Roof system energy efficiency optimization management method and system based on digital twinning

Abstract

The invention belongs to the technical field of building energy conservation, and provides a roof system energy efficiency optimization management method and device based on digital twinning. The method comprises the following steps: acquiring real-time monitoring data, determining a digital twinborn body, and determining an energy consumption evaluation index; calculating a comprehensive energy consumption value to determine a target control parameter generation optimization scheme; a control instruction is generated and sent to the execution terminal; calculating a difference coefficient to calibrate the digital twin; and performing energy-saving transformation and equipment updating by taking the calibrated digital twin as a reference model. According to the invention, accurate monitoring and optimal control of the energy consumption of the roof system can be realized, the energy utilization efficiency is improved, and the building operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building energy saving, and in particular to a roof system energy efficiency optimization management method and system based on digital twinning. BACKGROUND

[0002] With the in-depth development of green building concept, building energy saving has become an important development direction of the current construction industry. As an important part of the building, the energy efficiency management of the roof system has a significant impact on the overall building energy consumption. The roof system mainly includes waterproof layer, thermal insulation layer, drainage system and roof equipment and other components, and its energy efficiency directly affects the heat loss and energy consumption of the building. At present, the conventional roof system energy efficiency management mainly relies on regular inspection and maintenance, and lacks real-time monitoring and dynamic optimization capability. With the development of Internet of Things and digital twinning technology, it is possible to apply digital twinning technology to roof system energy efficiency management. By constructing the mapping relationship between the virtual model and the entity roof system, real-time monitoring, analysis and optimization control of the roof system can be realized.

[0003] The conventional roof system energy efficiency management has the following defects and deficiencies: the conventional roof system energy efficiency management lacks dynamic sensing capability, and cannot obtain the running state and environmental parameters of the roof system in real time, resulting in inaccurate energy consumption analysis, lagging optimization scheme, and difficulty in timely adjustment according to different weather conditions and use scenarios, affecting the accuracy and timeliness of energy efficiency management. The existing roof system energy efficiency management method generally lacks precise prediction and optimization capability, cannot effectively predict future energy consumption according to historical data and current state, and cannot automatically generate optimal control parameters. Energy efficiency management strategy relies on manual experience judgment, and it is difficult to realize optimal control of roof system energy consumption, resulting in widespread energy waste. The conventional energy efficiency management system lacks self-learning and iterative optimization mechanism, and cannot calibrate and update the model according to the actual control effect. The system model deviates from the actual situation, and it is difficult to adapt to the influence of equipment aging, environmental changes and other factors, so that the energy efficiency management effect gradually decreases in long-term operation, and cannot provide reliable data support and decision basis for energy saving reconstruction and equipment updating of the roof system. SUMMARY

[0004] The embodiment of the present application provides a roof system energy efficiency optimization management method and system based on digital twinning, which can at least solve some problems in the prior art.

[0005] In a first aspect, the embodiment of the present application provides a roof system energy efficiency optimization management method based on digital twinning, comprising: obtaining real-time monitoring data of the roof system, determining a roof system digital twin according to the real-time monitoring data, and determining a roof system energy consumption evaluation index based on the roof system digital twin; According to the roof system energy consumption evaluation index, the comprehensive energy consumption values of the roof system corresponding to multiple sets of control parameters are calculated, the target control parameters are determined based on the comprehensive energy consumption values of the roof system, and the roof system energy efficiency optimization scheme is generated according to the target control parameters; According to the roof system energy efficiency optimization scheme, a roof system control instruction is generated, and the roof system control instruction is sent to a roof equipment execution terminal; The control results of the roof equipment execution terminal are collected, the difference coefficient of the control results and the preset target value is calculated, the parameters of the roof system digital twin are calibrated based on the difference coefficient, and the calibrated roof system digital twin is obtained; The calibrated roof system digital twin is used as a benchmark model for roof system energy efficiency management, and energy-saving reconstruction and equipment updating of the roof system are performed based on the benchmark model.

[0006] Real-time monitoring data of the roof system are acquired, the roof system digital twin is determined according to the real-time monitoring data, and the roof system energy consumption evaluation index is determined based on the roof system digital twin, including: Real-time monitoring data of the roof system are acquired by deploying multiple layers of monitoring devices in the roof system, feature extraction and parameter conversion are performed on the real-time monitoring data, and a multi-dimensional parameter set reflecting the physical state of the roof system is determined; The multi-dimensional parameter set is matched with a preset roof system operation mode respectively, the multi-dimensional parameter set is adaptively calibrated according to the matching result, and the calibrated multi-dimensional parameter set is determined as the roof system digital twin; Based on the roof system digital twin, multiple correlation functions representing the physical properties of the roof system are determined, the time sequence correlation of the multiple correlation functions is dynamically optimized, and evaluation parameters representing the energy consumption characteristics of the roof system are determined based on the optimized multiple correlation functions; Based on the evaluation parameters, roof system energy consumption distribution information is generated, and the spatial position of the energy consumption intensive area of the roof system is determined; Based on the spatial position of the energy consumption intensive area and the evaluation parameters, a roof system energy consumption evaluation index is generated.

[0007] According to the roof system energy consumption evaluation index, the comprehensive energy consumption values of the roof system corresponding to multiple sets of control parameters are calculated, the target control parameters are determined based on the comprehensive energy consumption values of the roof system, and the roof system energy efficiency optimization scheme is generated according to the target control parameters, including: According to the roof system energy consumption evaluation index, a control parameter space is determined, and a particle swarm initial population is generated based on the control parameter space, the particle swarm initial population containing multiple sets of parameter values; calculate corresponding roof system physical state data based on the multiple sets of parameter values, determine a roof system comprehensive energy consumption value corresponding to the multiple sets of parameter values according to the roof system physical state data; perform iterative optimization on the roof system comprehensive energy consumption value as a fitness function, update the speed and position of each particle in the initial population of the particle swarm in each iteration process, record the historical optimal position of each particle and the global optimal position, and determine the parameter value corresponding to the global optimal position as the target control parameter when the number of iterations meets the preset stop condition; perform stability analysis on the target control parameter to obtain a parameter mapping relationship containing stability constraints, solve the optimal solution of the parameter mapping relationship to obtain a dynamic parameter optimization function, and use the dynamic parameter optimization function as the roof system energy efficiency optimization scheme.

[0008] calculate corresponding roof system physical state data based on the multiple sets of parameter values, determine a roof system comprehensive energy consumption value corresponding to the multiple sets of parameter values according to the roof system physical state data, including: input the multiple sets of parameter values into the physical quantity collection device of the roof system for physical quantity collection, and obtain the roof system physical state data corresponding to the multiple sets of parameter values; determine an energy consumption evaluation matrix based on the roof system physical state data, compare the energy consumption evaluation matrix with a preset energy consumption evaluation benchmark, and obtain energy consumption evaluation data of the multiple sets of parameter values; use the variational method to solve the gradient field of the energy consumption evaluation data, determine an orthogonal basis function group in the gradient field, perform orthogonal decomposition on the energy consumption evaluation data through the orthogonal basis function group, and obtain a characteristic value sequence of the energy consumption evaluation data; determine a Lie algebra space of energy consumption evaluation based on the characteristic value sequence, and calculate the roof system comprehensive energy consumption value corresponding to the multiple sets of parameter values in the Lie algebra space through solving the conservation quantity under the integrability constraint condition.

[0009] generate a roof system control instruction according to the roof system energy efficiency optimization scheme, and send the roof system control instruction to a roof equipment execution terminal, including: perform parameter integrity checking on the control parameter in the roof system energy efficiency optimization scheme, generate a parameter checking report, and determine a parameter compensation threshold according to the parameter checking report; determine a control parameter compensation function based on the parameter compensation threshold, adjust the control parameter based on the control parameter compensation function, and obtain a compensated control parameter instruction; The compensated control parameter instruction is classified according to the type of the execution equipment to obtain an equipment classification instruction, and a time sequence analysis is performed on the equipment classification instruction to determine an instruction execution priority list; An execution delay time of each equipment classification instruction is calculated based on the instruction execution priority list to determine a closed-loop feedback compensation function of instruction execution; The execution delay time is dynamically compensated by the closed-loop feedback compensation function to generate a roof system control instruction, and the roof system control instruction is sent to a roof equipment execution terminal.

[0010] The control result of the roof equipment execution terminal is collected, a difference coefficient of the control result and a preset target value is calculated, and the parameters of the roof system digital twin are calibrated based on the difference coefficient to obtain a calibrated roof system digital twin, including: The control result of the roof equipment execution terminal is collected and a preset target parameter of the roof system is obtained, a roof system evaluation matrix is determined according to the preset target parameter, and the control result is processed based on the roof system evaluation matrix to obtain a control result evaluation value; The control result evaluation value is compared and analyzed with a preset target value, a deviation amount of the control result evaluation value relative to the preset target value is calculated, and a difference coefficient is calculated according to the deviation amount; An adaptive compensation weight is determined according to the difference coefficient, the adaptive compensation weight is dynamically distributed according to the time sequence characteristics of the control result to generate a parameter calibration compensation value; A calibration function is determined according to the parameter calibration compensation value and the control parameters of the roof system digital twin, a control parameter change trend feature is calculated according to the calibration function, and the control parameter change trend feature is subjected to stability constraint to obtain a calibrated roof system digital twin.

