An environmental intelligent regulation method and system for concrete winter curing

Abstract

This invention discloses an intelligent environmental control method and system for winter curing of concrete, relating to the field of environmental monitoring technology. The system includes a control center connected to a building data acquisition module, an intelligent processing module, a curing analysis module, and a strategy execution module. A virtual-real twin space is constructed to transform the acquired data into graphs, obtaining dynamic graphs of concrete monitoring and environmental monitoring. Curve extraction is performed on the concrete monitoring graphs to obtain local monitoring coefficients and anomaly capture, identifying raw material mutation monitoring segments. Based on these raw material mutation monitoring segments, the environmental monitoring graphs are time-verified to obtain environmental mutation time periods. Based on these environmental mutation time periods, optimization simulation is performed in the virtual-real twin space to obtain optimized control strategies. Precise control parameters are achieved, reducing human error, ensuring concrete quality, and improving curing efficiency.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to an intelligent environmental control method and system for winter curing of concrete. Background Technology

[0002] With the rapid development of infrastructure construction in my country, concrete, as a major building material, is widely used in various projects. However, in the low temperatures of winter, the hardening rate of concrete slows down, its strength gain is affected, and it is prone to cracking and other quality problems. To ensure the quality and durability of concrete structures, winter curing becomes a crucial step.

[0003] Traditional methods for winter concrete curing typically involve heat preservation, heating, and humidification, relying on manual adjustments to the curing environment. This is labor-intensive, inefficient, and the curing effect is difficult to guarantee. Furthermore, because changes in ambient temperature and humidity are non-linear, manual adjustments are often difficult to control precisely, resulting in unsatisfactory concrete curing outcomes.

[0004] Advances in science and technology have provided new solutions for winter curing of concrete. This paper presents an intelligent environmental control method and system for winter curing of concrete. It monitors curing environment parameters in real time, analyzes the curing status of concrete through artificial intelligence algorithms, and automatically adjusts curing strategies to improve curing efficiency, ensure concrete quality, and reduce curing costs. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent environmental control method and system for winter curing of concrete.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] An intelligent environmental control system for winter curing of concrete includes a control center, which is connected to a building data acquisition module, an intelligent processing module, a curing analysis module, and a strategy execution module.

[0008] The building data acquisition module is used to collect concrete foundation data and environmental monitoring data of the concrete storage silo.

[0009] The intelligent processing module is used to construct a virtual-real twin space, perform graph transformation on concrete foundation data and environmental monitoring data to obtain dynamic graphs of concrete monitoring and environmental monitoring, extract curves from the dynamic graphs of concrete monitoring, and obtain local monitoring coefficients.

[0010] The maintenance analysis module is used to capture anomalies in local monitoring coefficients, obtain raw material mutation monitoring segments, and perform time verification on the environmental monitoring dynamic map based on the raw material mutation monitoring segments to obtain the environmental mutation time periods.

[0011] The strategy execution module is used to adjust the time period of environmental change to obtain an adjustable range. Based on the adjustable range, optimization simulation is performed through virtual and real twin space to obtain an optimized control strategy.

[0012] Preferably, the process of collecting concrete foundation data and environmental monitoring data includes:

[0013] Set up raw material collection terminals and environmental collection terminals;

[0014] Data is collected from the concrete storage silo through the raw material collection terminal to obtain concrete foundation data, and the collected concrete foundation data is time-stamped to obtain the raw material collection time.

[0015] Based on the raw material collection time, environmental monitoring data is obtained by collecting factors from the concrete storage silo through an environmental acquisition terminal.

[0016] Preferably, the process of converting concrete foundation data and environmental monitoring data into graphs includes:

[0017] A virtual twin space is constructed based on the concrete storage silo, and the concrete storage silo is mapped to the virtual twin space to obtain a virtual storage silo;

[0018] A two-dimensional rectangular coordinate system is constructed, and a foundation change curve is generated based on the concrete foundation data. The foundation change curve is then uploaded to the two-dimensional rectangular coordinate system to obtain a dynamic image of concrete monitoring.

[0019] Based on the concrete monitoring dynamic map, an environmental monitoring dynamic map is constructed from environmental monitoring data, and both the concrete monitoring dynamic map and the environmental monitoring dynamic map are uploaded to the virtual twin space.

[0020] Preferably, the process of extracting curves from the dynamic graph of concrete monitoring includes:

[0021] The dynamic graph of concrete monitoring was converted into a curve to obtain the foundation monitoring coefficient;

[0022] Set a range control base, perform scale statistics on the range control base, and obtain the variable window distance;

[0023] The amplification parameters are set according to the variable window spacing, and the range of the basic monitoring coefficients is increased by the amplification parameters to obtain the expanded monitoring coefficients;

[0024] By adjusting the base value of the range, the expanded monitoring coefficient is locally captured to obtain the local monitoring coefficient.

