A building energy-saving structure construction optimization method and system based on energy efficiency data driving

Abstract

The application discloses a kind of based on energy efficiency data-driven building energy-saving structure construction optimization method and system, it is related to building energy-saving construction optimization technical field, including acquisition energy efficiency design target data, building energy-saving target performance model is constructed in combination with building structure BIM model, construction influence factor mapping model is constructed based on influencing factor in building energy-saving structure construction process;Real-time acquisition construction site influence parameter, input construction influence factor mapping model, dynamically update construction stage building energy-saving performance prediction value;Based on construction stage building energy-saving performance prediction value, in combination with building energy-saving target performance model, dynamic energy efficiency simulation calculation is executed to generate energy efficiency performance risk early warning, and the construction optimization adjustment strategy for current construction state is output.The application realizes that building energy-saving target performance is dynamically benchmarked with construction process, and the intelligent control level of building energy-saving structure construction stage is improved, and energy-saving performance consistency is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building energy-saving construction optimization, in particular to a building energy-saving structure construction optimization method and system based on energy efficiency data driving. BACKGROUND

[0002] Under the background of rapid development of green building and energy-saving building technology, the building industry has higher requirements for consistency control between energy-saving performance design and construction phase. With the popularization of BIM (Building Information Modeling) technology, building energy efficiency design has gradually shifted from traditional static parameter design to whole life cycle performance optimization management. At the same time, intelligent monitoring and data-driven optimization methods in the construction process are constantly emerging, trying to realize dynamic adaptation of the construction process to the building energy-saving goal through real-time construction data feedback. However, the current mainstream construction optimization management still mainly relies on experience rules or coarse-grained progress control, lacks fine-grained energy efficiency performance prediction and closed-loop optimization capability, and cannot effectively support the construction quality assurance needs of high-performance energy-saving buildings.

[0003] The prior art has obvious deficiencies in the field of building energy-saving structure construction optimization. On the one hand, most methods still stay in the design stage energy efficiency simulation or post-performance detection, lack of dynamic benchmarking mechanism that real-time runs through the energy efficiency design goal in the construction process, resulting in a "performance gap" between design performance and completed performance. On the other hand, the existing construction monitoring system mainly focuses on construction progress and quality index monitoring, and fails to build a quantitative mapping relationship between construction process parameters, material performance parameters, construction environment parameters and building energy-saving performance, making it difficult to realize dynamic prediction of building energy-saving performance in the construction phase. In addition, there is a lack of intelligent optimization strategy generation mechanism based on real-time simulation feedback driving, making it difficult to adjust the construction scheme in a timely and accurate manner even if potential energy efficiency risks are found in the construction process. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing building energy-saving structure construction optimization method lacks a dynamic benchmarking mechanism between design and construction phases, cannot real-time predict building energy-saving performance in the construction process, and lacks an intelligent feedback mechanism for optimization strategy generation, and how to realize intelligent optimization control of building energy-saving structure construction based on energy efficiency data driving.

[0006] To solve the above technical problems, the application provides the following technical scheme: a building energy-saving structure construction optimization method based on energy efficiency data driving, which comprises collecting energy efficiency design target data, constructing a building energy-saving target performance model in combination with a building structure BIM model, and constructing a construction influence factor mapping model based on influence factors in a building energy-saving structure construction process; real-time collection of construction site influence parameters, input of the construction influence factor mapping model, dynamic updating of building energy-saving performance prediction values in the construction stage; based on the building energy-saving performance prediction values in the construction stage, in combination with the building energy-saving target performance model, dynamic energy efficiency simulation calculation is performed to generate an energy efficiency performance risk early warning, and a construction optimization adjustment strategy for the current construction state is output; the construction of the building energy-saving target performance model comprises, based on the thermal performance parameters of the external envelope structure, the window-to-wall ratio, the natural lighting coefficient, the thermal insulation performance parameters and the building heat load index determined in the building design stage, through integration with the building BIM model, a composite function relationship is used to fuse the multi-factor thermal performance, lighting utilization and energy consumption index, and the building energy-saving target performance model is constructed; the construction of the construction influence factor mapping model comprises, through historical construction data learning, simulation training and expert rule modeling, a nonlinear mapping relationship between the construction material performance parameters, the construction process parameters, the construction environment parameters and the building energy efficiency performance index is constructed, a composite mapping structure is used to comprehensively consider the multi-dimensional characteristics of the construction material, process and environment, a building energy-saving performance sensitivity index of the construction state is output, and the influence trend of the construction process on the target performance achievement degree is dynamically reflected.

