Intelligent production method and system for thin-wall concrete pole

Abstract

The invention relates to the technical field of production process control, and discloses an intelligent production method and system for a thin-wall concrete electric pole, and the method comprises the steps: obtaining the hardening state grade of the interior of concrete through constructing a hardening state evaluation model; dividing the interior of the electric pole into a plurality of control units, calculating a hardening unevenness index, and generating a hardening homogenization scheme; acquiring historical production data, and extracting a matched reference internal mold demolding scheme as an initial internal mold demolding parameter; according to the hardening state grade of the interior of the concrete, the initial internal mold demolding parameters are calibrated, and second internal mold demolding parameters are obtained; the inner mold demolding process is controlled according to the second inner mold demolding parameter, the demolding process is monitored in real time, and closed-loop optimization control is conducted; according to the method, the concrete internal hardening state and historical production experience are comprehensively considered, intelligent control over the thin-wall concrete pole production process is achieved through dynamic evaluation and calibration, and the production quality and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production process control, and more specifically, to an intelligent production method and system for thin-walled concrete poles. Background Art

[0002] Concrete poles are an important part of power lines and are widely used in power transmission and distribution lines of power grids at all levels. With the continuous development of power grid construction, the performance requirements for concrete poles are getting higher and higher. Energy saving, environmental protection, high strength, durability and lightweight have become the main development direction of concrete poles. Most of the existing concrete pole production uses traditional production processes and equipment, with low production efficiency and poor product consistency, which cannot meet the production requirements of thin-walled lightweight poles.

[0003] The Chinese patent application with publication number CN116041009A discloses a concrete for light anti-corrosion poles and its preparation method. The invention improves the tensile strength, bending strength, toughness and durability of concrete poles by adding microsilica fume, basalt fiber, glass fiber and other materials into the concrete, and reduces the viscosity of the concrete mixture by adding limestone powder, making the pole wall thinner and reducing the manufacturing and transportation costs. However, the invention does not consider the unevenness of the internal hardening process of concrete, which can easily lead to cracking and deformation of the poles, affecting product quality.

[0004] A Chinese patent application with publication number CN109186478A discloses an online detection method for the vertical height of recycled concrete poles. The method uses a CCD laser sensor to detect the vertical height of the steel bar after cold rolling online, which improves the detection accuracy. However, the method only detects and controls the verticality of the steel bars of the recycled concrete poles, and does not involve the monitoring and regulation of the concrete hardening process, making it difficult to ensure the overall quality of the poles.

[0005] Inner mold demoulding is a key link that affects the quality and efficiency of concrete pole production. The concrete pole production method in the existing technology lacks real-time monitoring and dynamic regulation of the concrete hardening process; the inner mold demoulding control is not precise and it is difficult to adapt to the production requirements of thin-walled lightweight poles. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent production method and system for thin-walled concrete poles, which collects the internal hardening data of concrete in real time, constructs a hardening state evaluation model, calculates the hardening unevenness index, dynamically adjusts the inner mold demolding parameters, and realizes closed-loop optimization control of the demolding process, thereby improving the production quality and efficiency of thin-walled concrete poles.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The first concrete hardening data of n detection sites related to the hardening state of the inside of the thin-walled concrete pole are collected in real time; a concrete internal hardening state evaluation model is constructed, and the hardening state level of the inside of the concrete is obtained according to the first concrete hardening data and the concrete internal hardening state evaluation model; the inside of the concrete pole is divided into n1 control units, and the hardening non-uniformity index is calculated according to the hardening state level of the inside of the concrete, and a corresponding hardening homogenization scheme is generated, wherein n and n1 are both positive integers;

[0009] Obtain historical production data of thin-walled concrete poles, extract a reference inner mold demoulding scheme matching current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; calibrate the initial inner mold demoulding parameter according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameter; the initial inner mold demoulding parameter includes an initial inner mold demoulding time, an initial inner mold demoulding speed, and an initial inner mold demoulding force;

[0010] The inner mold demoulding process is controlled according to the second inner mold demoulding parameter, and the demoulding process is monitored in real time to perform closed-loop optimization control.