[0025] Preferably, the process of anomaly capture for local monitoring coefficients includes:

[0026] Perform wavelet permutation on the local monitoring coefficients to obtain a local wavelet graph, set the selected variable window and upload it to the local wavelet graph;

[0027] Set a fluctuation threshold axis, and perform preliminary extraction of the local waveform based on the fluctuation threshold axis to obtain the primary abrupt change band;

[0028] By defining the primary mutation band using the selected variable window, the raw material mutation monitoring segment is obtained.

[0029] Preferably, the process of verifying the environmental monitoring dynamic map based on the raw material mutation monitoring segment includes:

[0030] The mutation acquisition time is obtained by time matching of the local waveform diagram based on the raw material mutation monitoring section;

[0031] The mutation collection time is uploaded to the environmental monitoring dynamic map. Anomalies are matched with the environmental monitoring dynamic map by mutation collection time to obtain the time period of environmental mutation.

[0032] Preferably, the process of adjusting the time period for environmental changes includes:

[0033] By matching the environmental monitoring dynamic graph with the time period of environmental abrupt change, the environmental abrupt change curve segment is obtained;

[0034] Based on the virtual-real twin space, the local wave pattern and the environmental monitoring dynamic map are normalized and statistically analyzed to obtain the concrete matching environment map.

[0035] Based on the concrete matching environment map, differential capture is performed on the raw material mutation monitoring section to obtain the total difference of raw material mutation, and an adjustable range is set and uploaded to the concrete matching environment map.

[0036] Preferably, the process of optimizing the simulation through a virtual-real twin space includes:

[0037] Based on the adjustable interval, interval statistics are performed on the concrete matching environment map to obtain abrupt change interval segments. The abrupt change interval segments are then captured by differential measurement to obtain the interval abrupt change difference value.

[0038] The total difference in raw material mutation is calculated based on the mutation difference in the interval to obtain the pre-adjustment ratio. The environmental matching of the concrete matching environment diagram is performed based on the mutation interval to obtain the environmental matching interval.

[0039] The environmental matching interval is simulated and adjusted by pre-adjustment ratio to obtain pre-adjustment instructions, and an optimal control strategy is generated based on the obtained pre-adjustment instructions.

[0040] Preferably, the process of simulating and adjusting the environmental matching interval through a pre-adjustment ratio includes:

[0041] Based on the pre-adjustment ratio, the environmental matching interval is pre-adjusted to obtain the environmental pre-adjustment instruction;

[0042] Based on the virtual twin space, the virtual storage chamber is simulated and corrected according to the pre-adjustment instructions of the environment, and the concrete matching environment map during the simulation correction process is recorded to obtain the original ring change map.

[0043] A cooling cycle is set, and the change diagram of the original correction loop is simulated and judged based on the cooling cycle to obtain the effect of the pre-adjustment command. The environmental pre-adjustment command is optimized based on the effect of the pre-adjustment command to obtain the optimized pre-adjustment command. The optimal control strategy is generated based on the environmental pre-adjustment command and the optimized pre-adjustment command.

[0044] Based on the above-mentioned intelligent environmental control system for winter curing of concrete, the present invention also provides an intelligent environmental control method for winter curing of concrete, comprising the following steps:

[0045] Step 1: Collect concrete foundation data and environmental monitoring data for the concrete storage silo;

[0046] Step 2: Construct a virtual twin space, perform graph transformation on concrete foundation data and environmental monitoring data to obtain dynamic graphs of concrete monitoring and environmental monitoring, extract curves from the dynamic graphs of concrete monitoring to obtain local monitoring coefficients;

[0047] Step 3: Capture anomalies in local monitoring coefficients to obtain raw material mutation monitoring segments. Based on the raw material mutation monitoring segments, verify the time of environmental monitoring dynamics to obtain the environmental mutation time periods.

[0048] Step 4: Adjust the time period of environmental abrupt changes to obtain an adjustable range. Based on the adjustable range, perform optimization simulation through virtual twin space to obtain an optimized control strategy.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] 1. Collect concrete foundation data and environmental data of storage silos separately, convert the concrete foundation data into curves and extract mutation features to obtain raw material mutation monitoring segments; improve the speed and accuracy of concrete anomaly investigation and identification, enable environmental control at the abnormal time point as quickly as possible to ensure concrete quality, greatly shorten the curing cycle and improve construction efficiency.

[0051] 2. Then, based on the raw material mutation monitoring segment, match the corresponding time period of environmental data, and simulate the mutation time period by constructing a virtual space to obtain the initial control strategy. At the same time, optimize the instructions with poor simulation effect to obtain the optimized control strategy. This can reduce the rework and repair costs caused by improper maintenance, accurately simulate resource consumption, and rationally allocate resources to achieve good maintenance results. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0054] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, an intelligent environmental control system for winter curing of concrete includes a control center, which is connected to a building acquisition module, an intelligent processing module, a curing analysis module, and a strategy execution module.

