Intelligent monitoring-based concrete precast product production management system and method

Abstract

The application discloses a concrete prefabricated part production management system and method based on intelligent monitoring, relates to the technical field of production management, and realizes real-time collection of concrete production data in the production process of the concrete prefabricated part, analyzes the internal structure evolution of the concrete prefabricated part based on the concrete production data, generates an initial damage model containing spatial distribution information, acquires an expected service environment of the concrete prefabricated part, simulates the dynamic expansion process of damage in the expected service environment based on the initial damage model, performs full life cycle performance degradation deduction, analyzes the service life of the concrete prefabricated part based on the full life cycle performance degradation deduction, analyzes whether the design service requirement is met based on the service life of the concrete prefabricated part, and comprehensively reflects the performance change of the prefabricated part in the service process by comprehensively considering the coupling effect of mechanical damage and environmental damage, so that the accuracy and practicability of service life prediction of the concrete prefabricated part are improved.

Description

Technical Field

[0001] This invention relates to the field of production management technology, and in particular to a production management system and method for precast concrete components based on intelligent monitoring. Background Technology

[0002] The long-term durability and service life of precast concrete components are core issues restricting the safety and economy of infrastructure. Current production management of precast concrete components has the following limitations: Existing technologies typically treat newly produced precast concrete components as ideal components with completely uniform material properties, ignoring the uneven spatial distribution of internal micro-defects caused by fluctuations in the production process, resulting in a disconnect between the model and the actual production situation. Existing technologies for simulating damage evolution mostly rely on static or linear assumptions, failing to consider the dynamic fluctuations of environmental factors such as temperature and humidity in the expected service environment, as well as the time-varying characteristics of damage propagation. They cannot reflect how the unique initial defects of concrete components interact and evolve with environmental loads. Most existing technologies analyze the manufacturing stage of components separately from their service use stage. Production data is only used for quality control and is not used for life prediction. Life prediction is based only on the performance of standard test pieces and general environmental levels, making it difficult to pinpoint life shortcomings. To address the aforementioned problems, this invention provides a production management system and method for precast concrete components based on intelligent monitoring. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a production management system and method for precast concrete components based on intelligent monitoring. This invention integrates the coupling effect of mechanical damage and environmental damage, comprehensively reflects the performance changes of precast components during service, and improves the accuracy and practicality of predicting the service life of precast concrete components.

[0004] To achieve the above objectives, the present invention provides a method for production management of precast concrete components based on intelligent monitoring, comprising the following specific steps: Step 1: Collect concrete production data in real time during the production of precast concrete components; Step 2: Analyze the internal structural evolution of precast concrete components based on concrete production data to generate an initial damage model containing spatial distribution information. Step 3: Obtain the expected service environment of the precast concrete component, simulate the dynamic expansion process of damage in the expected service environment based on the initial damage model, and perform full life cycle performance degradation simulation. Step 4: Analyze the service life of precast concrete components based on full life-cycle performance degradation. Step 5: Analyze whether the service life of the precast concrete components meets the design service requirements.

[0005] Preferably, step one includes the following specific steps: During the production of precast concrete components, concrete production data is collected in real time, including concrete temperature, wave velocity, and acoustic emission energy.

[0006] Preferably, step two includes the following specific steps: Step 21: Input the concrete temperature into the hydration degree calculation formula to obtain the degree of cement hydration reaction; input the degree of cement hydration reaction into the porosity calculation formula to obtain the concrete porosity evolution value; input the concrete porosity evolution value into the concrete porosity damage value calculation formula to obtain the concrete porosity damage value. Step 22: Input the wave velocity into the elastic modulus calculation formula to obtain the elastic modulus of concrete, and obtain the concrete elastic modulus damage value based on the difference between the value 1 and the ratio of the concrete elastic modulus to the initial concrete elastic modulus. Step 23: Obtain the acoustic emission damage value based on the ratio of acoustic emission energy to the maximum acoustic emission energy; Step 24: Obtain the initial damage value of concrete by weighted summation of concrete porosity damage value, concrete elastic modulus damage value and acoustic emission damage value.