[0011] Furthermore, the constructing of the concrete internal hardening state evaluation model, according to the first concrete hardening data and the concrete internal hardening state evaluation model, obtaining the concrete internal hardening state grade includes:

[0012] Establish the evolution model of concrete internal temperature field and concrete internal strain field;

[0013] The concrete internal temperature field evolution model and the concrete internal strain field evolution model are coupled to obtain the concrete internal hardening state evaluation model;

[0014] Inputting the first concrete hardening data into the concrete internal hardening state evaluation model to obtain the temperature time history curve and the strain time history curve of n detection points;

[0015] Select m1 characteristic moments, and extract the temperature and strain of n detection points at m1 characteristic moments from the temperature-time curve and the strain-time curve; m1 is a positive integer;

[0016] The temperature T of each detection point at each characteristic moment i,j and strain E i,j The hardening state level of each detection site at each characteristic moment is determined by comparing with the preset first temperature threshold T1, second temperature threshold T2, first strain threshold E1 and second strain threshold E2; i is the index of the detection site, 1≤i≤n; j is the index of the characteristic moment, 1≤j≤m1; E i,j represents the strain at the i-th detection site and the j-th characteristic moment.

[0017] Furthermore, the method for determining the hardening state level of each detection site at each characteristic moment includes:

[0018] When T i,j <T1, if E i,j <E1, the concrete is in the plastic stage, marked as the first hardening grade;

[0019] When T1≤T i,j ≤T2, if E1≤E i,j ≤E2, the concrete is in the setting stage and is marked as the second hardening grade;

[0020] When T i,j >T2, if E i,j >E2, the concrete is in the strength development stage and is marked as the third hardening grade.

[0021] Furthermore, the control unit is divided according to the length direction and radial direction of the pole, and each control unit contains at least one detection site;

[0022] The calculation of the hardening unevenness index according to the hardening state level inside the concrete includes:

[0023] Count the number of detection sites at the first hardening level, the second hardening level, and the third hardening level in each control unit at different characteristic moments, and calculate the proportion of the number of detection sites at the three hardening levels in the number of all detection sites in the control unit respectively;

[0024] Calculate the absolute value of the difference in the number of hardening state level detection sites between adjacent control units at different characteristic moments, sum the absolute values ​​of the difference in the proportions and divide them by the number of adjacent control unit pairs to obtain the hardening non-uniformity index at the characteristic moment; the number of adjacent control unit pairs is determined by the number of divided control units;

[0025] The average of the hardening non-uniformity index at each characteristic moment is taken to obtain a comprehensive hardening non-uniformity index.

[0026] Further, the generating of the corresponding hardening homogenization scheme includes:

[0027] Determine whether the comprehensive hardening unevenness index is greater than the preset unevenness threshold; if it is greater than the unevenness threshold, it indicates that the hardening inside the concrete pole is uneven, and a corresponding hardening homogenization plan needs to be generated; according to the corresponding hardening homogenization plan, adjust the maintenance parameters of different control units.

[0028] Furthermore, the calibrating of the initial inner mold demoulding parameters according to the hardening state level inside the concrete includes:

[0029] The first hardening level is assigned to 1, the second hardening level is assigned to 2, and the third hardening level is assigned to 3. The divided n1 control units are used as basic units for calibrating the initial inner mold demolding parameters. The initial inner mold demolding parameters are calibrated according to the hardening state level of each detection point in each control unit to obtain the second inner mold demolding parameters, which include the second inner mold demolding time, the second inner mold demolding speed and the second inner mold demolding force.

[0030] Furthermore, the real-time monitoring of the demoulding process and the closed-loop optimization control include:

[0031] According to the second inner mold demoulding parameter, the inner mold demoulding program is started; during the inner mold demoulding process, the second concrete hardening data is collected in real time, and according to the preset inner mold detachment evaluation model, it is judged whether the inner mold of each control unit is fully detached from the concrete;

[0032] If the inner mold of the i'th control unit is not fully separated from the concrete, adjust the inner mold demoulding speed and inner mold demoulding force of the i'th control unit until the inner mold of the i'th control unit is fully separated from the concrete; if the inner mold of each control unit is fully separated from the concrete, maintain the current inner mold demoulding speed and inner mold demoulding force until the inner mold is completely separated.