[0056] The building data acquisition module is used to collect concrete foundation data and environmental monitoring data of the concrete storage silo.

[0057] The intelligent processing module is used to construct a virtual-real twin space, perform graph transformation on concrete foundation data and environmental monitoring data to obtain dynamic graphs of concrete monitoring and environmental monitoring, extract curves from the dynamic graphs of concrete monitoring, and obtain local monitoring coefficients.

[0058] The maintenance analysis module is used to capture anomalies in local monitoring coefficients, obtain raw material mutation monitoring segments, and perform time verification on the environmental monitoring dynamic map based on the raw material mutation monitoring segments to obtain the environmental mutation time periods.

[0059] The strategy execution module is used to adjust the time period of environmental change to obtain an adjustable range. Based on the adjustable range, optimization simulation is performed through virtual and real twin space to obtain an optimized control strategy.

[0060] In practical applications, winter temperatures are low. When the temperature drops below 0℃, the water in the concrete freezes. The expansion of frozen water damages the internal structure of the concrete, leading to reduced strength, decreased durability, and quality problems such as cracks and spalling. Simultaneously, the hydration reaction of cement is crucial for the increase of concrete strength, and this reaction requires suitable temperature and humidity conditions. Low winter temperatures significantly slow down or even stop the hydration reaction. Proper curing can create a favorable environment for cement hydration, ensuring the normal increase of concrete strength. By intelligently controlling the environmental parameters of the concrete, quality problems caused by freezing and drying can be effectively avoided, ensuring the strength, durability, and integrity of the concrete structure, thereby ensuring the quality and safety of the entire project. The process of collecting concrete foundation data and environmental monitoring data includes:

[0061] Set up raw material collection terminals and environmental collection terminals;

[0062] Data is collected from the concrete storage silo through the raw material collection terminal to obtain concrete foundation data, and the collected concrete foundation data is time-stamped to obtain the raw material collection time.

[0063] The concrete storage silo refers to a storage area used for storing concrete in winter. The concrete basic data refers to the basic data of the stored concrete itself, including mechanical property data, physical property data, and durability data. Among them, the mechanical property data are parameter data that can represent the mechanical properties of concrete, including but not limited to cubic compressive strength, axial compressive strength, and elastic modulus; the physical property data includes but is not limited to density, porosity, water absorption, and thermal conductivity; and the durability data includes but is not limited to frost resistance, impermeability, and carbonation resistance.

[0064] Based on the raw material collection time, environmental monitoring data is obtained by collecting factors from the concrete storage silo through an environmental data acquisition terminal.

[0065] The aforementioned factor collection refers to the collection of environmental factors at all locations of the concrete storage silo to obtain environmental monitoring data. The environmental monitoring data includes the internal environment of the concrete and the environment of the storage silo. The internal environment of the concrete refers to the actual environmental parameters inside the concrete stored in the concrete storage silo, including but not limited to temperature, humidity, and gas content. The storage silo environment refers to the regional spatial environmental parameters of the concrete storage silo, including meteorological parameters and air quality parameters. Meteorological parameters include temperature, humidity, wind speed, and light intensity, while air quality parameters include carbon dioxide concentration, harmful gas content, and air pressure.

[0066] Specifically, data is collected simultaneously through the raw material collection terminal and the environmental collection terminal. For example, during the raw material collection time T1, data is collected through the raw material collection terminal and the environmental collection terminal respectively, so the concrete foundation data and environmental monitoring data at time T1 are obtained at the same time. That is, the corresponding environmental monitoring data is obtained at the same time as the collection time of each concrete foundation data.

[0067] A virtual twin space is constructed based on the concrete storage silo. The concrete storage silo is mapped to the virtual twin space to obtain a virtual storage silo. A transmission link is constructed between the virtual storage silo and the concrete storage silo.

[0068] The virtual twin space represents a virtual space used to transform the concrete storage silo into a virtual three-dimensional storage silo model, i.e., a virtual storage silo. The transformed virtual three-dimensional storage silo model has the same structure and function as the real concrete storage silo. The transmission link is a channel for transmitting data and information between the concrete storage silo and the virtual storage silo.

[0069] Construct a two-dimensional rectangular coordinate system for the raw material collection time, upload the obtained concrete foundation data to the two-dimensional rectangular coordinate system, generate the foundation change curve, and record it as a concrete monitoring dynamic graph in the two-dimensional rectangular coordinate system;

[0070] The generated foundation change curve is based on the concrete foundation data corresponding to each raw material collection time. At the time point corresponding to the horizontal axis, the corresponding concrete foundation data can be found on the vertical axis. Connecting them according to the time sequence of raw material collection time, the foundation change curve is obtained. Based on the mechanical performance data, physical performance data, and durability performance data included in the concrete foundation data, the corresponding foundation change curve includes mechanical performance curve, physical performance curve, and durability curve. Therefore, the concrete monitoring dynamic graph includes all foundation change curves of the concrete foundation data.