[0007] As a preferred scheme of the building energy-saving structure construction optimization method based on energy efficiency data driving, the energy efficiency design target data comprises the thermal performance parameters of the external envelope structure, the window-to-wall ratio, the natural lighting coefficient, the thermal insulation performance parameters and the building heat load index determined in the building design stage.

[0008] As a preferred scheme of the building energy-saving structure construction optimization method based on energy efficiency data driving, the construction of the construction influence factor mapping model comprises, through historical construction data learning, simulation training and expert rule modeling, a mapping relationship between the construction material performance parameters, the construction process parameters, the construction environment parameters and the building energy efficiency performance index is constructed.

[0009] As a preferred scheme of the building energy-saving structure construction optimization method based on energy efficiency data driving, the construction site influence parameters comprise the construction material performance parameters, the construction process parameters and the real-time construction environment parameters.

[0010] As a preferred scheme of the building energy-saving structure construction optimization method based on energy efficiency data driving provided in the application, the dynamic updating of the construction stage building energy-saving performance prediction value comprises adopting a time-weighted sliding prediction mechanism, fusing historical prediction values and current prediction results, and smoothing the construction process performance prediction curve.

[0011] As a preferred scheme of the building energy-saving structure construction optimization method based on energy efficiency data driving provided in the application, the dynamic energy efficiency simulation calculation for generating the energy efficiency performance risk early warning comprises outputting a construction stage performance deviation index, and outputting energy efficiency performance risk early warnings of different grades according to a preset performance risk threshold interval.

[0012] As a preferred scheme of the building energy-saving structure construction optimization method based on energy efficiency data driving provided in the application, the construction optimization adjustment strategy of the current construction state comprises optimizing a construction material selection scheme, adjusting a construction process parameter, optimizing a construction environment control strategy, and dynamically adjusting a construction plan and a process sequence.

[0013] Another object of the application is to provide a building energy-saving structure construction optimization system based on energy efficiency data driving, which can solve the problem that the current building energy-saving structure construction optimization method cannot predict the building energy-saving performance in real time during the construction process by collecting construction site influence parameters in real time, inputting a construction influence factor mapping model, and dynamically updating the construction stage building energy-saving performance prediction value.

[0014] As a preferred scheme of the building energy-saving structure construction optimization system based on energy efficiency data driving provided in the application, the system comprises an energy efficiency target modeling module, a construction dynamic prediction module, and an optimization adjustment control module; the energy efficiency target modeling module is used to build a benchmark for the construction stage building energy-saving performance; the construction dynamic prediction module is used to output an immediate influence trend of the construction process state on the building energy-saving performance in real time, and output a dynamic performance prediction during the construction process; and the optimization adjustment control module is used to perform real-time simulation and risk assessment on the construction stage building energy-saving performance deviation, and drive the construction optimization adjustment.

[0015] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the building energy-saving structure construction optimization method based on energy efficiency data driving.

[0016] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the building energy-saving structure construction optimization method based on energy efficiency data driving.

[0017] The application provides a building energy-saving structure construction optimization method based on energy efficiency data driving. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 The overall flowchart of the building energy-saving structure construction optimization method based on energy efficiency data driving provided by the first embodiment of the present application.

