CN116562461A - Indoor air state monitoring point optimal arrangement method suitable for EnKF data assimilation - Google Patents
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Abstract
The invention discloses an indoor air state monitoring point optimal arrangement method suitable for EnKF data assimilation, and belongs to the field of building environment testing. Firstly, a working condition sample set is constructed, candidate air state measuring point positions are set according to engineering case requirements and feasibility, spearman correlation coefficients between candidate measuring point states and boundary condition parameter variables are calculated, standard deviations of the reserved candidate measuring point states in each boundary condition parameter variable interval are calculated according to air state analog values of the candidate measuring points under corresponding working conditions, the number m of measuring points is not less than the total number n of condition parameter variables, the measuring point state variable quantity caused by unit condition parameter variable changes in each boundary condition parameter variable interval is calculated according to the standard deviations, and then extreme values of coefficient matrixes corresponding to the candidate measuring point combinations when the candidate measuring point combinations are used for data synchronization are calculated.
Description
Technical Field
The invention belongs to the field of building environment testing, and particularly relates to an indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation.
Background
Indoor air condition assessment is the basis for air conditioning ventilation system operation optimization. Because the air state is disturbed by various factors indoors and outdoors, the air state has the characteristic of time-space non-uniformity, a large number of measuring instruments are required to be arranged in a wide space area to acquire the accurate air state parameter distribution condition, high equipment initial investment and maintenance cost are caused, inconvenience is brought to residents, and the air state parameter distribution system is not feasible in actual engineering. The integrated Kalman filtering (Ensemble Kalman Filter, enKF) data assimilation technology can organically combine limited measurement data with Computational Fluid Dynamics (CFD) simulation of multiple physical fields, so that boundary condition parameters of the current working condition of a numerical model are calculated by a small amount of air state parameter measurement data, and global distribution information of the multiple physical fields is obtained through the CFD. Although the method can play a positive role in building environment simulation and evaluation, the performance of the method is greatly influenced by the measured data attribute. Environmental condition monitoring points in actual engineering are often arranged according to area indexes or by relying on artificial experience, and cannot provide useful measurement data for EnKF data assimilation in a targeted manner. When using the EnKF data assimilation method, the appropriate state monitoring points are usually found in a trial-and-error mode, which wastes a great deal of time cost and calculation resources.
Disclosure of Invention
Based on the defects, the invention provides an indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation, which solves the problem of poor data assimilation effect caused by improper arrangement of measuring points.
The technology adopted by the invention is as follows: an indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation comprises the following steps:
firstly, constructing a working condition sample set in a self-adaptive manner, constructing an original basic working condition sample set through extremum combination of boundary condition variables of each system, carrying out CFD simulation on working condition samples in the basic working condition sample set to obtain a corresponding physical field simulation result set, then creating a median parameter variable working condition through linear interpolation of adjacent working condition samples in the physical field simulation result set, simultaneously estimating an indoor physical field under the median parameter variable through linear interpolation, comparing the physical field estimated through interpolation with a physical field obtained through CFD simulation under the same parameter variable, calculating an error of the physical field linear interpolation, supplementing the median parameter variable working condition into the physical field simulation result set if the error is larger than a set limit value, supplementing a physical field CFD simulation result corresponding to the working condition into the physical field simulation result set if the error is smaller than the limit value, and then not supplementing so on until errors between a linear interpolation result and a CFD simulation result of the median parameter variable working condition of the adjacent samples are smaller than the limit value, and completing construction of the physical field simulation result set corresponding to the basic working condition sample set;
setting candidate air state measuring point positions by combining engineering case requirements and feasibility, setting m candidate measuring points at equal intervals in a building space, and deriving air state simulation values of the candidate measuring points under corresponding working conditions according to physical field CFD simulation results of the working condition samples;
step three, calculating Spearman correlation coefficient between the candidate measuring point state and the boundary condition parameter variable according to the basic working condition sample set and the air state simulation value of each candidate measuring point under the corresponding working condition, so as to analyze monotonicity of the candidate measuring point state in the boundary condition parameter variable interval, if Representing the air state at station j, j=1, 2,3, …, m; for any s i If yes, reserving the j measuring points, otherwise, eliminating the j measuring points from the candidate measuring points; wherein, the condition parameter variable with uncertainty in the boundary condition of the system is s i I=1, 2,3, …, n, the variable vector consisting of s= [ s ] 1 s 2 … s i … s n ] T Which is provided withN is the total number of conditional parameter variables; in the EnKF algorithm, the key EnKF filter equation is shown in formula (1),