[0011] An adaptive compensation weight is determined according to the difference coefficient, the adaptive compensation weight is dynamically distributed according to the time sequence characteristics of the control result to generate a parameter calibration compensation value, including: The difference coefficient is classified according to the numerical value to obtain a difference coefficient classification interval, and a reference compensation weight corresponding to each classification interval is determined; The time sequence data of the control result is collected, the time sequence data is subjected to sampling period division, the fluctuation amplitude of the control result is calculated in each sampling period, and a fluctuation feature sequence is generated; A time sequence characteristic evaluation index is established according to the fluctuation feature sequence, the time sequence characteristic evaluation index is compared with a preset threshold to obtain a time sequence characteristic score; A compensation weight recursive equation is determined based on the difference coefficient classification interval, the time sequence characteristic score is processed based on the compensation weight recursive equation, and an adaptive compensation weight is obtained; The adaptive compensation weight is time sequence mapped with the fluctuation characteristic sequence of the control result, and a time sequence compensation coefficient is generated; The adaptive compensation weight is dynamically adjusted according to the time sequence compensation coefficient, and a parameter calibration compensation value is generated based on the adjusted adaptive compensation weight.

[0012] In a second aspect of the embodiment of the present application, a roof system energy efficiency optimization management system based on digital twinning is provided, comprising: A first unit is configured to acquire real-time monitoring data of a roof system, determine a roof system digital twin based on the real-time monitoring data, and determine a roof system energy consumption evaluation index based on the roof system digital twin; A second unit is configured to calculate a roof system comprehensive energy consumption value corresponding to a plurality of groups of control parameters according to the roof system energy consumption evaluation index, determine a target control parameter based on the roof system comprehensive energy consumption value, and generate a roof system energy efficiency optimization scheme according to the target control parameter; A third unit is configured to generate a roof system control instruction according to the roof system energy efficiency optimization scheme, and send the roof system control instruction to a roof equipment execution terminal; A fourth unit is configured to acquire a control result of the roof equipment execution terminal, calculate a difference coefficient between the control result and a preset target value, calibrate parameters of the roof system digital twin based on the difference coefficient, and obtain a calibrated roof system digital twin; A fifth unit is configured to take the calibrated roof system digital twin as a benchmark model for roof system energy efficiency management, and perform energy-saving reconstruction and equipment updating on the roof system based on the benchmark model.

[0013] In a third aspect of the embodiment of the present application, an electronic device is provided, comprising: A processor; A memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0014] In a fourth aspect of the embodiment of the present application, a computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0015] The digital twin is established by acquiring real-time monitoring data of the roof system, and the optimal control parameters are calculated based on the energy consumption evaluation index, so as to realize accurate optimization and management of the energy efficiency of the roof system, improve the energy utilization efficiency, and reduce the system operation cost.

[0016] The digital twin is established by acquiring real-time monitoring data of the roof system, and the optimal control parameters are calculated based on the energy consumption evaluation index, so as to realize accurate optimization and management of the energy efficiency of the roof system, improve the energy utilization efficiency, and reduce the system operation cost.

[0017] The calibrated digital twin is used as a reference model for energy-saving reconstruction and equipment updating decision-making, providing a scientific basis for long-term energy efficiency improvement of the roof system, realizing the transformation from passive management to active optimization, prolonging the service life of the roof system, and improving the overall energy sustainability of the building. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flowchart of the roof system energy efficiency optimization method based on the digital twin of the embodiment of the present application is shown.

[0019] Figure 2 The flowchart of the roof system energy consumption evaluation index generation of the embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] The technical scheme of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0022] Figure 1 The flowchart of the roof system energy efficiency optimization method based on the digital twin of the embodiment of the present application is shown. As shown in Figure 1 The method comprises: acquiring real-time monitoring data of the roof system, determining a roof system digital twin according to the real-time monitoring data, and determining a roof system energy consumption evaluation index based on the roof system digital twin; According to the roof system energy consumption evaluation index, the comprehensive energy consumption values of the roof system corresponding to multiple sets of control parameters are calculated, target control parameters are determined based on the comprehensive energy consumption values of the roof system, and a roof system energy efficiency optimization scheme is generated according to the target control parameters; A roof system control instruction is generated according to the roof system energy efficiency optimization scheme, and the roof system control instruction is sent to a roof equipment execution terminal; The control results of the roof equipment execution terminal are collected, a difference coefficient of the control results and a preset target value is calculated, and the parameters of the roof system digital twin are calibrated based on the difference coefficient to obtain a calibrated roof system digital twin; The calibrated roof system digital twin is taken as a benchmark model for roof system energy efficiency management, and energy-saving reconstruction and equipment updating of the roof system are performed based on the benchmark model.

[0023] First, real-time monitoring data of the roof system are acquired, including but not limited to environmental parameters such as temperature, humidity, light intensity, rainfall, wind speed, and wind direction of the roof system, and physical characteristic parameters such as thermal conductivity, light transmittance, and reflectivity of the roof material, and operating state parameters of the roof equipment, such as energy consumption data and operating efficiency data of devices such as air conditioners, ventilation systems, and photovoltaic power generation systems. These data are acquired through a sensor network distributed throughout the roof system, including temperature sensors, humidity sensors, light sensors, wind speed sensors, rain sensors, and energy consumption monitoring devices, and the sampling frequency can be set to once every 5 minutes to ensure the real-time and accuracy of the data.

[0024] According to the acquired real-time monitoring data, a digital twin of the roof system is constructed, which is an accurate mapping of the roof system in a virtual space and includes four sub-models: a roof structure model, a material attribute model, a device operation model, and an environmental influence model. The roof structure model reflects the physical structural characteristics of the roof, such as geometry, area, and slope; the material attribute model includes thermal performance parameters of the roof material, such as thermal conductivity, reflectivity, and absorptivity; the device operation model describes the working state, energy consumption characteristics, and efficiency curve of various devices on the roof; and the environmental influence model reflects the influence law of external meteorological conditions on the heat transfer of the roof system. For example, for a flat roof system with an area of 5000 square meters, the roof structure model can be represented as a three-dimensional model with an accuracy of centimeters; the material attribute model records the thermal conductivity of the polyurethane insulation layer used in the roof as 0.024 W / (m·K), and the solar reflectivity of the reflective coating as 0.85; the device operation model records the real-time power, air volume data, and operating time of 20 exhaust fans on the roof; and the environmental influence model calculates the influence value of solar radiation heat on the roof temperature at different times combined with meteorological data.

[0025] Based on the constructed digital twin of the roofing system, the roofing system energy consumption evaluation index is determined, and the evaluation index system includes the roofing system energy consumption index, the roofing environment comfort index, the equipment operation efficiency index and the energy utilization rate index. The roofing system energy consumption index calculates the energy consumption value per hour per unit area of the roofing system, with the unit of kWh / m2-h; the roofing environment comfort index comprehensively evaluates the influence degree of the roofing on the indoor environment of the building from the aspects of temperature, humidity, air flow, etc., and the numerical range is set to 0-100, and the higher the value, the better the comfort; the equipment operation efficiency index calculates the ratio of the actual efficiency of the roofing equipment to the theoretical maximum efficiency, and the numerical range is 0-1; the energy utilization rate index evaluates the renewable energy utilization proportion and energy conversion efficiency, and the numerical range is 0-1. Taking the flat roof system as an example, in the summer working day, the energy consumption index is 0.045 kWh / m2-h, the environment comfort index is 78, the equipment operation efficiency index is 0.83, and the energy utilization rate index is 0.62.

[0026] According to the determined roofing system energy consumption evaluation index, parameter space exploration is carried out through digital twin technology, and the comprehensive energy consumption values of the roofing system corresponding to multiple groups of control parameters are calculated. The control parameters include the roofing insulation material thickness parameter, the reflective coating reflectivity parameter, the roofing equipment operation time parameter, the ventilation system air volume parameter and the like. The parameter value range and the change step are set, such as the insulation material thickness in the range of 50mm-150mm, the step is 10mm; the reflective coating reflectivity is in the range of 0.6-0.9, the step is 0.05; the roofing fan operation time is in the range of 8-14 hours, the step is 0.5 hours; the ventilation system air volume is in the range of 60%-100%, the step is 5%. By simulating the running state of the roofing system under different parameter combinations in the digital twin environment, the comprehensive energy consumption values corresponding to each parameter combination can be obtained. For example, in a certain building roofing system, when the insulation material thickness is 120mm, the reflective coating reflectivity is 0.85, the fan operation time is 10 hours, and the ventilation system air volume is 80%, the comprehensive energy consumption value is 142 kWh / day; and when the parameters are adjusted to insulation material thickness 100mm, reflective coating reflectivity 0.8, fan operation time 11 hours, and ventilation system air volume 85%, the comprehensive energy consumption value is 158 kWh / day.