[0020] Figure 2 The overall flowchart of the building energy-saving structure construction optimization system based on energy efficiency data driving provided by the third embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings in the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the present application.

[0022] Embodiment 1, refer to Figure 1For an embodiment of the present application, a building energy saving structure construction optimization method based on energy efficiency data driving is provided, comprising:

[0023] S1: Collect energy efficiency design target data, combine building structure BIM model to construct building energy saving target performance model, and construct construction influence factor mapping model based on influence factors in building energy saving structure construction process.

[0024] Further, collect energy efficiency design target data determined in the building design stage, the energy efficiency design target data including external envelope thermal performance parameters, window-wall ratio, natural lighting coefficient, thermal insulation performance parameters, building heat load index in the design stage, etc.; based on the above data, combine the building structure BIM model to construct the building energy saving target performance model as the energy efficiency performance benchmark baseline in the construction process.

[0025] The building energy saving target performance model is expressed as:

[0026]

[0027] Ψ target is the building energy saving target performance index, U is the heat transfer coefficient of the external envelope, A is the total area of the external envelope (m 2 ), γ U is the structure thermal performance sensitivity adjustment coefficient (dimensionless), α is the heat transfer coefficient weight index, β is the area weight index, η WWR is the window-wall ratio influence coefficient, θ is the window-wall ratio modulation coefficient, φ daylight is the sunshine utilization coefficient, κ is the sunshine utilization weight index, δ is the denominator normalization adjustment factor, ΔT is the indoor-outdoor temperature difference in the design stage, λ is the temperature difference smoothing adjustment parameter, H insul is the equivalent thermal resistance of the thermal insulation layer, Q design is the unit area heat load in the building design stage, Q ref is the reference building heat load benchmark value, μ is the heat load normalization adjustment coefficient, and ν is the heat load index term adjustment coefficient.

[0028] Ψ target is in the range of [0.5, 3.0], and Ψ target <1.0 indicates that the target building energy efficiency level is high (better than the industry benchmark); 1.0≤Ψ target ≤2.0 indicates that it meets the design standard target; Ψ target >2.0 indicates that there is a large energy efficiency optimization space in the design stage (which needs to be focused on in the construction stage).

[0029] It should be noted that for the influencing factors in the construction process of building energy saving structure, a construction influencing factor mapping model is constructed to establish the mapping relationship between construction process parameters, construction material performance parameters, construction environment parameters and building energy efficiency performance indicators; the mapping model can dynamically quantify the influence sensitivity of the construction process on building energy efficiency performance through historical data learning, simulation training or expert rule modeling.

[0030] The construction influencing factor mapping model is represented as:

[0031]

[0032] wherein Φ impact is the building energy efficiency performance sensitivity index output by the construction influencing factor mapping model, M is the comprehensive performance parameter of construction materials (such as thermal conductivity, density, etc.), ξ m is the material parameter adjustment coefficient, ρ is the material influence index, C is the key parameter of construction process (such as joint control quality index, spraying uniformity index, etc.), χ c is the process influence adjustment coefficient, θ1 is the process influence power index, ω c is the process parameter reduction adjustment coefficient, E is the comprehensive parameter of construction environment (such as construction temperature and humidity coupling index), ζ e is the environmental influence slope parameter, μ e is the environmental influence equilibrium point, γ is the Sigmoid normalization index, η is the historical data knowledge adjustment coefficient, H j is the jth feature parameter in the historical construction data (such as construction progress stability, personnel skill coefficient, construction equipment state index, etc.), ψ j is the corresponding feature weight, κ j is the historical feature power index, and k is the total number of feature parameters.

[0033] Φ impact is in the range of [0, 5.0], wherein Φ impact <1.0 indicates that the construction influence on building energy efficiency performance is small, and the construction process is highly friendly to energy efficiency performance, 1.0≤Φ impact ≤3.0 indicates that the construction influence is within the acceptable range of design, and the construction optimization space is moderate, Φ impact >3.0 indicates that the construction influence has a greater risk to building energy efficiency performance, and needs to be taken as a key optimization control.

