A fuzzy PID closed-loop control method for injection quantity of high-pressure fuel injection system based on physical mechanism-data driven fusion

Abstract

The purpose of the present invention is to provide a fuzzy PID closed-loop control method for the injection quantity of a high-pressure fuel injection system based on a fusion of physical mechanism and data drive, which belongs to the field of engine fuel injection. The method uses the collected injector inlet pressure and solenoid valve excitation current as data drive, and uses the physical mechanism of monotonicity loss and initial condition loss of the dynamic injection process as constraints to train a Transformer time series neural network model; calculates the difference and difference percentage between the predicted injection quantity and the target injection quantity, and realizes real-time closed-loop control of the injection quantity of the next injection by superimposing the injection pulse width control signal output by the fuzzy PID controller. By combining physical mechanism and data drive, the present invention solves the problems of insufficient generalization ability and poor interpretability of pure data-driven models, and can accurately predict the injection rate and injection quantity online and in real time under complex system working conditions, thereby realizing closed-loop control of the injection quantity.

Description

Technical Field

[0001] The present invention relates to a fuel injection control method, in particular to an injection quantity control method. Background Art

[0002] High-pressure fuel injection is a key core technology in internal combustion engines, including dual-fuel and low-carbon fuel engines. It achieves precise control of the combustion process by storing fuel at high pressure in a chamber and injecting it through a precisely controlled injector. As engines evolve toward higher thermal efficiency and ultra-high injection pressures, the transient dynamic response of fuel injection systems becomes increasingly complex. This makes precise control of fuel injection, particularly the consistency of injection quantities across cylinders and system stability, a key challenge and key aspect of fuel injection system design and control.

[0003] Traditional fuel injection systems are mostly controlled using open-loop control or closed-loop control based on low-dimensional feedback variables such as speed and torque. However, fuel injection is affected by nonlinear factors such as pressure fluctuations and fluid inertia effects. This severely limits the control accuracy based on static models or linear controllers, making it impossible to accurately adjust the injection amount in highly dynamic environments, making it difficult to meet the needs of modern engines.

[0004] With the widespread application of machine learning technology, data-driven fuel injection quantity prediction models have gradually become a research hotspot. These models, trained and trained on large amounts of historical data, can predict injection quantities to a certain extent. However, purely data-driven neural networks have long suffered from over-reliance on data sampling, difficulty ensuring robustness and generalization, and a lack of deep understanding of physical mechanisms, resulting in poor interpretability. Furthermore, purely data-driven models may exhibit poor generalization and robustness when faced with operating conditions outside of the training data or abnormal conditions, making them unable to effectively cope with complex transient changes in the system or extreme operating conditions. Therefore, methods that rely solely on data-driven methods struggle to achieve high-precision, real-time injection quantity prediction and control in the highly dynamic environment of fuel injection systems. Summary of the Invention

[0005] The purpose of the present invention is to provide a fuzzy PID closed-loop control method for the injection quantity of a high-pressure fuel injection system based on physical mechanism-data driven fusion, which can solve the problems of the inability to accurately predict the injection law and injection quantity in real time online and the inability to accurately control the injection quantity in closed loop.

[0006] The object of the present invention is achieved like this:

[0007] The present invention provides a fuzzy PID closed-loop control method for injection quantity of a high-pressure fuel injection system based on physical mechanism-data driven fusion, which is characterized by comprising the following steps:

[0008] (1) An injector is provided, the injector inlet is connected to a high-pressure fuel pipe, a single-shot injection instrument is installed below the injector, a current clamp is clamped on the wire of the high-speed solenoid valve of the injector, a pressure sensor is installed at the connection between the injector and the high-pressure fuel pipe, the current clamp, the pressure sensor and the single-shot injection instrument are all connected to a data acquisition unit, the excitation current signal of the high-speed solenoid valve and the pressure signal at the injector inlet are measured, the injection rate of the injector is measured using the single-shot injection instrument, and a data set of the excitation current, injector inlet pressure and injection rate under different rail pressure conditions and injection pulse width conditions is obtained, and the data in the data set is batch normalized;

