A method and system for the production of functionalized concrete

Abstract

The application relates to a functionalized concrete preparation method and system, and relates to the technical field of civil construction materials, solves the problems that the two types of methods lack "function-structure-process" system regulation at present, and the stability, economy and multifunctional synergy of the materials are difficult to be considered, and the method comprises the following steps: after a granulator starting signal is obtained, preset initial particle size cement particles are put in, the granulator is started to rotate and water is sprayed according to a preset water-cement ratio, the particle size and the water content are monitored in real time, and the rotating speed is dynamically adjusted until the standard is reached; after the granulation is completed, the nozzle is switched to spray a functional material, the coating thickness and uniformity are monitored, and the parameters are optimized until the standard is reached; then, cold pressing and quality evaluation are performed, and maintenance is performed after the standard is reached, and finally, the functionalized concrete is obtained. The application has the following effects: a continuous functional network is efficiently formed, the cement-based material is endowed with various functions, and the process and performance are considered.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of civil construction materials, in particular to a preparation method and system of functionalized concrete. BACKGROUND

[0002] With the development of intelligent buildings, intelligent transportation and special engineering fields, cement-based materials are upgrading from traditional structural materials to multifunctional integrated materials, which need to meet the composite requirements of mechanical bearing, electrical conductivity, temperature control and the like to adapt to intelligent operation and maintenance and special environment application scenarios.

[0003] At present, the preparation technology of functionalized concrete mainly includes two types: one is a direct doping method, in which functional components such as carbon nanotubes and phase change materials are directly mixed into cement paste to impart specific functions to the material through physical mixing; and the other is a surface treatment method, in which a functional layer is constructed on the surface of the material through surface coating, powder wrapping and the like to realize local functional enhancement.

[0004] There are three major problems in the prior art: first, in the direct doping method, the functional materials are prone to agglomeration and uneven dispersion, it is difficult to form a continuous functional network, and high dosage can supplement the function but increase the cost and deteriorate the mechanical properties; second, in the surface treatment method, the interface between the functional layer and the matrix is weak and prone to peeling, and the function is limited to the surface and cannot be covered three-dimensionally; and third, both methods lack system regulation of "function-structure-technology", which makes it difficult to balance stability, economy and multifunctional synergy, and restricts engineering application. SUMMARY

[0005] In order to efficiently form a continuous functional network, impart diverse functions to cement-based materials, and balance processability and performance, the application provides a preparation method and system of functionalized concrete.

[0006] In a first aspect, the application provides a preparation method of functionalized concrete, which adopts the following technical scheme:

[0007] A preparation method of functionalized concrete, comprising:

[0008] An initiation signal of a granulator is obtained, and the granulator is brought into a running preparation state in response to the initiation signal, and cement particles with a preset initial particle size are put into the granulator in the preparation state;

[0009] The granulator is started to run at a preset rotating speed, and a plurality of groups of preset nozzles are driven to spray deionized water to the cement particles at a preset water-cement ratio; during the granulation, the particle size and moisture content of the particles are monitored in real time by a preset related sensor, compared with a preset granulation standard, and the rotating speed of the granulator is dynamically adjusted; when the particle size and moisture content of the particles both meet the preset granulation standard, the granulation is completed;

[0010] After the granulation is completed, the spraying materials of the multiple groups of nozzles are switched to the preset functional materials, and the spraying is performed at a preset pressure; meanwhile, the thickness and uniformity of the coating on the particle surface are monitored through the preset related sensors, and the granulator speed, nozzle trajectory and spraying rate are compared with the preset coating standard and optimized, and when the thickness and uniformity of the coating meet the preset coating standard, the spraying is completed.

[0011] After the spraying is completed, the particles perform a preset cold pressing process, and after the cold pressing process is completed, a preset quality evaluation is performed, and when the preset quality standard is reached, a curing process is entered and the curing process is performed.

[0012] When the curing process is completed, the required functionalized concrete is obtained.

[0013] By adopting the above technical scheme, through intelligent control of granulation, spraying, cold pressing and curing, multifunctionalization and high performance of cement-based materials are realized. Precise spraying ensures uniform distribution of functional materials, cold pressing forming optimizes material structure, improves mechanical properties, electrical conductivity, temperature control and other multifunctional properties, while reducing production cost and improving production efficiency, which has significant engineering application value and industrialization potential.

[0014] In a second aspect, the present application provides a functionalized concrete preparation system, which adopts the following technical scheme:

[0015] A functionalized concrete preparation system includes a memory, a processor, and a program stored on the memory and executable on the processor. The program can be loaded and executed by the processor to implement the functionalized concrete preparation method of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of a functionalized concrete preparation method according to an embodiment of the present application.

[0017] Figure 2 is a schematic diagram of the granulation stage of cement particles according to an embodiment of the present application.

[0018] Figure 3 is a schematic diagram of cold pressing forming according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The present application will be further described in detail below with reference to the accompanying drawings.

[0020] REFERENCE Figure 1 A functionalized concrete preparation method according to the present application includes:

[0021] In step S100, a granulator start signal is obtained, and the granulator is brought into a running preparation state in response to the start signal, and cement particles with a preset initial particle size are fed into the granulator in the preparation state.

[0022] Wherein, the granulator start signal: a signal sent by the control system, used to trigger the granulator to enter the running preparation state. The preset initial particle size: refers to the initial particle size range of the cement particles put into the granulator, usually 7-200 microns. Running preparation state: the state of the granulator after receiving the start signal, at this time the device performs self-checking to ensure that all systems are running normally, and prepares for the subsequent granulation process.

[0023] The overall necessary process is described as follows:

[0024] 1. The automatic control system triggers the start signal: the automatic control system automatically triggers the start signal according to the preset production plan. The signal is transmitted to the control system of the granulator through the industrial control system (such as PLC), ensuring the integrity and accuracy of the signal.

[0025] 2. Device self-checking and parameter initialization: after receiving the start signal, the granulator automatically performs device self-checking to check whether each component is running normally, including the nozzle, rotating device, sensor, etc. After self-checking is completed, the control system initializes the settings according to the preset process parameters, including the nozzle, rotating device, sensor, etc.

[0026] 3. Put in cement particles: the automatic device puts in cement particles with a preset initial particle size (7-200 microns) into the granulator. This process is completed through an automatic conveying system to ensure uniform distribution and accurate input of the particles.

[0027] 4. Dynamic adjustment and monitoring: after the granulator starts, the integrated laser particle size analyzer and humidity sensor are used to monitor the particle size and moisture content in real time. The control system uses the PID control algorithm to dynamically adjust the speed of the granulator based on the feedback data, ensuring that the particle size and moisture content are within the preset range.

[0028] Step S200, start the granulator to run at a preset speed, and drive the preset multiple groups of nozzles to spray deionized water to the cement particles at a preset water-cement ratio; during granulation, the particle size and moisture content are monitored in real time by the preset related sensors, compared with the preset granulation standard, and the speed of the granulator is dynamically adjusted; when the particle size and moisture content meet the preset granulation standard, the granulation is completed.

[0029] Wherein, the preset speed: the rotating speed of the granulator during operation, which is usually adjusted according to the material characteristics and process requirements. Water-cement ratio: the ratio of sprayed deionized water to cement particles, which is an important parameter affecting the granulation effect. Related sensors: signal sensors, which are devices for real-time acquisition of process parameters, including particle size analyzers, humidity / pressure sensors, etc. The preset granulation standard: the standard value that the particle size and moisture content need to reach, used to judge whether the granulation is completed.

[0030] The overall necessary process can refer to Figure 2 , which is specifically described as follows:

[0031] Start the granulator to run: After receiving the start signal and completing the device self-checking and parameter initialization, the granulator starts to rotate at the preset speed. At the same time, the preset multiple groups of nozzles spray deionized water to the cement particles according to the preset water-cement ratio. This process is precisely controlled by the automatic control system to ensure the uniformity and continuity of the spraying.

[0032] Real-time monitoring and dynamic adjustment: During the granulation process, the particle size and moisture content of the particles are monitored in real time by the preset related sensors, i.e. signal sensors (such as laser particle size analyzers and humidity sensors). These sensors will feed back the monitoring data to the control system in real time.

[0033] The control system uses the PID control algorithm to dynamically adjust the speed of the granulator according to the feedback data. Specifically, if the monitored particle size or moisture content deviates from the preset granulation standard, the control system will calculate the speed adjustment amount according to the following formula:

[0034] ;

[0035] Wherein, is the speed adjustment amount, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the error between the target value and the actual value.

[0036] 3, Granulation completion judgment: When the particle size and moisture content of the particles meet the preset granulation standard, the control system judges that the granulation is completed, and automatically stops the water spraying and rotation, preparing for the next step of spraying functional materials.

[0037] Step S300, after the granulation is completed, switch the spraying material of the multiple groups of nozzles to the preset functional material, and spray at the preset pressure; at the same time, monitor the particle surface coating thickness and uniformity through the preset related sensors, compare with the preset coating standard and optimize the granulator speed, nozzle trajectory and spraying rate, when the coating thickness and uniformity meet the preset coating standard, the spraying is completed.

[0038] Wherein, the preset pressure: the pressure set by the nozzle when spraying the functional material, to ensure the uniformity and coverage effect of spraying. Functional material: a material that imparts specific functions (such as electrical conductivity, temperature control, electromagnetic shielding, etc.) to cement-based materials, such as carbon nanotubes, graphene, phase-change materials, etc. Coating thickness and uniformity: the thickness and uniformity of the coating formed on the surface of the particles by spraying the functional material, which is a key parameter affecting the functional performance of the material. Preset coating standard: the standard value that the coating thickness and uniformity need to reach, used to determine whether the spraying is complete.

[0039] The overall necessary process is described as follows:

[0040] 1. Switching nozzle material: After granulation is completed, the control system automatically switches the nozzle, changing the sprayed material from deionized water to the preset functional material. This process is precisely controlled through an automated control system, ensuring accurate switching of the sprayed material.

[0041] 2. Real-time monitoring and dynamic optimization: During the spraying process, the thickness and uniformity of the coating on the surface of the particles are monitored in real time by preset related sensors (such as multispectral sensors). These sensors feed the monitoring data back to the control system in real time.

[0042] The control system dynamically optimizes the rotational speed of the granulator, the trajectory of the nozzle, and the spraying rate based on the feedback data. Specifically, if the monitored coating thickness or uniformity deviates from the preset coating standard, the control system will calculate the adjustment amount according to the following formula:

[0043] ; wherein, is the parameter that needs to be adjusted (such as rotational speed, spraying rate, etc.).

[0044] 3. Spraying completion judgment: When the thickness and uniformity of the coating both meet the preset coating standard, the control system judges that the spraying is complete and automatically stops spraying, preparing for the next cold pressing forming process.

[0045] Step S400, after spraying is completed, the particles perform the preset cold pressing process, and after completing the cold pressing process, they undergo a preset quality assessment. When the preset quality standard is reached, they enter the curing process and perform the curing process.

[0046] Wherein, the cold pressing process: a process of applying pressure to functionalized particles at room temperature to shape them into the desired shape. Quality assessment: detection of the shaped material to ensure that it meets the predetermined quality standard. Curing process: a process of storing the shaped material in a suitable environment to harden and stabilize it.