wherein s' represents a condition parameter variable vector after data assimilation,for the monitoring point state vector, +.>For covariance between conditional parameter variable and monitoring point state prediction value, +.>For covariance among state predicted values of all monitoring points, Y is a measured value vector, M represents a projection matrix of the measured value vector to the predicted value vector, and C ∈∈ A covariance matrix of measurement errors;
to make the solution of s' unique, when the parameter variables of each boundary condition have different values, the states of the measuring points used for assimilation of EnKF data are not equal, so that in the variable interval of the parameter variables of each boundary condition, the states of the measuring points have monotonicity, namely:
step four, calculating the standard deviation of the state of the reserved candidate measuring points in each boundary condition parameter variable interval according to the air state analog value of each candidate measuring point under the corresponding working condition, comparing the standard deviation with the standard deviation of the instrument measuring error, ifIf true, reserving the j measuring points, otherwise, eliminating the j measuring points and sigma from the candidate measuring points j For air state error mark at measuring point jThe accuracy is poor; wherein, when there is an error in measurement, C ∈∈ Not equal to 0, i.e. the air state truth value exists within the measurement error range; in order to ensure the calculation stability, when the difference between the simulation value and the measured value is smaller than the measurement error, the simulation result is considered to be approximate to the real working condition; therefore, in the variable range of the boundary condition parameter, if the variable quantity of the state of the measuring point is always smaller than the measurement error, the method cannot be used for assimilating EnKF data, namely:
step five, the number m of measuring points required by EnKF data assimilation is not less than the total number n of conditional parameter variables, m=n is taken, the candidate measuring points reserved in the step four are combined according to 1 group for every m, and the reserved candidate measuring points are d, so that the method is formedThe number of candidate measuring point combinations, according to the air state analog value of each candidate measuring point under the corresponding working condition, calculating the Pearson correlation coefficient between the states of each candidate measuring point in each combination under the working condition change, so as to analyze the linear correlation between the states of the candidate measuring points, if->If any two measuring points in a certain combination are established, reserving the candidate measuring point combination, otherwise, eliminating the candidate measuring point combination; wherein, when the measurement is absolutely accurate, C ∈∈ =0, at which point equation (1) is made meaningful, then:
in the method, in the process of the invention,the covariance between the state predicted values of the measuring point a and the measuring point b is represented, wherein a, b=1, 2,3, …, m and m are the total number of the measuring points; />Representing the pearson correlation coefficient between the state predicted values of the measuring point a and the measuring point b; the state of any two measuring points used for EnKF data assimilation is required to be in nonlinear correlation according to the formula (6);
step six, calculating the measuring point state change quantity beta caused by the unit condition parameter variable change in each boundary condition parameter variable interval according to the basic working condition sample set and the air state simulation value of each candidate measuring point under the corresponding working condition i,j Then calculating extreme values of a coefficient matrix A corresponding to each candidate measuring point combination used for data assimilation, namely a minimum value min (|A|) and a maximum value max (|A|), if min (|A|) max (|A|) is larger than 0, reserving the measuring point combination, otherwise, removing the measuring point combination, wherein the reserved measuring point combination is judged to be the measuring point combination suitable for EnKF data assimilation, and sequentially outputting information such as the number of measuring points, the measuring point positions and measured state parameter objects contained in each combination to obtain a monitoring point arrangement scheme suitable for EnKF data assimilation, wherein the information comprises measured state parameters, the number of measuring points and the measuring point positions; wherein, for any boundary condition parameter variable:
β i ＝[β i,1 β i,2 … β i,j … β i,m ] (12)
let A be the coefficient matrix of the above equation set, namely:
to make Deltas i The rank of the equation set coefficient matrix is equal to the total number of condition parameter variables with unique solutions, namely:
rank(A)＝n (16)
when m=n, to hold rank (a) =n, then:
wherein alpha is i As conditional parameter variable s i A vector of ratios of the induced state prediction error to the total state prediction error,expressed in terms of parameter variabless i S in case of individual variation i Covariance with monitoring point state prediction value,/->To be under condition parameter variable s i Covariance between predictions of states of each monitoring point, Δs, with individual changes i As conditional parameter variable s i Is the correction amount of beta i To be at variable s i The air state change vector of each monitoring point per unit change, beta i,j The air state representing the measurement point j is at variable s i The amount of change in each unit change, Y j Is the measurement at station j.