[0071] Based on the concrete monitoring dynamic map, an environmental monitoring dynamic map is constructed according to the obtained environmental monitoring data, and curve markings are added to the environmental monitoring dynamic map to obtain the environmental change curve;

[0072] Furthermore, the constructed environmental monitoring dynamic map is also within a constructed two-dimensional rectangular coordinate system, and the raw material collection time corresponding to the horizontal axis time is the same. An environmental change curve is generated based on the environmental monitoring data, and the environmental change curve is uploaded to the two-dimensional rectangular coordinate system to obtain the environmental monitoring dynamic map. That is, the start time of the environmental monitoring dynamic map is exactly the same as the start time of the concrete monitoring dynamic map, and there are corresponding values ​​on the curve for the same raw material collection time. In particular, based on the internal environment of the concrete and the storage environment included in the environmental monitoring data, the environmental change curve also includes all curves corresponding to the internal environment of the concrete and the storage environment.

[0073] The obtained concrete monitoring dynamic images and environmental monitoring dynamic images are uploaded to the virtual twin space and associated with the virtual storage warehouse;

[0074] The process of extracting curves from dynamic concrete monitoring graphs to obtain local monitoring coefficients includes:

[0075] The obtained concrete monitoring dynamic graphs were curve-modified to obtain the foundation monitoring coefficients;

[0076] The curve conversion refers to converting the basic change curve in the concrete monitoring dynamic graph into a signal, namely the basic monitoring coefficient;

[0077] The range control base is set, and the range control base is expressed in the form of a function. In this embodiment, the range control base is selected by selecting a suitable wavelet function based on the signal characteristics of the basic monitoring coefficient. The wavelet function includes, but is not limited to, Morlet wavelet, Coiflets wavelet, and Daubechies wavelet.

[0078] The obtained range control baseline is subjected to scaling statistics to obtain the variable window spacing, which represents the length of the range control baseline;

[0079] Based on the obtained variable window spacing, set the amplification parameter, and then use the amplification parameter to increase the range of the basic monitoring coefficient to obtain the expanded monitoring coefficient;

[0080] The amplification parameter refers to the amplification factor set according to the length of the variable window spacing, which is the amplification parameter. The amplification parameter represents the factor by which each sampling point of the basic monitoring coefficient is amplified, and the amplification parameter is less than the variable window spacing.

[0081] The range expansion means that each sampling point of the basic monitoring coefficient is amplified by a corresponding factor according to the amplification parameter to obtain the expanded monitoring coefficient;

[0082] The obtained range control base is uploaded to the expanded monitoring coefficient, and the expanded monitoring coefficient is locally captured through the range control base to obtain the local monitoring coefficient;

[0083] The local capture means convolving the range-adjusted base coefficient with the expanded monitoring coefficient to obtain the local monitoring coefficient. This means that by amplifying the base monitoring coefficient, the coefficient mutation feature is highlighted, which is beneficial to enhance abnormal features and improve the accuracy of feature extraction.

[0084] Specifically, based on the mechanical performance curve, physical performance curve, and durability performance curve included in the basic change curve, the local monitoring coefficients correspond to the mechanical performance coefficient, physical performance coefficient, and durability performance coefficient.

[0085] The process of capturing anomalies in local monitoring coefficients to obtain raw material mutation monitoring segments includes:

[0086] The obtained local monitoring coefficients are subjected to wave permutation to obtain a local wave pattern;

[0087] The wave permutation represents the conversion of local monitoring coefficients into a waveform of a signal, i.e., a local wave diagram. For the mechanical performance coefficient, physical performance coefficient, and durability performance coefficient included in the local monitoring coefficients, the local wave diagram includes the waveforms corresponding to the mechanical performance coefficient, physical performance coefficient, and durability performance coefficient, respectively.

[0088] In particular, the horizontal axis time of the local waveform graph corresponds one-to-one with the raw material collection time of the concrete monitoring dynamic graph;

[0089] The selected variable window is set according to the obtained variable window distance. The selected variable window is a horizontal line segment with a fixed length, the length of which is equal to the length of the variable window distance. The horizontal line segment is parallel to the horizontal axis of the local ripple chart.

[0090] Upload the selected variable window to the local waveform;

[0091] Set a fluctuation threshold axis, which is a straight line parallel to the horizontal axis and is a pre-set safety threshold range. This means that waveforms that do not exceed the fluctuation threshold axis range are safe and without abnormalities. The obtained fluctuation threshold axis is then uploaded to the local waveform diagram.