[0047] The overall necessary process is described as follows: 1. Perform the cold pressing process: after spraying is completed, the functionalized particles are transferred to the cold pressing forming equipment. The hydraulic system intelligently matches the forming parameters such as the applied pressure and pressure holding time according to the material property database. The particles are transported to the mold cavity by a frequency conversion conveyor belt, and the hydraulic system applies a preset pressure to make the particles form in the mold. 2. Real-time feedback and automatic compensation: during the cold pressing process, the pressure sensor feeds back the forming density in real time, and the control system automatically compensates for pressure fluctuations according to the feedback to ensure that the forming density meets the requirement of relative density ≥ 92%. 3. Quality assessment: after the cold pressing forming is completed, the formed material is assessed for quality. This may include using X-ray CT scanning to detect porosity, ensuring that the porosity is < 3%, and using non-destructive testing modules for other quality assessments. 4. Enter the curing process: once the formed material meets the preset quality standards, they will be sent to the curing process. During the curing process, the material is subjected to 28-day wet curing in a standard curing chamber to complete the structural stabilization and hydration reaction of the bonding layer. 5. Functionalized concrete after curing: after the curing process is completed, the desired functionalized concrete is obtained. At this time, the concrete has the specific functions imparted by the sprayed functional materials, such as electrical conductivity, temperature control, or electromagnetic shielding.

[0048] Step S500, when the curing process is completed, the desired functionalized concrete is obtained.

[0049] The preset cold pressing process includes:

[0050] Step S410, the particles after completing the spraying are transported to the compression mold of a preset specification by a conveying device with a preset conveying rate.

[0051] Wherein, the preset conveying rate: refers to the speed of the conveying device transporting the particles, which is set according to the production efficiency and process requirements. Compression mold: a mold used to cold press the particles into a specific shape, the specification of which is preset according to the shape and size of the final product.

[0052] The overall necessary process is described as follows: 1. Preparation for delivery: The functionalized particles after spraying are initially arranged and aligned on the conveyor belt to ensure that the particles can enter the compression mold uniformly. 2. Start the conveying device: Start the conveying device according to the preset conveying rate. The rate is set considering the flowability of the particles and the uniformity in the subsequent compression process to avoid uneven accumulation of particles in the mold. 3. Delivery to the mold: The conveying device delivers the particles to the compression mold of the preset specification. The specification of the mold is determined in advance according to the size and shape of the required molded product to ensure that the final product meets the design requirements. 4. Monitor the delivery process: During the delivery process, the flow of particles is monitored by sensors to ensure the stability of the delivery rate and the uniform distribution of the particles. 5. Prepare for cold pressing: Once the particles are delivered to the mold, prepare to start the hydraulic press for the cold pressing process. At this time, the mold door is closed to ensure that the particles do not overflow during the cold pressing process.

[0053] Step S420, start the hydraulic press, and apply pressure to the particles in the mold according to the pre-set pressure matching the type of functional material, while maintaining the pre-set holding time.

[0054] Wherein, hydraulic press: a device that works on the principle of hydraulic pressure, used to apply pressure to the particles in the mold. Pre-set pressure: the pre-set pressure intensity according to the characteristics of the functional material and the required molding density. Stepwise pressure: gradually increasing the pressure in multiple stages according to the pre-set to optimize the molding effect and material performance. Holding time: the duration of maintaining the pre-set pressure after reaching it to ensure that the particles are fully formed.

[0055] The overall necessary process can be referred to Figure 3 , and is described as follows: 1. Start the hydraulic press: After the particles are delivered to the compression mold, start the hydraulic press to begin applying pressure to the particles in the mold. 2. Stepwise pressure: According to the type of functional material and the required molding characteristics, the hydraulic press applies pressure to the particles in multiple stages according to the pre-set pressure. This may include initial pressure, intermediate pressure, and final pressure to ensure that the particles are gradually and uniformly compressed. 3. Holding time: After reaching the target pressure, the hydraulic press maintains that pressure for a certain period of time, i.e. holding time. This time is pre-set according to the material characteristics and molding requirements to ensure that the particles have enough time to densify and adjust their structure under pressure. 4. Monitor pressure changes: During the pressure application process, the pressure changes in the mold are monitored in real time by pressure sensors to ensure the stability and uniformity of the pressure. 5. Prepare for the next process: Once the holding time is over, the hydraulic press will automatically release the pressure, preparing to remove the formed particles from the mold for the next process.

[0056] Step S430, stabilize the mold temperature in the pre-set temperature range through the water cooling system with pre-set temperature control accuracy.

[0057] Water cooling system: A cooling device that utilizes water circulation to regulate mold temperature. Preset temperature control accuracy: The temperature control accuracy that the water cooling system can maintain, usually measured in degrees Celsius. Preset temperature range: The pre-set mold temperature control range based on material properties and molding requirements.

[0058] The overall necessary process is described as follows: 1. Start the water cooling system: Start the water cooling system before the cold pressing process begins to ensure that the mold temperature remains stable during the pressing process. For example, if the preset temperature range is 20-25℃, the water cooling system will ensure that the mold temperature does not exceed this range. 2. Set temperature parameters: Set the preset temperature range and temperature control accuracy of the water cooling system according to the thermal physical properties of the material and the requirements of the molding process. For example, for heat-sensitive materials, the temperature control accuracy may need to be set within ±0.5℃. 3. Temperature monitoring and adjustment: During the cold pressing process, the temperature of the mold is monitored in real time by temperature sensors. If the sensor detects that the mold temperature has risen to 24℃, the water cooling system will automatically adjust the flow of cooling water to increase the cooling effect to maintain the mold temperature within the preset range. 4. Ensure temperature uniformity: The water cooling system design ensures that the temperature of each part of the mold is uniform, avoiding differences in molding quality caused by uneven temperature. For example, by arranging multiple cooling channels in different areas of the mold, the temperature distribution of the entire mold can be ensured to be uniform. 5. Coordinate with the cold pressing process: The control of the mold temperature is synchronized with the cold pressing process to ensure that the mold temperature remains suitable during the deformation of the particles and the formation of a three-dimensional network of functional materials. For example, if the thermal decomposition of the material needs to be avoided during the molding process, the water cooling system will ensure that the mold temperature does not exceed the thermal stability limit of the material.

[0059] Step S440, during the cold pressing process, the particles gradually deform into a cubic close-packed structure of the preset structural morphology under the action of pressure, and the functional material on the surface of the particles contacts each other to form a three-dimensional through network with a preset connectivity.

[0060] Cubic close-packed structure: refers to the close-packed structure of particles under the action of pressure, similar to the shape of a cube. Three-dimensional through network: a continuous network formed by the functional material on the surface of the particles under the action of pressure, which gives the material specific functions.

[0061] The overall necessary process is described as follows: 1. Particle deformation: During the cold pressing process, the particles gradually deform under the pressure applied by the hydraulic machine. For example, a spherical particle may be compressed into a shape closer to a cube to achieve a higher packing density. 2. Functional material contact: As the particles deform, the functional materials on their surfaces begin to contact each other. For example, if the particle surface is sprayed with conductive material, these materials will contact each other during the particle deformation process, forming a conductive path. 3. Forming a three-dimensional network: The particles are compressed into a predetermined cubic close-packed structure in the mold, and at the same time, the functional materials on the surface form a three-dimensional network with a predetermined connectivity under pressure. For example, to achieve good electrical conductivity, it may be necessary to ensure that the conductive materials are in sufficient contact to form a continuous conductive path. 4. Optimize the forming effect: During the entire cold pressing process, the deformation of the particles and the formation of the functional material network are optimized by precisely controlling the pressure and holding time. For example, for materials that require high electrical conductivity, it may be necessary to increase the pressure to ensure that the contact between the conductive materials is more intimate. 5. Ensure structural stability: By controlling the cold pressing process, the stability of the material structure after forming is ensured. For example, by appropriate pressure and temperature control, cracks or delamination of the material after forming can be avoided, thereby ensuring the mechanical strength and functional stability of the material.

[0062] Step S450, using a pressure sensor with a preset accuracy to monitor the actual density of the particles after forming in real time, when the actual density reaches the preset forming density standard, the cold pressing process is completed.

[0063] Wherein, forming density: refers to the density of the particles after cold pressing, which is an important indicator of forming quality. Pressure sensor: a sensor used to monitor the change in particle density during the forming process in real time.

[0064] The overall necessary process is described as follows: 1. Real-time monitoring of molding density: During the cold pressing process, the actual density of the granules after molding is monitored in real time by a pressure sensor with preset accuracy. This ensures accurate control of the molding density, which is crucial for ensuring the final performance of the material. 2. Reach the preset molding density standard: When the actual density reaches the preset molding density standard, it indicates that the granules have been sufficiently compressed and have formed the required dense packing structure. For example, if the preset molding density standard is 2.5 g / cm³, then when the actual density monitored by the sensor reaches or exceeds this value, it can be considered that the granules have reached the ideal molding density. 3. Automatically determine the completion of the cold pressing process: The control system automatically determines whether the cold pressing process is complete based on the feedback from the pressure sensor. Once the preset molding density is reached, the control system will automatically send a signal to end the cold pressing process. Simple example: Suppose we are producing a functionalized concrete with specific electrical conductivity properties. During the cold pressing process, we set the target value of the molding density to 2.7 g / cm³ to ensure good contact between the conductive materials. The pressure sensor monitors the molding density in real time, and when the monitored density stabilizes at 2.7 g / cm³ or higher, the control system automatically stops the hydraulic press from applying pressure, completing the cold pressing process. 4. Prepare for subsequent processes: After the cold pressing process is completed, the molded material will be removed and prepared for the curing process. During the curing process, the material will further harden, and its performance will be stabilized and improved.

[0065] If the required functionalized concrete is a conductive functional concrete, the complete preparation process is as follows:

[0066] First, the automatic control system sends a start signal to activate the granulator and perform a self-check to ensure that all components are working properly, preparing for subsequent operations. Next, the granulator rotates at a preset speed, and multiple groups of nozzles spray deionized water onto the cement particles according to the preset water-cement ratio. During this step, the integrated laser particle size instrument and humidity sensor monitor the particle agglomeration state in real time, and the PID control algorithm dynamically adjusts the rotation speed to achieve a gradient increase in particle size from the initial 7-200 microns to 1-3 millimeters, until the preset granulation standard is reached.

[0067] After granulation is completed, switch the sprayed material of multiple groups of nozzles to the preset functional material, and spray at a preset pressure. The multi-spectrum sensor monitors the coating thickness (accuracy ±2 microns) and uniformity in real time, and the machine learning algorithm dynamically optimizes the spraying parameters, including adjusting the spiral scanning trajectory of the nozzles (coverage rate ≥95%), the rotation speed of the granulator (30-150 rpm), and the spraying rate (5-15 mL / s), to ensure uniform coating of the functional layer on the surface of the cement particles.

[0068] Subsequently, the sprayed particles are transported to the compression mold by a conveying device with a preset conveying rate, the hydraulic machine is started, and the particles in the mold are subjected to grading pressure according to a preset pressure matching the type of functional material, while maintaining a preset pressure holding time. The mold temperature is stabilized at a preset temperature range, such as 25±2℃, by a water cooling system with a preset temperature control accuracy.

[0069] During the cold pressing process, the particles gradually deform into a cubic close-packed structure under pressure, and the functional material on the surface of the particles contacts each other to form a three-dimensional network. The actual density of the formed particles is monitored in real time by a pressure sensor with a preset accuracy, and when the actual density reaches a preset molding density standard, such as a relative density ≥ 92%, the cold pressing process is completed.

[0070] Finally, after the hydraulic pressure is released, the ejection mechanism automatically pushes out the formed test piece for quality evaluation, including testing the volume resistivity (<100Ω·m) by the four-probe method, analyzing the continuity of the functional network by an infrared thermal imager (temperature difference <5℃), and scanning by X-ray CT (porosity <3%). After meeting the standards, the formed test piece enters the curing process, and after curing, the intelligent preparation of the conductive concrete is realized. At this time, the concrete not only has excellent conductivity, but also is suitable for intelligent sensing, anti-interference, electromagnetic shielding, structural health monitoring and other engineering application scenarios.

[0071] If the required functionalized concrete is conductive functional concrete, after granulation, the spraying materials of multiple groups of nozzles are switched to the preset functional materials, and spraying is performed at a preset pressure, including the following steps:

[0072] Step S310, multiple groups of nozzles switch the spraying material to a preset functional material, which is a carbon-based conductive slurry matched with the demand for electrical conductivity. The carbon-based conductive slurry is a slurry containing carbon nanomaterials (such as conductive carbon black, graphene, carbon nanotubes) for spraying on the surface of cement particles to form a conductive network.

