CN116951681A - Air conditioner refrigerant quantity prediction method, air conditioner and storage medium - Google Patents
Air conditioner refrigerant quantity prediction method, air conditioner and storage medium Download PDFInfo
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- 239000003507 refrigerant Substances 0.000 title claims abstract description 98
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000003860 storage Methods 0.000 title claims abstract description 16
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000013178 mathematical model Methods 0.000 claims description 5
- 239000002826 coolant Substances 0.000 claims 1
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- 238000004378 air conditioning Methods 0.000 description 5
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- 238000004781 supercooling Methods 0.000 description 2
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- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
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- 238000004519 manufacturing process Methods 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Abstract
The application relates to the technical field of air conditioners, and provides a method for predicting the refrigerant quantity of an air conditioner, the air conditioner and a storage medium, wherein the method comprises the following steps of obtaining all key parameter data corresponding to different refrigerant quantities of the air conditioner; the key parameter data comprise air suction superheat data and air discharge superheat data; segmenting all key parameter data to form a plurality of segments; calculating the ratio of key parameter data corresponding to each segment to all data of the key parameter; inputting the ratio to a refrigerant quantity predicted value model, and calculating predicted refrigerant quantity; the refrigerant quantity predicted value model is obtained based on historical key parameter data training. The multi-dimensional linear method is established based on a large amount of random data, and the proportion of key parameters is adopted for analysis, so that the multi-dimensional linear method has higher accuracy.
Description
Technical Field
The application relates to the technical field of air conditioners, and provides a method for predicting refrigerant quantity of an air conditioner, the air conditioner and a storage medium.
Background
When the air conditioning system operates, the refrigerant quantity is an indispensable factor, and the effect of the multi-split air conditioner is directly influenced by the refrigerant quantity; the prediction of the refrigerant quantity of an air conditioning system is a very necessary technology; the multi-split air conditioner is one of air conditioning systems, and has more running parts, more complex running mechanism and more running parameters compared with the conventional air conditioning system; parameters such as operation mode, ambient temperature, compressor frequency, high pressure, low pressure, discharge temperature, discharge superheat, suction temperature, suction superheat, supercooling, electronic expansion valve opening, solenoid valve opening, oil return temperature, etc.
In the prior art, a mode of extracting key parameters (such as high pressure and suction superheat) is adopted, but when the refrigerant quantity is judged, a certain specific data is adopted instead of the big data, and when the certain specific data is adopted for judging the refrigerant quantity, judgment errors are easy to occur.
For example, patent CN113654182a discloses a method for detecting refrigerant leakage, a computer readable storage medium and an air conditioner, but the calculated refrigerant quantity extracted by the method is a specific piece of data, namely a key parameter pressure value, which is more prone to error; patent CN113739348A discloses a method for detecting refrigerant state, an air conditioner and a storage medium, wherein the model established by the method is a neural network model, and a multidimensional linear model is not provided; patent CN110131838A provides a control method, apparatus, computer device and storage medium for an air conditioning unit, which extracts a specific piece of data, and does not provide a clear calculation method of a linear formula.
Disclosure of Invention
In order to solve the problems, the application provides a refrigerant quantity prediction method of an air conditioner, the air conditioner and a storage medium, wherein the method establishes a multidimensional linear method based on a large amount of random data, analyzes the proportion of key parameters, does not simply judge the refrigerant quantity through data of one point, provides a clear linear formula calculation method, and has higher accuracy.
The technical scheme provided by the application is as follows:
a refrigerant quantity prediction method of an air conditioner comprises the following steps:
acquiring all key parameter data corresponding to different refrigerant quantities of an air conditioner; the key parameter data comprise air suction superheat data and air discharge superheat data;
segmenting all key parameter data to form a plurality of segments;
calculating the ratio of key parameter data corresponding to each segment to all data of the key parameter; establishing a multi-order mathematical model of the relation between the sectional key parameters and the refrigerant quantity, solving the calculation coefficients of different sectional key parameter data through a matrix, and constructing a refrigerant quantity predicted value model based on the calculation coefficients;
and inputting the ratio of all the data of the key parameters into a refrigerant quantity predicted value model, and calculating the predicted refrigerant quantity.