[0033] A thin-walled concrete pole intelligent production system, which is used to implement the above-mentioned thin-walled concrete pole intelligent production method, the system comprises:

[0034] Homogenization control module: used to collect the first concrete hardening data of n detection sites related to the internal hardening state of thin-walled concrete poles in real time; construct a concrete internal hardening state evaluation model, and obtain the hardening state level of the concrete according to the first concrete hardening data and the concrete internal hardening state evaluation model; divide the interior of the concrete pole into n1 control units, calculate the hardening unevenness index according to the hardening state level of the concrete, and generate a corresponding hardening homogenization plan, where n and n1 are both positive integers;

[0035] Demolding parameter calculation module: used to obtain historical production data of thin-walled concrete poles, extract a reference inner mold demoulding scheme that matches the current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; calibrate the initial inner mold demoulding parameter according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameter; the initial inner mold demoulding parameter includes initial inner mold demoulding time, initial inner mold demoulding speed and initial inner mold demoulding force;

[0036] Closed-loop optimization module: used to control the inner mold demoulding process according to the second inner mold demoulding parameters, and monitor the demoulding process in real time to perform closed-loop optimization control.

[0037] An electronic device comprises a memory, a central processing unit and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned intelligent production method of thin-walled concrete poles is implemented.

[0038] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the above-mentioned intelligent production method for thin-walled concrete poles.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] By collecting hardening data from multiple detection sites inside the concrete pole in real time and building a hardening state evaluation model, the hardening state inside the concrete can be more accurately grasped. This method greatly improves the monitoring accuracy of the production process and helps to ensure the quality of the final product. According to the hardening state level inside the concrete, the initial inner mold demoulding parameters are intelligently calibrated to obtain more reasonable demoulding parameters. This method can significantly reduce the damage and defects that may occur during the demoulding process and improve the integrity and surface quality of the product. By establishing the evolution model of the temperature field and strain field inside the concrete, the hardening process of the concrete can be more accurately predicted, which helps to optimize the production time and reduce unnecessary waiting, thereby improving the overall production efficiency. By accurately controlling the inner mold demoulding process, product scrapping caused by improper demoulding can be reduced, and the waste of raw materials can be reduced. At the same time, the optimized production process can also reduce energy consumption and further reduce production costs. By calculating the hardening unevenness index and generating the corresponding hardening homogenization scheme, the uniformity of the hardening inside the concrete pole can be effectively improved, which not only improves the quality of a single product, but also ensures that the batch-produced products have higher consistency. According to historical production data and current production conditions, the production parameters are dynamically adjusted. This method enables the production process to better adapt to different raw materials, environmental conditions and production requirements, and improves production flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 It is a principle flow chart of the intelligent production method of thin-walled concrete poles in the present invention;

[0043] Figure 2A flow chart of a method for obtaining the internal hardening state grade of concrete in the thin-walled concrete pole intelligent production method of the present invention;

[0044] Figure 3 A flow chart of a method for calculating a hardening non-uniformity index and generating a corresponding hardening homogenization scheme in the thin-walled concrete pole intelligent production method of the present invention;

[0045] Figure 4 It is a functional module diagram of the intelligent production system of thin-walled concrete poles in the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] See also Figure 1 As shown, this embodiment provides an intelligent production method for thin-walled concrete poles, including:

[0049] Step S1000, collecting first concrete hardening data of n detection sites related to the hardening state of the inside of the thin-walled concrete pole in real time; constructing a concrete internal hardening state evaluation model, and obtaining the hardening state level of the inside of the concrete according to the first concrete hardening data and the concrete internal hardening state evaluation model; dividing the inside of the concrete pole into n1 control units, calculating the hardening non-uniformity index according to the hardening state level of the inside of the concrete, and generating a corresponding hardening homogenization scheme; n1 is a positive integer;

[0050] Furthermore, step S1000 includes:

[0051] Step S1100, collecting first concrete hardening data of n detection points related to the hardening state inside the pole in real time; the first concrete hardening data includes strain and temperature inside the concrete; n is a positive integer;

