Intelligent preparation method and system for self-healing concrete based on dynamic optimization algorithm

Abstract

The invention discloses a self-healing concrete intelligent preparation method and system based on a dynamic optimization algorithm. The method comprises the following steps: S1, feeding: feeding materials to a premixing module by a raw material bin and a repairing agent carrier storage bin; s2, premixing: monitoring the water content of the aggregate fed into the raw material bin in real time by a premixing module, and dynamically adjusting the rotating speed of the premixing module through the water content; s3, gradient stirring: after premixing, feeding the materials into a gradient stirring module, and sequentially stirring; s31, firstly entering a low-speed paddle for stirring, monitoring the stirring resistance moment and the slurry viscosity distribution in real time, and dynamically adjusting the rotating speed of the paddle according to the torque-viscosity relationship; s32, performing ultrasonic homogenization on the materials subjected to low-speed stirring, monitoring the dispersity of each substance in low-speed stirring in real time through an identification camera, calculating the concentration gradient, monitoring the particle size distribution of a carrier at a discharge port of an ultrasonic homogenizer to obtain the particle size of a target aggregate, and dynamically adjusting the ultrasonic frequency according to the particle size of the target aggregate to obtain the target aggregate. And dynamically adjusting ultrasonic power and action time according to the concentration gradient.

Description

Technical Field

[0001] The present invention relates to the technical field of self-healing concrete preparation, and specifically to an intelligent preparation method and system for self-healing concrete based on a dynamic optimization algorithm. Background Technique

[0002] Existing methods for self-healing concrete mainly focus on the preparation of carriers containing repair agents or mainly on laboratory preparation. For example, "A Self-healing Concrete Composition and Its Preparation Method" with publication number IN438846B and "A Photo-magnetic Coupling Type Self-healing Concrete and Its Preparation Method and Device" with publication number CN114873956B. When used in on-site mixing, usually only the concrete and the carrier containing the repair agent are simply mixed at a fixed power and for a fixed time on-site. This step causes the following problems in actual use: 1. Existing mixing methods may not be able to evenly disperse the healing agent and cannot be adjusted according to actual situations. In particular, microorganisms or microcapsules are prone to rupture during the mixing process. 2. The simple mixing at a fixed power and for a fixed time has high energy consumption, cannot be adjusted according to the actual mixing situation, strengthen or reduce the mixing intensity, and has poor intelligence. Summary of the Invention

[0003] The present invention provides an intelligent preparation method and system for self-healing concrete based on a dynamic optimization algorithm, which solves the problems that existing mixing methods may not be able to evenly disperse the healing agent and cannot be adjusted according to actual situations. In particular, microorganisms or microcapsules are prone to rupture during the mixing process, the simple mixing at a fixed power and for a fixed time has high energy consumption, cannot be adjusted according to the actual mixing situation, strengthen or reduce the mixing intensity, and has poor intelligence.

[0004] To solve the above technical problems, the technical solutions adopted by the present invention are as follows: An intelligent preparation method for self-healing concrete based on a dynamic optimization algorithm, comprising the following steps: S1. Feeding: The raw material bin and the storage bin for the repair agent carrier feed materials into the premixing module. S2. Premixing: The premixing module monitors the moisture content of the aggregate fed from the raw material bin in real time and dynamically adjusts the rotation speed of the premixing module according to the moisture content. S3. Gradient mixing: After premixing, the materials are sent to the gradient mixing module for mixing in sequence. S31. First, enter the low-speed blade mixing, monitor the mixing resistance torque and the slurry viscosity distribution in real time, and dynamically adjust the blade rotation speed according to the torque-viscosity relationship, and the rotation speed does not exceed 50 rpm. S32. The materials after low-speed stirring enter the ultrasonic homogenization. The dispersion degree of each substance in the low-speed stirring is monitored in real time by an identification camera, the concentration gradient is calculated, and the particle size distribution of the carrier is monitored at the outlet of the ultrasonic homogenizer to obtain the target aggregate particle size. The ultrasonic frequency is dynamically adjusted according to the target aggregate particle size, and the ultrasonic power and action time are dynamically adjusted according to the concentration gradient.