[0009] (2) Construct the Transformer time series neural network model framework, use structural loss and regression loss as the basic loss functions of the network model, use initial condition loss and monotonicity loss as the physical mechanism loss functions of the network model, and use the sum of the basic loss function and the physical mechanism loss function as the total loss function of the network model;

[0010] (3) Based on the data set established in step (1), the Transformer time series neural network model is trained so that the model can predict the injection rate at the same moment based on the excitation current and injector inlet pressure time series data. After each round of training, it is checked whether the error is lower than the preset 5% threshold. If the error meets the requirement, the iteration is stopped. Otherwise, the training iteration is continued until the error between the predicted injection rate and the measured injection rate meets the threshold requirement.

[0011] (4) Based on the real-time measurement of the excitation current signal of the high-pressure fuel injection system and the pressure signal at the injector inlet, the Transformer time series neural network model established in step (3) is used to predict the injection rate in real time, and the real-time injection quantity of each injection is obtained by time integration; the real-time injection quantity is compared with the target injection quantity, and the injection quantity deviation and the injection quantity deviation percentage are calculated by fuzzy rules; the fuzzy controller fuzzifies the deviation and the deviation percentage according to the defined fuzzy rule base, outputs the fuzzy control quantity through fuzzy reasoning, and obtains the parameter adjustment quantity of the PID controller through the defuzzification process; by superimposing the injection pulse width control signal output by the fuzzy PID controller, the real-time closed-loop control of the injection pulse width of the next injection is completed.

[0012] The present invention may also include:

[0013] 1. In step (1), the rail pressure conditions include all pressure conditions between the idle rail pressure and the rated pressure with an interval of 10 MPa; the injection pulse width conditions include all injection pulse width conditions between the minimum start-up pulse width and the rated pulse width with an interval of 0.2 ms.

[0014] 2. The data batch normalization in step (1) is specifically as follows:

[0015]

[0016] Where x * is the processed data; represents the mean of unnormalized data; represents the standard deviation of the data; β and γ are the translation parameter and scale parameter respectively; ε is the margin used to prevent division by zero.

[0017] 3. Step (2) specifically includes:

[0018] (21) Define the input data format. The time series data of the excitation current and the injector inlet pressure are used as input features, namely X1 and X2, respectively. The input format is (B, T, F), where B is the batch size, T is the sampling step, and F is the number of input features. X1 and X2 are combined to form a complete input matrix

[0019] (22) Create a position code and add it to the input features to provide time sequence information for the input data. The position code vector PE is calculated as:

[0020]

[0021] Where i is the dimension index, d model is the embedding dimension;

[0022] (23) Establish the Transformer encoder layer, specifically:

[0023] Capturing the long-term temporal dependencies of input time series data through multi-head self-attention layers:

[0024]

[0025] Where Q, K, and V are query matrix, key matrix, and value matrix, respectively. k It is the attention dimension;

[0026] A feedforward neural network is built after each attention layer, which is expressed as:

[0027] FFN(x)=max(0,XW1+b1)W2+b2

[0028] Where W and b are the weight and bias of the feedforward neural network, respectively. Subscripts 1 and 2 represent the first and second layers of the feedforward network, respectively.