[0092] The local waveform is initially extracted based on the obtained fluctuation threshold axis to obtain the primary abrupt change band;

[0093] It should be further explained that, in the specific implementation process, the preliminary extraction is to determine the range of abrupt changes in the local waveform by the fluctuation threshold axis to obtain the primary abrupt change band, and then further narrow the range to determine the final range of abnormal abrupt changes. Here, preliminary extraction means to perform threshold truncation on the local waveform according to the fluctuation threshold axis to obtain the waveform curve that exceeds the fluctuation threshold axis, which is denoted as the primary abrupt change band.

[0094] The primary mutation band is ultimately defined based on the selected variation window to obtain the raw material mutation monitoring segment;

[0095] The process of ultimate limitation includes:

[0096] The primary abrupt band width is obtained by performing width statistics on the primary abrupt band.

[0097] The width statistics represent the distance between the two endpoints of the waveform curve segment of the primary abrupt band, thus obtaining the primary waveform width;

[0098] The obtained primary waveform width is compared with the selected variable window width. If the primary waveform width is greater than or equal to the selected variable window width, the primary change waveform band corresponding to the primary waveform width is recorded as the raw material change monitoring segment. If the primary waveform width is less than the selected variable window width, the primary change waveform band corresponding to the primary waveform width is recorded as the raw material qualified monitoring segment.

[0099] Based on the obtained raw material mutation monitoring segment, the local waveform diagram is time-matched to obtain the mutation acquisition time. The mutation acquisition time includes the mutation start point and the mutation end point. Since there is a one-to-one correspondence between the horizontal axis time of the local waveform diagram and the raw material acquisition time of the concrete monitoring dynamic diagram, the raw material acquisition time corresponding to the waveform can be found in the local waveform diagram. That is, the mutation start point represents the raw material acquisition time at the start point corresponding to the raw material mutation monitoring segment in the local waveform diagram, and the mutation end point represents the raw material acquisition time at the end point corresponding to the raw material mutation monitoring segment in the local waveform diagram.

[0100] The obtained mutation collection time is uploaded to the environmental monitoring dynamic map. Anomalies are matched with the environmental monitoring dynamic map by mutation collection time to obtain the time period of environmental mutation.

[0101] The abnormal pairing means pairing the time of mutation with the time represented by the horizontal axis in the environmental monitoring dynamic graph to find the corresponding time point, that is, the raw material collection time corresponding to the time point matched in the environmental monitoring dynamic graph, and thus obtaining the environmental mutation time period.

[0102] Obtain dynamic environmental monitoring graphs, and perform curve matching on the dynamic environmental monitoring graphs through the time periods of environmental abrupt changes to obtain environmental abrupt change curve segments;

[0103] The curve matching refers to the part of the environmental change curve corresponding to the time period of environmental change in the environmental monitoring dynamic map, which is the environmental change curve segment. According to the environmental change curve, including all curves corresponding to the internal environment of concrete and the storage warehouse environment, the environmental change curve segment can be obtained for each type of environmental change curve.

[0104] Based on the virtual-real twin space, the obtained local wave pattern and the environmental monitoring dynamic map are normalized and statistically analyzed to obtain the concrete matching environment map.

[0105] The normalized statistics represent mapping the local waveform and the environmental monitoring dynamic graph to the same two-dimensional rectangular coordinate system. The starting points of the curves in the local waveform and the environmental monitoring dynamic graph are the same. The corresponding raw material collection time, raw material mutation monitoring segment, environmental mutation time period and environmental mutation curve segment are marked in the two-dimensional rectangular coordinate system. That is, the raw material mutation monitoring segment and the environmental mutation curve segment can be obtained at the same time during the environmental mutation time period.

[0106] Based on the concrete matching environment map, differential capture is performed on the obtained raw material mutation monitoring section to obtain the total difference of raw material mutation;

[0107] The differential capture means that in the concrete matching environment map, the horizontal distance between the highest and lowest points of the raw material mutation monitoring section is statistically analyzed to obtain the height difference of the raw material mutation monitoring section, which is the total difference of raw material mutation.

[0108] Based on the concrete matching environment map, an adjustable range is set according to the obtained raw material collection time. The adjustable range represents a time interval with an adjustable length.

[0109] The obtained adjustable range is uploaded to the concrete matching environment map and coincides with the starting point of the raw material mutation monitoring section.

[0110] Based on the obtained adjustable intervals, interval statistics are performed on the concrete matching environment map to obtain abrupt change interval segments;

[0111] The interval statistics refer to the portion of raw material mutation monitoring segment covered by the adjustable interval in the concrete matching environment diagram as the mutation interval segment.