[0052] Specifically, the production process of thin-walled concrete poles includes the inner membrane centrifugal molding and the inner membrane and outer membrane demoulding process. Step S1100 uses distributed optical fiber sensing technology to finely perceive the internal hardening state of the concrete pole production process. Traditional methods mainly rely on experience to judge the degree of solidification of concrete, and it is difficult to grasp the actual hardening process inside. By arranging distributed optical fiber sensors on the inner mold, the strain, temperature and other parameters inside the concrete can be collected in real time and continuously to fully reflect the dynamic changes of the hardening process. Select a suitable type of distributed optical fiber sensor, such as Brillouin optical fiber sensor or Raman optical fiber sensor; Brillouin optical fiber sensor and Raman optical fiber sensor are widely used distributed sensors with high sensitivity, good stability, high spatial resolution and other characteristics, which can meet the needs of monitoring the production process of concrete poles. The working principle of the Brillouin optical fiber sensor is to use the periodic change of the grating structure in the optical fiber to cause the wavelength drift of the reflection spectrum, and to achieve distributed sensing by detecting the change law of the reflection spectrum with external strain and temperature. The Raman fiber optic sensor uses the relationship between the intensity of inelastic scattered light (i.e., Raman scattered light) in the optical fiber and the temperature, and obtains the temperature distribution along the optical fiber by measuring the time difference between the incident light and the Raman scattered light at different positions.

[0053] Evenly wind n distributed fiber optic sensors on the surface of the inner mold, and control the sensor spacing at 5-10cm; wind the optical fiber on the inner mold at a spacing of 5-10cm, and a relatively fine spatial sampling can be obtained, and each optical fiber can cover dozens to hundreds of sensor units. At the same time, the balance between the fiber optic layout density and the data processing efficiency should be taken into account, and a suitable layout scheme should be selected. Install the inner mold on the centrifugal forming machine and connect it to the data acquisition instrument to realize real-time monitoring of the entire process of concrete pouring and centrifugal forming, and the acquisition frequency can reach the second level. Through the sensor layout and data acquisition of step S1100, the shortcomings of the traditional method of large perception blind area and low sampling frequency are overcome, and the accurate perception of the hardening state inside the concrete pole is realized, which is the basis for intelligent control. Distributed fiber optic sensing uses the characteristics of light transmission in the optical fiber to turn a fiber into a "sensor array", which greatly simplifies the wiring structure and saves installation space, and is very suitable for monitoring the internal state of narrow and long components. At the same time, the optical fiber itself has the characteristics of high temperature resistance and electromagnetic interference resistance, which can meet the demanding requirements of the concrete pouring environment. Through the collaborative perception of multiple internal parameters, a three-dimensional data portrait of the entire process of concrete pole hardening was constructed, making subsequent status assessment and quality prediction more accurate and reliable.

[0054] Step S1200, constructing a concrete internal hardening state evaluation model, and obtaining the concrete internal hardening state grade according to the first concrete hardening data and the concrete internal hardening state evaluation model;

[0055] Furthermore, if Figure 2 As shown, step S1200 includes:

[0056] Step S1210, establishing a concrete internal temperature field evolution model and a concrete internal strain field evolution model;

[0057] Step S1220, coupling the concrete internal temperature field evolution model and the concrete internal strain field evolution model to obtain a concrete internal hardening state assessment model;

[0058] Step S1230, inputting the first concrete hardening data into the concrete internal hardening state assessment model to obtain the temperature time history curve and the strain time history curve of n detection points;

[0059] Step S1240, selecting m1 characteristic moments, extracting the temperature and strain of n detection points at the m1 characteristic moments from the temperature-time curve and the strain-time curve; m1 is a positive integer;

[0060] Step S1250: calculate the temperature T of each detection point at each characteristic moment. i,j and strain E i,j The first temperature threshold T1, the second temperature threshold T2, the first strain threshold E1 and the second strain threshold E2 are compared to determine the hardening state level of each detection site at each characteristic moment; i is the index of the detection site, 1≤i≤n; j is the index of the characteristic moment, 1≤j≤m1; T i,j represents the temperature of the i-th detection point at the j-th characteristic moment; E i,j represents the strain at the i-th detection site and the j-th characteristic moment.