Further, the invention provides an indoor air state monitoring point optimizing arrangement system suitable for EnKF data assimilation, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor processes and executes the computer program to realize the indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation as described above, and outputs a monitoring point arrangement scheme suitable for EnKF data assimilation.
The invention has the advantages and beneficial effects that: the invention determines an effective measurement scheme for the indoor multi-physical-field EnKF data assimilation, so that the measurement data and the boundary condition parameters obtained after numerical simulation assimilation have unique solutions, and the corresponding multi-physical-field simulation result has high accuracy. The invention can determine the minimum number of the monitoring devices required in the measurement and reasonably control the investment cost. The invention simultaneously avoids heavy manual trial-and-error work and saves a large amount of calculation resources.
Drawings
FIG. 1 is a technical flow chart of an optimized arrangement of indoor air state monitoring points for EnKF data assimilation
Figure 2 is a graph of an adaptively constructed operating mode sample,
wherein (a) adaptively constructs a working condition sample schematic, (b) adaptively constructs a working condition sample flow chart;
FIG. 3 is a drawing of a reduced scale model of a building space;
figure 4 is a map of candidate station locations,
wherein (a) a temperature candidate measuring point numbering chart, (b) a wind speed measuring point numbering chart;
FIG. 5 is a graph of measurement scheme optimization screening results;
FIG. 6 is a graph of simulated error versus wind speed field;
FIG. 7 is a temperature field simulated error versus graph;
Detailed Description
The boundary conditions of the system directly act on the distribution of the multiple physical fields in the space, the influence degree of each factor in the boundary conditions on the air states of different space points is different, the possibility that the air environment formed under different working conditions has the same state at part of positions exists is obvious that the unique and accurate boundary condition parameters cannot be obtained if the position points are used as air state monitoring points for EnKF data assimilation. Therefore, the key to optimizing the monitoring scheme is to analyze the correlation between the air state of each location point and each factor in the boundary conditions of the system. Starting from the basic principle of the EnKF algorithm, the invention derives the basic principle that the measurement scheme should meet, and creates an indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation, and the invention is further described with reference to the accompanying drawings and specific embodiments.
