Model prediction control method for telescopic hydraulic boom crane based on data driving

Abstract

The invention discloses a telescopic hydraulic boom crane model prediction control method based on data driving, relates to the technical field of telescopic hydraulic boom cranes, solves the problem of control precision caused by oil density change, and innovatively provides two error calibration strategies; on the basis of a time adjustment strategy, the hydraulic time is dynamically corrected and the oil volume is accurately controlled by utilizing an inverse ratio relation between density and volume, so that the actual telescopic length is consistent with model prediction; according to the throttle valve adjusting strategy, the hydraulic resistance is flexibly adjusted according to the pressure difference value, and fine regulation and control of the oil flow are achieved; the two strategies supplement each other and can be flexibly selected or cooperatively used according to actual working conditions, and the adaptability and the control precision of the system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of telescopic hydraulic arm cranes, and in particular to a data-driven model predictive control method for telescopic hydraulic arm cranes. Background Art

[0002] In the field of modern engineering construction, telescopic hydraulic boom cranes have become key equipment in scenarios such as port loading and unloading, bridge construction, and building installation due to their flexible and efficient operating capabilities. They achieve precise control of the telescopic boom through a hydraulic system, relying on the matching of preset models with actual working conditions to ensure operational accuracy. However, the oil density in the hydraulic system varies significantly with temperature, and this characteristic poses a severe challenge to the crane's control accuracy.

[0003] During actual operation, prolonged crane operation or drastic ambient temperature fluctuations can cause the hydraulic oil temperature to rise or fall. According to the principles of fluid mechanics, oil density and temperature have a nonlinear relationship. When the temperature changes, the oil's physical properties, such as density and viscosity, change, affecting the hydraulic system's pressure distribution, flow stability, and actuator response speed. For example, rising oil temperature reduces oil density and viscosity, leading to increased pressure loss, a deviation between the hydraulic pump's actual output flow and the theoretical value, and difficulty matching the telescopic boom's actual extension and retraction rate and stroke with the preset model. When the oil temperature drops, oil density increases, fluidity deteriorates, and hydraulic resistance increases significantly, which can also cause control lag and reduced accuracy.