[0112] Differential capture is performed on the obtained mutation intervals to obtain the interval mutation difference value;

[0113] The interval mutation difference represents the horizontal distance between the highest and lowest points in the mutation interval segment;

[0114] The total difference in raw material mutations is calculated based on the obtained interval mutation differences to obtain the pre-adjustment ratio, which is denoted as Y. Q1 represents the interval abrupt change difference. This indicates the total difference in raw material mutations;

[0115] Based on the obtained mutation intervals, environmental matching is performed on the concrete matching environment map to obtain the environmental matching interval. Here, environmental matching refers to the part of the curve corresponding to the mutation interval in the concrete matching environment map, which is denoted as the environmental matching interval.

[0116] The environmental matching interval is simulated and adjusted by pre-adjustment ratio to obtain pre-adjustment instructions, and an optimal control strategy is generated based on the obtained pre-adjustment instructions.

[0117] It needs further explanation that, in the specific implementation process, the adjustable range has corresponding mutation intervals and environmental matching intervals. This means that within an adjustable range, the environmental matching interval corresponding to the mutation interval has abnormal mutations. This is because a change in a certain data point in the environmental monitoring data causes abnormal fluctuations in the concrete foundation data corresponding to the mutation interval within the adjustable range, which may lead to a deterioration in the performance and service effect of the concrete. Therefore, it is necessary to simulate and adjust the corresponding environmental matching interval in a virtual-real twin space according to the characteristics of the mutation interval, integrate the simulation and adjustment process, generate the corresponding optimal control strategy, and simulate the operation of the optimal control strategy in the virtual-real twin space. At the same time, the strategy that is not ideal in the simulation operation is optimized to achieve the best curing effect. The process of simulating and adjusting the environmental matching interval includes:

[0118] Based on the obtained pre-adjustment ratio, the environmental matching interval is pre-adjusted to obtain the environmental pre-adjustment instruction;

[0119] The pre-adjustment means increasing or decreasing the environmental monitoring data corresponding to the environmental matching interval by Y% or Y% according to the pre-adjustment ratio. "Increasing or decreasing the environmental monitoring data corresponding to the environmental matching interval by Y% or Y%" is the environmental pre-adjustment instruction. The increase or decrease is determined based on the content of the concrete foundation data curve corresponding to the abrupt change interval. For example, for the cube compressive strength in the concrete foundation data, if the cube compressive strength decreases to the abrupt change interval, then the corresponding environmental matching interval needs to increase the humidity in the concrete storage silo, that is, increase the humidity in the virtual storage silo by Y% within the environmental matching interval. If Y=5, then increase the humidity in the virtual storage silo by 5% within the environmental matching interval. So "increasing the humidity in the virtual storage silo by 5% within the environmental matching interval" is the corresponding environmental pre-adjustment instruction.

[0120] Based on the virtual twin space, the virtual storage chamber is simulated and corrected according to the obtained environmental pre-adjustment instructions, and the concrete matching environment diagram during the simulation correction process is recorded to obtain the original correction loop change diagram.

[0121] The simulation correction refers to applying the environmental pre-adjustment command to the environmental matching interval segment corresponding to the virtual storage chamber in the virtual twin space, adjusting according to the environmental pre-adjustment command based on the environmental matching interval segment, and recording the changes in the concrete matching environment diagram after adjustment to obtain the original correction loop change diagram.

[0122] In particular, each concrete foundation data has a corresponding abrupt change interval and can be matched with the corresponding environmental matching interval. Each simulation correction only changes one variable, that is, only one environmental monitoring data is adjusted. This allows for more accurate effect monitoring, and at the same time, it can identify which environmental factors affect the curing effect of concrete and take timely remedial measures.

[0123] Set a cooling cycle, and perform simulation judgment on the original loop change diagram based on the cooling cycle to obtain the pre-adjustment command effect. The pre-adjustment effect includes pre-adjustment qualified command and pre-adjustment invalid command.

[0124] The cooling cycle refers to a pre-set period of time during which, after simulation correction, the abrupt change intervals in the original loop change diagram need to be restored to the normal level and are no longer abrupt change intervals. If this condition is met, the environmental pre-adjustment command corresponding to the simulation correction is recorded as a qualified pre-adjustment command; otherwise, it is recorded as an invalid pre-adjustment command.

[0125] Based on the effect of the obtained pre-tuning instructions, the environmental pre-tuning instructions are optimized to obtain optimized pre-tuning instructions;

[0126] Furthermore, since each concrete foundation data corresponding to the virtual storage chamber may have a corresponding mutation range, each mutation range needs to be pre-adjusted to obtain environmental pre-adjustment instructions. However, this environmental pre-adjustment instruction may not be able to achieve the best curing effect and needs further optimization. Therefore, it is necessary to simulate and run each environmental pre-adjustment instruction in the initial pre-adjustment strategy in the virtual twin space and monitor whether the running result is the optimal adjustment scale, and finally obtain the optimized pre-adjustment instruction. The optimized pre-adjustment instruction means modifying and optimizing the environmental pre-adjustment instruction by changing the pre-adjustment ratio.