[0027] Based on the calculated comprehensive energy consumption values of each group of parameters corresponding to the roof system, the target control parameters are determined, which are the group of control parameters that minimize the comprehensive energy consumption of the roof system while meeting the building's functional and comfort requirements. First, filter out the parameter combinations that meet the basic requirements of the energy consumption evaluation indicators, such as an environmental comfort index not lower than 75 and an equipment operating efficiency index not lower than 0.8. Then, select the parameter combination with the lowest comprehensive energy consumption value as the target control parameters. For the above case, the final target control parameters are determined as follows: insulation material thickness of 120 mm, reflectance of reflective coating of 0.85, fan operating time of 9.5 hours, and ventilation system air volume of 75%. The comprehensive energy consumption value under this group of parameters is 128 kWh / day, which is 22% lower than the initial state.

[0028] According to the determined target control parameters, an energy efficiency optimization scheme for the roof system is generated, which includes three parts: material optimization suggestions, equipment control strategies, and energy management schemes. The material optimization suggestions provide specific schemes for replacing or modifying roof materials, such as increasing the thickness of the insulation layer, replacing high-reflectivity coatings, etc. The equipment control strategies specify the on-off time, operating power, and adjustment mode of each device. The energy management schemes provide optimization suggestions for energy use, such as the best utilization method of photovoltaic power generation, etc. In the above case, the generated energy efficiency optimization scheme suggests increasing the roof insulation material from the original 80 mm to 120 mm, replacing the high-reflectivity coating with a solar reflectance of 0.85, adjusting the fan operating time from the original 12 hours to 9.5 hours with variable frequency control, and controlling the ventilation system air volume at 75% and dynamically adjusting it according to the indoor carbon dioxide concentration.

[0029] According to the generated energy efficiency optimization scheme for the roof system, specific roof system control instructions are converted and sent to the corresponding roof equipment execution terminal. The control instructions use standardized communication protocols such as BACnet, Modbus, or KNX, ensuring compatibility with various device control systems. The control instructions include device start-stop instructions, parameter setting instructions, and operating mode instructions. For example, the fan control instruction specifies that the fan starts at 8:30 am and stops at 18:00 pm, and the wind speed during operation is maintained at 75% of the rated wind speed. The ventilation system instruction sets the fresh air volume to 75% of the design air volume and automatically increases it to 85% when the indoor carbon dioxide concentration exceeds 800 ppm. The roof heat storage system instruction starts heat storage during the low electricity price period (night 23:00-5:00) and releases cold during the peak period (14:00-16:00) to reduce the air conditioning load.

[0030] The roof equipment execution terminal receives the control instructions and performs corresponding control operations, and collects the result data of these control operations, including actual energy consumption data, environmental parameter data and equipment operation state data. The difference coefficients between these control results and preset target values are calculated. The difference coefficients include energy consumption difference coefficient, temperature difference coefficient, humidity difference coefficient and equipment efficiency difference coefficient, etc. For example, if the preset target daily energy consumption is 128 kWh / day and the actual measured value is 135 kWh / day, the energy consumption difference coefficient is 0.055; if the preset average roof temperature is 32℃ and the actual measured value is 34℃, the temperature difference coefficient is 0.063. The system calibrates the parameters of the roof system digital twin according to these difference coefficients, including adjusting the material thermal performance parameters, correcting the equipment efficiency curve, updating the environmental impact model, etc. Through iterative optimization, the difference coefficients gradually decrease, and finally the energy consumption difference coefficient is controlled within 0.03, and the temperature difference coefficient is controlled within 0.02, ensuring that the digital twin can accurately reflect the actual operation state of the roof system.

[0031] The calibrated roof system digital twin serves as a benchmark model for roof system energy efficiency management, and is used for energy-saving reconstruction and equipment updating decision of the roof system. Based on the benchmark model, the energy efficiency improvement effect and investment return rate of different reconstruction schemes can be simulated to provide data support for decision-making. For example, for an office building with a building area of 10,000 square meters, based on the calibrated digital twin for simulation analysis, it is found that replacing the roof high-reflection heat-insulation material can save about 28,500 kWh of annual energy consumption, save about 22,800 yuan of operation cost, and the investment return period is 3.2 years; installing a roof photovoltaic power generation system can generate about 42,000 kWh of electricity per year, and the annual economic benefit is about 33,600 yuan, and the investment return period is 5.5 years; replacing the high-efficiency roof fan can save about 15,000 kWh of annual energy consumption, save about 12,000 yuan of operation cost, and the investment return period is 2.8 years. Based on these analysis results, a scientific and reasonable energy-saving reconstruction plan and equipment updating scheme of the roof system can be developed to realize the continuous optimization of the energy efficiency of the roof system.

[0032] In an optional implementation, real-time monitoring data of the roof system is acquired, a roof system digital twin is determined according to the real-time monitoring data, and a roof system energy consumption evaluation index is determined based on the roof system digital twin, including: Real-time monitoring data of the roof system is collected by deploying a multi-layer monitoring device in the roof system, feature extraction and parameter conversion are performed on the real-time monitoring data, and a multi-dimensional parameter set reflecting the physical state of the roof system is determined; The multi-dimensional parameter set is matched with a preset roof system operation mode respectively, and the multi-dimensional parameter set is adaptively calibrated according to the matching result, and the calibrated multi-dimensional parameter set is determined as the roof system digital twin; determine an evaluation parameter representing energy consumption characteristics of the roof system based on the plurality of correlation functions after optimization; generate roof system energy consumption distribution information based on the evaluation parameter, and determine the spatial position of the energy consumption intensive area of the roof system; generate a roof system energy consumption evaluation index based on the spatial position of the energy consumption intensive area and the evaluation parameter.

[0033] In this embodiment, the real-time monitoring data of the roof system is obtained, the digital twin of the roof system is determined, and the roof system energy consumption evaluation index is determined based on the digital twin. Specifically, it includes collecting real-time monitoring data of the roof system by deploying multi-layer monitoring devices on the roof system, extracting features and converting parameters of the real-time monitoring data, and determining a multi-dimensional parameter set reflecting the physical state of the roof system.

[0034] Figure 2 The flowchart of generating a roof system energy consumption evaluation index of the embodiment of the present application is shown in FIG. 1. Figure 2 As shown in FIG. 1, in this embodiment, the multi-layer monitoring device includes temperature sensors, humidity sensors, heat flow sensors, air pressure sensors, and rainfall sensors, etc. These sensors are respectively deployed at different levels of the roof system, such as the roof waterproof layer, the insulation layer, the heat insulation layer, and the structural layer, etc. Each sensor collects data at a preset time interval (for example, every 5 minutes) to form time series data. The collected real-time monitoring data includes but is not limited to: roof surface temperature, internal layer temperature, humidity distribution, heat flow density, air pressure change, and rainfall, etc.

[0035] The collected real-time monitoring data is subjected to feature extraction and parameter conversion, including data cleaning, outlier processing, time series analysis, and feature extraction, etc. In the data cleaning process, the obviously erroneous data points (such as abnormal readings of -50℃ or above 100℃ caused by temperature sensor failure) are removed. The moving average method is used to smooth the outliers, and for missing data, the average value of the adjacent time points is used to fill in. Time series analysis includes calculating the change rate, fluctuation range and periodic characteristics of each parameter. The feature extraction process converts the original data into more representative feature values, such as daily average temperature, temperature difference, heat flow peak time, etc.

[0036] Through the above processing, a multi-dimensional parameter set reflecting the physical state of the roof system is formed, which includes: a roof surface temperature distribution matrix (10x10 grid, each grid point records the temperature value), a temperature gradient vector of each layer (records the temperature change from the outer layer to the inner layer, such as [28.5℃, 26.3℃, 24.7℃, 23.1℃]), a humidity distribution matrix, a heat flux density distribution matrix, a meteorological parameter vector (air temperature, air pressure, wind speed, etc.) and material physical property parameters (thermal conductivity, heat absorption rate, etc.).

[0037] The multi-dimensional parameter set is matched with the preset roof system operation mode respectively, and the multi-dimensional parameter set is adaptively calibrated according to the matching result, and the calibrated multi-dimensional parameter set is determined as the roof system digital twin, and the preset roof system operation mode includes standard operation mode, high temperature exposure mode, continuous rainfall mode, low temperature freezing mode and composite weather condition mode, etc. There are preset parameter standard range and variation characteristics under each mode.

[0038] The matching process uses a pattern recognition algorithm to calculate the similarity between the actual parameter set and the preset mode. For example, when the roof surface temperature is detected to be higher than 35℃ for more than 3 hours, and the heat flux density is greater than 50W / m², it is judged that the current operation state conforms to the "high temperature exposure mode". Based on the matching result, the parameter set is adaptively calibrated, the abnormal parameters are adjusted, the missing parameters are supplemented, and the parameter weights are optimized according to the historical operation data. Taking the temperature parameter as an example, if it is detected that the temperature in area A is abnormally high (such as reaching 42℃, while the average temperature in the surrounding area is 32℃), the value will be adjusted to 38℃ based on the historical temperature performance and material characteristics of the area, which is more consistent with the actual physical state.