Further, the maximum value and the minimum value of the key data are obtained, the difference value of the maximum value and the minimum value is divided by the number of segments, and the key parameter data are equally divided into multiple segments.
Further, the method for acquiring all key parameter data corresponding to different refrigerant amounts of the air conditioner specifically comprises the following steps: and acquiring all corresponding key parameter data within the range of 10% -150% of the standard refrigerant quantity of the system of the air conditioner.
Further, the method for calculating the predicted refrigerant quantity comprises the following steps:
F(x)＝A 1 *f(SH≤M 1 )+A 2 *f(M 1 ＜SH≤M 2 )+A 3 *f(M 2 ＜SH≤M 3 )+……
+A m-1 *f(M m-2 ＜SH≤M m-1 )+A m *f(M m-1 ＜SH)+B 1 *g(DIS≤N 1 )+B 2 *g(N 1 ＜DIS≤N 2 )+B 3 *g(N 2 ＜DIS≤N 3 )+……+B n-1 *g(N n-2 ＜DIS≤N n-1 )+B n *g(N n-1 ＜DIS)；
f (x) is the proportion of the predicted refrigerant quantity relative to the standard refrigerant quantity of the system; a is that 1 、A 2 、……A m 、B 1 、B 2 ……B n To calculate coefficients; SH is the suction superheat degree, DIS is the exhaust superheat degree; dividing the suction superheat into M equal parts, wherein the endpoint value is M 1 、M 2 、……M m-1 The method comprises the steps of carrying out a first treatment on the surface of the Dividing the superheat degree of the exhaust gas into N equal parts, wherein the endpoint values are N respectively 1 、N 2 ……N n-1 ；f(M m-2 ＜SH≤M m-1 ) Representing a certain set of dataMiddle, (M) m-2 ，M m-1 ]The proportion of the sum of all suction superheat values corresponding to the sections in the sum of all suction superheat values; g (N) n-2 ＜DIS≤N n-1 ) Represents a certain group of data, (N) n-2 ，N n-1 ]And the proportion of the sum of all the exhaust superheat values corresponding to the sections in the sum of all the exhaust superheat values.
Further, the expression of the multi-order matrix model is as follows;
F(1)＝A 1 *M(1，1)+A 2 *M(1，2)+……+A m *M(1，m)
+B 1 *N(1，1)+B 2 *N(1，2)+……+B n *N(1，n)
F(2)＝A 1 *M(2，1)+A 2 *M(2，2)+……+A m *M(2，m)
+B 1 *N(2，1)+B 2 *N(2，2)+……+B n *N(2，n)
……
F(m)＝A 1 *M(m，1)+A 2 *M(m，2)+……+A m *M(m，m)
+B 1 *N(m，1)+B 2 *N(m，2)+……+B n *N(m，n)
……
F(m+n)＝A 1 *M(m+n，1)+A 2 *M(m+n，2)+……+A m *M(m+n，m)
+B 1 *N(m+n，1)+B 2 *N(m+n，2)+……+B n *N(m+n，n)；
A 1 、A 2 、……A m 、B 1 、B 2 ……B n to calculate coefficients; f (1), F (2), … … F (m), … … and F (m+n) represent the proportion of the historical predicted refrigerant quantity of the 1 st, 2 nd, … … th, m, … … th and m+n th groups relative to the standard refrigerant quantity of the system; dividing the suction superheat into M sections, wherein the endpoint values are M respectively 1 、M 2 、……M m-1 The method comprises the steps of carrying out a first treatment on the surface of the Dividing the superheat degree of the exhaust gas into N equal parts, wherein the endpoint values are N respectively 1 、N 2 ……N n-1 The method comprises the steps of carrying out a first treatment on the surface of the M (1, 1), M (1, 2) … … M (M, M), M (m+n, M) are f (SH.ltoreq.M) 1 )、f(M 1 ＜SH≤M 2 )、f(M m-1 ＜SH)、f(M m-1 < SH) value; n (1, 1), N (1, 2) … … N (N, N), M (m+n, N) are f (DIS. Ltoreq.N) 1 )、f(N 1 ＜DIS≤N 2 )、f(N m-1 ＜DIS)、f(N m-1 < DIS); f (M) m-2 ＜SH≤M m-1 ) Represents a certain group of data, (M) m-2 ，M m-1 ]The proportion of the sum of all suction superheat values corresponding to the sections in the sum of all suction superheat values; g (N) n-2 ＜DIS≤N n-1 ) Represents a certain group of data, (N) n-2 ，N n-1 ]And the proportion of the sum of all the exhaust superheat values corresponding to the sections in the sum of all the exhaust superheat values.