[0127] Specifically, modifying the pre-adjustment ratio to optimize the environmental pre-adjustment command means expanding or shrinking the pre-adjustment ratio by a factor of k. This k factor is determined based on the original loop change diagram. The "expansion or reduction" is determined by the relationship between the concrete foundation data and the environmental monitoring data. For example, if the cubic compressive strength in the concrete foundation data decreases to a sudden change interval, then increasing the humidity in the virtual storage chamber by Y% within the environmental matching interval would be changed to increasing the humidity in the virtual storage chamber by k×Y% within the environmental matching interval.

[0128] The optimal control strategy is generated by combining environmental pre-adjustment instructions with optimized pre-adjustment instructions. This means that, according to the time sequence of raw material collection, environmental pre-adjustment instructions that have the effect of qualified pre-adjustment instructions are retained, while environmental pre-adjustment instructions that have the effect of invalid pre-adjustment instructions are replaced with the corresponding optimized pre-adjustment instructions, thus obtaining the optimal control strategy. By simulating different environmental control strategies in a virtual twin space, large-scale testing and adjustments are not required in actual engineering, avoiding material waste, equipment damage, and increased labor costs caused by strategy errors in actual operation. Furthermore, the simulation results can be used to reverse-optimize the adjustment instructions, achieving the goal of bidirectional optimization of the control strategy.

[0129] Based on the above-mentioned intelligent environmental control system for winter curing of concrete, the present invention also provides an intelligent environmental control method for winter curing of concrete, comprising the following steps:

[0130] Step 1: Collect concrete foundation data and environmental monitoring data for the concrete storage silo;

[0131] Step 2: Construct a virtual twin space, perform graph transformation on concrete foundation data and environmental monitoring data to obtain dynamic graphs of concrete monitoring and environmental monitoring, extract curves from the dynamic graphs of concrete monitoring to obtain local monitoring coefficients;

[0132] Step 3: Capture anomalies in local monitoring coefficients to obtain raw material mutation monitoring segments. Based on the raw material mutation monitoring segments, verify the time of environmental monitoring dynamics to obtain the environmental mutation time periods.

[0133] Step 4: Adjust the time period of environmental abrupt changes to obtain an adjustable range. Based on the adjustable range, perform optimization simulation through virtual twin space to obtain an optimized control strategy.

[0134] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent environmental control system for winter curing of concrete, comprising a control center, characterized in that, The control center is connected to a building acquisition module, an intelligent processing module, a maintenance analysis module, and a strategy execution module; The building data acquisition module is used to collect concrete foundation data and environmental monitoring data of the concrete storage silo. The intelligent processing module is used to construct a virtual-real twin space, perform graph transformation on concrete foundation data and environmental monitoring data to obtain dynamic graphs of concrete monitoring and environmental monitoring, extract curves from the dynamic graphs of concrete monitoring, and obtain local monitoring coefficients. The process of extracting curves from dynamic concrete monitoring graphs includes: Curveification is performed on the dynamic graph of concrete monitoring to obtain the foundation monitoring coefficient. Curveification means converting the foundation transformation curve in the dynamic graph of concrete monitoring into a signal to obtain the foundation monitoring coefficient. Set the range control base, which is a suitable wavelet function selected based on the signal characteristics of the basic monitoring coefficients; Scale statistics are performed on the range control base to obtain the variable window spacing, which represents the length of the statistical range control base. The amplification parameters are set according to the variable window spacing, and the range of the basic monitoring coefficients is increased by the amplification parameters to obtain the expanded monitoring coefficients; Local monitoring coefficients are obtained by locally capturing the expanded monitoring coefficients through range-controlled cardinality. Local capture means convolving the range-controlled cardinality with the expanded monitoring coefficients to obtain the local monitoring coefficients. The maintenance analysis module is used to capture anomalies in local monitoring coefficients, obtain raw material mutation monitoring segments, and perform time verification on the environmental monitoring dynamic map based on the raw material mutation monitoring segments to obtain the environmental mutation time periods. The strategy execution module is used to adjust the time period of environmental change to obtain an adjustable range. Based on the adjustable range, optimization simulation is performed through virtual and real twin space to obtain an optimized control strategy.

2. The intelligent environmental control system for winter curing of concrete according to claim 1, characterized in that, The process of collecting concrete foundation data and environmental monitoring data includes: Set up raw material collection terminals and environmental collection terminals; Data is collected from the concrete storage silo through the raw material collection terminal to obtain concrete foundation data, and the collected concrete foundation data is time-stamped to obtain the raw material collection time. Based on the raw material collection time, environmental monitoring data is obtained by collecting factors from the concrete storage silo through an environmental acquisition terminal.