[0034] S2: Real-time collection of construction site influencing parameters, input of construction influencing factor mapping model, and dynamic updating of building energy saving performance prediction value in construction stage.

[0035] Further, in the construction process of building energy-saving structure, the system collects real-time energy efficiency related key parameters in the construction process based on multiple types of data collection devices arranged on the construction site. The parameters include: construction material measured performance parameters M: the key indicators of the thermal conductivity, density, thickness uniformity, moisture absorption rate, etc. of the current batch of materials are dynamically determined through material detection equipment; construction process parameters C: the quality of the joint processing, the uniformity of spraying, the stability coefficient of construction rhythm, etc. are collected in real time through process quality detection instruments or construction quality monitoring records; real-time construction environment parameters E: the environmental impact factors such as temperature, humidity, wind speed, dew point temperature, surface drying speed, etc. are monitored in real time through the array of on-site environmental sensors.

[0036] The above collected real-time parameters are input variables of the construction influence factor mapping model Φ impact , and the energy efficiency performance sensitivity index under the current construction state is calculated in real time , wherein t is the current time slice, representing the dynamic time dimension of the construction process.

[0037] The following dynamic prediction updating mechanism is used to update the building energy-saving performance prediction value in the construction phase in a rolling manner:

[0038] The calculated at the current time is compared with the building energy-saving target performance model Ψ target in real time, and the prediction deviation

[0039]

[0040] The deviation value reflects the immediate deviation degree of the current construction state to the target performance.

[0041] In order to realize the dynamic smoothing update of the performance prediction value in the construction phase, the system uses a time-weighted sliding prediction mechanism to integrate the latest round of into the historical prediction trajectory, and update the building energy-saving performance prediction value in the current phase

[0042]

[0043] , wherein λ t is the time dynamic adjustment weight (generally determined according to the construction phase progress or sliding window strategy, such as smaller in the initial stage and larger in the later stage).

[0044] By continuously sampling time sequence, the prediction curve trend is extracted, and the performance trend curve is fitted (such as using LOESS smoothing, Savitzky-Golay filtering, etc.); the performance change rate Predict whether the construction trend is deviating or converging.

[0045] S3: Based on the predicted building energy-saving performance value at the construction stage, combined with the building energy-saving target performance model, perform dynamic energy efficiency simulation calculation to generate energy efficiency performance risk warning, and output construction optimization adjustment strategy for the current construction state.

[0046] Further, based on the real-time updated predicted building energy-saving performance value at the construction stage combined with the building energy-saving target performance model Ψ target , the system performs dynamic energy efficiency simulation calculation throughout the construction process.

[0047] Real-time acquisition of the predicted performance value at the construction stage at the current time t and comparison and analysis with the building energy-saving target performance model Ψ target to calculate the real-time performance deviation index The calculation formula is:

[0048]

[0049] Among them, represents the relative deviation degree between the predicted building energy-saving performance value at the current time at the construction stage and the design target performance, reflecting the immediate influence level of the current construction state on the achievement of building energy-saving performance.

[0050] According to the preset performance risk threshold interval τ low ,τ high , the dynamic threshold value is determined The specific rules are:

[0051] When , it is determined that the construction state has an excellent influence on building energy-saving performance, and no adjustment is needed; when , it is determined that the construction state has a certain performance deviation trend, and the system generates a low-level risk warning and records the deviation trend; when , it is determined that the construction state has a significant performance deviation risk, and the system automatically generates a high-level energy efficiency performance risk warning and prompts the construction management system to trigger the optimization adjustment strategy.

[0052] It should be noted that the performance risk threshold interval τ low ,τ high is set as follows:

[0053] τ low = 0.05, τ high = 0.15

[0054] When (deviation ≤ 5%), the construction state is excellent and no adjustment is needed; when (Deviation in the interval of 5%-15%), there is a certain deviation trend, generate low-grade risk warning, suggest optimization adjustment; when (deviation > 15%), there is a significant performance deviation risk, generate high-level risk warning, need to take immediate optimization intervention measures.