The application also provides an air conditioner which comprises a processor, a memory and a refrigerant quantity prediction program which is stored in the memory and can run on the processor, wherein the method is adopted when the refrigerant quantity prediction program is executed by the processor.
The application also provides a computer readable storage medium, wherein the computer readable storage medium stores a refrigerant quantity prediction program, and the refrigerant quantity prediction program adopts the method when being executed by a processor.
Advantageous effects
The patent provides a multi-dimensional linear refrigerant quantity prediction method of an air conditioner based on big data, which is characterized in that key parameters including air suction superheat degree and air discharge superheat degree are extracted from multiple groups of air conditioner modeling basic data with different refrigerant quantities, a mathematical model is built, a refrigerant quantity calculation formula is established through matrix solving, and refrigerant quantity judgment is not simply carried out through data of one point.
The system refrigerant quantity is judged in real time, and the accumulated data is used for judging, and meanwhile, when key parameters are extracted, the common action of the suction superheat degree and the exhaust superheat degree is adopted, so that the error of single data is avoided; the method establishes a multidimensional linear model based on a large amount of random data, analyzes the proportion of key parameters, judges the refrigerant quantity through data of one point instead of simply, provides a clear linear formula calculation method, can realize refrigerant quantity prediction of one thousandth, and has higher accuracy.
Drawings
FIG. 1 is a flow chart of a method for predicting the refrigerant quantity of an air conditioner;
fig. 2 is a flow chart for constructing a refrigerant quantity prediction model of an air conditioner.
Detailed Description
Example 1
As shown in fig. 1 and 2, the present embodiment provides a method for predicting the refrigerant quantity of an air conditioner, which includes the following steps:
acquiring all key parameter data corresponding to different refrigerant quantities of an air conditioner; the key parameter data comprise air suction superheat data and air discharge superheat data;
segmenting all key parameter data to form a plurality of segments;
calculating the ratio of key parameter data corresponding to each segment to all data of the key parameter;
inputting the ratio to a refrigerant quantity predicted value model, and calculating predicted refrigerant quantity; the refrigerant quantity predicted value model is obtained based on historical key parameter data training.
The refrigerant quantity predicted value model is obtained through the following steps:
acquiring key parameter data corresponding to different refrigerant quantities of an air conditioner; the key parameter data comprise historical inspiration superheat data and historical exhaust superheat data;
segmenting the historical air suction superheat data and the historical air discharge superheat data respectively to form a plurality of segmented historical air suction superheat data and segmented historical air discharge superheat data;
calculating a historical air suction superheat ratio of each piece of historical air suction superheat data and historical air suction superheat data; calculating a historical exhaust superheat ratio of each piece of historical exhaust superheat data and historical exhaust superheat data;
and establishing a multi-order matrix model based on the historical suction superheat ratio, the historical exhaust superheat ratio and the corresponding refrigerant quantity, solving to obtain calculation coefficients corresponding to the historical suction superheat ratio and the historical exhaust superheat ratio, and establishing a refrigerant quantity predicted value model based on the calculation coefficients.