3. The intelligent environmental control system for winter curing of concrete according to claim 1, characterized in that, The process of converting concrete foundation data and environmental monitoring data into graphs includes: A virtual twin space is constructed based on the concrete storage silo, and the concrete storage silo is mapped to the virtual twin space to obtain a virtual storage silo; A two-dimensional rectangular coordinate system is constructed, and a foundation change curve is generated based on the concrete foundation data. The foundation change curve is then uploaded to the two-dimensional rectangular coordinate system to obtain a dynamic image of concrete monitoring. Based on the concrete monitoring dynamic map, an environmental monitoring dynamic map is constructed from environmental monitoring data, and both the concrete monitoring dynamic map and the environmental monitoring dynamic map are uploaded to the virtual twin space.

4. The intelligent environmental control system for winter curing of concrete according to claim 1, characterized in that, The process of anomaly capture for local monitoring coefficients includes: Perform wavelet permutation on the local monitoring coefficients to obtain a local wavelet graph, set the selected variable window and upload it to the local wavelet graph; Set a fluctuation threshold axis, and perform preliminary extraction of the local waveform based on the fluctuation threshold axis to obtain the primary abrupt change band; By defining the primary mutation band using the selected variable window, the raw material mutation monitoring segment is obtained.

5. The intelligent environmental control system for winter curing of concrete according to claim 4, characterized in that, The process of verifying the environmental monitoring dynamic chart based on the raw material mutation monitoring segment includes: The mutation acquisition time is obtained by time matching of the local waveform diagram based on the raw material mutation monitoring section; The mutation collection time is uploaded to the environmental monitoring dynamic map. Anomalies are matched with the environmental monitoring dynamic map by mutation collection time to obtain the time period of environmental mutation.

6. The intelligent environmental control system for winter curing of concrete according to claim 5, characterized in that, The process of adjusting the settings for periods of sudden environmental changes includes: By matching the environmental monitoring dynamic graph with the time period of environmental abrupt change, the environmental abrupt change curve segment is obtained; Based on the virtual-real twin space, the local wave pattern and the environmental monitoring dynamic map are normalized and statistically analyzed to obtain the concrete matching environment map. Based on the concrete matching environment map, differential capture is performed on the raw material mutation monitoring section to obtain the total difference of raw material mutation, and an adjustable range is set and uploaded to the concrete matching environment map.

7. The intelligent environmental control system for winter curing of concrete according to claim 6, characterized in that, The process of optimization simulation through virtual twin space includes: Based on the adjustable interval, interval statistics are performed on the concrete matching environment map to obtain abrupt change interval segments. The abrupt change interval segments are then captured by differential measurement to obtain the interval abrupt change difference value. The total difference in raw material mutation is calculated based on the mutation difference in the interval to obtain the pre-adjustment ratio. The environmental matching of the concrete matching environment diagram is performed based on the mutation interval to obtain the environmental matching interval. The environmental matching interval is simulated and adjusted by pre-adjustment ratio to obtain pre-adjustment instructions, and an optimal control strategy is generated based on the obtained pre-adjustment instructions.

8. The intelligent environmental control system for winter curing of concrete according to claim 7, characterized in that, The process of simulating and adjusting the environmental matching interval using a pre-adjustment ratio includes: The environmental matching interval is pre-adjusted according to the pre-adjustment ratio to obtain the environmental pre-adjustment instruction; Based on the virtual twin space, the virtual storage chamber is simulated and corrected according to the pre-adjustment instructions of the environment, and the concrete matching environment diagram during the simulation correction process is recorded to obtain the original loop change diagram. A cooling cycle is set, and the change diagram of the original correction loop is simulated and judged based on the cooling cycle to obtain the effect of the pre-adjustment command. The environmental pre-adjustment command is optimized based on the effect of the pre-adjustment command to obtain the optimized pre-adjustment command. The optimal control strategy is generated based on the environmental pre-adjustment command and the optimized pre-adjustment command.

9. An intelligent environmental control method for an intelligent environmental control system for winter curing of concrete according to any one of claims 1 to 8, characterized in that, Includes the following steps: Step 1: Collect concrete foundation data and environmental monitoring data for the concrete storage silo; Step 2: Construct a virtual twin space, perform graph transformation on concrete foundation data and environmental monitoring data to obtain dynamic graphs of concrete monitoring and environmental monitoring, extract curves from the dynamic graphs of concrete monitoring to obtain local monitoring coefficients; Step 3: Capture anomalies in local monitoring coefficients to obtain raw material mutation monitoring segments. Verify the environmental monitoring dynamic map based on the raw material mutation monitoring segments to obtain the environmental mutation time periods. Step 4: Adjust the time period of environmental abrupt changes to obtain an adjustable range. Based on the adjustable range, perform optimization simulation through virtual twin space to obtain an optimized control strategy.

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