Example 1
In order to realize the optimal arrangement of indoor air state monitoring points, as shown in fig. 1, the method for the optimal arrangement of indoor air state monitoring points suitable for EnKF data assimilation is provided, and comprises the following steps:
step one, as shown in fig. 2, a working condition sample set is adaptively constructed, an original basic working condition sample set is firstly constructed through extremum combination of boundary condition variables of each system, working condition samples in the basic working condition sample set are subjected to CFD simulation to obtain a corresponding physical field simulation result set, then a median parameter variable working condition is built through linear interpolation of adjacent working condition samples in the physical field simulation result set, meanwhile, an indoor physical field under the median parameter variable is estimated through linear interpolation, the physical field estimated through interpolation is compared with a physical field obtained through CFD simulation under the same parameter variable, an error of the linear interpolation of the physical field is calculated, if the error is larger than a set limit value, the median parameter variable working condition is supplemented into the physical field simulation result set, meanwhile, a physical field CFD simulation result corresponding to the working condition is supplemented into the physical field simulation result set, if the error is smaller than the limit value, supplementation is not needed, and the same until errors between the linear interpolation result and the CFD simulation result of the median parameter variable of the adjacent working condition are smaller than the limit value, and the physical field simulation result of the basic working condition set and the corresponding physical field simulation result set are completed;
setting candidate air state measuring point positions by combining engineering case requirements and feasibility, setting m candidate measuring points at equal intervals in a building space, and deriving air state simulation values of the candidate measuring points under corresponding working conditions according to physical field CFD simulation results of the working condition samples;
step three, calculating Spearman correlation coefficient between the candidate measuring point state and the boundary condition parameter variable according to the basic working condition sample set and the air state simulation value of each candidate measuring point under the corresponding working condition, so as to analyze monotonicity of the candidate measuring point state in the boundary condition parameter variable interval, if Representing the air state at station j, j=1, 2,3, …, m for any s i If yes, reserving the j measuring points, otherwise, eliminating the j measuring points from the candidate measuring points; wherein, the condition parameter variable with uncertainty in the boundary condition of the system is s i I=1, 2,3, …, n, the variable vector consisting of s= [ s ] 1 s 2 … s i … s n ] T Wherein n is the total number of conditional parameter variables;
in the EnKF algorithm, the key EnKF filter equation is shown in formula (1),
wherein s' represents a condition parameter variable vector after data assimilation,for the monitoring point state vector, +.>For covariance between conditional parameter variable and monitoring point state prediction value, +.>For covariance among state predicted values of all monitoring points, Y is a measured value vector, M represents a projection matrix of the measured value vector to the predicted value vector, and C ∈∈ A covariance matrix of measurement errors;
to make the solution of s' unique, when the parameter variables of each boundary condition have different values, the states of the measuring points used for assimilation of EnKF data are not equal, so that in the variable interval of the parameter variables of each boundary condition, the states of the measuring points have monotonicity, namely:
step four, calculating the standard deviation of the state of the reserved candidate measuring points in each boundary condition parameter variable interval according to the air state analog value of each candidate measuring point under the corresponding working condition, comparing the standard deviation with the standard deviation of the instrument measuring error, ifIf true, reserving the j measuring points, otherwise, eliminating the j measuring points and sigma from the candidate measuring points j The standard deviation of the air state error at the measuring point j; wherein, when there is an error in measurement, C ∈∈ Not equal to 0, i.e. the air state truth value exists within the measurement error range; to ensure computational stability, a simulated junction is identified when the difference between the simulated and measured values is less than the measurement errorThe result is similar to the real working condition; therefore, in the variable range of the boundary condition parameter, if the variable quantity of the state of the measuring point is always smaller than the measurement error, the method cannot be used for assimilating EnKF data, namely:
step five, as the number m of measuring points required by EnKF data assimilation is not less than the total number n of condition parameter variables, taking m=n, combining the candidate measuring points reserved in the step four according to 1 group for every m, and setting the number d of reserved candidate measuring points to form the methodThe number of candidate measuring point combinations, according to the air state analog value of each candidate measuring point under the corresponding working condition, calculating the Pearson correlation coefficient between the states of each candidate measuring point in each combination under the working condition change, so as to analyze the linear correlation between the states of the candidate measuring points, if->If any two measuring points in a certain combination are established, reserving the candidate measuring point combination, otherwise, eliminating the candidate measuring point combination; wherein, when the measurement is absolutely accurate, C ∈∈ To make equation (1) meaningful at this time, then=0:
in the method, in the process of the invention,the covariance between the state predicted values of the measuring point a and the measuring point b is represented, wherein a, b=1, 2,3, …, m and m are the total number of the measuring points; />Representing the pearson correlation coefficient between the state predicted values of the measuring point a and the measuring point b; the state of any two measuring points used for EnKF data assimilation is required to be in nonlinear correlation according to the formula (6);
step six, calculating the measuring point state change quantity beta caused by the unit condition parameter variable change in each boundary condition parameter variable interval according to the basic working condition sample set and the air state simulation value of each candidate measuring point under the corresponding working condition i,j Then calculating extreme values of a coefficient matrix A corresponding to each candidate measuring point combination used for data assimilation, namely a minimum value min (|A|) and a maximum value max (|A|), if min (|A|) max (|A|) is larger than 0, reserving the measuring point combination, otherwise, removing the measuring point combination, wherein the reserved measuring point combination is judged to be the measuring point combination suitable for EnKF data assimilation, and sequentially outputting information such as the number of measuring points, the measuring point positions and measured state parameter objects contained in each combination to obtain a monitoring point arrangement scheme suitable for EnKF data assimilation, wherein the information comprises measured state parameters, the number of measuring points and the measuring point positions; wherein, for any boundary condition parameter variable:
β i ＝[β i,1 β i,2 … β i,j … β i,m ] (12)
let A be the coefficient matrix of the above equation set, namely:
to make Deltas i The rank of the equation set coefficient matrix is equal to the total number of condition parameter variables with unique solutions, namely:
rank(A)＝n (16)
when m=n, to hold rank (a) =n, then:
wherein alpha is i As conditional parameter variable s i A vector of ratios of the induced state prediction error to the total state prediction error,expressed in the condition parameter variable s i S in case of individual variation i Covariance with monitoring point state prediction value,/->To be under condition parameter variable s i Covariance between predictions of states of each monitoring point, Δs, with individual changes i As conditional parameter variable s i Is the correction amount of beta i To be at variable s i The air state change vector of each monitoring point per unit change, beta i,j The air state representing the measurement point j is at variable s i The amount of change in each unit change, Y j Is the measurement at station j.
It follows that the measurement scheme for EnKF data assimilation needs to satisfy the following five-point principle:
(one), any two measuring point states used for EnKF data assimilation are in nonlinear correlation,
secondly, in the variable range of the boundary condition parameter, the standard deviation of the variable quantity of the state of the measuring point is larger than the standard deviation of the measuring error,
(III) in each boundary condition parameter variable change interval, the measuring point state has monotonicity, namely:
fourthly, the number of the measuring points is not less than the total number of the condition parameter variables, and m is more than or equal to n;
(fifth), when m=n, the coefficient matrix determinant composed of the measurement state change caused by the unit boundary condition parameter variable change is not 0,
example 2
The embodiment discloses an indoor air state monitoring point optimizing arrangement system suitable for EnKF data assimilation, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor processes and executes the computer program to realize the indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation as described in the embodiment 1, and outputs a monitoring point arranging scheme suitable for EnKF data assimilation.
Example 3
An ideal building space scale model is shown in fig. 3, where the cell space dimensions are length x width x height=1 m x 0.75m x 0.5m. Two air conditioner air supply outlets 1 are arranged on the top plate and supply air indoors cooperatively, and return air inlets 2 are symmetrically arranged below two side wall surfaces. Indoor heat sources are uniformly distributed on the ground, and other wall surfaces of the building have no heat transfer. The known system composition of the indoor thermal environment of the building comprises 3 boundary condition parameter variables with uncertainty, namely air conditioner air supply speed, air supply temperature and ground heat dissipation capacity, and the corresponding operation ranges are shown in table 1.
TABLE 1 boundary condition parameter variable value intervals
The objective of this embodiment is to analyze the indoor air temperature and flow velocity distribution, and known that the candidate measuring points in the two air states are uniformly distributed in the space length, width and height directions, and fig. 4 shows the position coordinates and the numbers of the candidate measuring points of the temperature and the wind speed.