Example 2
The modeling basic data of the patent are multiple groups of multi-online basic operation data with different refrigerant quantities; the refrigerant quantity covers the refrigerant quantity which can occur in normal operation of the multi-split air conditioner, such as 10% -150% of the refrigerant quantity; the number of groups of the basic operation data is more than or equal to 2, a plurality of groups which are distributed evenly are arranged between the minimum refrigerant quantity and the maximum refrigerant quantity, and the model accuracy built by the larger number of groups is higher.
The on-line modeling basic data of each group of refrigerant quantity is a group of big data, and the on-line modeling basic data comprises a unit refrigerating and heating mode, and the operation parameters of different external machines when outputting, wherein the operation parameters generally comprise an operation mode, an environment temperature, a compressor frequency, a high pressure, a low pressure, an exhaust temperature, an exhaust superheat degree, an air suction temperature, an air suction superheat degree, a supercooling degree, an electronic expansion valve opening degree and the like;
the key parameters that this patent draws are inspiration superheat and exhaust superheat, and the extraction mode is: calculating the proportion of the air suction superheat degree or the exhaust superheat degree in the whole data in a certain value range;
suction superheat = compressor suction temperature detected by the unit-low pressure side saturation temperature; the air suction temperature of the compressor is measured by a compressor air suction temperature sensing bulb; the low-pressure side saturation temperature is converted into a corresponding saturation temperature by measuring a low-pressure value through a low-pressure sensor;
exhaust superheat = compressor exhaust temperature detected by the unit-high pressure saturation temperature; the exhaust temperature of the compressor is measured by a compressor exhaust temperature sensing bulb; the high-pressure side saturation temperature is converted into a corresponding saturation temperature by measuring a high-pressure value through a high-pressure sensor;
establishing a multi-level mathematical model based on the suction superheat degree and the exhaust superheat degree by multiple groups of multi-online modeling basic data with different refrigerant amounts, and solving through a matrix to obtain a refrigerant amount calculation formula;
the following are provided:
F(x)＝A 1 *f(SH≤M 1 )+A 2 *f(M 1 ＜SH≤M 2 )+A 3 *f(M 2 ＜SH≤M 3 )+……
+A m-1 *f(M m-2 ＜SH≤M m-1 )+A m *f(M m-1 ＜SH)+B 1 *g(DIS≤N 1 )+B 2 *g(N 1 ＜DIS≤N 2 )+B 3 *g(N 2 ＜DIS≤N 3 )+……+B n-1 *g(N n-2 ＜DIS≤N n-1 )+B n *g(N n-1 ＜DIS)；
f(M m-2 ＜SH≤M m-1 ) In a certain group of data, the suction superheat is equal to (M m-2 ，M m-1 ]The proportion of the components is as follows; g (N) n-2 ＜DIS≤N n-1 ) In a certain group of data, the degree of superheat of the exhaust gas is set at (N n-2 ，N n-1 ]The proportion of the components is as follows; SH represents the suction superheat degree; DIS represents the degree of superheat of the exhaust gas; dividing the suction superheat into M equal parts, wherein the endpoint value is M 1 、M 2 、……M m-1 The method comprises the steps of carrying out a first treatment on the surface of the Dividing the superheat degree of the exhaust gas into N equal parts, wherein the endpoint values are N respectively 1 、N 2 ……N n-1 ；