[0073] The necessary process is described as follows: The control system automatically switches the nozzle material to ensure smooth transition from deionized water to conductive slurry.

[0074] Step S320, the nozzles start spraying the preset pressure according to the viscosity of the conductive slurry and the adsorption characteristics of the particle surface to ensure uniform adhesion of the slurry.

[0075] The specific process is as follows: Start spraying: the control system issues an instruction, and the nozzles start spraying according to the preset pressure parameters. The preset pressure is usually set between 0.2 and 0.5 MPa, which is determined according to the viscosity characteristics of the conductive slurry and the surface adsorption capacity of the cement particles to ensure that the slurry can be uniformly and effectively attached to the surface of the particles.

[0076] Step S330, the spray head runs along a pre-set trajectory designed to cover the entire surface of the granules, ensuring a continuous and uniform coating of the conductive material.

[0077] The process is described as follows: 1. Pre-set trajectory: The spray head moves along a pre-set path designed to ensure that the conductive material covers every surface of the granules, forming a continuous conductive network. The trajectory takes into account the geometry of the granules and the required uniformity of the coating. 2. Start spraying: Under the instructions of the control system, the spray head begins to move along the pre-set trajectory and sprays the conductive slurry. During the spraying process, the spray head maintains a stable speed and pressure to ensure uniformity of the coating. Trajectory design: The trajectory may include multiple back-and-forth or spiral movements to ensure that all surfaces are covered. 3. Full surface coverage: The movement trajectory of the spray head is designed to reach all surface areas of the granules, whether flat or curved. This usually involves precise movement of the spray head in multiple axes, ensuring no missed areas. 4. Coating quality monitoring: During the spraying process, sensors are used to monitor the uniformity and continuity of the coating. If the sensors detect that the coating is too thin or too thick in certain areas, the control system will automatically adjust the spray head's movement trajectory or spraying parameters. Coating thickness: The target thickness of the coating may be set within the range of 20 to 30 microns to ensure good conductive performance. 5. Complete coating: When the entire surface of the granules is uniformly covered and the coating quality meets the pre-set standards, the spraying process is complete. At this point, the granule surface has formed a uniform layer of conductive material coating, ready for the next step of cold pressing. Coverage rate: The target coverage rate of the coating is set at least 95% to ensure the formation of an effective conductive network.

[0078] If the required functionalized concrete is a conductive functional concrete, the following processing steps are included after the spraying of the pre-set functional material is complete:

[0079] Step S301, start the pre-set improved Monte Carlo-percolation combined algorithm to simulate the three-dimensional structure of the conductive network of the granules after spraying, input the pre-set key parameters of the conductive function, and output the network connectivity index.

[0080] In this invention, the Monte Carlo method is used to simulate the random distribution of the conductive network. Percolation theory is used to describe the formation and connectivity of conductive paths in materials. The improved Monte Carlo-percolation combined algorithm combines the Monte Carlo method and percolation theory to simulate the three-dimensional structure of the conductive network and calculate the network connectivity index (NCI) through random sampling and connectivity evaluation.

[0081] The necessary process is described as follows: 1. Input parameters: coating coverage: real-time monitoring of coating thickness and uniformity by multispectral sensor to ensure coating coverage ≥ 95%. Contact angle of conductive material: contact angle of conductive material with cement particle surface, θ < 30°, to ensure the wettability and adhesion of the material. Coefficient of variation (CV) of local spraying thickness: coefficient of variation of local spraying thickness, CV < 15%, to ensure the uniformity of coating thickness. 2. Random sampling and simulation: use Monte Carlo method to randomly generate the distribution of conductive particles, simulate the three-dimensional structure of the conductive network. Through random sampling, a large number of possible conductive paths are generated to evaluate their connectivity. 3. Connectivity evaluation: apply percolation theory to evaluate the connectivity of the conductive network. By calculating the number and quality of connected paths in the network, the network connectivity index (NCI) is output. 4. Result output If NCI is greater than the preset threshold (such as 0.85), the conductive network connectivity is considered good, and the next step of cold pressing process is automatically entered. If NCI is less than or equal to the preset threshold, a preset hierarchical adjustment mechanism is triggered to adjust the spraying parameters and re-simulate until NCI is greater than the preset threshold.

[0082] Step S302, if the network connectivity index is greater than the preset network connectivity index threshold, the preset adaptive parameters of the cold pressing process are automatically associated, the spraying-cold pressing parameter linkage instruction is generated, the particle size, spraying parameters and network connectivity index are recorded in the preset blockchain process database, and the particles are transported to the cold pressing process according to the linkage instruction.

[0083] Spraying-cold pressing parameter linkage instruction: automatically generated parameter instruction for coordinating spraying and cold pressing processes according to network connectivity index evaluation results.

[0084] The necessary process is described as follows: 1. Adaptive parameter association: if NCI is greater than 0.85, the system automatically associates the preset adaptive parameters of the cold pressing process to generate the spraying-cold pressing parameter linkage instruction. 2. Parameter recording: record the particle size, spraying parameters and network connectivity index in the preset blockchain process database to ensure the traceability and consistency of the parameters. 3. Instruction transmission: transmit the generated linkage instruction to the cold pressing process to ensure that the cold pressing process is executed according to the adaptive parameters.

[0085] Step S303, if the network connectivity index is less than or equal to the preset network connectivity index threshold, a preset hierarchical adjustment mechanism is triggered, and after adjustment, re-simulation is performed until the network connectivity index is greater than the preset network connectivity index threshold.

[0086] Hierarchical adjustment mechanism: a multi-level adjustment strategy for gradually optimizing spraying parameters when the conductive network connectivity is insufficient until the network connectivity meets the preset threshold.

[0087] The necessary process is described as follows: 1. Triggering condition: when the network connectivity index (NCI) is less than or equal to a preset threshold (such as 0.85), the hierarchical adjustment mechanism is triggered. 2. Hierarchical adjustment strategy as follows: 2.1, the basic adjustment layer as follows: increase the spraying pass: from 2 to 3, ensure the uniformity of coating thickness. Improve atomization pressure: the atomization pressure is raised to 0.5 MPa, improve the uniformity and adhesion of the coating. Add dispersant: add 0.1% dispersant in the material, eliminate the agglomeration phenomenon of conductive material. 2.2, the parameter optimization layer as follows: adjust the scanning speed of the spray head: reduce the scanning speed of the spray head by 20%, ensure the uniformity of the coating. Extend the particle fluidization time: extend the particle fluidization time by 30 seconds, improve the coating coverage rate of the particle surface. 2.3, emergency intervention layer: start the ultrasonic auxiliary vibration device: start the 40 kHz ultrasonic auxiliary vibration device, improve the dispersion uniformity. 3. Re-simulation and evaluation: after each adjustment, re-run the improved Monte Carlo-seepage combined algorithm, recalculate the network connectivity index (NCI). If the NCI is greater than the preset threshold (such as 0.85), it is considered that the adjustment is successful, and the next step of cold pressing process is entered. If the NCI is still less than or equal to the preset threshold, the next level of adjustment is continued until the NCI is greater than the preset threshold.

[0088] If the required functionalized concrete is a mechanical property enhanced concrete, the complete preparation process is as follows:

[0089] Step 1, start signal triggers granulator self-checking and enters preparation state, then 7-200 μm ordinary Portland cement particles are put into the hopper. Step 2, the granulator rotates at 50-200 rpm, four groups of ultrasonic atomizing nozzles spray deionized water with water-cement ratio of 0.25-0.35; laser particle size analyzer and humidity sensor monitor in real time, PID algorithm dynamically adjusts speed, so that the particle size gradually increases from 7-200 μm to 1-3 mm.

[0090] Step 3, after granulation, the nozzle material is switched to the preset functional material, and the nozzle material is sprayed at a preset pressure, and the multi-spectral sensor monitors the coating thickness with an accuracy of ±2 μm; the machine learning model optimizes the nozzle trajectory in real time (coverage rate ≥95%), so that the fiber-nanoparticle is uniformly coated. Step 4, the functionalized particles are sent into the mold by a frequency conversion conveyor belt at 0.5-2 m / s, the hydraulic machine matches the 20-100 MPa grading pressure (PVA system takes the upper limit) according to the material library, the pressure is maintained for 60-300 s, the mold temperature is water-cooled to 25±2℃, the pressure sensor closed-loop compensation ±3% fluctuation, forming a "brick-mud" dense packing structure like pearl layer. Step 5, after cold pressing, the X-ray CT scanning porosity is less than 3%, and there is no defect after ultrasonic nondestructive testing, then it enters standard curing; after 28d wet curing, high toughness and high impact resistance mechanical enhanced concrete is obtained.

[0091] If the required functionalized concrete is a mechanical property enhanced concrete, after the completion of the granulation, switch the spraying material of the multiple groups of nozzles to the preset functional material, and spray at a preset pressure, including:

[0092] Step S3A0, the multiple groups of nozzles switch the spraying material to the preset functional material, which is a flexible reinforcing phase matching the mechanical property enhancement requirement. Flexible reinforcing phase: refers to a flexible material that can enhance the toughness of concrete, such as PVA fiber, rubber particles, nano-SiO2, etc., which forms a "brick-mud" biomimetic structure with the cement matrix. Preset functional material: the system calls the flexible slurry (such as PVA+ nano-SiO2 suspension) matching the mechanical enhancement requirement from the formula library, and directly introduces the nozzle material from deionized water to the slurry channel through the double valve switching module.

[0093] The necessary process is as follows: after the completion of the granulation, the system instantaneously switches the multiple groups of nozzles to the flexible reinforcing phase slurry, that is, a toughening slurry containing PVA fibers or rubber particles; the slurry is pre-prepared and filtered and is ready for use after the switching valve actuates.

[0094] Step S3B0, the nozzles start spraying at a preset pressure, which is pre-set according to the viscosity of the flexible reinforcing phase and the surface adsorption characteristics of the cement particles to dynamically match the material adhesion requirement.

[0095] Pre-set pressure: the system pre-calculates and locks the spraying pressure value according to the viscosity of the flexible reinforcing phase slurry and the surface adsorption energy of the cement particles, to ensure uniform adhesion of the slurry and prevent dripping.

[0096] The necessary process is as follows: after the nozzles are started, the flexible reinforcing phase slurry is uniformly sprayed at a preset pressure to the still rolling granulation particles; the pressure is automatically given by the system after real-time comparison of the slurry viscosity and the particle surface energy, to ensure that the slurry is neither blown away nor firmly attached.

[0097] Step S3C0, the nozzles run according to a preset trajectory, which is a preset spiral scanning path, and the granulator runs at a preset speed range to cooperate with the rolling of the particles, to ensure that the coverage of the functional material on the particles reaches a preset coverage rate.

[0098] Among them, the preset spiral scanning path: the nozzles move according to a spiral trajectory in three-dimensional space, to ensure that the functional material is uniformly covered on the surface of the rolling particles. Obtaining method: generated by the built-in "spiral-coating algorithm" of the system, the input parameters include target coverage rate, nozzle flow, particle size, etc., and the specific parameters (such as pitch, number of turns, etc.) of the spiral scanning path are output.

[0099] Pre-set rotation speed range: The range of rotation speed that the granulator maintains during the spraying process, ensuring uniform rolling of the particles during spraying and improving the coverage of functional materials. Acquisition method: The optimal rotation speed range is determined through experiments and simulations and stored in the system's recipe library, which is automatically called according to different functional materials and particle characteristics.

[0100] Pre-set coverage: The expected coverage range of functional materials on the surface of particles, usually expressed in percentage. Acquisition method: Real-time monitoring of coating thickness and uniformity by multi-spectral sensors, combined with pre-set algorithms to dynamically adjust spraying parameters, ensuring the pre-set coverage is achieved.