According to the extreme values of boundary condition parameter variables in table 1, 8 basic working condition samples are established to form an original sample set, and the boundary condition parameter variable values corresponding to the samples are shown in table 2. And carrying out CFD simulation on the working condition samples in the set to obtain a set of corresponding physical field (air flow field and temperature field) simulation results. Then, working condition samples in the set are linearly interpolated to create a median parameter variable working condition, and meanwhile, the linear interpolation is used for estimating an indoor physical field under the median parameter variable, for example: the air supply wind speed of 0.4 is obtained by linear interpolation of working condition 1 and working condition 2 in table 2m/s, air supply temperature of 14 ℃ and ground heat flow density of 30W/m 2 And simultaneously, the air flow field and the temperature field obtained by CFD simulation of the working condition 1 and the working condition 2 are respectively and linearly interpolated to estimate the air flow field and the temperature field under the working condition of the median parameter variable. Comparing the interpolation estimated physical field with the physical field obtained by utilizing CFD simulation under the corresponding neutral parameter variable working condition, calculating the error of linear interpolation, if the error between the interpolation result of the air flow field or the temperature field and the CFD simulation result is larger than a set limiting value (the set relative error limiting value is set to be 3 percent in the present case), supplementing the neutral parameter variable working condition to the working condition sample set, and meanwhile supplementing the physical field CFD simulation result corresponding to the working condition to the physical field set, and if the error is smaller than the limiting value, supplementing the physical field CFD simulation result without supplementing the physical field sample set. And the same is repeated until the error between the linear interpolation result and the CFD simulation result of the neutral parameter variable working condition physical field of the adjacent samples is smaller than a limit value, and the working condition sample set and the CFD simulation result set of the corresponding physical field are constructed. The final set of operating condition samples for this example was calculated to be 25 samples in total, as shown in table 3.
TABLE 2 original set of base operating condition samples
Table 3 adaptively generated working condition sample set
According to the positions of the candidate measuring points, the temperature and flow velocity simulation values of the air at the positions of the candidate measuring points under various working conditions are derived from the physical field CFD simulation results of the working condition samples, the Spearman correlation coefficient between the states (temperature and flow velocity) of the candidate measuring points and the boundary condition parameter variable is calculated on the basis of the temperature and flow velocity simulation values, and the condition judgment is carried out, wherein the temperature or wind speed candidate measuring points are reserved if the Spearman correlation coefficient=1 between the temperatures or flow velocity of the candidate measuring points and the boundary condition parameter variable, otherwise, the temperature or wind speed measuring points are removed from the candidate measuring points.
And then, calculating the standard deviation of the state of the candidate measuring point reserved in the preamble step in each boundary condition parameter variable interval according to the simulation result of the working condition sample, and comparing the standard deviation with the standard deviation of the instrument measuring error. In each boundary condition parameter variable interval, if the standard deviation of the air temperature change of the candidate temperature measuring point is larger than the standard deviation of the measuring error of the temperature sensor (0.1 ℃ in the present case), the candidate temperature measuring point is reserved, otherwise, the temperature measuring point is removed. Similarly, if the standard deviation of the air flow rate change of the candidate wind speed measuring point is larger than the standard deviation of the measuring error of the wind speed sensor (0.01 m/s in the present case), the candidate wind speed measuring point is reserved, otherwise, the wind speed measuring point is removed.
Since there are 3 boundary condition parameter variables in this embodiment, at least 3 measurement points are required for EnKF data assimilation, and the candidate temperature or wind speed measurement points retained in the preceding step are combined in 1 group for every 3 measurement points. And calculating the Pearson correlation coefficient between the states of the candidate measuring points in each combination when the working condition is changed according to the simulation result of the working condition sample, if the Pearson correlation coefficient between the air states (the air temperature of the temperature measuring point or the air flow rate of the wind speed measuring point) of any two measuring points in the candidate measuring point combination is not 1, namely, the air states of any two measuring points are nonlinear correlated, reserving the candidate measuring point combination, otherwise, eliminating the candidate measuring point combination.