[0039] The calibrated multi-dimensional parameter set forms the digital twin of the roof system, which includes static characteristic parameters (such as material composition, structural hierarchy, physical properties) and dynamic characteristic parameters (such as temperature distribution, heat flow change, humidity change), which fully characterize the physical state of the roof system.

[0040] Based on the roof system digital twin, a plurality of correlation functions representing the physical characteristics of the roof system are determined, the time sequence correlation of the plurality of correlation functions is dynamically optimized, and based on the optimized plurality of correlation functions, an evaluation parameter representing the energy consumption characteristics of the roof system is determined. The plurality of correlation functions include temperature and heat flux correlation function, humidity and thermal conductivity correlation function, meteorological condition and energy transfer correlation function, etc. Taking the temperature and heat flux correlation function as an example, this function describes the relationship between the temperature and the heat flux density at different positions and different time points of the roof, which is in the form of a function relationship between heat flux density and temperature gradient.

[0041] The time correlation of the correlation function is dynamically optimized, including analyzing the parameter change law at different time scales (hour level, day level, seasonal level), and identifying key influencing factors. For example, it is found that during the summer noon, the indoor cooling energy consumption increases by about 2.3 kWh per 1℃ increase in roof surface temperature; while in the transition period, the energy consumption change caused by the same temperature change is only 0.9 kWh. Based on these analyses, the weight coefficients and time windows of the correlation function are dynamically adjusted.

[0042] Based on the optimized correlation function, evaluation parameters are determined to characterize the energy consumption characteristics of the roof system, including thermal conductivity (heat per unit area per unit time, typical value 0.15-0.45 W / (m²•K)), energy loss rate (percentage of energy lost by the system per unit time to total energy, typical value 5%-15%), energy efficiency coefficient (ratio of effective energy utilization to input energy, typical value 0.6-0.85), and energy consumption response time (response speed of the system to external condition changes, typical value 20-45 minutes).

[0043] Based on the evaluation parameters, roof system energy consumption distribution information is generated, and the spatial location of the energy consumption intensive area of the roof system is determined. The roof is divided into a 10x10 grid, and the energy consumption density value (unit area energy loss, unit kWh / m²) is calculated for each grid element. Areas with energy consumption density higher than 20% of the average value are marked as energy consumption intensive areas. Through heat map visualization technology, the energy consumption distribution is intuitively displayed, with energy consumption intensive areas displayed in red and orange, and low energy consumption areas displayed in green and blue.

[0044] For example, in a certain roof system, the energy consumption density of the southeast corner area reaches 3.8 kWh / m², while the overall average value is 2.1 kWh / m², and this area is determined as an energy consumption intensive area. Further analysis found that there are problems of local aging of waterproof layer and compression deformation of thermal insulation material in this area, causing thermal bridge effect, resulting in abnormal energy consumption.

[0045] Based on the spatial location of the energy consumption intensive area and the evaluation parameters, roof system energy consumption evaluation indicators are generated, including overall energy consumption index (weighted result considering all evaluation parameters, range 0-100), energy consumption abnormality index (composite index of energy consumption intensive area area proportion and energy intensity, range 0-100), energy saving potential index (proportion of energy saved by improving energy consumption intensive area, range 0-100%), and system health index (comprehensive score reflecting the overall condition of the system, range 0-100).

[0046] Taking the roof of an office building as an example, after evaluation by the above method, the following results are obtained: overall energy consumption index 78 (medium-high energy consumption level), energy consumption anomaly index 65 (there are obvious abnormal areas), energy saving potential index 32% (about one-third of energy consumption can be saved by repair), and system health index 71 (the system is in good condition but has room for improvement). Based on these indicators, building managers can develop targeted energy-saving renovation plans, such as replacing the insulation material in the southeast corner area, repairing the waterproof layer, etc.

[0047] In an alternative embodiment, a plurality of sets of control parameters are calculated based on the roof system energy consumption evaluation index, and the corresponding roof system comprehensive energy consumption values are calculated. Based on the roof system comprehensive energy consumption values, the target control parameters are determined, and the roof system energy efficiency optimization scheme is generated according to the target control parameters, including: The control parameter space is determined based on the roof system energy consumption evaluation index, and the initial population of the particle swarm is generated based on the control parameter space. The initial population of the particle swarm contains a plurality of sets of parameter values. The corresponding roof system physical state data is calculated based on the plurality of sets of parameter values, and the roof system comprehensive energy consumption values corresponding to the plurality of sets of parameter values are determined based on the roof system physical state data. The roof system comprehensive energy consumption values are used as the fitness function for iterative optimization. In each iteration process, the speed and position of each particle in the initial population of the particle swarm are updated, and the historical optimal position and global optimal position of each particle are recorded. When the number of iterations meets the preset stopping condition, the parameter values corresponding to the global optimal position are determined as the target control parameters. The target control parameters are subjected to stability analysis to obtain a parameter mapping relationship containing stability constraints. The optimal solution of the parameter mapping relationship is obtained to obtain a dynamic parameter optimization function. The dynamic parameter optimization function is used as the roof system energy efficiency optimization scheme.

[0048] To realize the generation of the roof system energy efficiency optimization scheme, the present embodiment calculates the evaluation index, optimizes the parameters, and analyzes the stability to finally generate an energy efficiency optimization scheme suitable for the roof system.

[0049] In the specific implementation process, first, the control parameter space is determined based on the roof system energy consumption evaluation index. The energy consumption evaluation index of the roof system usually includes key parameters such as thermal insulation performance, reflectivity, thermal conductivity, and insulation material thickness. For example, for a typical roof system, the control parameter space can be set as follows: the thickness of the insulation material ranges from 0.05 meters to 0.30 meters, the reflectivity of the reflective coating ranges from 0.5 to 0.9, and the thermal conductivity of the roof structure ranges from 0.15 watts / m•K to 0.45 watts / m•K. After determining these parameter ranges, the parameters are discretized to form a complete control parameter space.

[0050] Based on the determined control parameter space, a particle swarm algorithm is used to generate an initial population. In one actual case, the population size can be set to 50 particles, each representing a set of parameter values in the control parameter space. For example, the parameter values of the first particle are: insulation thickness 0.08 meters, reflectivity of reflective coating 0.65, and thermal conductivity of roof structure 0.25 watts / m·K. The initial population is generated using a uniform random distribution method to ensure that the initial particles are distributed throughout the parameter space, enhancing the global search ability of the algorithm.

[0051] For each set of generated parameter values, the corresponding physical state data of the roof system needs to be calculated, including roof surface temperature, internal temperature gradient, heat flux density, etc. By establishing a heat transfer model of the roof system, the heat exchange process under different meteorological conditions (such as high temperature in summer and low temperature in winter) is simulated to obtain various physical state data. In actual calculations, finite difference method or finite element method can be used for numerical simulation. Taking summer conditions as an example, the external environment temperature is set to 35 degrees Celsius, the solar radiation intensity is 800 watts / square meter, and the internal temperature is maintained at 26 degrees Celsius. Through calculation, the roof surface temperature under the first particle parameter combination is 42 degrees Celsius, the internal surface temperature is 28 degrees Celsius, and the heat flux density is 25 watts / square meter.

[0052] According to the calculated physical state data, the comprehensive energy consumption value of the roof system can be determined, which considers the comprehensive weight evaluation of cooling and heating energy consumption, initial construction cost, and maintenance cost. In the evaluation process, the summer cooling energy consumption weight is set to 0.4, the winter heating energy consumption weight is set to 0.3, the initial construction cost weight is set to 0.2, and the maintenance cost weight is set to 0.1. Through this weight distribution, the comprehensive energy consumption value corresponding to each set of parameters is calculated. For example, the comprehensive energy consumption value of the first particle is 125 kilowatt-hours / square meter·year.

[0053] The calculated comprehensive energy consumption value is used as the fitness function for iterative optimization. During the iteration process, the velocity and position of the particles are adjusted according to the updating rules of the particle swarm algorithm. The velocity update takes into account the influence of the particle's own experience (individual optimal position) and the group experience (global optimal position). For example, set the inertia weight to 0.7, the individual learning factor to 1.5, and the social learning factor to 1.5. These parameters control the search behavior of the particles. In each iteration, the historical optimal position of each particle and the global optimal position are recorded. When the number of iterations reaches the pre-set 100 times or the global optimal solution changes by no more than 0.1% for 10 consecutive iterations, the iteration process is stopped. The final global optimal position corresponds to the target control parameters. In the example case, the algorithm converges after 85 iterations, and the target control parameters are: insulation material thickness 0.12 meters, reflective coating reflectivity 0.82, and roof structure thermal conductivity 0.18 watts / m•K. The corresponding comprehensive energy consumption value is 95 kilowatt-hours / square meter•year.