[0061] The method for determining the hardening state level of each detection site at each characteristic moment includes:

[0062] When T i,j <T1, if E i,j <E1, the concrete is in the plastic stage and is marked as the first hardening grade; if E i,j ≥E1, the concrete is in an abnormal state and further analysis of the cause is required;

[0063] When T1≤T i,j ≤T2, if E1≤E i,j ≤E2, the concrete is in the setting stage and is marked as the second hardening grade; otherwise, the concrete is in an abnormal state and further analysis of the cause is required;

[0064] When T i,j >T2, if E i,j >E2, the concrete is in the strength development stage and is marked as the third hardening grade; otherwise, the concrete is in an abnormal state and further analysis of the cause is required.

[0065] Specifically, concrete hardening is a complex physical and chemical process, which is affected by multiple factors such as hydration reaction kinetics and microstructure evolution. Concrete is initially liquid, and gradually turns into solid after coagulation and hardening. Its strength and stiffness continue to increase, accompanied by internal temperature changes and autogenous shrinkage deformation. In order to accurately evaluate the hardening state inside concrete, a reasonable mathematical model needs to be established to describe it. First, based on the theory of hydration reaction kinetics, the evolution model of the temperature field inside concrete is established by considering factors such as the mineral composition, temperature, and humidity of cement. Commonly used temperature field models include Arrhenius exponential function and hyperbolic function. On the basis of the temperature field model, combined with the composition and mix parameters of concrete, the temperature distribution and change law of the hardening process are further predicted. By comparing the numerical simulation with the experimental measurement results, the model is calibrated and optimized to improve its accuracy and applicability. At the same time, based on the theory of concrete mesostructure evolution, the generation of hydration products and the changes in capillary pores during the hardening process are analyzed, and the evolution model of the internal strain field is established. The hardening process of concrete is accompanied by volume changes, which are mainly manifested as autogenous shrinkage deformation. Autogenous shrinkage is caused by the change of capillary tension caused by cement hydration, which is a volume deformation. The concrete autogenous shrinkage prediction model based on capillary pore size distribution provides a description of the strain field distribution during the concrete hardening process. The model equation is:

[0066]

[0067] Among them, ε au (t') is the autogenous contraction strain at time t', ε au0 is the ultimate autogenous shrinkage strain; τ' and β' are parameters related to the composition of concrete and need to be determined according to the specific concrete mix ratio, raw material properties, admixture types, etc. By calibrating these parameters, the distribution of autogenous shrinkage strain field during concrete hardening can be predicted more accurately. At the same time, by introducing volume constraints, such as the constraint effect of steel bars, the model can also simulate the development of self-constrained stress inside concrete.

[0068] On the basis of establishing the evolution model of temperature field and strain field, the coupling analysis of the two fields is further carried out. The temperature change and volume deformation of concrete hardening process affect each other. The strain will change the pore structure and affect the thermal conductivity. The temperature change will cause thermal expansion and change the strain level. Therefore, it is necessary to mathematically establish the coupling equation of temperature field and strain field to collaboratively solve the spatiotemporal evolution of the two fields. Common solution methods include step-by-step iteration method and fully coupled direct method. The step-by-step iteration method is to set the time step, calculate a certain field first, and then use the calculation result as the input of another field, and iterate alternately until the solutions of the two fields converge. The fully coupled direct method is to establish a set of simultaneous equations including temperature and strain, solve the two fields at the same time, and obtain a coupled solution. The temperature and strain time history curves obtained by coupling calculation can comprehensively reflect the deformation development and heat accumulation of concrete hardening process, and provide a basis for quantitative evaluation of hardening state.