[0005] Preferably, the formula for dynamically adjusting the rotation speed according to the water content of the aggregate in S2 is: ; Wherein, is the rotation speed of the spiral blade, is the throughput, is the bulk density of the material, is the compensation coefficient, and the empirical value is 0.8 - 1.2.

[0006] More preferably, the in S2 includes and , is the theoretical throughput of the screw conveyor, which can be calculated by the following formula: ; Wherein, is the diameter of the spiral blade, unit: m; is the pitch, unit: m, usually ; is the rotation speed of the spiral shaft, unit: rpm; is the filling coefficient, generally taking 0.3 - 0.45, adjusted according to the fluidity of the material; is the bulk density of the material, unit: kg / m³; is When, the actual throughput measured by running the screw conveyor is used to inversely deduce the correction coefficient : ; The theoretical throughput and the inversely deduced correction coefficient are brought into the formula for dynamically adjusting the rotation speed according to the water content of the aggregate to obtain the theoretical rotation speed, and run according to the theoretical rotation speed, and the actual throughput is detected. Compare with , and adjust the size of in real time until .

[0007] Furthermore, the spiral inclination angle in S2 is 15° - 45°.

[0008] Preferably, the formula for dynamically adjusting through the torque-viscosity relationship in S31 is: ; where is the real-time torque, is the slurry viscosity, , are blade geometric constants obtained by experimental fitting.

[0009] More preferably, in S31 , During the fitting process of the experiment, by monitoring the real-time torque, real-time rotational speed, and real-time viscosity under experimental conditions, the corrected and are obtained through the recursive least squares method.

[0010] Preferably, the formula for dynamically adjusting the ultrasonic frequency according to the target aggregate particle size in S32 is: ; where is the ultrasonic frequency, is the sound velocity correction coefficient, is the target aggregate particle size, is the elastic modulus of the ultrasonic action object, is the density of the ultrasonic action object.

[0011] More preferably, the formula for dynamically adjusting the ultrasonic power and action time according to the concentration gradient in S32 is: ; where is the real-time ultrasonic power, is the initial reference power, is the power attenuation coefficient, is the exponential attenuation term, is the dynamic power adjustment amplitude, is the sum of the multi-component concentration gradient change rates.

[0012] Furthermore, S32 also includes the following steps: gradually reduce the energy input over time to adapt to the changing needs of the mixing process from "vigorous dispersion" to "fine homogenization". When detecting an increase in inhomogeneity, increase the power to strengthen dispersion; otherwise, reduce the power.

[0013] Self-healing concrete intelligent preparation system based on dynamic optimization algorithm, including a raw material bin and a repair agent carrier storage bin, for the implementation of the above-mentioned self-healing concrete intelligent preparation method based on dynamic optimization algorithm. The raw material bin and the repair agent carrier storage bin are both connected to the inlet of the premixing module, and the outlet of the premixing module is connected to the inlet of the gradient mixing module; The premixing module includes a screw conveyor. A microwave moisture sensor is provided between the inlet of the screw conveyor and the outlet of the raw material bin for monitoring the moisture content of the aggregate. And the microwave moisture sensor forms a linkage with the screw conveyor through an externally connected programmable industrial PLC controller; The gradient mixing module includes a paddle mixer and an ultrasonic homogenizer connected in series along the feeding direction. A torque sensor is provided on the paddle main shaft of the paddle mixer for monitoring the mixing resistance torque. An ultrasonic echo viscosity sensor is provided on the side wall of the tank body of the paddle mixer for monitoring the viscosity distribution of the slurry. The torque sensor and the ultrasonic echo viscosity sensor form a linkage with the paddle mixer through an externally connected programmable industrial PLC controller; A transparent observation window is provided on one side of the paddle mixer. A high-speed recognition camera is provided at the transparent observation window for AI to recognize the dispersion degree of the repair agent carrier in the slurry in the tank. An online particle size distribution detector is provided at the outlet of the ultrasonic homogenizer for monitoring the distribution of the repair agent carrier at the outlet of the ultrasonic homogenizer. The high-speed recognition camera and the online particle size distribution detector form a linkage with the ultrasonic homogenizer through an externally connected programmable industrial PLC controller.