[0004] At present, traditional crane control methods are mostly based on constant oil parameters to establish control models, and do not fully consider the dynamic characteristics of oil density changes with temperature, resulting in large control errors in actual operations. This error not only affects operational efficiency, but may also cause safety accidents such as cargo falling and equipment collisions. Therefore, how to effectively solve the control accuracy problem caused by oil density changes has become a key technical bottleneck in improving the reliability and safety of telescopic hydraulic arm crane operations, and it is urgently needed to be broken through by innovative control methods and technical means. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a data-driven model predictive control method for a telescopic hydraulic arm crane, which solves the control accuracy problem caused by changes in oil density.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data-driven model predictive control method for a telescopic hydraulic boom crane, comprising the following steps: Step 1: Based on the input drive data, the extension and retraction rate of the crane during actual operation and the extension and retraction rate of the corresponding model during virtual operation are verified to assess whether there is any error in the crane; Step 2: If there is an error in the crane, confirm the hydraulic oil temperature of the crane's telescopic hydraulic arm. Based on the confirmed specific temperature, lock the current hydraulic oil density data. The specific method is as follows: Use ρ1=ρ0[1-β×(W1-W0)] to confirm the current hydraulic oil density data ρ1, where β is the preset coefficient factor, W1 is the current hydraulic oil temperature, W0 is the set hydraulic oil temperature, and ρ0 is the set hydraulic oil density data; Step 3: Based on the density data confirmed by the current hydraulic oil, confirm the actual difference between the actual hydraulic oil and the virtual hydraulic oil, determine the characteristic time based on the difference characteristics and execute it in the following way: Based on the determined density data ρ1 and density data ρ0, the actual characteristic ratio Bz is confirmed using Bz=(ρ1÷ρ0); Based on the hydraulic time associated with the drive data and the telescopic rate V2 associated with the virtual operation process, the following formula is used: hydraulic time × V2 = characteristic length. The cross-sectional area A associated with the oil corresponding to the telescopic hydraulic arm is then determined, and the following formula is used: characteristic length × A = virtual oil volume Tz0. Based on Tz0÷Tz1=Bz, the actual hydraulic oil volume Tz1 is confirmed. The following formula is used: (Tz1-Tz0)÷A=scalar length. The following formula is used: scalar length÷V1=scalar time. The following formula is used: characteristic time=(hydraulic time-scalar time) to confirm the characteristic time that needs to be executed during the actual operation of the current crane. Real-time control is then performed on it. The timing starts from the initial moment of hydraulic pressure. When the total duration of the characteristic time is reached, the hydraulic pressure is stopped to complete the control process associated with the current drive data. Alternatively, the throttle flow rate of the throttle valve can be adjusted based on the difference characteristics to adjust the hydraulic resistance value and complete the control process associated with the current drive data. The specific method is as follows: The oil mass during the actual operation is calibrated as M1, and the oil mass during the virtual operation is calibrated as M0; Based on the determined virtual oil volume Tz0 and the associated density data ρ0, the specific value of M0 is confirmed using: M0=Tz0×ρ0; Then, based on the actual characteristic ratio Bz, the value of M1 is determined using the formula: M1 = Bz × M0. When Bz and M0 are known, the value of M1 can be determined. Based on the associated cross-sectional area A, use P1=M1g÷A to confirm the actual pressure data P1, where g is the acceleration of gravity. Then use P0=M0g÷A to confirm the standard pressure data P0, and use: CZ=(P0-P1) to confirm the pressure difference, and calibrate the confirmed pressure difference as the pressure value to be adjusted; If the pressure to be adjusted is a positive value, the hydraulic resistance is increased; If the pressure to be adjusted is a negative value, reduce the hydraulic resistance; Hydraulic resistance formula based on throttle valve: : If you need to increase the hydraulic resistance, increase the value LL. Increase the original value, and the specific value of the increase is the pressure value to be adjusted; If you need to reduce the hydraulic resistance, you can reduce the value LL to make Reduce the original value, and the specific value of the reduction is the absolute value of the pressure value to be adjusted; LL is the specific flow of the throttle valve, C d is the flow coefficient, which is generally 0.6-0.62, and ρ1 is the determined density data. is the determined pressure difference data, where M is the area of the throttle port, and M, C d and ρ1 are all constant values in the current application scenario.

[0007] Preferably, the driving data is pressure data and hydraulic time of the hydraulic pump.

[0008] Preferably, in the step three, the characteristic time is determined and executed based on the difference characteristics.

[0009] Furthermore, the step three also includes the following adjustment method: Locking the extension and retraction rate of the crane during actual operation for a certain period of time in the determined process, and determining its time length Ts; The length difference Tc is locked using (V1×Ts)-(V2×Ts)=Tc, and the mass difference Mc1 is confirmed using Tc×A×ρ1=Mc1. The pressure data difference Pc1 is locked using Pc1=(Mc1×g)÷A. V1 is the telescopic rate of the corresponding telescopic hydraulic arm of the crane, and V2 is the telescopic rate associated with the corresponding model during virtual operation: If Pc1 is negative, reduce the value LL again to make Reduce the original value to |Pc1|; If Pc1 is positive, then by increasing the value LL, The original value is improved, and the specific value of the increase is Pc1.