[0007] Preferably, step three includes the following specific steps: Step 31: Obtain the expected service environment of the precast concrete component, which includes ambient temperature, ambient relative humidity and maximum equivalent stress; Step 32: Input the ambient temperature and ambient humidity into the environmental damage evolution rate calculation formula to obtain the environmental damage evolution rate, and input the maximum equivalent stress into the mechanical damage evolution rate calculation formula to obtain the mechanical damage evolution rate; Step 33: Obtain the total damage evolution rate by weighted summation of environmental damage evolution rate and mechanical damage evolution rate; Step 34: Input the total damage evolution rate into the concrete damage increment calculation formula to obtain the concrete damage increment, and obtain the concrete damage prediction value based on the sum of the initial concrete damage value and the concrete damage increment. Step 35: Construct a performance degradation model and obtain performance strength prediction values ​​based on concrete damage prediction values.

[0008] Preferably, step four includes the following specific steps: When the predicted performance strength value drops to the performance critical value, the corresponding prediction time is the service life of the precast concrete component.

[0009] Preferably, step five includes the following specific steps: The service life of precast concrete components is compared with the design service life. If the service life of the precast concrete components is greater than or equal to the design service life, it means that the precast concrete components meet the design service requirements. If the service life of the precast concrete components is less than the design service life, it means that the precast concrete components do not meet the design service requirements.

[0010] This invention also provides a precast concrete component production management system based on intelligent monitoring, comprising: The production data acquisition module is used to collect concrete production data in real time during the production of precast concrete components. The initial damage model generation module is used to analyze the internal structural evolution of precast concrete components based on concrete production data and generate an initial damage model containing spatial distribution information. The performance degradation simulation module is used to obtain the expected service environment of precast concrete components, simulate the dynamic expansion process of damage in the expected service environment based on the initial damage model, and perform full life cycle performance degradation simulation. The service life prediction module is used to extrapolate and analyze the service life of precast concrete components based on the performance degradation throughout their entire life cycle. The service life analysis module is used to analyze whether the service life of precast concrete components meets the design service requirements.

[0011] The present invention also provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above-described intelligent monitoring-based precast concrete component production management method by calling the computer program stored in the memory.

[0012] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described intelligent monitoring-based concrete precast component production management method.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: During the production of precast concrete components, concrete production data is collected in real time. Based on this data, the internal structural evolution of the precast concrete components is analyzed, generating an initial damage model containing spatial distribution information. The expected service environment of the precast concrete components is obtained. Based on the initial damage model, the dynamic expansion process of damage in the expected service environment is simulated, and a full life-cycle performance degradation prediction is performed. Based on this full life-cycle performance degradation prediction, the service life of the precast concrete components is analyzed. Based on the service life of the precast concrete components, it is analyzed whether the design service requirements are met. This invention comprehensively reflects the performance changes of precast components during service by integrating the coupling effect of mechanical damage and environmental damage, thus improving the accuracy and practicality of predicting the service life of precast concrete components. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the production management method for precast concrete components based on intelligent monitoring according to the present invention. Figure 2 This is a schematic diagram of step two of the intelligent monitoring-based precast concrete component production management method of the present invention. Figure 3 This is a flowchart illustrating step three of the intelligent monitoring-based precast concrete component production management method of the present invention. Figure 4 This is a schematic diagram of the overall framework of the intelligent monitoring-based precast concrete component production management system of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0017] Please see Figure 1 This invention provides a method for production management of precast concrete components based on intelligent monitoring, including the following specific steps: Step 1: Collect concrete production data in real time during the production of precast concrete components; In this embodiment, step one includes the following specific steps: According to the set size requirements, the precast concrete component is evenly divided into several grid units, and the coordinates of each grid unit are obtained. During the production process of the precast concrete component, concrete production data is collected in real time and mapped to the corresponding grid units. The concrete production data includes concrete temperature, wave velocity, and acoustic emission energy. The discrete concrete temperature data collected by the embedded temperature sensor is used to generate a continuous concrete temperature field through an interpolation algorithm, and the spatiotemporal temperature distribution is recorded. The discrete wave velocity data collected by the ultrasonic transducer is used to generate a wave velocity field through grid interpolation, and the spatiotemporal distribution of the wave velocity is recorded. Based on the arrival time difference of the acoustic emission sensor, the damage location can be located, and an acoustic emission energy distribution map can be drawn in combination with the energy parameters.