[0055] It should also be noted that the performance deviation index of the construction phase based on real-time calculation and its corresponding performance risk level determination results, the system dynamically generates optimization adjustment strategies for the current construction state.

[0056] When the system determines that the current deviation is in the low-risk interval , the system generates fine-tuning optimization strategies based on the collected construction material performance parameters M, construction process parameters C, and construction environment parameters E, combined with historical construction optimization experience library and expert rule library, to optimize the construction process parameters C, adjust the joint treatment process, spraying construction sequence, and construction rhythm configuration, improve process consistency and construction quality; optimize the construction environment parameters E, adjust the temperature and humidity control strategy of the construction section, optimize the construction time period selection, and reduce the impact of environmental disturbance on material performance; optimize the construction material selection parameters M, for materials with batch differences, preferentially allocate high consistency batches, or optimize the material ratio scheme. When the system determines that the current deviation is in the high-risk interval , the system generates key intervention type optimization strategies, and the construction management system issues high-priority optimization suggestions in parallel, including but not limited to: starting the construction phase special process review process, conducting on-site verification of key construction process quality; strengthening the material entry detection and re-inspection process to screen key material index abnormalities that cause performance deviation; dynamically adjusting the construction plan, implementing optimization rearrangement for the process section or process that has the greatest impact on the current deviation, such as adjusting key construction personnel configuration, construction rhythm, and construction window arrangement; based on the dynamic trend of the current and Ψ target , predict the risk evolution trend of subsequent construction phases, and develop optimization intervention schemes in advance to form proactive optimization control capabilities.

[0057] During the optimization strategy generation process, multi-dimensional feature sensitivity analysis is performed on the real-time collected data M, C, and E to preferentially locate the parameter dimensions with higher contribution to the performance deviation in the current phase, and optimization strategies are generated accordingly to ensure the operability, real-time performance, and effectiveness of the optimization measures.

[0058] After the optimization strategy is generated by the system, it is fed back to the construction site management personnel through the construction management platform to guide the adjustment operations during construction; at the same time, the implementation results of the optimization strategy and the system simulation prediction results form a closed-loop feedback, updating the historical optimization knowledge base and improving the intelligence and self-adaptation ability of subsequent optimization strategy generation.

[0059] Embodiment 2, one embodiment of the present application, provides a building energy saving structure construction optimization method based on energy efficiency data driving. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiment are carried out for scientific demonstration.

[0060] Firstly, a green three-star certified office building project A is selected as a pilot project, and the total building area is about 38,000 m 2 , BIM+energy saving simulation platform integrated design is adopted in the design stage, and the building energy saving target performance model is constructed according to the S1 step of the present application, and the design target year total energy consumption index is ≤110kWh / m 2 ·year.

[0061] In the construction preparation stage, firstly, the thermal performance parameters of the building envelope determined in the design stage are collected and sorted (such as the heat transfer coefficient of external wall is 0.40W / m 2 ·K, the heat transfer coefficient of roof is 0.35W / m 2 ·K), window-wall ratio (42%), natural lighting coefficient (0.58), thermal insulation performance parameters (XPS insulation board design thickness 60mm), building heat load index in design stage (heating load design target ≤25W / m 2 ). Through the BIM model linkage simulation platform, the building energy saving target performance model is established as the energy efficiency benchmark in the construction stage.

[0062] Then, according to the requirement of S1, combined with the historical data of similar projects, the construction influence factor mapping model is trained, and the mapping relationship covers the construction material parameters (thermal conductivity, density), construction technology parameters (seam treatment score, spraying uniformity), construction environment parameters (temperature and humidity, wind speed).