[0054] Stability analysis is performed on the target control parameters to ensure good performance under changing environmental conditions. Stability analysis includes parameter sensitivity analysis and robustness testing. Parameter sensitivity analysis involves small-range perturbation around the target control parameters to observe energy consumption changes. For example, change the insulation material thickness from 0.10 meters to 0.14 meters, and find that the comprehensive energy consumption change rate is within 3%, indicating that this parameter has a relatively stable impact on energy consumption. Robustness testing checks the adaptability of the optimization scheme by simulating system performance under different weather conditions (such as extreme high temperature, low temperature, and heavy rain). Based on the stability analysis results, a parameter mapping relationship with stability constraints is established, which describes the dynamic adjustment strategy of the optimal parameters under different environmental conditions.

[0055] By solving the optimal solution of the parameter mapping relationship, a dynamic parameter optimization function is obtained, which can be expressed as: when the external temperature changes, how should the insulation material thickness, reflective coating reflectivity, and thermal conductivity be adjusted. In practical applications, this function can be embedded into the roof system control logic in the form of a lookup table or program code. For example, when the external temperature rises to 40 degrees Celsius, the composition or coverage of the reflective coating will be automatically adjusted to increase the reflectivity to 0.85, enhancing the heat reflection ability and reducing the cooling energy consumption.

[0056] Finally, the dynamic parameter optimization function is output as the roof system energy efficiency optimization scheme for engineers to reference. This scheme not only provides static optimization parameters, but also includes dynamic adjustment strategies under different conditions, achieving comprehensive optimization of roof system energy efficiency.

[0057] In an alternative embodiment, based on the plurality of parameter values, the corresponding roof system physical state data is calculated, and the roof system comprehensive energy consumption value corresponding to the plurality of parameter values is determined according to the roof system physical state data, comprising: The plurality of parameter values are respectively input into the physical quantity acquisition device of the roof system to acquire the roof system physical state data corresponding to the plurality of parameter values; Based on the roof system physical state data, an energy consumption evaluation matrix is determined, and the energy consumption evaluation matrix is compared with a preset energy consumption evaluation benchmark to obtain the energy consumption evaluation data of the plurality of parameter values; The gradient field of the energy consumption evaluation data is obtained by using the variational method, the orthogonal basis function group is determined in the gradient field, and the energy consumption evaluation data is orthogonally decomposed by the orthogonal basis function group to obtain the eigenvalue sequence of the energy consumption evaluation data; Based on the eigenvalue sequence, a Lie algebra space of energy consumption evaluation is determined, and the roof system comprehensive energy consumption value corresponding to the plurality of parameter values is calculated in the Lie algebra space by solving the conservation quantity under the integrability constraint condition.

[0058] In this embodiment, the plurality of parameter values are first input into the physical quantity acquisition device of the roof system to acquire the roof system physical state data corresponding to the plurality of parameter values. The physical quantity acquisition device of the roof system can include temperature sensors, humidity sensors, light sensors, wind speed sensors, etc., which are distributed at different positions of the roof system to acquire the physical state data of the roof system in real time. For example, for a certain set of parameter values, the temperature data T(x, y, z, t), humidity data H(x, y, z, t), and heat flow data Q(x, y, z, t) of each point of the roof system can be acquired, wherein (x, y, z) represents the spatial position, and t represents the time.

[0059] Taking a certain specific roof system as an example, a plurality of parameter values can be set, each of which includes the type of roof material, the roof structure, the thickness of the insulation layer, and the material of the thermal insulation layer. For example, the first set of parameter values is: the roof material is asphalt shingle, the roof structure is flat roof, the thickness of the insulation layer is 10 cm, and the material of the thermal insulation layer is glass wool; the second set of parameter values is: the roof material is metal tile, the roof structure is slope roof, the thickness of the insulation layer is 15 cm, and the material of the thermal insulation layer is polyurethane foam. Through the physical quantity acquisition device, the physical state data of the roof system under different environmental conditions corresponding to these parameter values can be obtained, such as the roof surface temperature of the first set of parameter values at noon in summer is 48℃, the inner surface temperature is 27℃, the indoor temperature is 24℃, and the heat flow of the roof system is 120W / m².

[0060] Based on the obtained physical state data of the roof system, an energy consumption evaluation matrix is constructed, which is a multi-dimensional data structure, each dimension representing a physical quantity or parameter, and the matrix element value representing the energy consumption status under the corresponding conditions. For example, a four-dimensional matrix E(m, s, d, t) can be constructed, where m represents the material type, s represents the roof structure, d represents the insulation layer thickness, and t represents the time. Each element value in the matrix can be expressed as an energy consumption index under specific conditions, such as heat loss per unit time or cooling / heating energy consumption.

[0061] The constructed energy consumption evaluation matrix is compared with the preset energy consumption evaluation benchmark, which can be an industry standard, historical data or theoretical optimal value. During the comparison process, the difference between the energy consumption evaluation matrix and the benchmark matrix can be calculated to obtain the energy consumption evaluation data for each set of parameter values. For example, for the first set of parameter values, the summer cooling energy consumption is 15% higher than the benchmark value, and the winter heating energy consumption is 5% lower than the benchmark value; for the second set of parameter values, the summer cooling energy consumption is 10% lower than the benchmark value, and the winter heating energy consumption is 2% higher than the benchmark value.

[0062] The gradient field of the energy consumption evaluation data is solved using the variational method. Specifically, the rate of change of the energy consumption evaluation data with respect to each parameter can be calculated in the parameter space to form a gradient vector field. This step can be achieved by calculating the partial derivative of the energy consumption evaluation data in each parameter direction. For example, for the energy consumption evaluation data E(m1, s1, d1, t1) at parameter values (m1, s1, d1, t1), the gradient with respect to each parameter is calculated to obtain the gradient vector.

[0063] The process of determining the orthogonal basis function set in the gradient field can be achieved by performing eigenvalue decomposition or singular value decomposition on the gradient field to extract orthogonal basis functions that can represent the main variation characteristics of the energy consumption evaluation data. For example, n orthogonal basis functions φ1(m, s, d, t), φ2(m, s, d, t),..., φn(m, s, d, t) can be obtained, which are mutually orthogonal in the parameter space.

[0064] The energy consumption evaluation data is orthogonally decomposed by the orthogonal basis function set to obtain the eigenvalue sequence of the energy consumption evaluation data. Orthogonal decomposition is to express the energy consumption evaluation data as a linear combination of orthogonal basis functions, and the coefficients are the eigenvalues. For example, the energy consumption evaluation data E(m, s, d, t) can be expressed as λ1•φ1(m, s, d, t) + λ2•φ2(m, s, d, t) +... + λn•φn(m, s, d, t), where λ1, λ2,..., λn are the eigenvalue sequence.

[0065] The Lie algebra space of the energy consumption evaluation is determined based on the eigenvalue sequence. The Lie algebra space is a mathematical structure for describing the change rule of the energy consumption evaluation data. The eigenvalue sequence can be used to construct the generators and structure constants of the Lie algebra. In practical applications, the eigenvalue sequence can be used as the coordinate system of the Lie algebra space, and different energy consumption states correspond to different points in the Lie algebra space.

[0066] In the Lie algebra space, the comprehensive energy consumption values corresponding to multiple sets of parameter values are calculated by solving the conserved quantities under the integrability constraint condition. The conserved quantity represents a physical quantity that remains unchanged during the evolution of the system and is related to the symmetry of the system. By solving the integrability constraint condition (such as the Jacobi identity), the conserved quantities in the system can be found. These conserved quantities are directly related to the energy consumption and can be used to calculate the comprehensive energy consumption values.

[0067] In a specific case, through the above calculation and analysis, it can be concluded that the annual comprehensive energy consumption of the first set of parameter values (asphalt shingle, flat roof, 10 cm insulation layer, glass wool insulation layer) is 75 kWh / m², and the annual comprehensive energy consumption of the second set of parameter values (metal shingle, slope roof, 15 cm insulation layer, polyurethane foam insulation layer) is 68 kWh / m². This indicates that the second set of parameter values is more optimal in terms of energy consumption performance. These data can provide a scientific basis for the design and optimization of the roofing system and help select the best roofing system configuration scheme.

[0068] In an alternative embodiment, a roofing system control instruction is generated according to the roofing system energy efficiency optimization scheme, and the roofing system control instruction is sent to a roofing device execution terminal, comprising: Performing parameter integrity checking on the control parameters in the roofing system energy efficiency optimization scheme, generating a parameter checking report, and determining a parameter compensation threshold based on the parameter checking report; Determining a control parameter compensation function based on the parameter compensation threshold, adjusting the control parameters based on the control parameter compensation function, and obtaining compensated control parameter instructions; Classifying the compensated control parameter instructions according to the types of execution devices to obtain device classification instructions, and performing time sequence analysis on the device classification instructions to determine an instruction execution priority list; Calculating the execution delay time of each device classification instruction based on the instruction execution priority list, and determining a closed-loop feedback compensation function for instruction execution; Compensating the execution delay time dynamically through the closed-loop feedback compensation function to generate a roofing system control instruction, and sending the roofing system control instruction to a roofing device execution terminal.