[0018] Step 2: Analyze the internal structural evolution of precast concrete components based on concrete production data to generate an initial damage model containing spatial distribution information. Please see Figure 2 In this embodiment, step two includes the following specific steps: Step 21: Input the concrete temperature into the hydration degree calculation formula to obtain the degree of cement hydration reaction. The hydration degree calculation formula can be: , In the formula, Let t represent the degree of hydration at time t. The degree of cement hydration ranges from 0 to 1, where 0 represents completely unhydrated and 1 represents completely hydrated. This refers to the time required for complete cement hydration, specifically the point at which the degree of hydration reaches 1 (or is close to 1). Typically, complete cement hydration takes 28 days. Activation energy is the energy barrier required for a hydration reaction, reflecting the ease with which the reaction occurs, and is measured in J / mol. is the gas constant, with a value of 8.314 J / (mol·K), used to describe the thermodynamic properties of an ideal gas. for The concrete temperature at any given moment. for The reference temperature of concrete at any given time can be taken as the standard curing temperature of concrete, which is usually 293.15K (20℃). It is an exponential function with the real number e as its base. For time differentiation, Temperature of a single grid cell in a precast concrete component Below, the cumulative reaction degree from time 0 to time t is used to input the cement hydration reaction degree into the porosity calculation formula to obtain the evolution value of concrete porosity. The porosity calculation formula can be: , In the formula, Let t be the evolution value of concrete porosity for grid cell coordinates (x, y, z) at time t. The initial porosity of concrete is measured using the mercury intrusion porosimetry method before concrete molding (or before hydration). The necessary porosity refers to the porosity that cannot be filled by hydration products during the hydration process (such as gel pores and some capillary pores). When hydration is complete, the porosity of the concrete is measured, and the remaining porosity at this point is the necessary porosity. The evolution value of the concrete porosity is then imported into the concrete porosity damage value calculation formula to obtain the concrete porosity damage value. The concrete porosity damage value calculation formula can be: , In the formula, The value represents the concrete porosity damage value for grid cell coordinates (x, y, z). An increase in porosity reflects a more severe degree of deterioration of the concrete material. Step 22: Input the wave velocity into the elastic modulus calculation formula to obtain the elastic modulus of concrete. The elastic modulus calculation formula can be: , In the formula, Let t be the elastic modulus of concrete with grid cell coordinates (x, y, z). The density of concrete is obtained through weighing and volume measurement of test blocks; the density of ordinary concrete is approximately 2300-2500 kg / m³. The longitudinal wave velocity is typically 3000-4500 m / s for concrete. Lower wave velocities indicate lower material stiffness, resulting in more severe damage. Poisson's ratio is determined through material mechanics tests. For concrete, it is usually taken as 0.15-0.20. The decrease in elastic modulus reflects the damage and deterioration of concrete materials. The damage value of concrete elastic modulus is obtained based on the difference between the numerical value 1 and the ratio of concrete elastic modulus to the initial concrete elastic modulus. Through static compression tests, stress-strain curves are plotted, and the slope of the initial linear segment is taken as the initial concrete elastic modulus. Step 23: Obtain the acoustic emission damage value based on the ratio of acoustic emission energy to the maximum acoustic emission energy. During the service of the precast concrete component, the acoustic emission signal is continuously monitored. The acoustic emission sensor automatically calculates the energy of each event and records the time series data. After the monitoring is completed, the energy peak value of all acoustic emission events in the entire test cycle is extracted from the acoustic emission data as the maximum acoustic emission energy. The acoustic emission energy reflects the propagation of microcracks inside the material. The higher the energy, the more severe the damage. Step 24: Obtain the initial damage value of concrete by weighted summation of concrete porosity damage value, concrete elastic modulus damage value and acoustic emission damage value, and output a dataset containing the coordinates of each grid cell and its corresponding initial damage value of concrete.