[0014] Advantages of the present invention: (1) By introducing premixing and gradient mixing including low-speed paddles and ultrasonic homogenization, the mixing effect and uniformity are ensured; (2) By introducing an algorithm to achieve automatic control and intelligence, the parameters of premixing and gradient mixing are adjusted according to the real-time material conditions, avoiding "overmixing" or "undermixing" in the traditional fixed power mode, and at the same time reducing ineffective energy such as, saving energy and reducing consumption. Secondly, the breakage of the repair agent carrier is further reduced, significantly improving the repair effect and performance consistency. Description of the drawings

[0015] Figure 1 is a schematic diagram of the system of the present invention; In the figure: 1, raw material bin; 2, repair agent carrier storage bin; 3, screw conveyor; 4, microwave moisture sensor; 5, paddle mixer; 6, torque sensor; 7, ultrasonic echo viscosity sensor; 8, transparent observation window; 9, high-speed recognition camera; 10, ultrasonic homogenizer; 11, online particle size distribution detector. Detailed implementation manners

[0016] As follows, the embodiments will be further described with reference to the drawings.

[0017] As shown Figure 1 in the figure, as a preferred Embodiment 1, a self-healing concrete intelligent preparation method based on a dynamic optimization algorithm includes the following steps: S1. Feeding: The raw material silo and the repair agent carrier storage silo feed materials into the premixing module; S2. Premixing: The premixing module monitors the moisture content of the aggregate fed from the raw material silo in real time and dynamically adjusts the rotation speed of the premixing module according to the moisture content; perform premixing in advance to ensure the premixing effect; S3. Gradient stirring: After premixing, the materials are sent to the gradient stirring module for stirring in sequence; S31. First, enter the low-speed paddle stirring, monitor the stirring resistance torque and the slurry viscosity distribution in real time, and dynamically adjust the paddle rotation speed according to the torque-viscosity relationship, with the rotation speed not exceeding 50 rpm; avoid the rupture of microorganisms or microcapsules; S32. The materials after low-speed stirring enter the ultrasonic homogenization. The dispersion degree of each substance in the low-speed stirring is monitored in real time through an identification camera, the concentration gradient is calculated, and the carrier particle size distribution is monitored at the outlet of the ultrasonic homogenizer to obtain the target aggregate particle size. The ultrasonic frequency is dynamically adjusted according to the target aggregate particle size, and the ultrasonic power and the action time are dynamically adjusted according to the concentration gradient.

[0018] Achieve intelligent control, avoid "overmixing" or "undermixing" in the traditional fixed power mode, and at the same time reduce ineffective energy, such as energy conservation and consumption reduction. Secondly, further reduce the damage of the repair agent carrier, and significantly improve the repair effect and performance consistency.

[0019] As a preferred Embodiment 2, the formula for dynamically adjusting the rotation speed according to the aggregate moisture content in S2 is: ; where is the rotation speed of the spiral blade, is the throughput (m 3 / h), is the bulk density of the material, is the compensation coefficient, and the empirical value is 0.8 - 1.2.

[0020] More preferably, the in S2 includes and , is the theoretical throughput of the screw conveyor, which can be calculated by the following formula: ; where is the diameter of the spiral blade, unit: m; is the pitch, unit: m, usually ; is the rotational speed of the screw shaft, unit: rpm; is the filling coefficient, generally taken as 0.3 - 0.45, adjusted according to the fluidity of the material; is the bulk density of the material, unit: kg / m³; is When, the actual throughput measured by operating the screw conveyor. The actual flow rate can be calculated by the marker tracking method. Tracer particles are introduced at the inlet, and the time difference is detected at the outlet to calculate the actual flow rate, and the correction coefficient is deduced inversely. : ; The theoretical throughput and the inversely deduced correction coefficient are substituted into the formula for dynamically adjusting the rotational speed according to the aggregate moisture content to obtain the theoretical rotational speed. Operate according to the theoretical rotational speed and detect the actual throughput , and is compared with , and the size of is adjusted in real time until .