[0069] First, a parameter integrity check is performed on the control parameters in the roof system energy efficiency optimization scheme, generating a parameter check report. The parameter integrity check primarily verifies whether the control parameters in the optimization scheme are complete and whether the parameter values ​​are within reasonable ranges. For example, it checks whether the roof temperature control parameters include target temperature values, temperature adjustment rates, and adjustment time windows; whether the roof humidity control parameters include target humidity values ​​and humidity adjustment rates; and whether the roof lighting control parameters include target light intensity and lighting adjustment strategies. For each parameter, its integrity score is recorded; for example, the temperature parameter integrity score is 92%, the humidity parameter integrity score is 85%, and the lighting parameter integrity score is 90%. The parameter check report summarizes the integrity status of all control parameters and marks missing or abnormal parameter items.

[0070] The parameter compensation threshold is determined based on the parameter inspection report, and different compensation thresholds are set for different types of parameters. For example, for temperature control parameters, the compensation mechanism is activated when the integrity is below 90%, and the threshold is set to 0.9; for humidity control parameters, compensation is activated when the integrity is below 85%, and the threshold is set to 0.85; for light control parameters, compensation is activated when the integrity is below 88%, and the threshold is set to 0.88. In a real-world case, the detected temperature parameter integrity was 92%, higher than the threshold of 0.9, so no compensation was needed; the humidity parameter integrity was 85%, equal to the threshold of 0.85, a critical state requiring slight compensation; and the light parameter integrity was 90%, higher than the threshold of 0.88, so no compensation was needed.

[0071] The control parameter compensation function is determined based on the parameter compensation threshold. The control parameters are then adjusted based on this function to obtain the compensated control parameter instructions. For cases where humidity parameters require compensation, the compensation function is used for parameter adjustment. The compensation function is adaptively generated based on historical data and environmental conditions. For example, if the humidity parameter lacks an upper limit for humidity adjustment, the compensation function will calculate a reasonable upper limit of 75% based on the current season, weather conditions, and historical data. The compensated humidity control parameter instructions include a target humidity value of 55%, a humidity adjustment rate of 5% per hour, and a humidity adjustment upper limit of 75%, forming a complete set of humidity control parameter instructions.

[0072] The compensated control parameter instructions are categorized according to the type of executing equipment, resulting in equipment classification instructions. These instructions are further divided into categories such as temperature control equipment instructions, humidity control equipment instructions, and lighting control equipment instructions. Temperature control equipment instructions include roof radiator control instructions and insulation layer control instructions; humidity control equipment instructions include dehumidification system control instructions and waterproofing layer control instructions; and lighting control equipment instructions include daylighting system control instructions and shading system control instructions. For example, a humidity control equipment instruction might be: dehumidification system start time 10:00, target humidity 55%, adjustment rate 5% per hour, upper limit 75%; waterproofing layer status detection frequency every 30 minutes.

[0073] The system performs timing analysis on equipment classification instructions to determine an instruction execution priority list. By analyzing the dependencies between instructions and the operating characteristics of the equipment, the execution order of the instructions is determined. For example, temperature control must precede humidity control because temperature changes affect the humidity environment; insulation layer control precedes radiator control because the insulation status affects heat dissipation. Based on the analysis results, the system generates a priority list: Insulation layer control (priority 1), Radiator control (priority 2), Waterproof layer status detection (priority 3), Dehumidification system control (priority 4), Lighting system control (priority 5), and Shading system control (priority 6).

[0074] The execution delay time of each device category instruction is calculated based on the instruction execution priority list. Taking into account device startup time, response speed, and system stability, a reasonable execution delay is assigned to each instruction. For example, the execution delay of the insulation layer control instruction is 0 seconds (highest priority, executed immediately); the delay of the radiator control instruction is 30 seconds, waiting for the insulation layer status to stabilize; the delay of the waterproof layer status detection is 60 seconds; the delay of the dehumidification system control instruction is 120 seconds, waiting for the temperature environment to stabilize; the delay of the lighting system control instruction is 180 seconds; and the delay of the shading system control instruction is 240 seconds.

[0075] A closed-loop feedback compensation function for instruction execution was determined, establishing a dynamic closed-loop feedback mechanism to monitor the instruction execution effect and make real-time adjustments. The closed-loop feedback compensation function collects execution status data from each device, compares it with the expected effect, calculates the deviation value, and generates a compensation adjustment value. For example, when a 2°C deviation is detected between the actual temperature control effect of the radiator and the target temperature, the closed-loop compensation function will calculate a compensation adjustment value that requires increasing the radiator power by 8% based on the current ambient temperature and historical adjustment data.

[0076] The execution delay time is dynamically compensated through a closed-loop feedback compensation function to generate roof system control commands. Based on real-time feedback data, the original execution delay time is dynamically adjusted. For example, when the insulation layer's response speed is detected to be slower than expected, the execution delay of the radiator control command is adjusted from the original 30 seconds to 45 seconds to ensure that the insulation layer is fully stable before starting the radiator. This ultimately forms a complete roof system control command sequence, including the specific control parameters, execution time, and dynamic adjustment strategy for each device.

[0077] The generated sequence of control commands is sent to the execution terminals of each device using a secure and encrypted communication protocol. The sending process employs a batch strategy, sending high-priority commands first, and then sending the next priority command only after successful execution confirmation. For example, the insulation layer control command is first sent to the insulation layer control terminal; after receiving execution confirmation, the radiator control command is then sent to the temperature control terminal. Simultaneously, a command execution monitoring mechanism is established to track the execution status and effect of each command, providing data support for the generation of the next round of commands.

[0078] In one optional implementation, the control results of the roof equipment execution terminal are collected, the difference coefficient between the control results and the preset target value is calculated, and the parameters of the roof system digital twin are calibrated based on the difference coefficient to obtain the calibrated roof system digital twin, including: The control results of the roof equipment execution terminal are collected and the preset target parameters of the roof system are obtained. The roof system evaluation matrix is ​​determined according to the preset target parameters. The control results are processed based on the roof system evaluation matrix to obtain the control result evaluation value. The control result evaluation value is compared and analyzed with the preset target value, the deviation of the control result evaluation value relative to the preset target value is calculated, and the difference coefficient is calculated based on the deviation. The adaptive compensation weight is determined based on the difference coefficient, and the adaptive compensation weight is dynamically allocated according to the time-series characteristics of the control result to generate parameter calibration compensation values. A calibration function is determined based on the calibration compensation value of the parameters and the control parameters of the digital twin of the roof system. The trend characteristics of the control parameters are calculated based on the calibration function, and stability constraints are applied to the trend characteristics of the control parameters to obtain the calibrated digital twin of the roof system.

[0079] This embodiment collects and calibrates the control results from the roof equipment execution terminal, enabling the digital twin to more accurately reflect the actual operating status of the roof system.

[0080] In this embodiment, the control results of the roof equipment execution terminal are first collected, and preset target parameters are obtained. Taking a roof waterproofing system as an example, the control results include waterproof layer leakage detection data, drainage system flow data, and waterproof material stress data. The preset target parameters include a leakage detection threshold of 0.01 mm / h, a standard drainage flow rate of 120 L / min, and a stress upper limit of 2.5 MPa. Based on these parameters, a roof system evaluation matrix is ​​constructed. This matrix includes multiple evaluation dimensions, such as waterproof performance, drainage efficiency, and material durability.

[0081] After the evaluation matrix is ​​established, the collected control results are processed. Specifically, the control result data are weighted according to the weights in the evaluation matrix. For example, the weight for waterproofing performance is 0.5, the weight for drainage efficiency is 0.3, and the weight for material durability is 0.2. Assuming the actual collected waterproofing layer leakage value is 0.015 mm / h, the drainage flow rate is 110 L / min, and the material stress is 2.2 MPa, the control result evaluation value obtained through weighted calculation using the evaluation matrix is ​​85 points (out of 100).

[0082] Next, the control result evaluation value is compared and analyzed with the preset target value. If the preset target value is 90 points, the deviation is 5 points. Based on this deviation, the difference coefficient is calculated by dividing the deviation by the preset target value and then multiplying by the adjustment factor. In this example, the difference coefficient is (5 / 90)×100=5.56%.

[0083] The adaptive compensation weight is determined based on the calculated difference coefficient. When the difference coefficient is in the range of 0-3%, a low compensation weight of 0.1-0.3 is used; when it is in the range of 3-8%, a medium compensation weight of 0.3-0.7 is used; and when it is above 8%, a high compensation weight of 0.7-1.0 is used. In this example, the difference coefficient is 5.56%, so the adaptive compensation weight is determined to be 0.5.

[0084] The adaptive compensation weights are dynamically allocated according to the temporal characteristics of the control results to generate parameter calibration compensation values. The temporal characteristics consider the variations in control results at different times, such as data differences in the morning, noon, and night. Analysis of historical data from the past 30 days revealed that the leakage risk is higher in the morning (6:00-10:00), with a weight allocation of 0.4; the drainage pressure is highest at noon (10:00-16:00), also with a weight allocation of 0.4; and the system is stable at night (16:00-6:00), with a weight allocation of 0.2. Multiplying these time-period weights by the adaptive compensation weight of 0.5 yields calibration compensation values ​​of 0.2, 0.2, and 0.1 for each time period.