[0010] The present invention provides a data-driven model predictive control method for a telescopic hydraulic boom crane. Compared with the prior art, it has the following advantages: This invention innovatively proposes two error calibration strategies. The time-adjustment strategy utilizes the inverse relationship between density and volume to dynamically correct hydraulic time, precisely controlling oil volume and ensuring that actual telescopic length is consistent with model predictions. The throttle valve adjustment strategy flexibly adjusts hydraulic resistance based on pressure differentials to achieve refined control of oil flow. These two strategies complement each other and can be flexibly selected or used in conjunction with actual operating conditions, significantly improving the system's adaptability and control accuracy. Not only does it consider the direct impact of oil density changes on flow rate and pressure, but it also deeply analyzes the indirect effects caused by density changes, such as changes in hydraulic resistance and flow rate. Furthermore, by introducing speed difference analysis within a specific time period, the error calibration process is further refined, comprehensively covering the key factors affecting control accuracy and building a complete and systematic precision control system. The entire control process does not require tedious manual intervention and can automatically complete error detection, density calculation, strategy selection and parameter adjustment, achieving high-precision and automated control goals. It effectively reduces the error rate of manual operation and improves operating efficiency, providing a strong guarantee for the stable and precise operation of telescopic hydraulic arm cranes under complex working conditions, and promoting the development of crane control technology towards intelligence and refinement. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Schematic diagram of the process of the present invention; Figure 2 The diagram shows a method for adjusting the hydraulic resistance of the present invention. DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0013] See also Figure 1 The present application provides a data-driven model predictive control method for a telescopic hydraulic boom crane, comprising the following steps: Step 1. During the predictive control process, there is a preset crane model. The corresponding crane model is a preset model, which is set in combination with the actual crane. It is more common in the existing technology. When the relevant personnel are operating, they input the specified parameters on the corresponding display screen, and the operation trajectory of the corresponding telescopic hydraulic arm will be displayed. This type of operation trajectory belongs to the output of the corresponding model. That is to say, I input the hydraulic pressure of a hydraulic arm and an execution time (that is, the duration of the corresponding hydraulic pressure), and the corresponding hydraulic arm will move to the specified position. Because in the actual automation control process, the operator performs simulation operation based on the set working parameters, and when the standard is met, the actual operation is performed. Therefore, in the actual operation process, the corresponding hydraulic oil of the corresponding hydraulic arm will change due to the influence of temperature. When the oil density changes, it will cause related errors in the control accuracy. In order to adjust such numerical errors, a more accurate control effect can be achieved; Priority is given to verifying the extension and retraction rate of the crane during actual operation and the extension and retraction rate of the corresponding model during virtual operation based on the input drive data, which is the pressure data and hydraulic time of the hydraulic pump, to assess whether there is an error in the crane. If an error exists, it means that the density of the corresponding oil changes due to temperature changes after operation, but the model operates according to the preset density, so the oil density in the model will not change. Then, when the density of the corresponding oil changes during actual operation, it will lead to an operation error in the actual operation. A related positioning sensor is set in the telescopic rod, and the extension and retraction rate can be locked in real time based on the change of the corresponding positioning value. The specific methods for conducting specific assessments are: Input the determined driving data, and after input, confirm the telescopic rate V1 of the corresponding telescopic hydraulic arm of the crane, and then confirm the telescopic rate V2 associated with the corresponding model during the virtual operation process; Identify whether V1 and V2 satisfy: V1=V2. If so, no processing is performed; If not, an error calibration signal is generated and subsequent related steps are executed to perform error calibration. This type of situation is a situation where an error exists.

[0014] For example, a truck crane with a rated lifting capacity of 50 tons uses a multi-stage hydraulic cylinder to drive its telescopic hydraulic arm. The predictive control system pre-sets a crane model based on the hydraulic oil density (860 kg / m³) at room temperature (25°C). When performing a lifting task, the operator inputs the driving data through the display screen: the hydraulic pump pressure is set to 20MPa, the hydraulic action time is 15 seconds, and the system simulates the operation according to the preset model, and concludes that the telescopic hydraulic arm's telescopic rate V2 during the virtual operation is 0.12m / s. During actual crane operation, the hydraulic system continuously operates, causing the hydraulic oil temperature to rise to 45°C and the oil density to drop to 840 kg / m³. At this point, the positioning sensor installed on the telescopic rod collects data in real time and calculates the actual telescopic rate (V1) to be 0.13 m / s. The system compares V1 and V2 and finds that V1 (0.13 m / s) ≠ V2 (0.12 m / s), generating an error calibration signal. The system initiates the error calibration process. Combined with the 45°C data from the hydraulic oil temperature sensor, the system invokes the oil density-temperature compensation algorithm to recalculate the extension and retraction rate correction parameters based on the actual hydraulic oil density. These corrected control parameters are then sent to the crane control system, enabling real-time calibration of the hydraulic arm's extension and retraction motion, ensuring that subsequent operations meet operational accuracy requirements.