[0101] The necessary process is described as follows:

[0102] Trajectory generation and parameter setting are as follows:

[0103] Trajectory generation: The system generates a spiral scanning path based on the target coverage (e.g., 95%) and particle size (e.g., 1.5mm) using the "spiral-coating algorithm". For example, the pitch is set to 2.5mm and the number of turns is 60.

[0104] Parameter setting: The spray head is started at the pre-set pressure (e.g., 0.35MPa), and the granulator is operated at the pre-set rotation speed range (e.g., 80-100rpm).

[0105] Spraying and rolling coordination is as follows:

[0106] Spraying process: The spray head uniformly sprays the flexible reinforcing phase slurry along the spiral scanning path, ensuring that the slurry is evenly attached to the surface of the rolling particles.

[0107] Rolling coordination: The granulator maintains the pre-set rotation speed range during the spraying process, ensuring uniform rolling of the particles and improving the coverage of functional materials.

[0108] If the required functionalized concrete is a mechanical property-enhanced concrete, the pre-set cold pressing process includes:

[0109] Step S4A0, the functionalized particles after flexible reinforcing phase spraying are transported to the pre-set specification compression mold through the variable frequency conveying device with pre-set conveying rate. Among them, the pre-set conveying rate: the fixed speed of the conveying device when conveying functionalized particles, ensuring that the particles enter the compression mold uniformly and continuously. Acquisition method: According to the particle size, mold specification and production requirements, the optimal conveying rate is determined through experiments and simulations and stored in the system's recipe library. Variable frequency conveying device: a conveying system that can adjust the conveying speed according to pre-set parameters, ensuring accurate control of the conveying process. Compression mold of pre-set specification: mold for cold pressing forming, whose specification is designed according to the size and shape of the required concrete block. Acquisition method: Manufactured according to design requirements, mold specifications are stored in the system's recipe library, and the system automatically selects the appropriate mold according to the recipe.

[0110] The necessary processes are described as follows: 1. Functionalized particle preparation: The functionalized particles sprayed with flexible reinforcement phase are moved from the spraying area to the inlet of the conveying system. 2. Conveying rate setting: The system calls the preset conveying rate (such as 0.5-2 m / s) from the formula library and sets the operating frequency of the variable frequency conveying device. 3. Particle conveying: The functionalized particles are uniformly conveyed to the inlet of the compression mold at a preset speed by the variable frequency conveying device.

[0111] Step S4B0, start the hydraulic machine, call the preset material property database, press the particles in the mold according to the preset graded pressure matching the flexible reinforcement phase type, while maintaining the preset holding time.

[0112] Wherein, hydraulic machine: a device that uses hydraulic principle to apply pressure, used for cold pressing forming. Preset material property database: a database that stores different material properties (such as flexible reinforcement phase type, particle properties, etc.) and their corresponding cold pressing parameters. Preset graded pressure: pressure applied in stages according to the flexible reinforcement phase type and particle properties, ensuring uniform deformation of particles and formation of functional material network. Preset holding time: time maintained at each pressure stage, ensuring sufficient deformation of particles and stability of functional material network.

[0113] The necessary processes are described as follows: 1. Hydraulic machine start: the system calls the preset graded pressure and holding time matching the flexible reinforcement phase type from the formula library. Start the hydraulic machine and press the particles in the mold according to the preset parameters. 2. Graded pressure application: the hydraulic machine applies pressure in stages according to the preset graded pressure, ensuring uniform deformation of particles. For example, for PVA fiber reinforcement phase, the preset graded pressure may be 20 MPa, 50 MPa, 80 MPa. 3. Holding time maintenance: maintain the preset holding time at each pressure stage to ensure sufficient deformation of particles and stability of functional material network. For example, the holding time at each pressure stage may be 60 seconds, 120 seconds, 180 seconds.

[0114] Step S4C0, stabilize the mold temperature in the preset temperature range by the water cooling system with preset temperature control accuracy.

[0115] Preset temperature control accuracy: the accuracy range of the water cooling system that can accurately control the temperature of the mold, ensuring the stability of the mold temperature during cold pressing. Acquisition method: through the temperature sensor and controller settings of the water cooling system, usually with an accuracy range of ±2℃. Water cooling system: a cooling system that removes heat through circulating water to maintain the stability of the mold temperature. Preset temperature range: the target temperature range that the mold temperature needs to maintain during cold pressing, usually set according to material properties and process requirements.

[0116] The necessary processes are described as follows: 1. Water cooling system start: The system calls the preset temperature range (such as 25±2℃) and temperature control accuracy (such as ±2℃) from the formula library. Start the water cooling system and start circulating cooling water. 2. Temperature monitoring and adjustment: The water cooling system monitors the mold temperature in real time through the temperature sensor. The controller automatically adjusts the flow and temperature of the cooling water according to the monitoring data to ensure that the mold temperature is stable within the preset range. 3. Real-time feedback and compensation: The system monitors the mold temperature in real time, and if the temperature exceeds the preset range, automatically adjusts the cooling water parameters for compensation. For example, if the mold temperature rises above 27℃, the system automatically increases the cooling water flow; if the temperature drops below 23℃, the system automatically reduces the cooling water flow.

[0117] Step S4D0, during the cold pressing process, collect strain data of functionalized particles in different areas of the mold according to the preset number, and simultaneously start the pressure sensor of the preset accuracy to monitor the overall forming density of the functionalized particles after cold pressing.

[0118] Wherein, the preset number: the number of strain data collection points preset during the cold pressing process, to ensure comprehensive monitoring of functionalized particles in different areas of the mold. Acquisition method: according to the mold specifications and particle distribution, determine the optimal number of collection points through experiments and simulations, and store them in the system formula library.

[0119] Strain data: data reflecting the deformation degree of functionalized particles during cold pressing, used to evaluate the uniform deformation and stress distribution of particles. Acquisition method: real-time collection by high-precision strain sensors, which are distributed in different areas of the mold.

[0120] Pressure sensor of preset accuracy: a sensor used to monitor the overall forming density of functionalized particles after cold pressing, whose accuracy directly affects the accuracy of the monitoring results. Acquisition method: select appropriate pressure sensors according to the requirements of the cold pressing process, and store their parameters in the system formula library.

[0121] Overall forming density: the density of functionalized particles after cold pressing, an important indicator of forming quality.

[0122] The necessary processes are described as follows: 1. Strain data collection: the system calls the preset number of collection points from the formula library, which are distributed in different areas of the mold. During the cold pressing process, high-precision strain sensors collect strain data from these points in real time to ensure comprehensive monitoring of particle deformation. 2. Pressure sensor start: the system calls the pressure sensor parameters of the preset accuracy from the formula library to start the pressure sensor. The pressure sensor monitors the overall forming density after cold pressing in real time to ensure that the forming quality meets the requirements.

[0123] 3. Data synchronization and analysis: Strain data and pressure data are transmitted synchronously to the control system for real-time analysis of particle deformation and molding density. If abnormal strain data or molding density is detected, the system automatically triggers a warning and adjusts the cold pressing parameters.

[0124] Step S4E0, calculate the local stress ratio based on the strain data, if the stress ratio exceeds the preset threshold, automatically trigger the preset three-level control for the hydraulic press head and the pressing process, the preset three-level control includes adjusting the inclination of the press head, switching the pressing sequence, activating the vibration auxiliary; if the pressure sensor detects pressure fluctuation of the hydraulic system, automatically compensate the pressure according to the preset deviation threshold.

[0125] Wherein, the local stress ratio: the ratio of local stress to average stress, used to evaluate the uniformity of stress distribution in different areas of the mold. The preset threshold: the maximum allowed value of the local stress ratio, exceeding this value triggers the control mechanism. The preset three-level control: a set of hierarchical automatic control mechanism, including adjusting the inclination of the press head, switching the pressing sequence, activating the vibration auxiliary, used to optimize the cold pressing process. Pressure fluctuation: unstable change of hydraulic system pressure, which may affect the quality of cold pressing. The preset deviation threshold: the maximum allowed deviation of hydraulic system pressure fluctuation, exceeding this value triggers the pressure compensation mechanism.

[0126] Pressure fluctuation monitoring and compensation: the system monitors the hydraulic system pressure in real time through high-precision pressure sensors. If the pressure fluctuation exceeds the preset deviation threshold (such as ±0.1MPa), the system automatically compensates the pressure to ensure stable pressure.

[0127] Step S4F0, combine the strain control results and density monitoring data, call the preset finite element simulation model, dynamically optimize the preset pressure holding gradient and holding time of the hydraulic press.

[0128] For details, please refer to steps S4F1 to S4F5, which are not repeated here.

[0129] Step S4G0, when the molding density of functionalized particles reaches the preset standard, the stress ratio is stable within the preset threshold, and the holding time meets the optimized preset value, the cold pressing process is determined to be completed.

[0130] The necessary process is described as follows: 1. Molding density monitoring: the system monitors the overall molding density after cold pressure molding in real time through high-precision pressure sensors. If the molding density reaches the preset standard (such as relative density ≥ 92%), the next step is continued. 2. Stress ratio monitoring: the system monitors the strain data of different areas in the mold in real time through strain sensors, and calculates the local stress ratio. If the stress ratio is stable within the preset threshold (such as 1.05-1.15), the next step is continued. 3. Pressure holding time monitoring: the system monitors the pressure holding time in real time to ensure that the pressure holding time of each pressure stage meets the optimized preset value. If the pressure holding time reaches the preset value (such as 120 seconds), the next step is continued. 4. Comprehensive evaluation and judgment: the system comprehensively evaluates the molding density, stress ratio and pressure holding time. If all conditions are met, it is determined that the cold pressure process is completed. If any one does not meet, the system automatically triggers the corresponding control mechanism to continue optimizing the cold pressure process.

[0131] If the required functionalized concrete is a mechanical property enhanced concrete, the local stress ratio is calculated based on the strain data. If the stress ratio exceeds the preset threshold, the system automatically triggers the preset three-level control of the hydraulic press head and the pressing process, including the following steps:

[0132] Step S4E1, collect the original strain values of different areas of the mold. The original strain value is the functionalized particle strain data collected directly by the preset strain sensor.

[0133] Wherein, the preset strain sensor: a sensor for real-time monitoring of functionalized particle strain in different areas of the mold, the position and number of which are preset according to the mold specifications and process requirements.

[0134] The necessary process is described as follows: 1. Sensor preparation: the system calls the parameters of the preset strain sensor from the formula library, including the position, number and accuracy of the sensor. Ensure that all strain sensors are in normal working condition and prepare to collect data. 2. Strain data collection: during the cold pressure process, the strain sensor collects the strain data of the functionalized particles in different areas of the mold in real time. The system stores the collected strain data in a temporary data buffer, ready for subsequent processing. 3. Data preliminary processing: the system preliminarily processes the collected original strain values, including data filtering and formatting, to ensure the accuracy and usability of the data. For example, low-pass filtering is used to remove high-frequency noise and ensure the stability of the strain data.

[0135] Step S4E2, call the preset multi-source data weighted fusion algorithm, combine the previous granulation particle size distribution and spraying thickness uniformity data to correct the original strain value, and calculate the local stress ratio through the preset stress conversion model.

[0136] Among them, the multi-source data weighted fusion algorithm: an algorithm for combining data from multiple sources (such as granulation particle size distribution, spraying thickness uniformity, etc.) to improve the accuracy and reliability of the data through weighted fusion. The preset stress conversion model: a mathematical model for converting the corrected strain value to stress value, and then calculating the local stress ratio. The preset stress conversion model is obtained as follows: through material characteristics and experimental data, stored in the system formula library. The model is usually based on the stress-strain relationship of the material.