[0069] Distributed optical fiber sensing technology arranges n sensor detection sites on the inner membrane of concrete poles, which can collect temperature and strain data of concrete hardening process at different parts. The collected massive monitoring data is input into the hardening state evaluation model. Through numerical simulation calculation, the temperature time history curve and strain time history curve at each sensor detection site can be obtained, that is, the evolution law of the internal hardening state over time. However, the evaluation of the hardening state level does not directly use the entire time history curve, but extracts the data of certain characteristic moments in the curve. According to the process of concrete hardening, it is necessary to select a series of representative characteristic moments, such as the moment of pouring completion, the moment of coagulation initiation, the moment of final coagulation, and the moment when the strength reaches the design value. The selection of characteristic moments should take into account the comprehensiveness of data and the efficiency of judgment, covering the key nodes of the hardening process and avoiding data redundancy. The temperature and strain data of each sensor detection site at the characteristic moment are extracted and compared with the preset temperature and strain thresholds, which can be divided into plastic stage, coagulation stage, strength development stage, etc. Since multiple sensor detection sites are arranged on the inner membrane, the hardening process of different parts of the concrete pole may be different. Therefore, it is necessary to determine the temperature and strain data of all sensor detection sites one by one to obtain the hardening state level of each detection site at the characteristic moment.

[0070] The temperature thresholds T1 and T2 reflect the key temperature points of the concrete hardening process, such as the initial setting temperature and the final setting temperature. The strain thresholds E1 and E2 reflect the key deformation points of the hardening process, such as the critical value of setting shrinkage and the inflection point of strength growth. To determine these thresholds, it is necessary to refer to the specifications and literature, and to combine them with the actual project, and obtain them through a large number of trial mix tests and data analysis.

[0071] The basic logic of the judgment is to first judge the approximate stage of concrete based on temperature, and then judge the abnormality of the stage based on strain. When the temperature is lower than T1, the concrete is basically in the plastic stage, the gel structure has not yet been formed inside, and the deformation is mainly plastic deformation. If the strain is also small at this time and does not exceed E1, the concrete is a normal plastic body and can be formed at will. However, if the strain has exceeded E1, there may be problems such as local premature coagulation and excessive porosity. The concrete is in an abnormal state and needs to be analyzed and countermeasures taken. When the temperature is between T1 and T2, the concrete enters the coagulation stage and gradually changes from a plastic body to an elastic body. Under normal circumstances, the strain should be between E1 and E2, the reaction volume begins to shrink, and the strength gradually increases. If the strain is too small or too large, it indicates that the coagulation process is abnormal, such as insufficient hydration reaction, cracks inside, etc., and the cause needs to be analyzed and the process adjusted. When the temperature exceeds T2, the concrete enters the strength development stage, and a stable gel structure is basically formed inside. Under normal circumstances, the strain will exceed E2, the reaction strength will further increase, and the shrinkage deformation will tend to be gentle. If the strain is still small, it may be caused by hydration stagnation, weak internal bonding, etc., and the reasons need to be analyzed and the mix ratio needs to be improved. In addition to the state determination at a single point, it is also necessary to pay attention to the evolution of the state of each point over time. Since multiple sensors are arranged on the inner membrane, the hardening process may be inconsistent for different parts of the pole. Therefore, it is necessary to draw a state-time distribution diagram of each point, analyze whether there are abnormal conditions such as sudden state changes and excessive state gradients, and take targeted measures respectively.

[0072] Step S1250 selects characteristic moments, extracts key temperature and time-varying data, and compares and determines with preset thresholds, thereby discretizing the continuous hardening process into several typical stages, simplifying the computational complexity of state determination and facilitating rapid evaluation and response; making full use of the advantages of distributed fiber optic sensing to achieve spatial distribution perception of the hardening state, which helps to identify local defects; the criteria provided by step S1250 can more comprehensively evaluate the hardening state inside the concrete pole, providing data support for quality control and process optimization.

[0073] Step S1200 constructs a concrete internal hardening state assessment model that comprehensively considers the two key parameters of temperature and strain, avoiding the biased assessment caused by considering only a single parameter, and improving the accuracy and reliability of the hardening state determination. By establishing the evolution model of the temperature field and strain field inside the concrete and conducting coupling analysis, the complex physical and chemical process of concrete hardening is mathematically described, laying a theoretical foundation for the quantitative assessment of the hardening state. Taking full advantage of the advantages of distributed optical fiber sensing, the massive monitoring data collected is input into the hardening state assessment model for calculation, and the evolution law of the hardening state of different parts of the concrete pole over time is obtained, realizing the refined perception of the internal hardening state. By partitioning the states of different sensor detection sites, local defects and abnormal conditions are identified. By drawing the time distribution diagram of the state of each point, the spatial inhomogeneity of the hardening process is mastered, providing data support and targeted suggestions for quality control and process optimization. In summary, the concrete internal hardening state evaluation model constructed in step S1200, combined with distributed optical fiber sensing data, can comprehensively evaluate the internal hardening state of concrete poles during production, quantitatively reflect the hardening process, identify local defects, and provide a key basis for quality improvement and production optimization. It has significant technical advantages and practical value.