[0063] After entering the construction stage, intelligent monitoring devices are arranged on the construction site, including high-precision material detection instruments, process quality monitoring terminals and environment real-time monitoring stations. According to the S2 step, the key influence parameters in the construction process are collected in real time, the construction influence factor mapping model is dynamically input, and the building energy saving performance prediction value in the construction stage is updated in real time. The monitoring cycle is set to 2 times rolling sampling per day, the construction stage span is 6 weeks, and the real-time update curve is automatically smoothed and fitted by the system.

[0064] In the S3 stage, the system compares the performance prediction value in the construction stage with the building energy saving target performance model every day, automatically calculates the performance deviation index, and dynamically generates risk warning and optimization adjustment strategy according to the preset performance risk threshold (5% excellent, 5%-15% deviation, >15% high risk), and feeds back to the construction management system. According to different risk levels, the construction technology, material use and environment control measures are dynamically adjusted on site, forming a closed-loop optimization control process to ensure that the building energy saving performance achieves the target.

[0065] Table 1 experimental data table

[0066]

[0067] By comparing the test data in the table, it can be observed that the application effect of the method of the present application in the actual construction process has obvious advantages. First, among the key influence parameters collected dynamically during the construction phase, the thermal conductivity of the construction materials is stably maintained in the interval of 0.039-0.042 W / m·K, and there is no batch fluctuation. Thanks to the real-time feedback of the material detection results of the system, the on-site construction team preferentially selects the batch of materials with high consistency according to the system suggestion, ensuring the consistency of the contribution of the materials to the building energy saving performance.

[0068] Secondly, in terms of construction process parameters, the score of joint treatment is steadily improved from 78 points in the first week of construction to 94 points in the sixth week, and the spraying uniformity index is improved from 85.2% to 94.0%, which shows that the optimization adjustment strategy driven by the dynamic performance risk early warning of the system can effectively guide the improvement of the construction process details and improve the process consistency level.

[0069] The construction environment parameters (temperature, humidity) also show a trend of stability. After the system generates optimization suggestions, the construction management team adjusts the construction time and environmental control measures according to the suggestions, effectively suppresses the humidity fluctuation, and reduces the negative impact on the construction quality.

[0070] More importantly, the building energy saving performance prediction value (normalized index) during the construction phase is smoothly decreased from 0.98 to 0.91, and the performance deviation Δperf is always controlled within 7.0%, which is in the preset low risk interval. The daily dynamic simulation feedback of the system guides the construction team to continuously optimize the material selection, process operation and environmental control, ensures the stable achievement of the energy saving performance design goal, and avoids the design-construction performance "fault" problem in the traditional construction process.

[0071] Compared with the prior art, the traditional construction phase usually lacks dynamic prediction and real-time optimization mechanism, and often discovers performance deviation through energy efficiency detection after completion, which is difficult to correct in time, resulting in high rework cost and poor performance consistency. While the present application realizes the dynamic consistency of energy saving performance and construction process through the whole process "collection-mapping-prediction-simulation-optimization" closed loop process, and realizes the dynamic consistency of energy saving performance and construction process, effectively improves the intelligent level of building energy saving construction and the engineering quality control ability, and has high novelty and engineering application value.

[0072] Example 3, refer to Figure 2 An embodiment of the present application provides an energy efficiency data driven building energy saving structure construction optimization system, which comprises an energy efficiency target modeling module 100, a construction dynamic prediction module 200, and an optimization adjustment control module 300.

[0073] S4: The energy efficiency target modeling module 100 is used to build a benchmark for the building energy efficiency performance in the construction phase. By collecting energy efficiency design target data in the building design phase and combining the building structure BIM model, a building energy efficiency target performance model is established.

[0074] It should also be noted that the energy efficiency target modeling module 100 completes the extraction of the thermal performance parameters of the external envelope structure, the window-to-wall ratio, the natural lighting coefficient, the thermal insulation performance parameters, and the building heat load index in the design phase. Based on the above data, a building energy efficiency target performance model is built, and the model is output to the construction dynamic prediction module 200 as a real-time prediction benchmark.