[0019] Step 3: Obtain the expected service environment of the precast concrete component, simulate the dynamic expansion process of damage in the expected service environment based on the initial damage model, and perform full life cycle performance degradation simulation. Please see Figure 3 In this embodiment, step three includes the following specific steps: Step 31: Obtain the expected service environment of the precast concrete component. The expected service environment includes ambient temperature, relative humidity, and maximum equivalent stress. The steps for obtaining ambient temperature and relative humidity are as follows: statistically analyze historical temperature and relative humidity data of the service area, organize the historical data into typical meteorological year data, and represent environmental conditions in a long-term statistical sense. The steps for obtaining the maximum equivalent stress are as follows: establish a three-dimensional finite element model based on the geometric dimensions, material properties, reinforcement, and support conditions of the precast concrete component; obtain common load combinations based on dead load, live load, wind load, snow load, and seismic action in the design documents; perform dynamic time history analysis; extract the stress time history of key parts; and select the maximum equivalent stress. The stress amplitude and frequency can be counted using the rainflow counting method. Among them, key parts are those whose bearing capacity is greater than the set stress threshold. For example: First, the finite element model needs to be established based on the design drawings. The three-dimensional solid of the concrete component needs to be accurately constructed in the general finite element software ABAQUS. The geometry is analyzed using constitutive models suitable for the nonlinear behavior of concrete (such as concrete damage plasticity models). For reinforcing bars, coupling is achieved by creating lines and embedding them into the concrete solid to accurately simulate their position and bond-slip effect. Fixed, hinged, or elastic boundary conditions are applied at the supports according to the actual support conditions. Subsequently, in the dynamic time history analysis, seismic waves or artificial waves that meet the specifications are used as the base excitation input to solve the complex stress-time history of key parts of the structure (such as the ends of reinforcing bars, prestressed anchorage zones, and joint interfaces). Finally, the rainflow counting method is used to perform fatigue load spectrum statistics: this algorithm identifies closed stress cycles by rotating the stress time history curve 90 degrees clockwise and passing through a series of rainflow paths. Its core steps are peak and valley value extraction, four-point cycle determination, and data reconstruction, thereby decomposing the random stress time history into a series of complete stress cycles, statistically analyzing each stress amplitude and its corresponding cycle number, and finally generating a stress spectrum for fatigue life calculation. Step 32: Input the ambient temperature and humidity into the environmental damage evolution rate calculation formula to obtain the environmental damage evolution rate. The environmental damage evolution rate calculation formula can be: , In the formula, For the environmental damage evolution rate, The environmental damage evolution coefficient reflects the sensitivity of a material to environmental damage. The unit is 1 / year. It is calibrated through environmental damage tests. Concrete specimens are subjected to constant temperature and humidity, and the relationship between the environmental damage evolution rate and time is fitted. The slope is the environmental damage evolution coefficient. The damage rate increases exponentially with increasing temperature, as shown by the temperature effect index. Environmental damage tests at different temperatures were used to fit the data. Different temperatures were set while maintaining constant humidity (50% RH), and temperature fluctuation cycles were performed. The damage evolution rate at different temperatures was measured, and the data was fitted. and To determine the temperature influence index, MATLAB is used. First, a set of environmental damage tests at different constant temperatures (e.g., 30°C, 40°C, 50°C, 60°C) is designed and executed, maintaining constant humidity (50% RH) at each temperature, and subjecting the concrete specimens to temperature fluctuation cycles. The environmental damage evolution rate under each constant temperature condition is calculated by periodically measuring the performance degradation of the specimens (e.g., decrease in elastic modulus or strength loss). Then, in MATLAB, different temperatures are used as independent variables, and the calculated corresponding damage evolution rate is used as the dependent variable. The `fit` function or curve fitting toolbox is used to select a power function model for nonlinear regression fitting. Therefore, the exponential term of the independent variable in the fitting result is the temperature influence index to be solved. This fitting process optimizes the parameters using the least squares method, and finally, the value of the parameter exponential term is directly read from or output from the fitted curve as the calibration result of the temperature influence index. For ambient temperature, For reference to ambient temperature, the value is usually taken as 298K (25℃). The damage rate index is determined by humidity; as humidity increases, the damage rate increases exponentially. Environmental damage experiments under different humidity levels were used to fit the data. Different humidity