[0021] Bulk density represents the unit volume mass (kg / m³) of the material in the loose state. The experimental measurement method is as follows: 1. Fill the dry aggregate into a standard container (such as a 10L bucket).

[0022] 2. Weigh after scraping the surface flat and calculate .

[0023] Moisture content correction: ; where is the aggregate moisture content (such as ).

[0024] As a more preferred Example 3, a set of data is taken as an example for illustration: 1. Parameter input: ; ; Initial ; 2. Theoretical rotational speed calculation: (obviously unreasonable, need to check unit conversion); After correcting the unit (assuming the unit is m³ / h, is kg / m³, need to unify the dimension): ; 3. Dynamic adjustment: The actual processing volume is detected , which is lower than the target value.

[0025] Adjust , and recalculate: ; After verification, the actual processing volume meets the standard and is locked .

[0026] Furthermore, the spiral inclination angle in S2 is 15° - 45°.

[0027] As a preferred embodiment 4, the formula for dynamically adjusting through the torque-viscosity relationship in S31 is: ; where is the real-time torque, is the slurry viscosity, , are the blade geometric constants obtained by experimental fitting.

[0028] More preferably, in S31 , During the experimental fitting process, by monitoring the real-time torque, real-time speed, and real-time viscosity under experimental conditions, the corrected and are obtained through the recursive least squares method.

[0029] Recursive Least Squares (RLS): Input data: Real-time speed (sampling period ); Real-time torque ; Real-time viscosity (measured by ultrasonic echo or rotational viscometer); Parameter vector: ; Model linearization: Take the logarithm of the original formula: ; Convert it into a linear equation: ; Recursive update: ; : Forgetting factor (usually taken as 0.95 - 0.99), which is used to reduce the weight of historical data and adapt to time - varying systems.

[0030] : Covariance matrix, with the initial value set as a diagonal matrix ( Take a larger value such as ).

[0031] As a more preferred Embodiment 5, one of the scenarios is taken as an illustration: When fibers are suddenly added during the mixing process of self - healing concrete (resulting in a sudden change in rheological properties): 1. Parameter changes: Original parameters: ,

[0032] After adding fibers: Increases to 0.18, Decreases to 1.2 2. Algorithm response: The online identification module updates to , (Convergence error < 5%).

[0033] The controller automatically reduces the rotational speed To prevent torque from exceeding the limit.

[0034] As a preferred Embodiment 6, the formula for dynamically adjusting the ultrasonic frequency according to the target aggregate particle size in S32 is: ; Where, Is the ultrasonic frequency, Is the sound velocity correction coefficient, Is the target aggregate particle size, Is the elastic modulus of the object under ultrasonic action, Is the density of the object under ultrasonic action.

[0035] Can be measured through experiments. Measure the stress - strain curve using a universal testing machine and calculate .

[0036] Can be calibrated through experiments: 1. Prepare standard samples (with known material components and density ); 2. Use an ultrasonic probe to measure the actual sound velocity (Time - of - flight method); 3. Calculate the theoretical sound velocity ; 4. Determine the correction factor: 。

[0037] Example: If the measured sound velocity in the concrete , and the theoretical calculation , then 。

[0038] As a more preferred embodiment 7, when the material components or process conditions change, it is necessary to update and in real time to ensure the frequency adjustment accuracy: 1. Use an ultrasonic probe: Monitor the sound velocity in real time ; 2. Use a torque sensor: Deduce the equivalent viscosity , and update in combination with the rheological model; Control logic: 。

[0039] As a preferred embodiment 8, the formula for dynamically adjusting the ultrasonic power and the action time according to the concentration gradient in S32 is: ; where is the real-time ultrasonic power, is the initial reference power, is the power attenuation coefficient, is the exponential attenuation term, is the dynamic power adjustment amplitude, is the sum of the multi-component concentration gradient change rates.