[0137] The necessary process is described as follows: 1. Call the multi-source data weighted fusion algorithm: the system calls the preset multi-source data weighted fusion algorithm from the formula library, which combines the previous granulation particle size distribution and spraying thickness uniformity data to correct the original strain value. The algorithm combines data from different sources through weighted averaging to improve the accuracy and reliability of the data. The weight distribution is based on experimental and simulation results to ensure that the contribution of each data source is reasonably reflected. 2. Correct the original strain value: the system corrects the original strain value according to the multi-source data weighted fusion algorithm. Specifically, the granulation particle size distribution data and the spraying thickness uniformity data are respectively assigned different weights, and the original strain value is corrected by weighted averaging. The correction formula is:

[0138] ;

[0139] where, is the original strain value, is the granulation particle size distribution data correction value, is the spraying thickness uniformity data correction value, and are the weights.

[0140] 3. Call the preset stress conversion model: the system calls the preset stress conversion model from the formula library, which is based on the stress-strain relationship of the material to convert the corrected strain value to stress value. For linear elastic materials, the stress calculation formula is: ; where, is the elastic modulus of the material, is the corrected strain value. Through this model, the system can accurately convert the strain value to stress value, providing a basis for subsequent stress ratio calculation.

[0141] 4. Calculate the local stress ratio: the system calculates the local stress ratio , the formula is:

[0142] ; where, is the local stress, is the average stress of all monitoring points. By calculating the local stress ratio, the system can evaluate the stress distribution uniformity in different areas of the mold, providing a basis for subsequent regulation.

[0143] Step S4E3, if the local stress ratio exceeds the preset threshold, start the preset stress cause decision tree algorithm, take the preset particle size deviation threshold, the preset thickness deviation threshold, and the preset initial density threshold as characteristic variables, and output the core cause judgment result of the granulation particle size segregation, the spraying thickness unevenness, or the excessive particle voids.

[0144] wherein, the preset threshold: the maximum allowed value of the local stress ratio, exceeding which triggers the regulation mechanism. The acquisition method of the preset threshold: determined through experiments and simulations, stored in the system formula library.

[0145] Stress cause decision tree algorithm: an algorithm based on decision tree, used to analyze and judge the specific reasons for the local stress ratio exceeding the threshold. The acquisition method of the stress cause decision tree algorithm: obtained through machine learning training, the input characteristic variables include particle size deviation, thickness deviation, and initial density deviation, and the output is the specific cause.

[0146] Characteristic variables: input variables for the decision tree algorithm, including the preset particle size deviation threshold, the preset thickness deviation threshold, and the preset initial density threshold. The acquisition method is as follows: determined through experiments and simulations, stored in the system formula library.

[0147] The necessary process is described as follows: 1. Local stress ratio evaluation: the system monitors the local stress ratio in real time, and if the local stress ratio exceeds the preset threshold (such as 1.15), the stress cause decision tree algorithm is triggered. 2. Feature variable extraction: the system calls the preset characteristic variables from the formula library, including the particle size deviation threshold, the thickness deviation threshold, and the initial density threshold. These variables are the best parameters determined through experiments and simulations. 3. Start the decision tree algorithm: the system starts the preset stress cause decision tree algorithm, inputs the characteristic variables, and the algorithm outputs the most likely cause of the local stress ratio exceeding the standard through a series of logical judgments. Possible causes include granulation particle size segregation, spraying thickness unevenness, or excessive particle voids. 4. Output the cause judgment result: the decision tree algorithm outputs the specific cause, and the system stores the result and uses it for subsequent regulation decisions. For example, if the algorithm determines that granulation particle size segregation is the main cause, the system will prioritize regulation for this problem.

[0148] Step S4E4, trigger three-level intelligent regulation according to the cause priority: for granulation particle size segregation, dynamically calculate the ram inclination angle through the preset particle size deviation-angle mapping algorithm; for spraying thickness unevenness, switch the partition pressure sequence by calling the preset pressure timing optimization algorithm; for excessive particle voids, start the preset density-vibration parameter adaptive algorithm to match the vibration frequency and amplitude.

[0149] Among them, the three-level intelligent regulation: a set of hierarchical automatic regulation mechanism, according to the priority of different incentives, trigger different control measures in turn, to optimize the cold pressing process. The way to get it is as follows: according to the material properties and process requirements design, stored in the system formula library. The priority of the incentives is as follows: according to the decision tree algorithm output of the incentives, determine the execution order of the control measures. The way to get it is as follows: through experiment and simulation, stored in the system formula library. Particle size deviation-angle mapping algorithm: an algorithm for dynamically calculating the tilt angle of the pressure head according to the particle size deviation. The way to get it is as follows: through experiment and simulation, stored in the system formula library. Pressing timing optimization algorithm: an algorithm for optimizing the pressing sequence to ensure uniform pressure distribution. The way to get it is as follows: through experiment and simulation, stored in the system formula library. Density-vibration parameter adaptive algorithm: an algorithm for matching vibration frequency and amplitude according to the particle void condition. The way to get it is as follows: through experiment and simulation, stored in the system formula library.

[0150] The necessary process is described as follows: 1. Determine the priority of the incentives: the system determines the execution order of the control measures according to the output results of the stress incentive decision tree algorithm. For example, if the decision tree algorithm determines that the particle size segregation during granulation is the main incentive, the control measures for particle size segregation are triggered first. 2. Trigger the first-level regulation: if the main incentive is the particle size segregation during granulation, the system calls the preset particle size deviation-angle mapping algorithm to dynamically calculate the tilt angle of the pressure head. The algorithm dynamically adjusts the tilt angle of the pressure head according to the particle size deviation to reduce the pressure in the stress concentration area. The specific formula is: ; wherein, is the tilt angle of the pressure head, is the particle size deviation, is the mapping coefficient (determined by experiment, assuming k=0.5° / mm).

[0151] 3. Trigger the second-level regulation: if the local stress ratio still exceeds the preset threshold after the first-level regulation, the system calls the preset pressing timing optimization algorithm to switch the partition pressing sequence. The algorithm optimizes the pressing sequence to ensure more uniform pressure distribution.

[0152] The specific logic is as follows: the system will first determine the size relationship between the local stress and the average stress to determine the pressing sequence. If the local stress is greater than the average stress, start pressing from the center; if the local stress is less than or equal to the average stress, start pressing from the edge.

[0153] 4. Trigger the third-level regulation: if the local stress ratio still exceeds the preset threshold after the second-level regulation, the system starts the preset density-vibration parameter adaptive algorithm to match the vibration frequency and amplitude. The algorithm dynamically adjusts the vibration parameters according to the particle void condition to improve the uniformity of the particles. The specific formula is as follows:

[0154] , ; wherein, is the vibration frequency, is the amplitude, is the particle porosity, is the adaptive coefficient.

[0155] Step S4E5, after each level of regulation, real-time strain data is collected by a pre-set closed-loop feedback algorithm to recalculate the local stress ratio. If it meets the standard, stop regulating; if it does not meet the standard, trigger the next level.

[0156] The closed-loop feedback algorithm is an algorithm used to monitor and adjust system parameters in real time to ensure that the system output meets the preset target. The acquisition method is determined through experiments and simulations and stored in the system formula library. This algorithm is usually based on real-time data acquisition and feedback control mechanisms.

[0157] The necessary process is described as follows: 1. Real-time data acquisition: after each regulation measure is implemented, the system collects strain data in different areas of the mold in real time through a pre-set closed-loop feedback algorithm. These data are obtained through high-precision strain sensors to ensure the accuracy and real-time nature of the data. 2. Strain data processing: the system processes the collected strain data, including data filtering and formatting, to remove noise and improve data reliability. The processed strain data are used to recalculate the local stress ratio. 3. Recalculation of local stress ratio: the system recalculates the local stress ratio based on the processed strain data through a pre-set stress conversion model. The specific steps are as follows: first, convert the processed strain data to stress values. Then calculate the ratio of local stress to average stress to obtain the local stress ratio. 4. Evaluation and decision: the system evaluates whether the recalculated local stress ratio meets the pre-set threshold. If the local stress ratio is stable within the pre-set threshold (e.g., 1.05-1.15), it is considered that the regulation measure is effective, and further regulation is stopped. If the local stress ratio still exceeds the pre-set threshold, the next level of regulation measure is triggered.

[0158] Step S4E6, when the three-level regulation does not meet the standard, generate a pre-sequence process parameter correction suggestion based on a pre-set parameter correlation model. The correction suggestion includes the granulation rotation speed adjustment range and the optimized value of the spraying rate.

[0159] wherein the preset parameter correlation model: a model for analyzing the relationship between the previous process parameters (such as granulation speed, spraying rate, etc.) and the current local stress ratio, and generating a correction suggestion. The acquisition method is as follows: determined through experiments and simulations, stored in the system formula library. This model is usually based on historical data and machine learning algorithms. Granulation speed adjustment range: suggest the range of adjusting the granulator speed to optimize the uniformity of the particles and reduce stress concentration. Acquisition method: generated by the preset parameter correlation model, based on the current local stress ratio and historical data of previous process parameters. Spraying rate optimization value: suggest the value of adjusting the spraying rate to optimize the uniformity of the coating and reduce stress concentration.

[0160] The necessary process is described as follows: 1. Evaluate the results of the third-level regulation: the system evaluates whether the local stress ratio after the third-level regulation reaches the preset threshold. If the local stress ratio still does not reach the preset threshold, it means that the current regulation measures have not effectively solved the problem, and the previous process parameters need to be corrected. 2. Call the preset parameter correlation model: the system calls the preset parameter correlation model to analyze the relationship between the previous process parameters (such as granulation speed, spraying rate, etc.) and the current local stress ratio. This model is based on historical data and machine learning algorithms and can generate a correction suggestion. 3. Generate a correction suggestion: Granulation speed adjustment range: the model generates a suggested range of granulation speed adjustment based on the current local stress ratio and historical data. The specific formula is: ;

[0161] wherein, is the current speed, is the granulation speed adjustment amplitude calculated according to the model.

[0162] Spraying rate optimization value: the model generates a suggested spraying rate optimization value based on the current local stress ratio and historical data . The specific formula is: ; wherein, is the current spraying rate, is the spraying rate adjustment amplitude calculated according to the model.

[0163] 4. Output the correction suggestion: the system outputs the generated correction suggestion for the operator to refer to or for the automatic adjustment system to use. The correction suggestion includes specific adjustment ranges and optimization values, helping to optimize the previous process parameters and improve the quality and stability of cold pressure forming.

[0164] If the required functionalized concrete is a mechanical property enhanced concrete, combine the strain regulation results and density monitoring data, call the preset finite element simulation model, and dynamically optimize the preset pressure holding gradient and holding time of the hydraulic press, including:

[0165] Step S4F1, collect key data of three-level regulation, including pressure head inclination angle, pressure sequence switching record, vibration parameters, and stress ratio change and density monitoring value after each level of regulation, and integrate into regulation parameter-stress-density correlation data.

[0166] Wherein, the regulation parameter-stress-density correlation data: the integrated data set, containing regulation parameters, stress ratio change and density monitoring value, used to analyze the influence of regulation measures on the forming process. The necessary process is described as follows: 1, collect key data: the system collects the following key data in the three-level regulation process: pressure head inclination angle: record the pressure head inclination angle after each adjustment; pressure sequence switching record: record the switching of pressure sequence, including switching from center to edge or from edge to center; vibration parameters: record the frequency and amplitude of vibration assistance; stress ratio change: record the local stress ratio after each regulation; density monitoring value: record the forming density after each regulation. 2, integrate data: the system integrates the collected key data into regulation parameter-stress-density correlation data. These data will be used for subsequent finite element simulation and optimization analysis. The integration process includes: pairing the regulation parameters such as pressure head inclination angle, pressure sequence switching record, vibration parameters with the corresponding stress ratio change and density monitoring value. Store these data in the system database to form a complete data set for subsequent analysis.

[0167] Step S4F2, call the preset particle-level cold pressing forming finite element simulation model, input the regulation parameter-stress-density correlation data into the model, and correct the initial conditions according to the regulation conditions.

