate the number of times the target temperature value is adjusted within a preset time period; If the number of adjustments exceeds the preset number of adjustments, the ratio of the number of adjustments to the preset number of adjustments is calculated as the number of adjustments control value, and the uniform setting value is adjusted in a positive correlation with the number of adjustments control value.

5. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 1, characterized in that, The method also includes the following steps: Environmental data values ​​are acquired based on environmental sensors installed within the window group space, and these environmental data values ​​are negatively correlated with the air quality within the window group space. If the environmental data value is greater than the preset quality warning value, then obtain the corresponding loop opening and loop period of the window group space; otherwise, set the loop opening and loop period to empty. If both the target difference and the environmental difference are less than the adjustment difference, the system enters the cyclic control mode. In the cyclic control mode, based on the non-empty cyclic opening and cyclic period, the opening of the window group is controlled to the cyclic opening by the cyclic period. The ratio of the environmental data value and the quality warning value is calculated as the quality control value. The duration for which the window group maintains the cyclic opening is adjusted according to the positive correlation of the quality control value.

6. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 1, characterized in that, The method also includes the following steps: Multiple temperature sensors are installed outdoors, each generating temperature data. The outdoor temperature value is calculated based on the multiple temperature data. The fluctuation value of the temperature sensor is calculated. If the fluctuation value is greater than the preset fluctuation reference value, the area corresponding to the temperature sensor in the window group space is marked as a dynamic area; otherwise, the area corresponding to the temperature sensor in the window group space is marked as a static area. Cancel the control operation of the window group in the dynamic area and continue for multiple cycles.

7. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 6, characterized in that, The method also includes the following steps: If the fluctuation value is less than the preset fluctuation reference value, the ratio of the fluctuation value to the fluctuation reference value is calculated as the fluctuation control value, and the opening of the window group in the static area is adjusted according to the negative correlation of the fluctuation control value.

8. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 1, characterized in that, The method also includes the following steps: Indoor light intensity values ​​are obtained based on light intensity sensors installed within a pre-defined window group space; If the target temperature value > indoor temperature value > outdoor temperature value, and the target temperature value is greater than the preset first temperature value, then compare the indoor light intensity value with the preset light intensity value. If the indoor light intensity value is greater than the set light intensity value, the ratio of the indoor light intensity value to the set light intensity value is calculated as the light intensity control value. The shading area of ​​the curtains on the window group is adjusted according to the negative correlation with the light intensity control value, and the shading area is positively correlated with the light flux of the window group.

9. The intelligent window group collaborative control method based on building energy consumption simulation according to claim 1, characterized in that, The method also includes the following steps: Indoor light intensity values ​​are obtained based on light intensity sensors installed within a pre-defined window group space; If the target temperature value is less than the indoor temperature value and less than the outdoor temperature value, and the target temperature value is greater than the preset second temperature value, then the indoor light intensity value is compared with the preset light intensity value; wherein, the second temperature value is greater than the first temperature value. If the indoor light intensity is greater than the set light intensity, the ratio of the indoor light intensity to the set light intensity is calculated as the light intensity control value. The shading area of ​​the curtains on the window group is adjusted according to the negative correlation of the light intensity control value, and the shading area is positively correlated with the light flux of the window group.

10. A smart window group collaborative control system based on building energy consumption simulation, characterized in that, Includes a processor, wherein the steps of the intelligent window group collaborative control method based on building energy consumption simulation as described in any one of claims 1-9 are executed.

Images