F (x) represents the proportion of the predicted refrigerant quantity relative to the standard refrigerant quantity of the system, if the standard refrigerant quantity of the system is 100%, the value range of F (x) is usually 10-150%, namely the refrigerant quantity accounts for 10-150% of the standard refrigerant quantity;
A 1 、A 2 、……A m 、B 1 、B 2 ……B n calculating coefficients for the refrigerant, and solving by establishing a mathematical model;
taking m+n groups of data with different refrigerant amounts as basic modeling data;
f (1), F (2), … … F (m), … … and F (m+n) represent refrigerant quantity modeling basic values of groups 1,2, … …, m, … … and m+n, and the refrigerant quantity basic values cover the common refrigerant quantity of the multi-split air conditioner;
m (1, 1) represents f (SH.ltoreq.M) in the 1 st set of modeling base data 1 ) Is a value of (2);
m (1, 2) tableIn the 1 st group modeling base data, f (M 1 ＜SH≤M 2 ) Is a value of (2);
……
m (1, M-1) represents f (M) in the 1 st set of modeling base data m-2 ＜SH≤M m-1 ) Is a value of (2);
m (1, M) represents f (M) in the 1 st set of modeling base data m-1 < SH) value;
m (M, M) represents f (M) in the M-th modeling base data m-1 < SH) value;
m (m+n, M) represents f (M) in the m+n-th group modeling base data m-1 < SH) value;
n (1, 1) represents g (DIS. Ltoreq.N) in the 1 st set of modeling base data 1 ) Is a value of (2);
n (1, 2) represents g (N) in the 1 st group modeling base data 1 ＜DIS≤N 2 )；
……
N (1, N-1) represents g (N) in the 1 st group modeling base data n-2 ＜DIS≤N n-1 ) Is a value of (2);
n (1, N) represents g (N) in the 1 st group modeling base data n-1 < DIS);
n (m+n, N) represents g (N) in the m+n-th group modeling base data n-1 < DIS);
data by m+n sets; an (m+n) × (m+n) order matrix can be established as follows:
F(1)＝A 1 *M(1，1)+A 2 *M(1，2)+……+A m *M(1，m)
+B 1 *N(1，1)+B 2 *N(1，2)+……+B n *N(1，n)
F(2)＝A 1 *M(2，1)+A 2 *M(2，2)+……+A m *M(2，m)
+B 1 *N(2，1)+B 2 *N(2，2)+……+B n *N(2，n)
……
F(m)＝A 1 *M(m，1)+A 2 *M(m，2)+……+A m *M(m，m)
+B 1 *N(m，1)+B 2 *N(m，2)+……+B n *N(m，n)
……
F(m+n)＝A 1 *M(m+n，1)+A 2 *M(m+n，2)+……+A m *M(m+n，m)
+B 1 *N(m+n，1)+B 2 *N(m+n，2)+……+B n *N(m+n，n)
namely: can be expressed as: matrix f=matrix mn×matrix AB
F(1)＝A1＊M(1，1)+A2＊M(1，2)+......+Am＊M(1，m)+B1＊N(1，1)+B2＊N(1，2)+......+Bn＊N(1，n)
F(2)＝A1＊M(2，1)+A2＊M(2，2)+......+Am＊M(2，m)+B1＊N(2，1)+B2＊N(2，2)+......+Bn＊N(2，n)
……
……
F(m)＝A1＊M(m，1)+A2＊M(m，2)+......+Am＊M(m，m)+B1＊N(m，1)+B2＊N(m，2)+......+Bn＊N(m，n)
F(m+1)＝A1＊M(m+1，1)+A2＊M(m+1，2)+......+Am＊M(m+1，m)+B1＊N(m+1，1)+B2＊N(m+1，2)+......+Bn＊N(m+1，n)
……
……
F(m+n)＝A1＊M(m+n，1)+A2＊M(m+n，2)+......+Am＊M(m+n，m)+B1＊N(m+n，1)+B2＊N(m+n，2)+......+Bn＊N(m+n，n)
The matrix F can be expressed as:
the matrix AB can be expressed as:
the matrix MN can be expressed as:
M(1，1)，M(1，2)，......，M(1，m)，N(1，1)，N(1，2)，......，N(1，n)
M(2，1)，M(2，2)，......，M(2，m)，N(2，1)，N(2，2)，......，N(2，n)
……
……
M(m，1)，M(m，2)，......，M(m，m)，N(m，1)，N(m，2)，......，N(m，n)
M(m+1，1)，M(m+1，2)，......，M(m+1，m)，N(m+1，1)，N(m+1，2)，......，N(m+1，n)
……
……
M(m+n，1)，M(m+n，2)，......，M(m+n，m)，N(m+n，1)，N(m+n，2)，......，N(m+n，n)