[0029] On this basis, a residual connection is added after each layer, and the outputs of the self-attention layer and the feedforward neural network layer are expressed as:

[0030] Output=LayerNorm(X+Attention)

[0031] Output = LayerNorm(X+FFN)

[0032] (24) Construct the total loss function of the model, specifically:

[0033] Construct regression loss E r , used to evaluate the prediction error of the model:

[0034]

[0035] Where, Represent the input excitation current array X1 and the input injector inlet pressure array X2 and predict the output injection rate through the Transformer model; y i is the actual value of the injection rate;

[0036] Constructing structure loss E s :

[0037]

[0038] Where ω and β are the weight and bias of the Transformer model respectively, is the number of layers in the model architecture, and n is the number of nodes in each layer;

[0039] Construct the initial condition loss E i :

[0040]

[0041] Where, t p-down is the initial moment when the ejector inlet pressure drops, v sound is the speed of sound in the pipeline, L is the distance from the ball valve to the pressure sensor;

[0042] Constructing monotonicity loss E m :

[0043]

[0044] Where m i is the monotonicity factor;

[0045] In summary, the total loss function of the model is:

[0046] E=E r +λ s E s +λ i E i +λ m E m

[0047] Where λs is the weight coefficient of the result loss, λ i is the weight coefficient of the initial condition loss, λ m is the weight coefficient of monotonicity loss.

[0048] 4. Step (4) is specifically:

[0049] (41) The real-time measured excitation current and injector inlet pressure time series data are input into the Transformer model, and then the output injection rate Q is calculated. rate (t), calculate the total injection quantity Q of each injection by time integration total :

[0050] Q total =∫Q rate (t)dt

[0051] The error e and error percentage δ between the real-time predicted injection amount and the target injection amount are calculated in real time:

[0052]

[0053] (42) The fuzzy controller is designed, including input fuzzification, definition of fuzzy rule base, fuzzy reasoning and defuzzification. The fuzzy controller obtains the PID parameter adjustment amount according to the injection amount deviation e and the deviation percentage δ. The adjustment formula of the fuzzy PID controller output is:

[0054] u(t)=(K p0 +ΔK p )·e+(K i0 +ΔK i )∫e dt+(K d0 +ΔK d )·δ

[0055] Where K p0 , K i0 and K d0 is the initial PID parameter, ΔK p , ΔK i and ΔK d is the parameter adjustment amount calculated by the fuzzy controller;

[0056] (43) By comparing the real-time predicted injection amount with the target injection amount, the fuzzy PID controller outputs the control signal T of the next injection pulse width according to the calculated PID adjustment amount. inject :

[0057] T inject =T inject,0 +u(t)

[0058] Where, T inject,0is the initial injection pulse width, T inject As the next injection pulse width, real-time closed-loop control of the injection amount is achieved.

[0059] The advantages of the present invention are:

[0060] (1) The present invention combines the advantages of physical mechanisms and data-driven models, taking advantage of the interpretability and robustness of physical mechanisms while enhancing the prediction accuracy of data-driven models in complex nonlinear systems, thus overcoming the problems of insufficient generalization ability and poor interpretability of pure data-driven models.

[0061] (2) The present invention only needs to install a pressure sensor at the injector inlet, and can use the timing data of the excitation current and the injector inlet pressure to realize the real-time online prediction of the injection pattern and injection amount without destroying the overall structure of the engine injector and combustion chamber, thereby improving the reliability and durability of the system.

[0062] (3) The present invention combines the fuzzy PID control algorithm to automatically adjust the parameters of the PID controller by fuzzifying the injection quantity deviation and deviation percentage, thereby realizing adaptive adjustment of the injection pulse width and ensuring the closed-loop control accuracy of the system on the injection quantity under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a flow chart of the present invention;

[0064] Figure 2 FIG. 1 is a diagram of the experimental device of the present invention;

[0065] Figure 3 This is the Transformer time series neural network model framework diagram;

[0066] Figure 4 This is the fuzzy PID control block diagram. DETAILED DESCRIPTION

[0067] The present invention will be described in more detail below with reference to the accompanying drawings:

[0068] Combine Figure 1-4 ,like Figure 1 As shown, the present invention provides a fuzzy PID closed-loop control method for injection quantity of a high-pressure fuel injection system based on physical mechanism-data driven fusion, comprising the following steps:

[0069] Step S1, such as Figure 2As shown, a current clamp is clamped on the wire of the injector high-speed solenoid valve, and a pressure sensor is installed at the connection between the injector and the high-pressure fuel pipe to measure the excitation current signal of the high-speed solenoid valve and the pressure signal at the injector inlet. In addition, a single injection instrument is used to measure the injection rate of the injector. The excitation current signal, injector inlet pressure signal and injection rate are stored in the host computer through the data acquisition unit, and the data in the data set are batch-processed and normalized to obtain data sets of excitation current, injector inlet pressure and injection rate under different rail pressure conditions and injection pulse width conditions.