levels (30%RH, 50%RH, 70%RH, 90%RH) were set while maintaining a constant temperature (25℃). Humidity fluctuation cycles were performed, and the damage evolution rate under different humidity levels was measured to fit the data. and The relationship was investigated, and the humidity effect index was determined using MATLAB. For ambient relative humidity, For reference humidity, a value of 50% is typically used. This represents the initial damage value of the concrete. This indicates the accelerating effect of damage on the environment; the more severe the damage, the easier it is for environmental substances to penetrate. As an environmental damage accelerator, reflecting the accelerating effect of damage on environmental damage, it was determined through damage-environment coupling experiments. Specimens with different damage levels were prepared (pre-treated with 10, 20, and 30 dry-wet cycles), and dry-wet cycles were performed under standard conditions (25℃, 50%RH). The damage evolution rate under different damage levels was measured, and the results were fitted. and To determine the environmental damage acceleration factor using MATLAB, the following steps are taken: First, the number of pre-damage cycles (e.g., 10, 20, 30) is used as the independent variable, and the corresponding measured damage evolution rate is used as the dependent variable. Next, the `polyfit` function is used for linear fitting to obtain the fitting coefficients. Then, the `polyder(p)` function is called to differentiate the polynomial and obtain the derivative coefficients. Finally, a specific pre-damage level (e.g., 20 cycles) is substituted into this derivative function, and the calculated function value is the environmental damage acceleration factor at that damage level. The maximum equivalent stress is then imported into the mechanical damage evolution rate calculation formula to obtain the mechanical damage evolution rate. The mechanical damage evolution rate calculation formula can be: , In the formula, For mechanical damage evolution rate, The mechanical damage evolution coefficient, expressed in units of 1 / year, reflects the material's sensitivity to mechanical damage and represents the percentage decrease in the material's strength under standard conditions each year. For the maximum equivalent stress, The fatigue stress threshold represents the critical stress at which a material will not suffer damage under fatigue loading. It is determined through the material's SN curve or fatigue limit test. The steps for obtaining the mechanical damage evolution coefficient are as follows: prepare multiple sets of concrete specimens with the same mix proportion as the precast parts, apply cyclic loading, and assign stress amplitudes of 0.8... 1.0 1.2 Maintaining constant temperature and humidity (25℃ and 50%RH) to eliminate environmental interference, concrete damage values ​​were measured by ultrasonic velocity changes. The mechanical damage evolution rate was obtained by the ratio of the concrete damage value change rate to the change time, and a fitting was performed. and Relationship; Step 33: Obtain the total damage evolution rate by weighted summation of environmental damage evolution rate and mechanical damage evolution rate; Step 34: Import the total damage evolution rate into the concrete damage increment calculation formula to obtain the concrete damage increment. The concrete damage increment calculation formula can be: , In the formula, For the increase in concrete damage, To predict the duration, the unit can be years. for Total damage evolution rate at time t, For time differentiation, the predicted value of concrete damage is obtained based on the sum of the initial concrete damage value and the concrete damage increment. In this embodiment, the damage state of each grid cell is updated hourly. Step 35: Construct a performance degradation model and obtain the predicted performance strength value based on the predicted concrete damage value. The performance strength prediction formula can be: , In the formula, for Predicted performance strength at time t. The initial performance index represents the compressive strength of the material before service, determined through a standard compressive strength test. The performance degradation coefficient is determined by the following steps: preparing multiple sets of concrete specimens with different degrees of damage, and subjecting them to the same mechanical load (cyclic loading, frequency 1 Hz, stress amplitude 1.0). Under environmental conditions (25℃ and 50%RH), the compressive strength of the specimens was measured, and the performance degradation coefficient was obtained using MATLAB. The specific steps for obtaining the performance degradation coefficient through MATLAB fitting are as follows: First, the compressive strength data of specimens with different damage levels in each group were normalized with their corresponding initial undamaged state strength to obtain a series of strength retention rate data points. Then, in MATLAB, with the damage level (such as the number of cyclic loadings, damage index, etc.) as the independent variable and the strength retention rate as the dependent variable, a suitable mathematical model (such as an exponential decay function or a power function) was selected and established using the fit function or cftool (curve fitting toolbox) to perform nonlinear regression fitting on the experimental data points. This fitting process automatically optimizes the model parameters. The final functional relationship calibrated by the experimental data, and the relationship between the function value (i.e., the fitted value of the strength retention rate) and the damage level, is defined as the performance degradation coefficient.