[0040] The following explanations are made for the above parameters: 1. : Real-time ultrasonic power Meaning: The real-time ultrasonic power setting value output by the algorithm, with the unit of watt (W).

[0041] Function: Dynamically adjust the ultrasonic energy input according to the material mixing state to optimize the dispersion effect and avoid damaging sensitive materials (such as microorganisms or microcapsules).

[0042] 2. : Initial reference power Meaning: The initial power setting value of the ultrasonic wave, usually determined based on experiments or experience.

[0043] Unit: Watt (W).

[0044] Function: Provide the basic energy input to ensure the uniformity in the initial stage of mixing. For example, a high initial power may be used to quickly break up agglomerates.

[0045] 3. : Power attenuation coefficient Meaning: The rate constant of the exponential decay term, which characterizes how fast the base power decays over time.

[0046] Unit: Reciprocal of time (e.g., s⁻¹).

[0047] Determination method: By experimental fitting or setting according to the time constant of the mixing process (e.g., if mixing takes 60 seconds to complete, it may be taken ).

[0048] Function: Gradually reduces the base power over time, avoiding excessive energy input in the later stage of mixing, thus saving energy consumption and reducing the risk of material damage.

[0049] 4. : Exponential decay term Physical meaning: Describes the process of the initial power gradually decaying over time.

[0050] Function: Reflects the natural downward trend of energy demand during the mixing process. As the material becomes more uniform, the base power demand decreases.

[0051] 5. : Dynamic power adjustment amplitude Meaning: The proportionality coefficient for adjusting power according to the change rate of the concentration gradient.

[0052] Unit: Watt (W·s) or calibrated according to the specific system.

[0053] Determination method: It needs to be calibrated in combination with the maximum power limit of the ultrasonic device and the material sensitivity (such as the compressive strength of microcapsules).

[0054] Function: Amplifies or reduces the impact of concentration gradient changes on power adjustment. For example, if a rapid change in concentration distribution (such as local agglomeration) is detected, then increase to quickly increase the power.

[0055] 6. : Sum of the change rates of multi-component concentration gradients Meaning: : The concentration distribution of the

[0056] th component (such as microorganisms, microcapsules, fibers, etc.).

[0057] : The change rate of concentration over time, reflecting the dynamic distribution of each component during the mixing process.

[0058] Measurement method: The concentration field is monitored in real time through a high-speed camera + AI image analysis or ultrasonic echo technology, and the local gradient change is calculated.

[0059] Function: Quantify the mixing non-uniformity. When the concentration changes sharply in a certain area (such as the formation or dispersion of aggregates), trigger dynamic power adjustment.

[0060] Furthermore, the step S32 further includes the following steps: gradually reduce the energy input over time to adapt to the changing needs of the mixing process from "intense dispersion" to "fine homogenization". When the detected non-uniformity intensifies, increase the power to strengthen the dispersion; otherwise, reduce the power.

[0061] This algorithm realizes dynamic power control through the superposition of two parts: 1. Basic power attenuation ( ): Gradually reduce the energy input over time to adapt to the changing needs of the mixing process from "intense dispersion" to "fine homogenization".

[0062] 2. Dynamic response adjustment ( ): Respond to the change of concentration distribution in real time. When the detected non-uniformity intensifies (such as ), immediately increase the power to strengthen the dispersion; otherwise, reduce the power.

[0063] Suppose during the stirring process, the system detects that the concentration of nano-SiO2 particles suddenly increases in a certain area ( ), the algorithm will increase to increase the ultrasonic power and use the cavitation effect to break the aggregates. At the same time, over time, the basic power gradually decreases to avoid the rupture of microcapsules due to excessive power in the later stage of mixing.