when the M+N modular basic data is given, the matrix F is known, the matrix MN is known, and the solution of the matrix AB can be obtained by solving the (m+n) x (m+n) order matrix, namely, the A can be obtained 1 、A 2 、……A m 、B 1 、B 2 ……B n Is a value of (2);
then for any group of multi-split data, the value of the refrigerant quantity F (x), F (x) =A can be obtained through the following formula 1 *f(SH≤M 1 )+A 2 *f(M 1 ＜SH≤M 2 )+A 3 *f(M 2 ＜SH≤M 3 )+……+A m-1 *f(M m-2 ＜SH≤M m-1 )+A m *f(M m-1 ＜SH)+B 1 *g(DIS≤N 1 )+B 2 *g(N 1 ＜DIS≤N 2 )+B 3 *g(N 2 ＜DIS≤N 3 )+……+B n-1 *g(N n-2 ＜DIS≤N n-1 )+B n *g(N n-1 ＜DIS)
In the patent, the group number of basic modeling data, m+n, can be adjusted according to the data quantity which can be provided actually, m and n are integers, and the number of m and n is not required to be consistent; the group number of m+n is less than or equal to the modeling basic data group number of the known refrigerant quantity; the larger the m+n value is, the more accurate the established refrigerant quantity prediction formula is; meanwhile, the refrigerant quantity prediction method can be optimized at any time according to the newly-added modeling data.
Example 3
An air conditioner comprising a processor, a memory, and a prediction program of an amount of refrigerant stored on the memory and operable on the processor, the prediction program of the amount of refrigerant employing the method of embodiment 1 when executed by the processor.
Example 4
A computer-readable storage medium having stored thereon a refrigerant amount prediction program that, when executed by a processor, employs the method of embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present application, and such modifications and variations should also be regarded as being within the scope of the application.
Claims (7)
1. The refrigerant quantity prediction method of the air conditioner is characterized by comprising the following steps of:
acquiring all key parameter data corresponding to different refrigerant quantities of an air conditioner; the key parameter data comprise air suction superheat data and air discharge superheat data;
segmenting all key parameter data to form a plurality of segments;
calculating the ratio of key parameter data corresponding to each segment to all data of the key parameter; establishing a multi-order mathematical model of the relation between the sectional key parameters and the refrigerant quantity, solving the calculation coefficients of different sectional key parameter data through a matrix, and constructing a refrigerant quantity predicted value model based on the calculation coefficients;
and inputting the ratio of all the data of the key parameters into a refrigerant quantity predicted value model, and calculating the predicted refrigerant quantity.
2. The method of claim 1, wherein maximum and minimum values of the key data are obtained, a difference between the maximum and minimum values is divided by the number of segments, and the key parameter data are equally divided into a plurality of segments.
3. The method for predicting the refrigerant quantity of an air conditioner according to claim 1, wherein the obtaining of all key parameter data corresponding to different refrigerant quantities of the air conditioner is specifically: and acquiring all corresponding key parameter data within the range of 10% -150% of the standard refrigerant quantity of the system of the air conditioner.