[0020] Step 4: Analyze the service life of precast concrete components based on full life-cycle performance degradation. In this embodiment, step four includes the following specific steps: When the predicted performance strength value drops to the performance critical value, the corresponding prediction time is the service life of the precast concrete component. The performance critical value can be obtained through engineering specifications. In this embodiment, it is set to 80% of the initial performance.

[0021] This embodiment extrapolates the performance degradation process of precast concrete components throughout their entire life cycle based on damage propagation paths and rates, outputting damage distribution cloud maps at different times and life prediction results for key areas. Through time-varying damage evolution simulation, it accurately captures the damage propagation behavior of concrete under the coupled action of complex environments and loads, ensuring the continuity and stability of the damage evolution process. The damage distribution cloud map can be used to intuitively identify high-risk areas, thereby assessing the durability degradation trend of key structural components.

[0022] Step 5: Analyze whether the service life of the precast concrete components meets the design service requirements.

[0023] In this embodiment, step five includes the following specific steps: The service life of the precast concrete component is compared with the design service life in the design documents. If the service life of the precast concrete component is greater than or equal to the design service life, it means that the precast concrete component meets the design service requirements. If the service life of the precast concrete component is less than the design service life, it means that the precast concrete component does not meet the design service requirements.

[0024] The steps for obtaining the weights in this embodiment are as follows: obtain historical concrete production data and historical concrete final service time; determine whether the historical service requirements are met by using the historical concrete final service time; import the obtained historical concrete production data into each step of this embodiment to obtain the judgment result of whether the precast concrete components meet the service requirements; import the two judgment results into MATLAB for fitting; and obtain the set of weight values ​​with the highest judgment accuracy.

[0025] Please see Figure 4 This invention also provides a precast concrete component production management system based on intelligent monitoring, including: The production data acquisition module is used to collect concrete production data in real time during the production of precast concrete components. The initial damage model generation module is used to analyze the internal structural evolution of precast concrete components based on concrete production data and generate an initial damage model containing spatial distribution information. The performance degradation simulation module is used to obtain the expected service environment of precast concrete components, simulate the dynamic expansion process of damage in the expected service environment based on the initial damage model, and perform full life cycle performance degradation simulation. The service life prediction module is used to extrapolate and analyze the service life of precast concrete components based on the performance degradation throughout their entire life cycle. The service life analysis module is used to analyze whether the service life of precast concrete components meets the design service requirements.

[0026] This invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above-described intelligent monitoring-based precast concrete component production management method by calling the computer program stored in the memory.

[0027] The electronic device can vary considerably depending on its configuration and performance. It may include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the intelligent monitoring-based precast concrete component production management method provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted here.

[0028] This invention also provides a computer-readable storage medium storing instructions that, when a computer program is run on a computer device, cause the computer device to execute the above-described intelligent monitoring-based concrete precast component production management method.

[0029] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0030] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, they generate in whole or in part the flow or function according to the embodiments of the present invention. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be solid-state drives.

[0032] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this invention.

[0033] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical or other forms.

[0034] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0035] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined in this invention may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for production management of precast concrete components based on intelligent monitoring, characterized in that, The specific steps include the following: Step 1: Collect concrete production data in real time during the production of precast concrete components; Step 2: Analyze the internal structural evolution of precast concrete components based on concrete production data to generate an initial damage model containing spatial distribution information. Step 3: Obtain the expected service environment of the precast concrete component, simulate the dynamic expansion process of damage in the expected service environment based on the initial damage model, and perform full life cycle performance degradation simulation. Step 4: Analyze the service life of precast concrete components based on full life-cycle performance degradation. Step 5: Analyze whether the service life of the precast concrete components meets the design service requirements.