[0168] Wherein, the particle-level cold pressing forming finite element simulation model: a simulation model based on finite element method, used to simulate the stress and deformation behavior of particles in cold pressing process, and predict the density and stress distribution in the forming process. The acquisition method is as follows: established by professional finite element analysis software (such as ABAQUS, ANSYS, etc.), the model parameters are calibrated according to experimental data and material characteristics, and stored in the system formula library.

[0169] Initial conditions: The initial state at the start of the simulation, including the initial position of the particles, initial stress, and initial density, etc. Obtaining method: According to the experimental data and the monitoring results of the previous process, set in the system formula library. The necessary process is described as follows: 1, call the finite element simulation model: the system calls the preset particle-level cold compaction finite element simulation model from the formula library. This model is based on the finite element method and can simulate the stress and deformation behavior of particles in the cold compaction process. 2, input the control parameter-stress-density correlation data: the system inputs the control parameter-stress-density correlation data collected and integrated in step S4F1 into the finite element simulation model. These data include the inclination angle of the pressure head, the pressure sequence switching record, the vibration parameters, the stress ratio change, and the density monitoring value. 3, correct the initial conditions: according to the input control parameter-stress-density correlation data, the system corrects the initial conditions of the finite element simulation model according to the control situation. The correction content includes: initial stress distribution: according to the stress ratio change data, the initial stress distribution is corrected. Initial density distribution: according to the density monitoring value, the initial density distribution is corrected. Particle position and state: according to the control parameters, the initial position and state of the particles are adjusted to ensure that the simulation results are closer to the actual forming process.

[0170] Step S4F3, input the preset target forming density, the preset stress uniformity threshold, and the preset maximum holding pressure time into the preset particle-level cold compaction finite element simulation model. The model takes the current state after three-level control as the starting point, simulates the density growth curve and stress uniformity change curve under different holding pressure gradient and holding time combinations based on the stress-density change trend in the correlation data, and the holding pressure gradient contains the preset pressure and preset pressure increasing rate of each stage.

[0171] Among them, the preset target forming density: the expected forming density, used to evaluate the forming quality. Obtaining method: determined by experiment and simulation, stored in the system formula library. The preset stress uniformity threshold: the uniformity standard of stress distribution, used to evaluate whether the stress distribution in the forming process is uniform. Obtaining method: determined by experiment and simulation, stored in the system formula library. The preset maximum holding pressure time: the maximum allowed time of the holding pressure process, used to control the efficiency of the forming process. Obtaining method: determined by experiment and simulation, stored in the system formula library. Holding pressure gradient: the change gradient of pressure in the holding pressure process, including the preset pressure and pressure increasing rate of each stage. Obtaining method: determined by experiment and simulation, stored in the system formula library.

[0172] The density growth curve is as follows: describes the change curve of the forming density with time under different holding pressure gradients and holding times. Obtaining method: obtained by finite element simulation model simulation.

[0173] Stress uniformity variation curve: describes the variation of stress uniformity over time under different holding pressure gradients and holding time. The acquisition method is as follows: obtained through finite element simulation model simulation.

[0174] The necessary process is described as follows: 1, input preset parameters: the system inputs the following preset parameters to the preset particle level cold compaction finite element simulation model: preset target forming density: the expected forming density. Preset stress uniformity threshold: the uniformity standard of stress distribution. Preset maximum holding time: the maximum allowed time for holding process. 2, set initial state: take the current state after three-level regulation as the starting point, including the current particle position, stress distribution and density distribution. These initial state data are obtained from the corrected initial conditions in step S4F2. 3, simulate different combinations: based on the stress-density variation trend in the associated data, the model simulates the density growth curve and stress uniformity variation curve under different holding pressure gradient and holding time combinations. The specific steps are as follows: holding pressure gradient: including preset pressure and pressure increasing rate of each stage. Holding time: different holding time combinations, from initial time to preset maximum holding time. 4, generate simulation results: the model outputs the density growth curve and stress uniformity variation curve under different holding pressure gradient and holding time combinations. These curves are used to evaluate the forming effect under different combinations.

[0175] Step S4F4, according to the simulation results and the three-level regulation type, determine the matching holding pressure gradient and holding time optimization scheme from the preset optimization scheme library.

[0176] Simulation results: the density growth curve and stress uniformity variation curve under different holding pressure gradient and holding time combinations obtained through finite element simulation model. Three-level regulation type: the specific regulation measure type taken in the three-level regulation process, such as pressure head inclination angle adjustment, pressure sequence switching, vibration parameter adjustment, etc. Preset optimization scheme library: stores multiple optimization schemes of holding pressure gradient and holding time combinations, each scheme corresponds to different forming targets and regulation types.

[0177] The necessary process is as follows: 1. Analyze the simulation results: the system analyzes the simulation results obtained in step S4F3, including the density growth curve and the stress uniformity change curve under different pressure maintaining gradient and pressure maintaining time combinations. Evaluate which combination can make the forming density reach the preset target and the stress uniformity within the preset threshold. 2. Determine the type of three-level regulation: the system determines the specific regulation measure type adopted in the three-level regulation process according to the key data collected in step S4F1. These types include pressure head inclination angle adjustment, pressure sequence switching, vibration parameter adjustment, etc. 3. Compare the optimization scheme library: the system compares the simulation results and regulation types from the preset optimization scheme library to find a matching optimization scheme. The optimization scheme library stores a variety of pressure maintaining gradient and pressure maintaining time combinations, each corresponding to different forming targets and regulation types. 4. Select the optimal scheme: the system selects the optimal pressure maintaining gradient and pressure maintaining time combination that can make the forming density reach the preset target and the stress uniformity within the preset threshold. This scheme will be used as the parameter setting for subsequent cold pressing.

[0178] Step S4F5, output the optimized pressure maintaining gradient and pressure maintaining time, and generate hydraulic press execution instructions.

[0179] The necessary process is as follows: 1. Output the optimized parameters: the system outputs the optimized pressure maintaining gradient and pressure maintaining time determined in step S4F4, including the pressure, pressure increasing rate and pressure maintaining time of each stage. 2. Generate execution instructions: the system generates hydraulic press execution instructions according to the optimized parameters, which will control the actual operation of the hydraulic press to ensure that the cold pressing process is carried out according to the optimized parameters. 3. Send instructions to the hydraulic press: the system sends the generated execution instructions to the hydraulic press control system, and the hydraulic press performs the corresponding pressure maintaining and pressure maintaining operation according to these instructions.

[0180] If the required functionalized concrete is temperature control functional concrete, its complete preparation process can be divided into the following main stages:

[0181] 1. Raw material preparation and wet granulation: ordinary Portland cement particles with a particle size of 7-200 microns are loaded into the intelligent granulator hopper, the "temperature control function" mode is selected through the man-machine interface and the process parameters are set, and the intelligent granulation system is started. The granulator rotates at a speed of 50-200 rpm, and four groups of symmetrically distributed ultrasonic atomizing nozzles spray deionized water (water-cement ratio 0.25-0.35) synchronously. The integrated laser particle size analyzer and humidity sensor monitor the particle agglomeration state in real time, and the speed is dynamically adjusted through the PID control algorithm to realize the gradient growth of the particle size from the initial 7-200 microns to 1-3 millimeters.

[0182] 2. Functional material spraying: The central control system automatically switches the spraying material to the preset pressure spraying of the preset functional material. For details, see steps S3a0 to S3f0. The multi-spectrum sensor monitors the coating thickness (accuracy ± 2 microns) and uniformity in real time, and the machine learning algorithm dynamically optimizes the spraying parameters to ensure uniform coating of the functional layer on the surface of the cement particles.

[0183] 3. Cold pressing: The functionalized particles are transported to the mold cavity by a variable frequency conveyor belt (0.5-2 meters / second), the hydraulic system intelligently matches the molding parameters according to the material characteristics database, applies a 20-100 MPa staged pressure (the upper limit for fatty alcohol systems and the lower limit for paraffin systems), maintains a 60-300 second pressure holding time, and controls the mold temperature at 25±2 degrees Celsius through a water cooling system. The pressure sensor provides real-time feedback on the molding density (relative density ≥ 92%), and the control system automatically compensates for pressure fluctuations (deviation <±3%).

[0184] 4. Stable control and parameter adjustment: The central control system performs stable control on the phase change material type temperature control concrete, and monitors the granulation moisture content (W) in real time through a near-infrared sensor. When W>6% is detected, the humidity compensation algorithm is automatically triggered, the cold pressing pressure is dynamically adjusted according to P=100 / (1+0.15(W-6))MPa, and the spraying amount is increased by 5-15%.

[0185] 5. Finished product output and performance verification: After the hydraulic pressure is released, the ejection mechanism automatically pushes out the molded test piece (dimensional tolerance ± 0.1 millimeters), which is evaluated by a non-destructive testing module: after X-ray CT scanning (porosity <3%), differential scanning calorimeter testing (phase change enthalpy attenuation rate <5%), etc., it meets the standards and enters the curing process. After curing, the intelligent preparation of temperature control function concrete is realized.

[0186] If the required functionalized concrete is temperature control function concrete, after granulation, switch the spraying material of multiple groups of nozzles to the preset functional material and spray at a preset pressure, including:

[0187] Step S3a0, call the preset environmental temperature prediction model, combine the phase change material phase change temperature threshold, and use the preset constant temperature control module to stabilize the spraying environment temperature in the non-phase change critical interval.

[0188] Wherein, the preset environmental temperature prediction model: a model based on historical data and real-time environmental data, used to predict the temperature change of the spraying environment, to ensure that the spraying process is carried out in the appropriate temperature range. Phase change material phase change temperature threshold: the temperature range at which the phase change material undergoes phase change, used to determine the temperature control target of the spraying environment. Constant temperature control module: a module for controlling the temperature of the spraying environment, to ensure that the temperature is stable within the preset range.

[0189] The necessary process is described as follows: 1. Call the prediction model: the system calls the preset environmental temperature prediction model, combined with the phase change temperature threshold of the phase change material, to predict the temperature change of the spraying environment. The model is based on historical data and real-time environmental monitoring data to ensure the accuracy of the prediction results. 2. Determine the temperature control target: according to the phase change temperature threshold of the phase change material, determine the temperature control target of the spraying environment. The target temperature should be stable in the non-phase change critical interval to avoid unnecessary phase change of the phase change material during the spraying process. 3. Start the constant temperature control module: the system starts the preset constant temperature control module, and the spraying environment temperature is stabilized in the preset range through heating or cooling system. The temperature sensor monitors the environmental temperature in real time and feeds back to the control system to ensure the accuracy of the temperature control.

[0190] Step S3b0, the multiple groups of spray heads switch the spraying material to the preset functional material, which is a phase change material matching the preset temperature control requirement. The preset functional material: the phase change material pre-selected according to the temperature control requirement, which can absorb or release heat in a specific temperature range to achieve temperature regulation function. Acquisition method: determined through experiments and simulations, stored in the system formula library. Common phase change materials include paraffin, PEG, fatty alcohol, inorganic hydrated salt, etc.

[0191] The necessary process is described as follows: 1. Select the preset functional material: the system calls the matching phase change material from the formula library according to the temperature control requirement. For example, if the goal is to prepare temperature control function concrete, the system may select paraffin as the phase change material, whose phase change temperature range is 25°C-30°C. 2. Switch the spray head material: the system controls the multiple groups of spray heads to switch to the preset phase change material. The spray head switches the material through pneumatic or electric valve to ensure that the switching process is fast and leak-free. The switching time is usually completed within 0.5 seconds to ensure the continuity of the spraying process. 3. Start spraying: the spray head starts to spray the phase change material at the preset pressure. The preset pressure is usually adjusted according to the viscosity of the phase change material and the adsorption characteristics of the particle surface to ensure that the material can be uniformly attached to the particle surface. For example, for paraffin, the preset pressure may be 0.3 MPa.