[0075] S5: The construction dynamic prediction module 200 is used to output the immediate influence trend of the construction process state on the building energy efficiency performance in real time, and output the dynamic performance prediction in the construction process.

[0076] It should also be noted that the construction dynamic prediction module 200 collects construction site influence parameters in real time, including construction material measured performance parameters, construction process parameters, and real-time construction environment parameters. According to the building energy efficiency target performance model provided by the energy efficiency target modeling module 100 as a prediction benchmark, the construction influence factor mapping model is dynamically input, the construction phase building energy efficiency performance prediction value is updated in real time, and the real-time performance prediction result and the corresponding deviation index are output to the optimization adjustment control module 300.

[0077] S6: The optimization adjustment control module 300 is used to perform real-time simulation and risk assessment on the construction phase building energy efficiency performance deviation, and drive construction optimization adjustment.

[0078] It should also be noted that the optimization adjustment control module 300 performs dynamic energy efficiency simulation calculation according to the performance prediction value output by the construction dynamic prediction module 200 in real time and the target performance model provided by the energy efficiency target modeling module 100, generates a performance deviation index, generates an energy efficiency performance risk warning according to a preset performance risk threshold interval, and automatically generates an optimization adjustment strategy for the current construction state based on the risk level, and feeds back to the construction management platform to drive the construction site optimization adjustment, forming a closed-loop control process of construction optimization. The optimization adjustment control module 300 can also form a closed-loop learning of the optimization adjustment execution result and the simulation feedback result, update the construction optimization knowledge base, and further improve the self-adaptive optimization capability of the system.

[0079] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0081] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0082] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be understood that the above-described embodiments are merely given as examples of the technical solution of the present application and are not to be interpreted in a limiting manner, and that for persons skilled in the art, modifications or equivalent replacements can be made to the technical solution of the present application without departing from the spirit and scope of the present application, and all such modifications or equivalent replacements shall be encompassed in the scope of the claims of the present application.

Claims

1. A method for optimizing the construction of energy-efficient building structures based on energy efficiency data, characterized in that, include: Collect energy efficiency design target data, combine the building structure BIM model to construct the building energy-saving target performance model, and construct the construction influence factor mapping model based on the influencing factors in the construction process of the building energy-saving structure. Real-time collection of construction site impact parameters, input into the construction impact factor mapping model Φ impact Dynamically update the predicted values ​​of building energy efficiency during the construction phase. Based on the predicted building energy efficiency performance during the construction phase, combined with the building energy efficiency target performance model Ψ target It performs dynamic energy efficiency simulation calculations to generate energy efficiency performance risk warnings and outputs construction optimization and adjustment strategies for the current construction status. The construction of the building energy-saving target performance model includes, based on the thermal performance parameters of the external envelope, window-to-wall ratio, natural daylighting coefficient, thermal insulation performance parameters and building heat load index determined in the building design stage, integrating with the building BIM model, using composite function relationships, and combining multi-factor thermal performance, daylighting utilization and energy consumption indexes to construct the building energy-saving target performance model; The construction of the construction impact factor mapping model includes learning from historical construction data, simulation training and expert rule modeling, constructing a nonlinear mapping relationship between construction material performance parameters, construction process parameters, construction environment parameters and building energy efficiency performance indicators, adopting a composite mapping structure, integrating the multi-dimensional characteristics of construction materials, processes and environment, outputting the sensitivity index of construction status to building energy-saving performance, and dynamically reflecting the trend of the impact of the construction process on the achievement of target performance. Dynamically update the predicted values ​​of building energy efficiency during the construction phase. Specifically, it includes: Real-time calculation of energy efficiency performance sensitivity indicators under the current construction conditions Where t is the current time slice, representing the dynamic time dimension of the construction process; To achieve dynamic and smooth updates of performance predictions during the construction phase, the system employs a time-weighted sliding prediction mechanism, updating the latest round of predictions. Incorporate historical forecast patterns to update current building energy efficiency performance forecasts. Where, λ t The weights are dynamically adjusted over time. Dynamic energy efficiency simulation calculations generate energy efficiency performance risk warnings, specifically including: Real-time acquisition of construction stage performance prediction values ​​at the current time t and the building energy efficiency target performance model Ψ target Comparative analysis was conducted to calculate the real-time performance deviation index. The calculation formula is as follows: in, It indicates the degree of relative deviation between the predicted value of building energy efficiency performance at the current construction stage and the design target performance, reflecting the immediate impact of the current construction status on the achievement of building energy efficiency performance; Based on the preset performance risk threshold range τ low ,τ high ,right The specific rules for dynamic threshold determination are as follows: when When the construction status is deemed to have an excellent impact on the building's energy efficiency, no adjustment is required; when When a performance deviation trend is detected during construction, the system generates a low-level risk warning and records the deviation trend; when When a construction status is determined to have a significant risk of performance deviation, the system automatically generates a high-level energy efficiency performance risk warning and prompts the construction management system to trigger optimization and adjustment strategies.