4. The method for predicting the refrigerant quantity of an air conditioner according to claim 1, wherein,
the method for calculating the predicted refrigerant quantity comprises the following steps:
F(x)＝A 1 *f(SH≤M 1 )+A 2 *f(M 1 ＜SH≤M 2 )+A 3 *f(M 2 ＜SH≤M 3 )+……+A m-1 *f(M m-2 ＜SH≤M m-1 )+A m *f(M m-1 ＜SH)+B 1 *g(DIS≤N 1 )+B 2 *g(N 1 ＜DIS≤N 2 )+B 3 *g(N 2 ＜DIS≤N 3 )+……+B n-1 *g(N n-2 ＜DIS≤N n-1 )+B n *g(N n-1 ＜DIS)；
f (x) is the proportion of the predicted refrigerant quantity relative to the standard refrigerant quantity of the system; a is that 1 、A 2 、……A m 、B 1 、B 2 ……B n To calculate coefficients; SH is the suction superheat degree, DIS is the exhaust superheat degree; dividing the suction superheat into M equal parts, wherein the endpoint value is M 1 、M 2 、……M m-1 The method comprises the steps of carrying out a first treatment on the surface of the Dividing the superheat degree of the exhaust gas into N equal parts, wherein the endpoint values are N respectively 1 、N 2 ……N n-1 ；f(M m-2 ＜SH≤M m-1 ) Represents a certain group of data, (M) m-2 ，M m-1 ]The proportion of the sum of all suction superheat values corresponding to the sections in the sum of all suction superheat values; g (N) n-2 ＜DIS≤N n-1 ) Represents a certain group of data, (N) n-2 ，N n-1 ]And the proportion of the sum of all the exhaust superheat values corresponding to the sections in the sum of all the exhaust superheat values.
5. The air conditioner coolant amount prediction method of claim 1, wherein the expression of the multi-order matrix model is;
F(1)＝A 1 *M(1，1)+A 2 *M(1，2)+……+A m *M(1，m)+B 1 *N(1，1)+B 2 *N(1，2)+……+B n *N(1，n)
F(2)＝A 1 *M(2，1)+A 2 *M(2，2)+……+A m *M(2，m)+B 1 *N(2，1)+B 2 *N(2，2)+……+B n *N(2，n)
……
F(m)＝A 1 *M(m，1)+A 2 *M(m，2)+……+A m *M(m，m)+B 1 *N(m，1)+B 2 *N(m，2)+……+B n *N(m，n)
……
F(m+n)＝A 1 *M(m+n，1)+A 2 *M(m+n，2)+……+A m *M(m+n，m)+B 1 *N(m+n，1)+B 2 *N(m+n，2)+……+B n *N(m+n，n)；
A 1 、A 2 、……A m 、B 1 、B 2 ……B n to calculate coefficients; f (1), F (2), … … F (m), … … and F (m+n) represent the proportion of the historical predicted refrigerant quantity of the 1 st, 2 nd, … … th, m, … … th and m+n th groups relative to the standard refrigerant quantity of the system; dividing the suction superheat into M sections, wherein the endpoint values are M respectively 1 、M 2 、……M m-1 The method comprises the steps of carrying out a first treatment on the surface of the Dividing the superheat degree of the exhaust gas into N equal parts, wherein the endpoint values are N respectively 1 、N 2 ……N n-1 The method comprises the steps of carrying out a first treatment on the surface of the M (1, 1), M (1, 2) … … M (M, M), M (m+n, M) are f (SH.ltoreq.M) 1 )、f(M 1 ＜SH≤M 2 )、f(M m-1 ＜SH)、f(M m-1 < SH) value; n (1, 1), N (1, 2) … … N (N, N), M (m+n, N) are f (DIS. Ltoreq.N) 1 )、f(N 1 ＜DIS≤N 2 )、f(N m-1 ＜DIS)、f(N m-1 < DIS); f (M) m-2 ＜SH≤M m-1 ) Represents a certain group of data, (M) m-2 ，M m-1 ]The proportion of the sum of all suction superheat values corresponding to the sections in the sum of all suction superheat values; g (N) n-2 ＜DIS≤N n-1 ) Represents a certain group of data, (N) n-2 ，N n-1 ]And the proportion of the sum of all the exhaust superheat values corresponding to the sections in the sum of all the exhaust superheat values.
6. An air conditioner, characterized in that the air conditioner comprises a processor, a memory and a refrigerant quantity prediction program stored on the memory and capable of running on the processor, wherein the refrigerant quantity prediction program adopts the method according to any one of claims 1-6 when being executed by the processor.
7. A computer-readable storage medium, wherein a program for predicting the amount of refrigerant is stored in the computer-readable storage medium, and the program for predicting the amount of refrigerant is executed by a processor by using the method according to any one of claims 1 to 6.
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