[0192] Step S3c0, start the sensor of preset accuracy to collect the moisture content and particle size data of the granulated particles, and determine the spraying pressure through the preset dynamic algorithm.

[0193] Among them, the sensor of preset accuracy: a high-precision sensor for real-time monitoring of the moisture content and particle size of the granulated particles. Moisture content: the content of water in the particles, usually expressed in percentage. Acquisition method: real-time monitoring by high-precision humidity sensor, data stored in system database. Preset dynamic algorithm: an algorithm for dynamically adjusting the spraying pressure according to the real-time monitoring of moisture content and particle size data to ensure the uniformity and stability of the spraying process.

[0194] The necessary processes are described as follows: 1. Start the sensor: the system starts the sensor with preset precision, including high-precision humidity sensor and laser particle size instrument, to collect the moisture content and particle size data of the granules in real time. These sensors are installed at the outlet of the granulator to ensure the real-time and accuracy of the data. 2. Data collection: the sensor collects the moisture content and particle size data of the granules in real time and transmits the data to the control system. For example, the humidity sensor collects the moisture content of 10%, and the laser particle size instrument collects the particle size of 1.5mm. 3. Dynamic algorithm calculation: the system calls the preset dynamic algorithm to dynamically adjust the spraying pressure according to the collected moisture content and particle size data. The specific algorithm is as follows:

[0195] Moisture content influence factor: calculate the influence factor according to the moisture content , the formula is:

[0196] , wherein, is the moisture content;

[0197] Particle size influence factor: calculate the influence factor according to the particle size , the formula is:

[0198] , wherein, represents the particle size of the granules;

[0199] Comprehensive influence factor: calculate the comprehensive influence factor by combining the influence factors of moisture content and particle size , the formula is: ;

[0200] Spraying pressure: adjust the spraying pressure according to the comprehensive influence factor , the formula is as follows:

[0201] ; wherein, is the basic spraying pressure, usually 0.3MPa.

[0202] 4. Determine the spraying pressure: the dynamic algorithm calculates the optimal spraying pressure according to the real-time data and sends the pressure value to the spraying system. For example, the algorithm calculates the spraying pressure of 0.35MPa.

[0203] Step S3d0, the nozzle runs according to the preset adaptive trajectory, and the trajectory pitch is dynamically adjusted according to the real-time particle size of the granules.

[0204] Pre-set adaptive trajectory: The spray head follows a pre-set path during the spraying process, which can be dynamically adjusted based on real-time monitoring of particle size to ensure uniformity of the coating. Acquisition method: Determined through experiments and simulations, stored in the system recipe library. The trajectory usually includes a spiral scanning path to ensure uniform coverage of the particle surface. Trajectory pitch: The distance between adjacent two circles of the spray head in the spiral scanning path, used to control the density and uniformity of spraying. Acquisition method: Adjusted by a dynamic algorithm based on real-time monitoring of particle size to ensure uniformity of the coating.

[0205] The necessary process is described as follows: 1. Start the spray head: the system starts the spray head, and the spray head runs according to the pre-set adaptive trajectory. The initial trajectory parameters (such as the initial pitch) of the spray head are set according to the pre-set value, which is usually based on the best parameters determined by experiments and simulations. 2. Real-time monitoring of particle size: the system monitors the particle size data of the particles in real time through the laser particle size analyzer, and transmits the data to the control system. These data are used to dynamically adjust the trajectory pitch of the spray head. 3. Dynamically adjust the trajectory pitch: the system calls the pre-set dynamic algorithm to dynamically adjust the trajectory pitch of the spray head according to the real-time monitoring of the particle size data. The specific algorithm is as follows: Trajectory pitch adjustment formula: . Wherein k is the adjustment coefficient, which is usually determined by experiments, is the target particle size, which is the pre-set optimal particle size; is the real-time particle size; is the actual adjusted pitch; is the basic pitch. Spray head operation: the spray head operates according to the adjusted trajectory pitch, ensuring that the coating is uniformly covered on the surface of the particles. The running speed and trajectory path of the spray head are dynamically adjusted according to real-time data to adapt to particles of different sizes.

[0206] Step S3e0, during the spraying process, the coating state is detected in real time by a pre-set monitoring system, and the defect category is identified according to a pre-set defect classification rule. The defect categories include missed spraying, thin coating, and bulging.

[0207] Wherein, the pre-set defect classification rule: refers to the rules pre-set for identifying coating defects. These rules are based on the parameters of the coating state to classify defects into different categories, such as missed spraying, thin coating, and bulging. Missed spraying: refers to the coating that is not sprayed in some areas, resulting in missing coating. Thin coating: refers to the coating thickness that is lower than the pre-set standard, which may result in insufficient functional performance. Bulging: refers to the local bulging of the coating in some areas, which may be caused by uneven spraying or the failure to discharge the gas inside the coating.

[0208] The necessary process is described as follows: 1. Real-time monitoring of coating state: During the spraying process, the preset monitoring system collects the state data of the coating on the particle surface in real time through sensors. These data include parameters such as the thickness, uniformity, continuity of the coating. For example, optical sensors can be used to detect the thickness of the coating, and laser sensors can be used to detect the uniformity of the coating. 2. Identify defect categories: According to the preset defect classification rules, the monitoring system analyzes the collected coating state data to identify whether the coating has defects and the category of the defects. For example, if the coating thickness is lower than the preset standard value, it is identified as "thin coating"; if the coating is missing in some areas, it is identified as "miss spraying"; if the coating appears local bulging, it is identified as "bump".

[0209] Step S3f0, according to the defect category, start the preset differential re-spraying mechanism.

[0210] The preset differential re-spraying mechanism refers to starting the corresponding re-spraying measures according to the identified defect category to repair the coating defects and ensure the coating quality. For example: for "miss spraying" defects, the system will adjust the trajectory of the spray head to ensure that the missed spraying area is re-sprayed. For "thin coating" defects, the system will increase the spraying time or increase the spraying pressure to increase the coating thickness. For "bump" defects, the system will adjust the spraying trajectory to avoid excessive spraying in the bump area, and may eliminate the bump through local air extraction and other measures.

[0211] After the preset constant temperature control module stabilizes the spraying environment temperature in the non-phase transition critical interval, before the multiple groups of spray heads switch the spraying material to the preset functional material, a phase change material function retention degree prediction and cross-stage parameter linkage step is also set, which is as follows:

[0212] Step Sa00, collect the preset key parameters of the previous granulation process (particle size distribution, average moisture content), the current spraying environment temperature, and the preset phase change material characteristic parameters (phase change enthalpy reference value, thermal cycle stability coefficient), input the functional retention degree prediction sub-model constructed by the preset LSTM neural network, and calculate the phase change enthalpy retention rate prediction value after cold pressing.

[0213] Among them, the phase change enthalpy reference value refers to the reference value of the heat absorbed or released by the phase change material during the phase change process, usually expressed in joules per gram (J / g). The thermal cycle stability coefficient refers to the stability coefficient of the phase change material in maintaining the phase change performance during multiple thermal cycles, usually expressed in dimensionless number.

[0214] Functional retention degree prediction sub-model: a model based on LSTM neural network, used to predict the functional retention degree of the phase change material after cold pressing, especially the retention rate of phase change enthalpy.

[0215] The necessary process is described as follows: 1. Collecting preset key parameters: particle size distribution and average moisture content: real-time monitoring of particle size distribution and average moisture content of the granulation process by integrated laser particle size analyzer and humidity sensor. Spray environment temperature: real-time monitoring of the temperature of the spraying environment by temperature sensor. Phase change material characteristic parameters: obtaining the phase change enthalpy reference value and thermal cycle stability coefficient of the phase change material from the material characteristic database. 2. Input LSTM neural network: input the collected preset key parameters (particle size distribution, average moisture content, spraying environment temperature, phase change enthalpy reference value, thermal cycle stability coefficient) into the function retention prediction sub-model constructed by the preset LSTM neural network. The model is trained by historical data and can predict the phase change enthalpy retention rate of the phase change material after cold pressing according to the input parameters. 3. Calculate the phase change enthalpy retention rate prediction value: the function retention prediction sub-model calculates the phase change enthalpy retention rate prediction value of the phase change material after cold pressing according to the input parameters. The prediction value is used to evaluate the retention of the function of the phase change material during the cold pressing process, to ensure that the material can still maintain good temperature control performance after cold pressing.

[0216] Step Sb00, if the prediction value is less than the preset function retention threshold, automatically backtrack and optimize the previous parameters according to the preset rules: if the granulation particle size deviation exceeds the preset threshold, adjust the granulation speed (correct according to the preset deviation-speed mapping relationship), if the spraying thickness uniformity exceeds the preset threshold, optimize the spray head trajectory (reduce the preset proportion of pitch) and the spraying speed (increase the preset proportion of speed), until the prediction value meets the standard.

[0217] Preset function retention threshold: refers to the minimum acceptable value of phase change enthalpy retention rate, used to ensure that the phase change material can still maintain sufficient functional performance after cold pressing. Granulation particle size deviation: refers to the deviation of the actual granulation particle size from the preset particle size standard, usually expressed in percentage. Preset deviation-speed mapping relationship: refers to the preset relationship between adjusting the speed of the granulator according to the granulation particle size deviation, usually a mathematical model or table. Spraying thickness uniformity: refers to the uniformity of the coating thickness after spraying, usually expressed by the coefficient of variation (CV). Preset proportion of pitch: refers to the adjustment proportion of the pitch of the spray head trajectory, used to optimize the spraying uniformity.

[0218] Preset proportion of speed: refers to the adjustment proportion of the spraying speed, used to optimize the coating thickness.

[0219] Backtracking and optimizing the previous parameters as follows:

[0220] Granulation particle size deviation check: check if the granulation particle size deviation exceeds the preset threshold. If it exceeds the preset threshold, adjust the speed of the granulator according to the preset deviation-speed mapping relationship. For example, if the particle size deviation is 10%, the preset mapping relationship may indicate adjusting the speed from 100 rpm to 120 rpm.

[0221] Spray thickness uniformity check: Check if the spray thickness uniformity exceeds the preset threshold. If it does, optimize the spray head trajectory and spray rate. Specific measures include: reducing the pitch of the spray head trajectory, for example, adjusting the pitch from 5 mm to 4 mm to improve the uniformity of the coating. Increase the spray rate, for example, increase the spray rate from 10 mL / s to 12 mL / s to ensure that the coating thickness meets the requirements.

[0222] Iterative optimization: After adjusting the parameters, re-perform the phase change enthalpy retention rate prediction (return to step Sa00). Repeat the above process until the phase change enthalpy retention rate prediction value reaches or exceeds the preset functional retention threshold.

[0223] Step Sc00, after the prediction value meets the preset threshold interval, pre-optimize the cold pressing parameters: if the prediction value is in the preset critical meeting interval, reduce the cold pressing pressure (preset amplitude) and extend the holding time (preset proportion); if it is in the preset stable meeting interval, match the preset basic cold pressing parameters and set the preset pressure compensation threshold.

[0224] Preset critical meeting interval: refers to the interval where the phase change enthalpy retention rate prediction value is close to but slightly lower than the preset functional retention threshold, usually indicating that the material functional retention is close to the minimum acceptable standard. Preset stable meeting interval: refers to the interval where the phase change enthalpy retention rate prediction value is significantly higher than the preset functional retention threshold, usually indicating that the material functional retention is stable and reliable.

[0225] The pre-optimized cold pressing parameters are as follows:

[0226] Preset critical meeting interval: if the prediction value is in the preset critical meeting interval, it means that the functional retention of the phase change material is close to the minimum acceptable standard, and a conservative cold pressing parameter adjustment strategy needs to be adopted. Reduce the cold pressing pressure: reduce the cold pressing pressure according to the preset amplitude. For example, if the preset amplitude is 10 MPa, reduce the cold pressing pressure from 100 MPa to 90 MPa. Extend the holding time: extend the holding time according to the preset proportion. For example, if the preset proportion is 20%, extend the holding time from 60 seconds to 72 seconds.