[0070] Step S2: Construct the Transformer time series neural network model framework, and use structural loss and regression loss as the basic loss functions of the network model. In addition, based on the one-dimensional pipeline fluid pressure fluctuation theory, the initial condition loss and monotonicity loss are used as the physical mechanism loss functions of the network model. The sum of the basic loss function and the physical mechanism loss function is used as the total loss function of the network model.

[0071] Step S3: Train the Transformer time series neural network model based on the data set established in step S1, so that the model can predict the injection rate at the same moment based on the excitation current and injector inlet pressure time series data. After each round of training, check whether the error is lower than the preset 5% threshold. If the error meets the requirement, stop the iteration; otherwise, continue the training iteration until the error between the predicted injection rate and the measured injection rate meets the threshold requirement.

[0072] In step S4, based on the excitation current signal and the pressure signal at the injector inlet measured in real time by the high-pressure fuel injection system, the Transformer time series neural network model established in step S3 is used to perform real-time prediction of the injection rate, and the real-time injection quantity for each injection is obtained through time integration. The real-time injection quantity is compared with the target injection quantity, and the injection quantity deviation and injection quantity deviation percentage are calculated using fuzzy rules. The fuzzy controller fuzzifies the deviation and deviation percentage based on a defined fuzzy rule base, outputs a fuzzy control variable through fuzzy reasoning, and obtains the parameter adjustment variable of the PID controller through the defuzzification process. By superimposing the injection pulse width control signal output by the fuzzy PID controller, real-time closed-loop control of the injection pulse width of the next injection is completed.

[0073] The operating conditions of the data set in step S1 are specifically as follows: the rail pressure conditions include all rail pressure conditions between the idle rail pressure and the rated rail pressure with an interval of 10 MPa; the injection pulse width conditions include all injection pulse width conditions between the minimum start pulse width and the rated pulse width with an interval of 0.2 ms.

[0074] The data batch normalization in step S1 is specifically as follows:

[0075]

[0076] Where x * is the processed data; represents the mean of unnormalized data; represents the data standard deviation; β and γ are the translation parameter and scale parameter, respectively, which are optimized by back propagation during training; ε is the margin used to prevent division by zero.

[0077] Specifically, step S2 is as follows: Figure 3 As shown, including:

[0078] Step S201: Define the input data format. The time series data of the excitation current and the injector inlet pressure are used as input features, namely X1 (excitation current) and X2 (injector inlet pressure). The input format is (B, T, F), where B is the batch size, T is the sampling step, and F is the number of input features. Combine X1 and X2 to form a complete input matrix.

[0079] Step S202: Create a position code and add it to the input feature to provide time sequence information for the input data. The position code vector PE is calculated as:

[0080]

[0081] Where i is the dimension index, d model is the embedding dimension.

[0082] Step S203, specifically, establish the Transformer encoder layer:

[0083] Capturing the long-term temporal dependencies of input time series data through multi-head self-attention layers:

[0084]

[0085] Where Q, K, and V are query matrix, key matrix, and value matrix, respectively. k It is the attention dimension.

[0086] A feedforward neural network is established after each attention layer to improve the nonlinear representation ability of the model, which is expressed as:

[0087] FFN(x)=max(0,XW1+b1)W2+b2 (4)

[0088] Where W and b are the weight and bias of the feedforward neural network, respectively. The subscripts 1 and 2 represent the first and second layers of the feedforward network, respectively.