[0015] Step 2: In case of errors in the crane, confirm the hydraulic oil temperature of the crane's telescopic hydraulic arm. Based on the confirmed specific temperature, lock the current hydraulic oil density data. Specifically, based on the determined V1 and V2, when V1>V2, it means that the oil density during actual operation is relatively low, so the flow rate is fast, which is usually caused by excessively high oil temperature. When V1<V2, it means that the oil density during actual operation is relatively high, and its flow rate will be slow, which is usually caused by excessively low oil temperature. Among them, the specific method of locking the density data corresponding to the current hydraulic oil is: The currently confirmed hydraulic oil temperature of the telescopic hydraulic arm is calibrated as W1, and the hydraulic oil temperature W0 is confirmed from the parameters set by the corresponding model (W0 is a preset value, which is the normal working oil temperature, prepared in advance by the relevant operators based on experience, generally 15°C), and the density data ρ0 of the hydraulic oil is confirmed simultaneously. ρ0 is the density data associated with W0. For example, it is known that the density of a mineral oil at 20°C is 860kg / m 3 ; Use ρ1=ρ0[1-β×(W1-W0)] to confirm the current hydraulic oil density data ρ1, where β is the preset coefficient factor. Generally, the β value of mineral oil is (6.5-7.0)×10 -4 / ℃, when W1 is 50℃, its β is generally 6.8×10 -4 / ℃; Step 3: Based on the density data of the current hydraulic oil, confirm the actual difference between the actual hydraulic oil and the virtual hydraulic oil, determine the characteristic time based on the difference characteristics and execute it. Or the throttle flow rate of the throttle valve is adjusted based on the difference characteristics, thereby adjusting the hydraulic resistance value to complete the control process associated with the current drive data; Specifically, there are two processing methods here. The first processing method is to adjust and confirm the actual hydraulic time of the hydraulic oil, thereby adjusting the associated hydraulic oil volume to ensure the associated control accuracy. There is a logical problem here, that is, the hydraulic pressure is constant, and the pushed cross-section is constant. Its Q=A×V, A is the area of the cross-section, V is the flow rate, and Q is the flow rate. Although the pressure value is constant, the oil density changes, which will cause the corresponding flow rate V to change. When the density decreases, the flow rate becomes faster, and the total volume of the hydraulic oil actually pushed will increase. The mass of the fluid being pushed is assumed to be constant, with mass = density × volume. Therefore, as density decreases, volume increases, resulting in an increase in flow rate. For example, based on the current hydraulic oil temperature of 45°C and the corresponding density of 840 kg / m³, combined with a constant hydraulic pump pressure of 20 MPa and a cross-sectional area A (assumed to be 0.01 m²), the system uses the flow formula Q = A × V. Due to the decrease in density, the flow rate V increases from the preset 0.12 m / s to 0.13 m / s. The actual flow rate Q1 (0.0013 m³ / s) exceeds the preset flow rate Q2 (0.0012 m³ / s). To ensure a constant mass of fluid being pushed (mass = density × volume), the system automatically shortens the hydraulic action time. The original hydraulic action time was set at 15 seconds, but after calculation, it was adjusted to 13.85 seconds. This ensures that the actual total volume of hydraulic fluid pushed matches the volume in the preset model, even with the reduced density and increased flow rate. This calibrates the actual extension and retraction rate to the virtual operating rate, ensuring control accuracy. The second approach involves adjusting the throttle valve aperture. Within the telescopic hydraulic arm, corresponding throttle orifices are located in the main cylinder and telescopic rod areas to control hydraulic oil flow. Larger orifices release oil faster and create less resistance, while smaller orifices slow oil release and increase resistance. This adjustment method can be used to calibrate the actual error. For example, at the orifices in the main cylinder and telescopic rod areas of the telescopic hydraulic arm, the system automatically adjusts based on the error. Initially, the throttle valve aperture is 5mm, resulting in faster oil release and less resistance. Upon detecting the error, the control system sends a command to the servo motor to drive the adjustment mechanism, reducing the throttle valve aperture to 4.8mm. The reduced aperture slows oil release and increases flow resistance, effectively suppressing excessive flow caused by reduced hydraulic oil density. As the oil outflow rate was adjusted, the actual telescopic rate gradually decreased. After real-time monitoring and fine-tuning, the