According to the simulation result of the working condition sample, calculating the state change quantity of the measuring point caused by the change of the unit condition parameter variable in each boundary condition parameter variable interval, and then calculating the extreme value of the corresponding coefficient matrix A, namely the minimum value min (A) and the maximum value max (A), when each candidate measuring point combination is used for data synchronization. If min (A) & max (A) & gt 0, determining that the combination is suitable for the measurement point combination of EnKF data assimilation, and sequentially outputting information such as the number of measurement points, the position of the measurement points and the measured state parameters contained in the combination as an optimized measurement scheme.
FIG. 5 shows the combination of measurement points for EnKF data assimilation with optimized screening output. After the non-optimized measurement scheme and the optimized measurement scheme are respectively used for assimilating EnKF data of the simulation of the wind speed and the temperature field of the cell, the obtained simulation errors are compared with each other, and the comparison is shown in fig. 6 and 7. Therefore, the adoption of the optimized arrangement method provided by the embodiment 1 can remarkably improve the assimilation precision of EnKF data.
Claims (2)
1. An indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation is characterized by comprising the following steps:
firstly, constructing a working condition sample set in a self-adaptive manner, constructing an original basic working condition sample set through extremum combination of boundary condition variables of each system, carrying out CFD simulation on working condition samples in the basic working condition sample set to obtain a corresponding physical field simulation result set, then creating a median parameter variable working condition through linear interpolation of adjacent working condition samples in the physical field simulation result set, simultaneously estimating an indoor physical field under the median parameter variable through linear interpolation, comparing the physical field estimated through interpolation with a physical field obtained through CFD simulation under the same parameter variable, calculating an error of the physical field linear interpolation, supplementing the median parameter variable working condition into the physical field simulation result set if the error is larger than a set limit value, supplementing a physical field CFD simulation result corresponding to the working condition into the physical field simulation result set if the error is smaller than the limit value, and then not supplementing so on until errors between a linear interpolation result and a CFD simulation result of the median parameter variable working condition of the adjacent samples are smaller than the limit value, and completing construction of the physical field simulation result set corresponding to the basic working condition sample set;
setting candidate air state measuring point positions by combining engineering case requirements and feasibility, setting m candidate measuring points at equal intervals in a building space, and deriving air state simulation values of the candidate measuring points under corresponding working conditions according to physical field CFD simulation results of the working condition samples;
step three, calculating Spearman correlation coefficients between the states of the candidate measuring points and parameter variables of boundary conditions according to the basic working condition sample set and the air state simulation values of the candidate measuring points under the corresponding working conditions, so as to analyze the states of the candidate measuring points at the boundaryMonotonicity in the condition parameter variable interval, ifRepresenting the air state at station j, j=1, 2,3, …, m; for any s i If yes, reserving the j measuring points, otherwise, eliminating the j measuring points from the candidate measuring points; wherein, the condition parameter variable with uncertainty in the boundary condition of the system is s i I=1, 2,3, …, n, the variable vector consisting of s= [ s ] 1 s 2 … s i … s n ] T Wherein n is the total number of conditional parameter variables; in the EnKF algorithm, the key EnKF filter equation is shown in formula (1),
wherein s' represents a condition parameter variable vector after data assimilation,for the monitoring point state vector, +.>For covariance between conditional parameter variable and monitoring point state prediction value, +.>For covariance among state predicted values of all monitoring points, Y is a measured value vector, M represents a projection matrix of the measured value vector to the predicted value vector, and C ∈∈ A covariance matrix of measurement errors;
to make the solution of s' unique, when the parameter variables of each boundary condition have different values, the states of the measuring points used for assimilation of EnKF data are not equal, so that in the variable interval of the parameter variables of each boundary condition, the states of the measuring points have monotonicity, namely:
step four, calculating the standard deviation of the state of the reserved candidate measuring points in each boundary condition parameter variable interval according to the air state analog value of each candidate measuring point under the corresponding working condition, comparing the standard deviation with the standard deviation of the instrument measuring error, ifIf true, reserving the j measuring points, otherwise, eliminating the j measuring points and sigma from the candidate measuring points j The standard deviation of the air state error at the measuring point j; wherein, when there is an error in measurement, C ∈∈ Not equal to 0, i.e. the air state truth value exists within the measurement error range; in order to ensure the calculation stability, when the difference between the simulation value and the measured value is smaller than the measurement error, the simulation result is considered to be approximate to the real working condition; therefore, in the variable range of the boundary condition parameter, if the variable quantity of the state of the measuring point is always smaller than the measurement error, the method cannot be used for assimilating EnKF data, namely:
step five, the number m of measuring points required by EnKF data assimilation is not less than the total number n of conditional parameter variables, m=n is taken, the candidate measuring points reserved in the step four are combined according to 1 group for every m, and the reserved candidate measuring points are d, so that the method is formedThe number of candidate measuring point combinations, according to the air state analog value of each candidate measuring point under the corresponding working condition, calculating the Pearson correlation coefficient between the states of each candidate measuring point in each combination under the working condition change, so as to analyze the linear correlation between the states of the candidate measuring points, if->If any two measuring points in a certain combination are established, reserving the candidate measuring point combination, otherwise, eliminating the candidate measuring point combination; wherein, when the measurement is absolutely accurate, C ∈∈ =0, at which point equation (1) is made meaningful, then:
in the method, in the process of the invention,the covariance between the state predicted values of the measuring point a and the measuring point b is represented, wherein a, b=1, 2,3, …, m and m are the total number of the measuring points; />Representing the pearson correlation coefficient between the state predicted values of the measuring point a and the measuring point b; the state of any two measuring points used for EnKF data assimilation is required to be in nonlinear correlation according to the formula (6);
step six, calculating the measuring point state change quantity beta caused by the unit condition parameter variable change in each boundary condition parameter variable interval according to the basic working condition sample set and the air state simulation value of each candidate measuring point under the corresponding working condition i,j And then calculating extreme values of a coefficient matrix A corresponding to each candidate measuring point combination used for data assimilation, namely a minimum value min (|A|) and a maximum value max (|A|), if min (|A|) max (|A|) is larger than 0, reserving the measuring point combination, otherwise, removing the measuring point combination, wherein the reserved measuring point combination is judged to be the measuring point combination suitable for EnKF data assimilation, and the reserved measuring point combination is selected from the plurality of setsSequentially outputting information such as the number of the measuring points, the measuring point positions, the measured state parameter objects and the like contained in the combination to obtain a monitoring point arrangement scheme suitable for EnKF data assimilation, wherein the information comprises the measured state parameters, the measuring point numbers and the measuring point positions; wherein, for any boundary condition parameter variable:
β i ＝[β i,1 β i,2 … β i,j … β i,m ] (12)
let A be the coefficient matrix of the above equation set, namely:
to make Deltas i The rank of the equation set coefficient matrix is equal to the total number of condition parameter variables with unique solutions, namely:
rank(A)＝n (16)
when m=n, to hold rank (a) =n, then:
wherein alpha is i As conditional parameter variable s i A vector of ratios of the induced state prediction error to the total state prediction error,expressed in the condition parameter variable s i S in case of individual variation i Covariance with monitoring point state prediction value,/->To be under condition parameter variable s i Covariance between predictions of states of each monitoring point, Δs, with individual changes i As conditional parameter variable s i Is the correction amount of beta i To be at variable s i The air state change vector of each monitoring point per unit change, beta i,j The air state representing the measurement point j is at variable s i The amount of change in each unit change, Y j Is the measurement at station j.
2. An indoor air state monitoring point optimizing arrangement system suitable for EnKF data assimilation comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the processor processes and executes the computer program to realize the indoor air state monitoring point optimizing arrangement method suitable for EnKF data assimilation according to claim 1 and output a monitoring point arrangement scheme suitable for EnKF data assimilation.
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