[0089] On this basis, a residual connection is added after each layer to prevent the gradient from disappearing and improve the training stability. The outputs of the self-attention layer and the feedforward neural network layer are expressed as:

[0090] Output=LayerNorm(X+Attention) (5)

[0091] Output=LayerNorm(X+FFN) (6)

[0092] Step S204: construct the total loss function of the model, specifically:

[0093] Construct regression loss E r , used to evaluate the prediction error of the model:

[0094]

[0095] Where, Represent the input excitation current array X1 and the input injector inlet pressure array X2 and predict the output injection rate through the Transformer model; y i is the actual value of the injection rate.

[0096] Constructing the structure loss E s , used to reduce overfitting of the model:

[0097]

[0098] Where ω and β are the weight and bias of the Transformer model respectively, is the number of model architecture layers, and n is the number of nodes in each layer.

[0099] Construct the initial condition loss E i , used to reduce overfitting of the model:

[0100]

[0101] Where, t p-down is the initial moment when the ejector inlet pressure drops, v sound is the speed of sound in the pipeline, and L is the distance from the ball valve to the pressure sensor.

[0102] Constructing the monotonicity loss E m , used to meet the actual physical mechanism and increase the model interpretability:

[0103]

[0104] Where m i is the monotonicity factor, which is equal to 1 if the physical monotonicity is positive and equal to -1 if the physical monotonicity is negative.

[0105] An embodiment of the monotonicity rule is as follows:

[0106] (a) Predicted monotonic relationship between injection rate and pressure fluctuations. When the needle valve cannot fully open and the rail-end compression wave is not reflected at the measurement point, the injection rate and the injector inlet pressure are monotonically opposite before the inflection point in the injection rate rising phase, and the same as the injector inlet pressure after the inflection point. When the rail-end compression wave is reflected at the measurement point, the injection rate and the injector inlet pressure are monotonically opposite before the inflection point in the injection rate rising phase, and the same as the injector inlet pressure during the period between the inflection point and the reflection at the measurement point. After the reflection at the measurement point, the injection rate and the injector inlet pressure are monotonically opposite. When the needle valve can fully open, the injection rate and the injector inlet pressure are monotonically opposite before the rail-end compression wave is reflected at the measurement point, and the same as the injector inlet pressure during the period between this moment and the inflection point in the injection rate closing phase. After the inflection point in the injection rate closing phase, the injection rate and the injector inlet pressure are monotonically opposite.

[0107] (b) Monotonic relationship between the predicted injection rate and the actual injection rate. The derivative of the predicted injection rate at two consecutive points is the same as the derivative of the actual injection rate at two consecutive points.

[0108] In summary, the total loss function of the model is:

[0109] E=E r +λ s E s +λ i E i +λ m E m (11)

[0110] Where λ s is the weight coefficient of the result loss, λ i is the weight coefficient of the initial condition loss, λ m is the weight coefficient of monotonicity loss.

[0111] Step S4, as Figure 4 As shown, specifically:

[0112] Step S401: Input the real-time measured excitation current and injector inlet pressure time series data into the Transformer model, and then calculate the output injection rate Q rate (t), calculate the total injection quantity Q of each injection by time integration total :

[0113] Q total =∫Q rate (t)dt (12)

[0114] The deviation e and deviation percentage δ between the real-time predicted injection quantity and the target injection quantity are calculated in real time:

[0115]

[0116] Step S402, designing a fuzzy controller, including:

[0117] Input fuzzification involves converting the deviation and deviation percentage between the real-time predicted injection quantity and the target injection quantity into fuzzy quantities. A fuzzy rule base is defined, which defines fuzzy rules based on the properties of the fuzzy quantity. Fuzzy reasoning involves reasoning about the input fuzzy quantity based on the fuzzy rule base to obtain the fuzzy control quantity. Defuzzification involves converting the fuzzy control quantity into the PID controller parameter adjustment quantity. Ultimately, the fuzzy controller obtains the PID parameter adjustment quantity based on the injection quantity deviation e and the deviation percentage δ. The adjustment formula for the fuzzy PID controller output is:

[0118] u(t)=(K p0 +ΔK p )·e+(K i0 +ΔK i )∫e dt+(K d0 +ΔK d )·δ (14)

[0119] Where K p0 , K i0 and K d0 is the initial PID parameter, ΔK p , ΔK i and ΔK d It is the parameter adjustment amount calculated by the fuzzy controller.