actual telescopic rate V1 was finally reduced from 0.13m / s to 0.12m / s, achieving error calibration and ensuring precise control of the crane's telescopic movement. The first processing method specifically includes (determining the feature time based on the difference feature and executing it): Based on the determined density data ρ1 and density data ρ0, the actual characteristic ratio Bz is confirmed using Bz=(ρ1÷ρ0); Based on the hydraulic time associated with the drive data and the telescopic rate V2 associated with the virtual operation process, the following formula is used: hydraulic time × V2 = characteristic length (that is, the specific length of telescopic length during the virtual operation process). The cross-sectional area A associated with the oil corresponding to the telescopic hydraulic arm is then determined, and the following formula is used: characteristic length × A = virtual oil volume Tz0. The ratio between the density data is the inverse ratio between the corresponding volumes. Here, the volume consistency is ensured, because in actual operation, in actual applications, due to the increase in ambient temperature or the long-term operation of the system, the oil temperature rises, and the oil density decreases from ρ0 to ρ1; according to the law of conservation of mass, the volume of the oil with the same mass m will increase from T0 to T1, that is, m=ρ0T0=ρ1T1, because ρ1<ρ0, so T1>T0, based on Tz0÷Tz1=Bz to confirm the actual hydraulic oil volume Tz1, use: (Tz1-Tz0)÷A=scalar length The characteristic time that needs to be executed during the actual operation of the crane is determined by the following formula: scalar length ÷ V1 = scalar time. The characteristic time is then determined by the following formula: (hydraulic time - scalar time). The characteristic time is then controlled in real time. The timing starts from the initial moment of the hydraulic pressure. When the total duration of the characteristic time is reached, the hydraulic pressure is stopped to complete the control process associated with the current drive data. This method can fully reduce the density error in the actual operation process, thereby effectively ensuring the actual hydraulic accuracy, making the specific accuracy in the actual operation process consistent with the accuracy of the model, and achieving better precision control effects. The second processing method (the specific method of adjusting the throttle flow rate of the throttle valve based on the difference characteristics) includes: In this method, the corresponding resistance needs to be adjusted to make the oil volume delivered during the actual operation consistent with the oil volume associated with the virtual process. Then, when the volumes are consistent, the actual characteristic ratio Bz is the mass ratio of the corresponding oil volumes. The oil mass during the actual operation is calibrated as M1, and the oil mass during the virtual operation is calibrated as M0. Combine Figure 2 Based on the determined virtual oil volume Tz0 and the associated density data ρ0, the specific value of M0 is confirmed using: M0=Tz0×ρ0; Then, based on the actual characteristic ratio Bz, the value of M1 is determined using the formula: M1 = Bz × M0. When Bz and M0 are known, the value of M1 can be determined. Based on the associated cross-sectional area A, use P1=M1g÷A to confirm the actual pressure data P1, where g is the acceleration of gravity. Then use P0=M0g÷A to confirm the standard pressure data P0, and use: CZ=(P0-P1) to confirm the pressure difference, and calibrate the confirmed pressure difference as the pressure value to be adjusted; If the pressure value to be adjusted is positive, the hydraulic resistance is increased. The specific value of the increase is the pressure value to be adjusted. Specifically, the density decreases, which will cause the associated actual pressure data to decrease, and the associated pressure difference will be a positive value, which means that the hydraulic resistance value needs to be increased to change the standard pressure data P0 to P1. The following content is the opposite; If the pressure to be adjusted is a negative value, reduce the hydraulic resistance; Hydraulic resistance formula based on throttle valve: : If you need to increase the hydraulic resistance, increase the value LL. Increase the original value, and the specific value of the increase is the pressure value to be adjusted; If you need to reduce the hydraulic resistance, you can reduce the value LL to make Reduce the original value, and the specific value of the reduction is the absolute value of the pressure value to be adjusted; LL is the specific flow of the throttle valve, C d is the flow coefficient, which is generally 0.6-0.62, and ρ1 is the determined density data. is the determined pressure difference data, where M is the area of the throttle port, and M, C d and ρ1 are all constant values in the current application scenario.