2. The energy efficiency data-driven construction optimization method for building energy-saving structures as described in claim 1, characterized in that: The energy efficiency design target data includes: The thermal performance parameters of the external envelope, window-to-wall ratio, natural lighting coefficient, thermal insulation performance parameters, and building heat load index determined during the architectural design phase are all included.

3. The method for optimizing the construction of energy-saving building structures based on energy efficiency data as described in claim 2, characterized in that: The construction influencing factor mapping model includes, By learning from historical construction data, conducting simulation training, and using expert rule modeling, a mapping relationship is constructed between construction material performance parameters, construction process parameters, construction environment parameters, and building energy efficiency performance indicators.

4. The method for optimizing the construction of energy-saving building structures based on energy efficiency data as described in claim 1, characterized in that: The construction site influencing parameters include, Performance parameters of construction materials, parameters of construction process, and real-time parameters of construction environment.

5. The energy efficiency data-driven construction optimization method for building energy-saving structures as described in claim 4, characterized in that: The dynamically updated predicted building energy efficiency performance values ​​during the construction phase include... A time-weighted sliding prediction mechanism is adopted to integrate historical prediction values ​​with current prediction results, thereby smoothing the performance prediction curve of the construction process.

6. The energy efficiency data-driven construction optimization method for building energy-saving structures as described in claims 1 and 5, characterized in that: The process of generating energy efficiency performance risk warnings through dynamic energy efficiency simulation calculations includes... Output performance deviation indicators during the construction phase, and output different levels of energy efficiency performance risk warnings based on preset performance risk threshold ranges.

7. The energy efficiency data-driven construction optimization method for building energy-saving structures as described in claim 6, characterized in that: The construction optimization and adjustment strategies for the current construction status include, Optimize the selection of construction materials, adjust construction process parameters, optimize construction environment control strategies, and dynamically adjust construction plans and sequence of procedures.

8. A building energy-saving structure construction optimization system based on energy efficiency data, used to implement the building energy-saving structure construction optimization method based on energy efficiency data as described in any one of claims 1 to 7, characterized in that: It includes an energy efficiency target modeling module (100), a construction dynamic prediction module (200), and an optimization and adjustment control module (300); The energy efficiency target modeling module (100) is used to construct a benchmark for the energy-saving performance of buildings during the construction phase; The construction dynamic prediction module (200) is used to output the real-time impact trend of the construction process status on the building's energy-saving performance and output the dynamic performance prediction during the construction process. The optimization and adjustment control module (300) is used to perform real-time simulation and risk assessment of the deviation of building energy-saving performance during the construction phase, and drive construction optimization and adjustment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the construction optimization method for building energy-saving structures based on energy efficiency data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the construction optimization method for building energy-saving structures based on energy efficiency data as described in any one of claims 1 to 7.

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