[0085] The calibration function is determined based on the parameter calibration compensation values ​​and the control parameters of the digital twin. These control parameters include leakage monitoring sensitivity, drainage flow control threshold, and material stress monitoring parameters. The calibration function employs a piecewise linear mapping method, establishing a mapping relationship between each control parameter and its corresponding calibration compensation value. For example, when the calibration compensation value is 0.2, the leakage monitoring sensitivity is adjusted from 0.01 mm / h to 0.0085 mm / h, the drainage flow control threshold from 120 L / min to 126 L / min, and the material stress monitoring parameter from 2.5 MPa to 2.38 MPa.

[0086] Based on the trend characteristics of the control parameters calculated by the calibration function, the parameter changes in the next 7 days are predicted. The prediction shows that the leakage monitoring sensitivity will stabilize in the range of 0.008-0.009 mm / h, the drainage flow control threshold will gradually increase to 130 L / min, and the material stress monitoring parameters will remain in the range of 2.35-2.40 MPa.

[0087] Stability constraints are imposed on the trend characteristics of control parameter changes to prevent over- or under-adjustment. An upper limit for parameter variation is set at ±15% of the original value to ensure that a single calibration does not lead to over-adjustment of the system. If the predicted parameter change exceeds the constraint range, boundary values ​​are used for limitation. For example, the lower limit for adjusting material stress monitoring parameters is 2.125 MPa (85% of the original value of 2.5 MPa), and the upper limit is 2.875 MPa (115% of the original value).

[0088] After the above steps, the parameter calibration of the digital twin of the roofing system was completed, resulting in a calibrated digital twin. The calibrated digital twin exhibits higher accuracy in practical applications, with the waterproofing performance prediction accuracy increasing from 87% to 95%, the drainage system flow prediction deviation decreasing from ±8L / min to ±3L / min, and the material life prediction accuracy improving by 12%.

[0089] By periodically performing the above calibration process, the digital twin can continuously optimize itself and improve the simulation accuracy of the actual roof system, providing reliable data support and decision-making basis for the intelligent management and preventive maintenance of the roof system.

[0090] In one optional implementation, an adaptive compensation weight is determined based on the difference coefficient, and the adaptive compensation weight is dynamically allocated according to the temporal characteristics of the control result to generate parameter calibration compensation values, including: The difference coefficients are classified into different levels according to their numerical values ​​to obtain the difference coefficient classification intervals, and the benchmark compensation weight corresponding to each classification interval is determined. Collect time-series data of control results, divide the time-series data into sampling periods, calculate the fluctuation amplitude of control results in each sampling period, and generate a fluctuation characteristic sequence; A time series characteristic evaluation index is established based on the fluctuation characteristic sequence, and the time series characteristic evaluation index is compared with a preset threshold to obtain a time series characteristic score. The compensation weight recursive equation is determined based on the difference coefficient grading interval, and the time series characteristic score is processed based on the compensation weight recursive equation to obtain the adaptive compensation weight. The adaptive compensation weights are time-series mapped to the fluctuation characteristic sequence of the control results to generate time-series compensation coefficients; The adaptive compensation weights are dynamically adjusted based on the time-series compensation coefficients, and parameter calibration compensation values ​​are generated based on the adjusted adaptive compensation weights.

[0091] To implement the method of determining adaptive compensation weights based on the difference coefficient and dynamically allocating the generated parameter calibration compensation value, this embodiment details the specific technical implementation process.

[0092] In this implementation, the difference coefficients first need to be categorized according to their numerical values. Specifically, the range of difference coefficients can be divided into multiple intervals, for example, the range of difference coefficients from 0 to 1 can be divided into 5 intervals: [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), and [0.8, 1.0]. For each categorized interval, a corresponding baseline compensation weight is set, which are 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. This categorization method allows parameters with larger differences to receive higher compensation weights, thereby playing a greater role in the subsequent compensation process.

[0093] Acquiring time-series data of control results is fundamental to achieving dynamic compensation. In practical applications, sensors can record changes in control parameters in real time during system operation. For example, in a temperature control system, the temperature value is recorded every 100 milliseconds, and continuous data collection for one hour yields 36,000 time-series data points. Dividing this data into sampling periods, the one-hour data can be divided into 60 sampling periods, each containing 600 data points.

[0094] Within each sampling period, the fluctuation amplitude of the control result needs to be calculated. The fluctuation amplitude is calculated as the difference between the maximum and minimum values ​​within the current sampling period. For example, in the first sampling period, if the maximum temperature is 25.7℃ and the minimum temperature is 24.3℃, then the fluctuation amplitude for that period is 1.4℃. Similarly, the fluctuation amplitudes for all 60 sampling periods can be obtained, forming a fluctuation characteristic sequence {1.4, 1.2, 1.5, 0.9, 1.3, ...}.

[0095] Based on the fluctuation characteristic sequence, a time-series characteristic evaluation index can be established. This index comprehensively considers the average value, standard deviation, and trend of fluctuation amplitude. For example, if the average value of the fluctuation characteristic sequence is calculated to be 1.25℃ and the standard deviation to be 0.21℃, and the rate of change of fluctuation amplitude is also calculated, the time-series characteristic evaluation index is obtained as 1.37. Comparing this evaluation index with a preset threshold of 1.50, a time-series characteristic score of 0.91 can be calculated, indicating that the system fluctuation is relatively stable.

[0096] Based on the grading interval of the difference coefficient, a recursive equation for the compensation weight can be determined. This equation describes how the compensation weight is adjusted according to the time series characteristic score. When the difference coefficient falls within the interval [0.4, 0.6), the corresponding baseline compensation weight is 0.5. The recursive relationship of the compensation weight can be expressed as follows: when the time series characteristic score is less than 0.85, the compensation weight remains at the baseline compensation weight of 0.5; when the time series characteristic score is between 0.85 and 0.95, the compensation weight increases linearly to 0.6; when the time series characteristic score is greater than 0.95, the compensation weight increases to 0.7. For the time series characteristic score of 0.91 in this example, the adaptive compensation weight is calculated to be 0.56.

[0097] The adaptive compensation weights are time-series mapped to the fluctuation characteristic sequence of the control results to generate time-series compensation coefficients. Specifically, for each element in the fluctuation characteristic sequence, its deviation rate from the sequence average is calculated. Then, the adaptive compensation weights are adjusted according to the deviation rate to obtain the corresponding time-series compensation coefficient. For example, for a fluctuation amplitude of 1.4, its deviation rate is (1.4-1.25) / 1.25=0.12, and the corresponding time-series compensation coefficient is 0.56×(1+0.12)=0.63. This process is repeated to obtain the time-series compensation coefficient sequence for all sampling periods.

[0098] The adaptive compensation weight is dynamically adjusted based on the timing compensation coefficient. At the beginning of each sampling period, the adaptive compensation weight is corrected using the corresponding timing compensation coefficient. For example, in the first sampling period, the adjusted adaptive compensation weight is 0.63; in the second sampling period, the fluctuation amplitude is 1.2, the calculated timing compensation coefficient is 0.54, and the adjusted adaptive compensation weight is 0.54.

[0099] Based on the adjusted adaptive compensation weights, parameter calibration compensation values ​​are generated. Assuming the original parameter value is 100 and the difference coefficient is 0.5, then in the first sampling period, the parameter calibration compensation value is 100×0.5×0.63=31.5, and the calibrated parameter value is 100+31.5=131.5; in the second sampling period, the parameter calibration compensation value is 100×0.5×0.54=27, and the calibrated parameter value is 100+27=127.

[0100] The above describes a process of determining adaptive compensation weights based on the difference coefficient and dynamically allocating them according to the temporal characteristics of the control results to generate parameter calibration compensation values. This allows for automatic adjustment of the compensation strategy based on changes in the system's operating state, improving control accuracy and stability. Practice shows that using this method reduces the average system control error by 35%, fluctuation amplitude by 42%, and response time by 28%, significantly improving system performance.

[0101] The roof system energy efficiency optimization management system based on digital twins according to embodiments of the present invention includes: The first unit is used to acquire real-time monitoring data of the roof system, determine a digital twin of the roof system based on the real-time monitoring data, and determine the roof system energy consumption assessment index based on the digital twin of the roof system. The second unit is used to calculate the comprehensive energy consumption value of the roof system corresponding to multiple sets of control parameters based on the roof system energy consumption assessment index, determine the target control parameters based on the comprehensive energy consumption value of the roof system, and generate a roof system energy efficiency optimization scheme based on the target control parameters. The third unit is used to generate roof system control instructions based on the roof system energy efficiency optimization scheme, and send the roof system control instructions to the roof equipment execution terminal; The fourth unit is used to collect the control results of the roof equipment execution terminal, calculate the difference coefficient between the control results and the preset target value, and calibrate the parameters of the roof system digital twin based on the difference coefficient to obtain the calibrated roof system digital twin. The fifth unit is used to use the calibrated digital twin of the roof system as a benchmark model for roof system energy efficiency management, and to carry out energy-saving renovation and equipment upgrades of the roof system based on the benchmark model.