[0120] Step S403: By comparing the real-time predicted injection amount with the target injection amount, the fuzzy PID controller outputs the control signal T for the next injection pulse width according to the calculated PID adjustment amount. inject :

[0121] T inject =T inject,0 +u(t) (15)

[0122] Where, T inject,0 is the initial injection pulse width. inject As the next injection pulse width, real-time closed-loop control of the injection amount is achieved.

Claims

1. A fuzzy PID closed-loop control method for injection quantity of a high-pressure fuel injection system based on physical mechanism-data driven fusion, characterized by: The steps include: (1) An injector is provided, the injector inlet is connected to a high-pressure fuel pipe, a single-shot injection instrument is installed below the injector, a current clamp is clamped on the wire of the high-speed solenoid valve of the injector, a pressure sensor is installed at the connection between the injector and the high-pressure fuel pipe, the current clamp, the pressure sensor and the single-shot injection instrument are all connected to a data acquisition unit, the excitation current signal of the high-speed solenoid valve and the pressure signal at the injector inlet are measured, the injection rate of the injector is measured using the single-shot injection instrument, and a data set of the excitation current, injector inlet pressure and injection rate under different rail pressure conditions and injection pulse width conditions is obtained, and the data in the data set is batch normalized; (2) Construct the Transformer time series neural network model framework, use structural loss and regression loss as the basic loss functions of the network model, use initial condition loss and monotonicity loss as the physical mechanism loss functions of the network model, and use the sum of the basic loss function and the physical mechanism loss function as the total loss function of the network model; (3) Based on the data set established in step (1), the Transformer time series neural network model is trained so that the model can predict the injection rate at the same moment based on the excitation current and injector inlet pressure time series data. After each round of training, it is checked whether the error is lower than the preset 5% threshold. If the error meets the requirement, the iteration is stopped. Otherwise, the training iteration is continued until the error between the predicted injection rate and the measured injection rate meets the threshold requirement. (4) Based on the real-time measurement of the excitation current signal of the high-pressure fuel injection system and the pressure signal at the injector inlet, the Transformer time series neural network model established in step (3) is used to predict the injection rate in real time, and the real-time injection quantity of each injection is obtained by time integration; the real-time injection quantity is compared with the target injection quantity, and the injection quantity deviation and the injection quantity deviation percentage are calculated by fuzzy rules; the fuzzy controller fuzzifies the deviation and the deviation percentage according to the defined fuzzy rule base, outputs the fuzzy control quantity through fuzzy reasoning, and obtains the parameter adjustment quantity of the PID controller through the defuzzification process; by superimposing the injection pulse width control signal output by the fuzzy PID controller, the real-time closed-loop control of the injection pulse width of the next injection is completed.

2. The method of fuzzy PID closed-loop control of injection quantity of a high-pressure fuel injection system based on physical mechanism and data-driven fusion according to claim 1 is characterized by: In step (1), the rail pressure conditions include all pressure conditions between the idle rail pressure and the rated pressure with an interval of 10 MPa; the injection pulse width conditions include all injection pulse width conditions between the minimum start-up pulse width and the rated pulse width with an interval of 0.2 ms.

3. The method of fuzzy PID closed-loop control of injection quantity of a high-pressure fuel injection system based on physical mechanism and data-driven fusion according to claim 1 is characterized by: The data batch normalization in step (1) is specifically as follows: Where x * is the processed data; represents the mean of unnormalized data; represents the standard deviation of the data; β and γ are translation parameters and scale parameters respectively; ε is a margin used to prevent division by zero.