2. The method for production management of precast concrete components based on intelligent monitoring according to claim 1, characterized in that, Step one includes the following specific steps: During the production of precast concrete components, concrete production data is collected in real time, including concrete temperature, wave velocity, and acoustic emission energy.

3. The method for production management of precast concrete components based on intelligent monitoring according to claim 2, characterized in that, Step two includes the following specific steps: Step 21: Input the concrete temperature into the hydration degree calculation formula to obtain the degree of cement hydration reaction; input the degree of cement hydration reaction into the porosity calculation formula to obtain the concrete porosity evolution value; input the concrete porosity evolution value into the concrete porosity damage value calculation formula to obtain the concrete porosity damage value. Step 22: Input the wave velocity into the elastic modulus calculation formula to obtain the elastic modulus of concrete, and obtain the concrete elastic modulus damage value based on the difference between the value 1 and the ratio of the concrete elastic modulus to the initial concrete elastic modulus. Step 23: Obtain the acoustic emission damage value based on the ratio of acoustic emission energy to the maximum acoustic emission energy; Step 24: Obtain the initial damage value of concrete by weighted summation of concrete porosity damage value, concrete elastic modulus damage value and acoustic emission damage value.

4. The method for production management of precast concrete components based on intelligent monitoring according to claim 3, characterized in that, Step three includes the following specific steps: Step 31: Obtain the expected service environment of the precast concrete component, which includes ambient temperature, ambient relative humidity and maximum equivalent stress; Step 32: Input the ambient temperature and ambient humidity into the environmental damage evolution rate calculation formula to obtain the environmental damage evolution rate, and input the maximum equivalent stress into the mechanical damage evolution rate calculation formula to obtain the mechanical damage evolution rate; Step 33: Obtain the total damage evolution rate by weighted summation of environmental damage evolution rate and mechanical damage evolution rate; Step 34: Input the total damage evolution rate into the concrete damage increment calculation formula to obtain the concrete damage increment, and obtain the concrete damage prediction value based on the sum of the initial concrete damage value and the concrete damage increment. Step 35: Construct a performance degradation model and obtain performance strength prediction values ​​based on concrete damage prediction values.

5. The method for production management of precast concrete components based on intelligent monitoring according to claim 4, characterized in that, Step four includes the following specific steps: When the predicted performance strength value drops to the performance critical value, the corresponding prediction time is the service life of the precast concrete component.

6. The method for production management of precast concrete components based on intelligent monitoring according to claim 5, characterized in that, Step five includes the following specific steps: The service life of precast concrete components is compared with the design service life. If the service life of the precast concrete components is greater than or equal to the design service life, it means that the precast concrete components meet the design service requirements. If the service life of the precast concrete components is less than the design service life, it means that the precast concrete components do not meet the design service requirements.

7. A precast concrete component production management system based on intelligent monitoring, used to implement the precast concrete component production management method based on intelligent monitoring as described in any one of claims 1-6, characterized in that, include: The production data acquisition module is used to collect concrete production data in real time during the production of precast concrete components. The initial damage model generation module is used to analyze the internal structural evolution of precast concrete components based on concrete production data and generate an initial damage model containing spatial distribution information. The performance degradation simulation module is used to obtain the expected service environment of precast concrete components, simulate the dynamic expansion process of damage in the expected service environment based on the initial damage model, and perform full life cycle performance degradation simulation. The service life prediction module is used to extrapolate and analyze the service life of precast concrete components based on the performance degradation throughout their entire life cycle. The service requirement analysis module is used to analyze whether the service life of precast concrete components meets the design service requirements.

8. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that can be called by the processor, and the processor executes the intelligent monitoring-based precast concrete component production management method according to any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the intelligent monitoring-based precast concrete component production management method as described in any one of claims 1-6.

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