[0016] As a further example of this embodiment: A specific time period of time V1 needs to be determined (the so-called specific time period means that when the corresponding speed is determined, a certain distance needs to be traveled before it can be determined. The numerical characteristics associated with the corresponding distance in this stage are not considered in the resistance determination scenario. Therefore, in order to make its value more accurate, this time period needs to be determined). The time length Ts of the specific time period is locked; Use: (V1×Ts)-(V2×Ts)=Tc to lock the length difference value Tc, and use: Tc×A×ρ1=Mc1 to confirm its mass difference Mc1, and then use: Pc1=(Mc1×g)÷A to lock the pressure data difference Pc1: If Pc1 is negative, reduce the value LL again to make Reduce the original value to |Pc1|; If Pc1 is positive, then by increasing the value LL, Improve the original value, the specific value of the increase is Pc1; In summary, combined with the actual processing application process, this application can achieve the optimal precision control effect in the control process, fully consider the difference changes in the density change process, and achieve a more refined processing process.

[0017] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0018] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data-driven model predictive control method for a telescopic hydraulic boom crane, characterized in that: The following steps are involved: Step 1: Based on the input drive data, the extension and retraction rate of the crane during actual operation and the extension and retraction rate of the corresponding model during virtual operation are verified to assess whether there is any error in the crane; Step 2: If there is an error in the crane, confirm the hydraulic oil temperature of the crane's telescopic hydraulic arm, and lock the current hydraulic oil density data based on the confirmed specific temperature; Step 3: Based on the density data of the current hydraulic oil, confirm the actual difference between the actual hydraulic oil and the virtual hydraulic oil, determine the characteristic time based on the difference characteristics and execute it. Or the throttle flow of the throttle valve is adjusted based on the difference characteristics, so as to adjust the hydraulic resistance value and complete the control process associated with the current drive data.

2. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 1, characterized in that: The driving data includes pressure data and hydraulic time of the hydraulic pump.

3. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 1, characterized in that: In step 1, the specific method of verifying the expansion and contraction rate is: Input the determined driving data, and after input, confirm the telescopic rate V1 of the corresponding telescopic hydraulic arm of the crane, and then confirm the telescopic rate V2 associated with the corresponding model during the virtual operation process; Identify whether V1 and V2 satisfy: V1=V2. If so, no processing is performed; If not, an error calibration signal is generated and subsequent related steps are executed to perform error calibration.

4. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 1, characterized in that: In step 2, the specific method of locking the density data corresponding to the current hydraulic oil is: The currently confirmed hydraulic oil temperature of the telescopic hydraulic arm is calibrated as W1, and its hydraulic oil temperature W0 is confirmed from the parameters set by the corresponding model, and its hydraulic oil density data ρ0 is simultaneously confirmed, where ρ0 is the density data associated with W0; The density data ρ1 of the current hydraulic oil is confirmed using ρ1=ρ0[1-β×(W1-W0)], where β is a preset coefficient factor.

5. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 4, characterized in that: In step 3, the characteristic time is determined based on the difference characteristics and the execution method is as follows: Based on the determined density data ρ1 and density data ρ0, the actual characteristic ratio Bz is confirmed using Bz=(ρ1÷ρ0); Based on the hydraulic time associated with the drive data and the telescopic rate V2 associated with the virtual operation process, the following formula is used: hydraulic time × V2 = characteristic length. The cross-sectional area A associated with the oil corresponding to the telescopic hydraulic arm is then determined, and the following formula is used: characteristic length × A = virtual oil volume Tz0. Based on Tz0÷Tz1=Bz, the actual hydraulic oil volume Tz1 is confirmed, and the following formula is used: (Tz1-Tz0)÷A=scalar length, and the following formula is used: scalar length÷V1=scalar time. Then, the following formula is used: characteristic time=(hydraulic time-scalar time) to confirm the characteristic time that needs to be executed during the actual operation of the current crane, and to control it in real time. The timing starts from the initial moment of the hydraulic pressure, and when the total duration of the characteristic time is reached, the hydraulic pressure is stopped to complete the control process associated with the current drive data.

6. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 4, characterized in that: In step 3, the specific method of adjusting the throttle flow rate of the throttle valve based on the difference characteristics is: The oil mass during the actual operation is calibrated as M1, and the oil mass during the virtual operation is calibrated as M0; Based on the determined virtual oil volume Tz0 and the associated density data ρ0, the specific value of M0 is confirmed using: M0=Tz0×ρ0; Then, based on the actual characteristic ratio Bz, the value of M1 is determined using the formula: M1 = Bz × M0. When Bz and M0 are known, the value of M1 can be determined. Based on the associated cross-sectional area A, use P1=M1g÷A to confirm the actual pressure data P1, where g is the acceleration of gravity. Then use P0=M0g÷A to confirm the standard pressure data P0, and use: CZ=(P0-P1) to confirm the pressure difference, and calibrate the confirmed pressure difference as the pressure value to be adjusted; If the pressure to be adjusted is a positive value, the hydraulic resistance is increased; If the pressure value to be adjusted is negative, reduce the hydraulic resistance.

7. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 6, characterized in that: The specific ways to reduce or increase the hydraulic resistance are: Hydraulic resistance formula based on throttle valve: : If you need to increase the hydraulic resistance, increase the value LL. Increase the original value, and the specific value of the increase is the pressure value to be adjusted; If you need to reduce the hydraulic resistance, you can reduce the value LL to make Reduce the original value, and the specific value of the reduction is the absolute value of the pressure value to be adjusted; LL is the specific flow of the throttle valve, C d is the flow coefficient, which is generally 0.6-0.62, ρ1 is the determined density data, is the determined pressure difference data, where M is the area of the throttle port, and M, C d and ρ1 are all constant values in the current application scenario.

8. The data-driven model predictive control method for a telescopic hydraulic boom crane according to claim 7, characterized in that: The step three also includes further adjustment methods: Locking the extension and retraction rate of the crane during actual operation for a certain period of time in the determined process, and determining its time length Ts; The length difference Tc is locked using (V1×Ts)-(V2×Ts)=Tc, and the mass difference Mc1 is confirmed using Tc×A×ρ1=Mc1. The pressure data difference Pc1 is locked using Pc1=(Mc1×g)÷A. V1 is the telescopic rate of the corresponding telescopic hydraulic arm of the crane, and V2 is the telescopic rate associated with the corresponding model during virtual operation: If Pc1 is negative, reduce the value LL again to make Reduce the original value to |Pc1|; If Pc1 is positive, then by increasing the value LL, The original value is improved, and the specific value of the increase is Pc1.

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