[0064] The self-healing concrete intelligent preparation system based on the dynamic optimization algorithm includes a raw material bin 1 and a repair agent carrier storage bin 2, which are used for the implementation of the above-mentioned self-healing concrete intelligent preparation method based on the dynamic optimization algorithm. The raw material bin 1 and the repair agent carrier storage bin 2 are both connected to the feed inlet of the premixing module, and the discharge outlet of the premixing module is connected to the feed inlet of the gradient stirring module; The premixing module includes a screw conveyor 3. A microwave moisture sensor 4 is provided between the feed inlet of the screw conveyor 3 and the discharge outlet of the raw material bin 1 to monitor the moisture content of the aggregate, and the microwave moisture sensor 4 forms a linkage with the screw conveyor 3 through an externally connected programmable industrial PLC controller; The gradient stirring module includes a paddle mixer 5 and an ultrasonic homogenizer 10 connected in series along the feeding direction. A torque sensor 6 is provided on the paddle main shaft of the paddle mixer 5 to monitor the stirring resistance torque. An ultrasonic echo viscosity sensor 7 is provided on the side wall of the tank body of the paddle mixer 5 to monitor the viscosity distribution of the slurry. The torque sensor 6 and the ultrasonic echo viscosity sensor 7 are in linkage with the paddle mixer 5 through an externally connected programmable industrial PLC controller; A transparent observation window 8 is provided on one side of the paddle mixer 5. A high-speed recognition camera 9 is provided at the transparent observation window 8 for AI recognition of the dispersion degree of the repair agent carrier in the slurry in the tank. A particle size distribution on-line detector 11 is provided at the outlet of the ultrasonic homogenizer 10 to monitor the distribution of the repair agent carrier at the outlet of the ultrasonic homogenizer 10. The high-speed recognition camera 9 and the particle size distribution on-line detector 11 are in linkage with the ultrasonic homogenizer 10 through an externally connected programmable industrial PLC controller.

Claims

1. An intelligent preparation method for self-healing concrete based on a dynamic optimization algorithm, characterized in that: The following steps are involved: S1. Feeding: the raw material bin and the repair agent carrier storage bin feed the premixing module; S2. Premixing: The premixing module monitors the moisture content of aggregates fed into the raw material bin in real time, and dynamically adjusts the speed of the premixing module according to the moisture content; S3, gradient stirring: after premixing, the materials are sent into the gradient stirring module for stirring in sequence; S31, first enter low-speed paddle stirring, monitor the stirring resistance torque and slurry viscosity distribution in real time, and dynamically adjust the paddle speed according to the torque-viscosity relationship, and the speed does not exceed 50rpm; S32. The material after low-speed stirring enters the ultrasonic homogenizer. The dispersion degree of each substance in the low-speed stirring is monitored in real time through the recognition camera, the concentration gradient is calculated, and the carrier particle size distribution is monitored at the outlet of the ultrasonic homogenizer to obtain the target agglomerate particle size. The ultrasonic frequency is dynamically adjusted according to the target agglomerate particle size, and the ultrasonic power and action time are dynamically adjusted according to the concentration gradient.

2. The method for preparing self-healing concrete based on a dynamic optimization algorithm according to claim 1 is characterized in that: The formula for dynamically adjusting the rotation speed according to the moisture content of aggregate in S2 is: ； in, is the spiral blade speed, is the processing volume, is the bulk density of the material, is the compensation coefficient, and its empirical value is 0.8~1.

2.

3. The method for intelligently preparing self-healing concrete based on a dynamic optimization algorithm according to claim 2 is characterized in that: The S2 Include and , is the theoretical processing capacity of the screw conveyor, which can be calculated by the following formula: ； in, is the diameter of the spiral blade, unit: m; is the pitch, unit: m, usually ; is the screw shaft speed, unit: rpm; is the filling factor, which is generally 0.30.45 and is adjusted according to the fluidity of the material; is the bulk density of the material, unit: kg / m³; for When the screw conveyor is running, the actual processing volume is measured and the correction factor is calculated. : ； Theoretical processing capacity and the correction factor deduced Substitute the formula for dynamically adjusting the rotation speed by the moisture content of aggregate to obtain the theoretical rotation speed, operate according to the theoretical rotation speed, and detect the actual processing capacity ,Will and Compare and adjust in real time Size, up to .