4. The method of claim 1 for controlling the injection quantity of a high-pressure fuel injection system using fuzzy PID closed-loop control based on physical mechanism and data-driven fusion, characterized by: Step (2) specifically includes: (21) Define the input data format. The time series data of the excitation current and the injector inlet pressure are used as input features, namely X1 and X2, respectively. The input format is (B, T, F), where B is the batch size, T is the sampling step, and F is the number of input features. X1 and X2 are combined to form a complete input matrix (22) Create a position code and add it to the input features to provide time sequence information for the input data. The position code vector PE is calculated as: Where i is the dimension index, d model is the embedding dimension; (23) Establish the Transformer encoder layer, specifically: Capturing the long-term temporal dependencies of input time series data through multi-head self-attention layers: Where Q, K, and V are query matrix, key matrix, and value matrix, respectively. k It is the attention dimension; A feedforward neural network is built after each attention layer, which is expressed as: FFN(x)=max(0,XW1+b1)W2+b2 Where W and b are the weight and bias of the feedforward neural network, respectively. Subscripts 1 and 2 represent the first and second layers of the feedforward network, respectively. On this basis, a residual connection is added after each layer, and the outputs of the self-attention layer and the feedforward neural network layer are expressed as: Output=LayerNorm(X+Attention) Output = LayerNorm(X+FFN) (24) Construct the total loss function of the model, specifically: Construct regression loss E r , used to evaluate the prediction error of the model: Where, Represent the input excitation current array X1 and the input injector inlet pressure array X2 and predict the output injection rate through the Transformer model; y i is the actual value of the injection rate; Constructing the structure loss E s : Where ω and β are the weight and bias of the Transformer model respectively, is the number of model architecture layers, and n is the number of nodes in each layer; Construct the initial condition loss E i : Where, t p-down is the initial moment when the ejector inlet pressure drops, v sound is the speed of sound in the pipeline, L is the distance from the ball valve to the pressure sensor; Constructing monotonicity loss E m : Where m i is the monotonicity factor; In summary, the total loss function of the model is: E=E r +λ s AND s +λ i AND i +λ m AND m Where λ s is the weight coefficient of the result loss, λ i is the weight coefficient of the initial condition loss, λ m is the weight coefficient of monotonicity loss.

5. The method of fuzzy PID closed-loop control of injection quantity of a high-pressure fuel injection system based on physical mechanism-data driven fusion according to claim 1, characterized in that: Step (4) is specifically as follows: (41) The real-time measured excitation current and injector inlet pressure time series data are input into the Transformer model, and then the output injection rate Q is calculated. rate (t), calculate the total injection quantity Q of each injection by time integration total : Q total =∫Q rate (t)dt The error e and error percentage δ between the real-time predicted injection amount and the target injection amount are calculated in real time: (42) The fuzzy controller is designed, including input fuzzification, definition of fuzzy rule base, fuzzy reasoning and defuzzification. The fuzzy controller obtains the PID parameter adjustment amount according to the injection amount deviation e and the deviation percentage δ. The adjustment formula of the fuzzy PID controller output is: u(t)=(K p0 +ΔK p )·e+(K i0 +ΔK i )∫e dt+(K d0 +ΔK d )·δ Where K p0 , K i0 and K d0 is the initial PID parameter, ΔK p , ΔK i and ΔK d is the parameter adjustment amount calculated by the fuzzy controller; (43) By comparing the real-time predicted injection amount with the target injection amount, the fuzzy PID controller outputs the control signal T of the next injection pulse width according to the calculated PID adjustment amount. inject : T inject =T inject,0 +u(t) Where, T inject,0 is the initial injection pulse width, T inject As the next injection pulse width, real-time closed-loop control of the injection amount is achieved.

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