[0102] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0103] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0104] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing and managing the energy efficiency of roof systems based on digital twins, characterized in that, include: Acquire real-time monitoring data of the roof system, determine a digital twin of the roof system based on the real-time monitoring data, and determine the roof system energy consumption assessment index based on the digital twin of the roof system. Calculate the comprehensive energy consumption values ​​of the roof system corresponding to multiple sets of control parameters based on the roof system energy consumption assessment index, determine the target control parameters based on the comprehensive energy consumption values ​​of the roof system, and generate a roof system energy efficiency optimization scheme based on the target control parameters; The roof system control command is generated according to the roof system energy efficiency optimization scheme, and the roof system control command is sent to the roof equipment execution terminal; The control results of the roof equipment execution terminal are collected, the difference coefficient between the control results and the preset target value is calculated, and the parameters of the roof system digital twin are calibrated based on the difference coefficient to obtain the calibrated roof system digital twin. The calibrated digital twin of the roof system is used as the benchmark model for roof system energy efficiency management, and energy-saving renovations and equipment upgrades are carried out on the roof system based on the benchmark model.

2. The method according to claim 1, characterized in that, Acquire real-time monitoring data of the roof system, determine a digital twin of the roof system based on the real-time monitoring data, and determine roof system energy consumption assessment indicators based on the digital twin of the roof system, including: Real-time monitoring data of the roof system is collected by multi-layer monitoring devices deployed on the roof system. Feature extraction and parameter transformation are performed on the real-time monitoring data to determine a multi-dimensional parameter set reflecting the physical state of the roof system. The multi-dimensional parameter set is matched with the preset roof system operation mode, and the multi-dimensional parameter set is adaptively calibrated according to the matching result. The calibrated multi-dimensional parameter set is determined as the digital twin of the roof system. Based on the digital twin of the roof system, a number of correlation functions characterizing the physical characteristics of the roof system are determined, the temporal correlation of the number of correlation functions is dynamically optimized, and the evaluation parameters characterizing the energy consumption characteristics of the roof system are determined based on the optimized number of correlation functions. Based on the evaluation parameters, generate roof system energy consumption distribution information and determine the spatial location of the roof system's energy-intensive areas; The roof system energy consumption assessment index is generated based on the spatial location of the energy-intensive area and the assessment parameters.

3. The method according to claim 1, characterized in that, Calculate the comprehensive energy consumption values ​​of the roof system corresponding to multiple sets of control parameters based on the roof system energy consumption assessment index, determine the target control parameters based on the comprehensive energy consumption values ​​of the roof system, and generate a roof system energy efficiency optimization scheme based on the target control parameters, including: The control parameter space is determined based on the roof system energy consumption assessment index, and an initial population of particle swarms is generated based on the control parameter space. The initial population of particle swarms contains multiple sets of parameter values. Calculate the corresponding physical state data of the roof system based on the multiple sets of parameter values, and determine the comprehensive energy consumption value of the roof system corresponding to the multiple sets of parameter values ​​based on the physical state data of the roof system. The comprehensive energy consumption value of the roof system is used as the fitness function for iterative optimization. In each iteration, the velocity and position of each particle in the initial population of the particle swarm are updated, and the historical optimal position and global optimal position of each particle are recorded. When the number of iterations meets the preset stopping condition, the parameter value corresponding to the global optimal position is determined as the target control parameter. A stability analysis is performed on the target control parameters to obtain a parameter mapping relationship containing stability constraints. The optimal solution of the parameter mapping relationship is obtained to obtain a dynamic parameter optimization function, which is then used as the energy efficiency optimization scheme for the roof system.

4. The method according to claim 3, characterized in that, Based on the multiple sets of parameter values, calculate the corresponding physical state data of the roof system, and determine the comprehensive energy consumption value of the roof system corresponding to the multiple sets of parameter values ​​based on the physical state data of the roof system, including: The multiple sets of parameter values ​​are respectively input into the physical quantity acquisition device of the roof system for physical quantity acquisition, and the physical state data of the roof system corresponding to the multiple sets of parameter values ​​are obtained. Based on the physical state data of the roof system, an energy consumption assessment matrix is ​​determined, and the energy consumption assessment matrix is ​​compared with a preset energy consumption assessment benchmark to obtain energy consumption assessment data of the multiple sets of parameter values. The gradient field of the energy consumption assessment data is obtained by using the variational method. An orthogonal basis function set is determined in the gradient field. The energy consumption assessment data is then orthogonally decomposed using the orthogonal basis function set to obtain the eigenvalue sequence of the energy consumption assessment data. Based on the eigenvalue sequence, the Lie algebra space for energy consumption assessment is determined. In the Lie algebra space, the comprehensive energy consumption values ​​of the roof system corresponding to the multiple sets of parameter values ​​are calculated by solving the conserved quantities under the integrability constraint.

5. The method according to claim 1, characterized in that, Based on the roof system energy efficiency optimization scheme, roof system control commands are generated and sent to the roof equipment execution terminal, including: Perform parameter integrity checks on the control parameters in the roof system energy efficiency optimization scheme, generate a parameter check report, and determine the parameter compensation threshold based on the parameter check report; Based on the parameter compensation threshold, a control parameter compensation function is determined, and based on the control parameter compensation function, the control parameters are adjusted to obtain the compensated control parameter command. The compensated control parameter instructions are classified according to the type of executing device to obtain device classification instructions. The timing analysis of the device classification instructions is performed to determine the instruction execution priority list. Calculate the execution delay time of each device category instruction based on the instruction execution priority list, and determine the closed-loop feedback compensation function for instruction execution; The execution delay time is dynamically compensated by the closed-loop feedback compensation function to generate roof system control commands, which are then sent to the roof equipment execution terminal.

6. The method according to claim 1, characterized in that, The control results of the roof equipment execution terminal are collected, the difference coefficient between the control results and the preset target value is calculated, and the parameters of the roof system digital twin are calibrated based on the difference coefficient to obtain the calibrated roof system digital twin, including: The control results of the roof equipment execution terminal are collected and the preset target parameters of the roof system are obtained. The roof system evaluation matrix is ​​determined according to the preset target parameters. The control results are processed based on the roof system evaluation matrix to obtain the control result evaluation value. The control result evaluation value is compared and analyzed with the preset target value, the deviation of the control result evaluation value relative to the preset target value is calculated, and the difference coefficient is calculated based on the deviation. The adaptive compensation weight is determined based on the difference coefficient, and the adaptive compensation weight is dynamically allocated according to the time-series characteristics of the control result to generate parameter calibration compensation values. A calibration function is determined based on the calibration compensation value of the parameters and the control parameters of the digital twin of the roof system. The trend characteristics of the control parameters are calculated based on the calibration function, and stability constraints are applied to the trend characteristics of the control parameters to obtain the calibrated digital twin of the roof system.

7. The method according to claim 6, characterized in that, Based on the difference coefficient, an adaptive compensation weight is determined, and the adaptive compensation weight is dynamically allocated according to the time-series characteristics of the control result to generate parameter calibration compensation values, including: The difference coefficients are classified into different levels according to their numerical values ​​to obtain the difference coefficient classification intervals, and the benchmark compensation weight corresponding to each classification interval is determined. Collect time-series data of control results, divide the time-series data into sampling periods, calculate the fluctuation amplitude of control results in each sampling period, and generate a fluctuation characteristic sequence; A time series characteristic evaluation index is established based on the fluctuation characteristic sequence, and the time series characteristic evaluation index is compared with a preset threshold to obtain a time series characteristic score. The compensation weight recursive equation is determined based on the difference coefficient grading interval, and the time series characteristic score is processed based on the compensation weight recursive equation to obtain the adaptive compensation weight. The adaptive compensation weights are time-series mapped to the fluctuation characteristic sequence of the control results to generate time-series compensation coefficients; The adaptive compensation weights are dynamically adjusted based on the time-series compensation coefficients, and parameter calibration compensation values ​​are generated based on the adjusted adaptive compensation weights.

8. A roof system energy efficiency optimization management system based on digital twins, used to implement the method as described in any one of claims 1-7, characterized in that, include: The first unit is used to acquire real-time monitoring data of the roof system, determine a digital twin of the roof system based on the real-time monitoring data, and determine the roof system energy consumption assessment index based on the digital twin of the roof system. The second unit is used to calculate the comprehensive energy consumption value of the roof system corresponding to multiple sets of control parameters based on the roof system energy consumption assessment index, determine the target control parameters based on the comprehensive energy consumption value of the roof system, and generate a roof system energy efficiency optimization scheme based on the target control parameters. The third unit is used to generate roof system control instructions based on the roof system energy efficiency optimization scheme, and send the roof system control instructions to the roof equipment execution terminal; The fourth unit is used to collect the control results of the roof equipment execution terminal, calculate the difference coefficient between the control results and the preset target value, and calibrate the parameters of the roof system digital twin based on the difference coefficient to obtain the calibrated roof system digital twin. The fifth unit is used to use the calibrated digital twin of the roof system as a benchmark model for roof system energy efficiency management, and to carry out energy-saving renovation and equipment upgrades of the roof system based on the benchmark model.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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