4. The method for intelligently preparing self-healing concrete based on a dynamic optimization algorithm according to claim 3 is characterized in that: The helical inclination angle in S2 is 15° to 45°.

5. The method for preparing self-healing concrete based on dynamic optimization algorithm according to claim 1 is characterized in that: The formula for dynamic adjustment through the torque-viscosity relationship in S31 is: ； in, is the real-time torque, is the slurry viscosity, , is the blade geometric constant, obtained through experimental fitting.

6. The method for intelligently preparing self-healing concrete based on a dynamic optimization algorithm according to claim 5 is characterized in that: In S31 , During the fitting process of the experiment, the real-time torque, real-time speed and real-time viscosity under the experimental conditions were monitored, and the corrected and .

7. The method for intelligently preparing self-healing concrete based on a dynamic optimization algorithm according to claim 1, characterized in that: The formula for dynamically adjusting the ultrasonic frequency according to the target aggregate particle size in S32 is: ； in, is the ultrasonic frequency, is the speed of sound correction factor, is the target aggregate size, is the elastic modulus of the object acted upon by ultrasound, is the density of the object acted upon by ultrasound.

8. The method for intelligently preparing self-healing concrete based on a dynamic optimization algorithm according to claim 7 is characterized in that: The formula for dynamically adjusting the ultrasonic power and action time according to the concentration gradient in S32 is: ； in, is the real-time ultrasound power, is the initial reference power, is the power attenuation coefficient, is an exponential decay term, is the dynamic power adjustment amplitude, is the sum of the rates of change of multi-component concentration gradients.

9. The method for intelligently preparing self-healing concrete based on a dynamic optimization algorithm according to claim 8, characterized in that: The S32 also includes the following steps: gradually reducing the energy input over time to adapt to the change in the mixing process from "violent dispersion" to "fine homogenization". When it is detected that the inhomogeneity is aggravated, the power is increased to enhance the dispersion; otherwise, the power is reduced.

10. A self-healing concrete intelligent preparation system based on a dynamic optimization algorithm, comprising a raw material warehouse and a repair agent carrier storage warehouse, characterized in that: Used for the implementation of the self-healing concrete intelligent preparation method based on the dynamic optimization algorithm as described in any one of claims 1 to 9, the raw material bin and the repair agent carrier storage bin are both connected to the feed port of the premixing module, and the discharge port of the premixing module is connected to the feed port of the gradient mixing module; The premixing module includes a screw conveyor, and a microwave moisture sensor is provided between the feed port of the screw conveyor and the discharge port of the raw material bin to monitor the moisture content of the aggregate, and the microwave moisture sensor is linked with the screw conveyor through an external programmable industrial PLC controller; The gradient stirring module comprises a paddle stirrer and an ultrasonic homogenizer connected in series along the feeding direction, a torque sensor is provided on the paddle main shaft of the paddle stirrer for monitoring the stirring resistance torque, an ultrasonic echo viscosity sensor is provided on the side wall of the tank of the paddle stirrer for monitoring the viscosity distribution of the slurry, and the torque sensor and the ultrasonic echo viscosity sensor are linked with the paddle stirrer through an external programmable industrial PLC controller; A transparent observation window is provided on one side of the paddle mixer, and a high-speed recognition camera is provided at the transparent observation window for AI to identify the dispersion of the repair agent carrier in the slurry in the tank. An online particle size distribution detector is provided at the outlet of the ultrasonic homogenizer for monitoring the distribution of the repair agent carrier at the discharge port of the ultrasonic homogenizer. The high-speed recognition camera and the online particle size distribution detector are linked to the ultrasonic homogenizer through an external programmable industrial PLC controller.

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