[0227] Preset stable meeting interval: if the prediction value is in the preset stable meeting interval, it means that the functional retention of the phase change material is stable and reliable, and the preset basic cold pressing parameters can be used. Match the preset basic cold pressing parameters: use the preset basic cold pressing parameters, including pressure and holding time. Set the preset pressure compensation threshold: set an allowed pressure fluctuation range to ensure the stability of the forming density. For example, set the pressure compensation threshold to ±3 MPa to cope with pressure fluctuations during cold pressing.

[0228] Step Sd00: After the optimized cold pressing parameters are verified by the preset finite element simulation model (meeting the preset molding density and preset network connectivity standards), the "spraying-cold pressing" process is triggered, and the parameters of the entire process are simultaneously uploaded to the preset blockchain database for traceability.

[0229] Preset finite element simulation model: A simulation model built based on the finite element method, used to simulate and verify the forming effect of materials during cold pressing, including key parameters such as forming density and network connectivity.

[0230] Based on the same inventive concept, embodiments of the present invention provide a system for preparing functionalized concrete, including a memory and a processor, wherein the memory stores information that can be run on the processor to implement, as described above. Figure 1 The procedure for the method shown.

[0231] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for the preparation of functionalized concrete, characterized in that, The application relates to a functionalized concrete production method and device. The method comprises the following steps: acquiring a granulator starting signal, and enabling the granulator to enter a running preparation state in response to the starting signal, and feeding cement particles with a preset initial particle size into the granulator in the preparation state; starting the granulator to rotate at a preset rotating speed, and driving a plurality of groups of nozzles to spray deionized water on the cement particles at a preset water-cement ratio; during the granulation, the particle size and the moisture content of the particles are monitored in real time through preset related sensors, and the rotating speed of the granulator is dynamically adjusted by comparing the particle size and the moisture content with preset granulation standards; when the particle size and the moisture content of the particles meet the preset granulation standards, the granulation is completed; after the granulation is completed, the spraying materials of the plurality of groups of nozzles are switched to preset functional materials, and the functional materials are sprayed at a preset pressure; meanwhile, the thickness and the uniformity of the coating on the particles are monitored through preset related sensors, and the rotating speed of the granulator, the trajectory of the nozzles and the spraying rate are optimized by comparing the thickness and the uniformity of the coating with preset coating standards; when the thickness and the uniformity of the coating meet the preset coating standards, the spraying is completed; after the spraying is completed, the particles execute a preset cold pressing process, and after the cold pressing process is completed, a preset quality evaluation is carried out; when the quality reaches a preset quality standard, a curing process is entered and the curing process is executed; 2. A method of preparing a functionalized concrete according to claim 1, characterized in that, after the curing process is completed, the required functionalized concrete is obtained. The preset cold pressing process comprises the following steps: the particles after the spraying are conveyed to a compression mold of a preset specification through a conveying device with a preset conveying speed; a hydraulic machine is started, and the particles in the mold are subjected to graded pressure in accordance with a preset pressure strength matched with the type of the functional materials, and a preset pressure holding time is maintained; a water cooling system with a preset temperature control precision is used to stabilize the temperature of the mold in a preset temperature range; during the cold pressing process, the particles are gradually deformed into a cubic close-packed structure with a preset structure form under the action of pressure, and the functional materials on the surface of the particles contact each other to form a three-dimensional network with a preset connectivity; 3. The method for preparing functionalized concrete according to claim 1, characterized in that, a pressure sensor with a preset precision is used to monitor the actual density of the particles after the particles are formed; when the actual density reaches a preset forming density standard, the cold pressing process is completed. if the required functionalized concrete is conductive functional concrete, after the granulation is completed, the spraying materials of the plurality of groups of nozzles are switched to preset functional materials, and the functional materials are sprayed at a preset pressure, which comprises the following steps: the spraying materials of the plurality of groups of nozzles are switched to preset functional materials, and the functional materials are carbon-based conductive slurries matched with the conductive functional requirements; the nozzles are started to spray at a preset pressure, and the preset pressure is set in advance according to the viscosity of the conductive slurry and the adsorption characteristics of the particle surface, so as to ensure that the slurry is uniformly attached; 4. A method of producing a functionalized concrete according to claim 3, characterized in that, the nozzles are operated along a preset trajectory, and the trajectory is a full-surface covering path matched with the continuity requirement of the conductive coating, and the functional material coating on the particles after the granulation is completed. if the required functionalized concrete is conductive functional concrete, after the spraying of the preset functional materials is completed, the following processing steps are further included: an improved Monte Carlo-seepage combined algorithm is started to perform three-dimensional simulation on the conductive network of the particles after the spraying, preset conductive functional key parameters are input, and a network connectivity index is output. If the network connectivity index is greater than the preset network connectivity index threshold, the preset adaptive parameters of the cold pressing process are automatically associated, the spraying-cold pressing parameter linkage instruction is generated, the granulation particle size, spraying parameters and network connectivity index are recorded through the preset blockchain process database, and the particles are transported to the cold pressing process according to the linkage instruction; If the network connectivity index is less than or equal to the preset network connectivity index threshold, a preset hierarchical adjustment mechanism is triggered, and after adjustment, the simulation is restarted until the network connectivity index is greater than the preset network connectivity index threshold.

5. The method for preparing functionalized concrete according to claim 2, characterized in that, If the required functionalized concrete is a mechanical property enhanced concrete, after granulation is completed, the spraying materials of the multiple groups of nozzles are switched to the preset functional materials, and spraying is performed at a preset pressure, including: The multiple groups of nozzles switch the spraying materials to the preset functional materials, which are flexible reinforcement phases matching the mechanical property enhancement requirements; The nozzles start spraying at a preset pressure, which is preset according to the viscosity of the flexible reinforcement phase and the surface adsorption characteristics of the cement particles to dynamically match the material adhesion requirements; The nozzles operate according to a preset trajectory, which is a preset spiral scanning path, and the granulator operates at a preset speed range to cooperate with the rolling of the particles, ensuring that the coverage of the functional materials on the particles reaches a preset coverage rate.

6. A method of preparing a functionalized concrete according to claim 5, characterized in that, If the required functionalized concrete is a mechanical property enhanced concrete, the preset cold pressing process includes: The functionalized particles after spraying of the flexible reinforcement phase are transported to the compression mold of a preset specification through a variable frequency conveying device with a preset conveying rate; The hydraulic machine is started, the material property database is called, and the particles in the mold are pressed according to the preset hierarchical pressure matching the type of the flexible reinforcement phase, while maintaining a preset holding pressure time; The mold temperature is stabilized in a preset temperature range through a water cooling system with a preset temperature control accuracy; During the cold pressing process, the strain data of the functionalized particles in different areas of the mold are collected according to a preset number, and a pressure sensor with a preset accuracy is started to monitor the overall forming density of the functionalized particles after cold pressing; Based on the strain data, the local stress ratio is calculated, and if the stress ratio exceeds the preset threshold, a preset three-level control is automatically triggered for the hydraulic press head and the pressing process, including adjusting the inclination of the press head, switching the pressing sequence, and activating the vibration auxiliary; if the pressure sensor detects pressure fluctuations in the hydraulic system, the pressure is automatically compensated according to a preset deviation threshold; Based on the strain control results and density monitoring data, a preset finite element simulation model is called to dynamically optimize the preset pressure holding gradient and holding time of the hydraulic machine; When the forming density of the functionalized particles reaches the preset standard, the stress ratio is stable within the preset threshold, and the holding time meets the optimized preset value, the cold pressing process is determined to be completed.

7. A method of producing a functionalized concrete according to claim 6, characterized in that, If the required functionalized concrete is a mechanical property enhanced concrete, based on the strain data, the local stress ratio is calculated, and if the stress ratio exceeds the preset threshold, the steps of automatically triggering a preset three-level control for the hydraulic press head and the pressing process include: The original strain values of different areas of the mold are collected, which are the strain data of the functionalized particles directly collected by a preset strain sensor; The preset multi-source data weighted fusion algorithm is called, the original strain value is corrected combined with the previous granulation particle size distribution and the spraying thickness uniformity data, and the local stress ratio is calculated through a preset stress conversion model; If the local stress ratio exceeds a preset threshold, a preset stress cause decision tree algorithm is started, a preset particle size deviation threshold, a preset thickness deviation threshold, and a preset initial density threshold are taken as characteristic variables, and a core cause judgment result of granulation particle size segregation, spraying thickness unevenness, or too many particle voids is output; According to the cause priority, three-level intelligent regulation and control is triggered: for granulation particle size segregation, a preset particle size deviation-angle mapping algorithm is used to dynamically calculate the inclination angle of the pressure head; for spraying thickness unevenness, a preset pressure timing optimization algorithm is called to switch the partition pressure sequence; for too many particle voids, a preset density-vibration parameter adaptive algorithm is started to match the vibration frequency and amplitude; After each level of regulation and control, the strain data is collected again through a preset closed-loop feedback algorithm, the local stress ratio is recalculated, if it meets the standard, the regulation and control is stopped, and if it does not meet the standard, the next level is triggered; When the three-level regulation and control does not meet the standard, a preset parameter correlation model is used to generate a parameter correction suggestion for the previous process, including the granulation rotation speed adjustment range and the spraying rate optimization value.

8. A method of producing a functionalized concrete according to claim 7, characterized in that, If the required functionalized concrete is a mechanical property enhanced concrete, the strain regulation and control result and the density monitoring data are combined, a preset finite element simulation model is called, and the preset pressure holding gradient and holding time of the hydraulic machine are dynamically optimized, including: Key data of the three-level regulation and control is collected, including the pressure head inclination angle, the pressure sequence switching record, the vibration parameters, the stress ratio change after each level of regulation and control, and the density monitoring value, which are integrated into the regulation and control parameter-stress-density correlation data; A preset particle-level cold pressing forming finite element simulation model is called, the regulation and control parameter-stress-density correlation data is input into the model, and the initial conditions are corrected according to the regulation and control situation; The preset target forming density, the preset stress uniformity threshold, and the preset maximum pressure holding time are input into the preset particle-level cold pressing forming finite element simulation model, which takes the current state after the three-level regulation and control as the starting point, simulates the density growth curve and the stress uniformity change curve under different pressure holding gradient and holding time combinations based on the stress-density change trend in the correlation data, and the pressure holding gradient includes the preset pressure and the preset pressure increasing rate at each stage; According to the simulation results and the three-level regulation and control type, the matching pressure holding gradient and holding time optimization scheme is determined from the preset optimization scheme library; The optimized pressure holding gradient and holding time are output, and the hydraulic machine execution instruction is generated.

9. The method for preparing functionalized concrete according to claim 2, characterized in that, If the required functionalized concrete is a temperature control functional concrete, after granulation, the spraying materials of multiple groups of nozzles are switched to a preset functional material, and the spraying is performed at a preset pressure, including: A preset environmental temperature prediction model is called, the phase change temperature threshold of the phase change material is combined, and the spraying environment temperature is stabilized in the non-phase change critical interval through a preset constant temperature control module; Multiple groups of nozzles switch the spraying material to a preset functional material, which is a phase change material matching the preset temperature control requirement; A sensor with preset precision is started to collect the moisture content and particle size data of the granules after granulation, a preset dynamic algorithm is used to determine the spraying pressure, and the nozzles are started to spray at the spraying pressure; The spray head runs according to a preset adaptive track, and the track pitch is dynamically adjusted according to the real-time particle size of the particles.

10. A system for the production of functionalized concrete, characterized by The computer program product comprises a memory, a processor and a program stored in the memory and capable of being executed on the processor, and the program can realize the preparation method of the functionalized concrete according to any one of claims 1 to 9 when loaded and executed by the processor.

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