[0074] Step S1300, dividing the interior of the concrete pole into n1 control units, calculating the hardening non-uniformity index according to the hardening state level inside the concrete, and generating a corresponding hardening homogenization scheme; n1 is a positive integer.

[0075] Furthermore, if Figure 3 As shown, step S1300 includes:

[0076] Step S1310, dividing the interior of the concrete pole into n1 control units according to the pole length direction and radial direction, each control unit including at least one detection site;

[0077] Step S1320, counting the number of detection sites at the first hardening level, the second hardening level, and the third hardening level in each control unit at different characteristic moments, and calculating the proportion of the number of detection sites at the three hardening levels in the number of all detection sites in the control unit;

[0078] Step S1330, calculating the absolute value of the proportion difference of the number of hardening state level detection sites between adjacent control units at different characteristic moments, summing up the absolute value of the proportion difference and dividing it by the number of adjacent control unit pairs to obtain the hardening non-uniformity index at the characteristic moment; the number of adjacent control unit pairs is determined by the number of divided control units;

[0079] Step S1340, taking the average of the hardening non-uniformity index at each characteristic moment to obtain a comprehensive hardening non-uniformity index;

[0080] The method of calculating the absolute value of the difference in the number of hardening state level sites between adjacent control units at different characteristic moments, summing the absolute values ​​of the difference in the proportions and dividing the sum by the logarithm of adjacent control units to obtain the hardening non-uniformity index at the characteristic moment includes:

[0081] Assume that at the jth characteristic moment, there are m pairs of adjacent control units. For the kth pair of adjacent control units (i*, i'), the absolute value of the difference in the proportion of the hardening state level is:

[0082]

[0083] in,

[0084] It represents the absolute value of the difference in the number of detection sites of the first hardening level between the kth pair of adjacent control units at the jth characteristic moment;

[0085] It represents the absolute value of the difference in the number of detection sites of the second hardening level for the kth pair of adjacent control units at the jth characteristic moment;

[0086] It represents the absolute value of the difference in the number of detection sites of the third hardening level at the jth characteristic moment of the kth pair of adjacent control units;

[0087] i* and i': are the indices of the control units, 1≤i*≤n1, 1≤i'≤n1;

[0088] It represents the ratio of the number of detection sites in the first hardening level in the i*th control unit to the number of all detection sites in the i*th control unit at the jth characteristic moment;

[0089] represents the proportion of the number of detection sites in the first hardening level in the i'th control unit to the number of all detection sites in the i'th control unit at the j'th characteristic moment;

[0090] It represents the proportion of the number of detection sites in the second hardening level in the i*th control unit to the number of all detection sites in the i*th control unit at the jth characteristic moment;

[0091] represents the proportion of the number of detection sites in the second hardening level in the i'th control unit to the number of all detection sites in the i'th control unit at the j'th characteristic moment;

[0092] It represents the proportion of the number of detection sites in the third hardening level in the i*th control unit to the number of all detection sites in the i*th control unit at the jth characteristic moment;

[0093] It represents the ratio of the number of detection sites in the third hardening level in the i'th control unit to the number of all detection sites in the i'th control unit at the j'th characteristic moment.

[0094] Then the hardening non-uniformity index at the jth characteristic moment is:

[0095]

[0096] Among them, I j Represents the hardening non-uniformity index at the jth characteristic moment.

[0097] Specifically, in step S1100, n distributed optical fiber sensors are wound on the inner mold at a spacing of 5-10 cm, forming multiple measuring points along the length direction of the pole. At the same time, considering that the centrifugally formed concrete pole is a hollow annular section, there are certain hardening differences in the radial direction. Therefore, in step S1310, the interior of the concrete pole is divided into n1 control units by a two-dimensional grid division method, taking into account both the length direction and the radial direction. Specifically, along the length direction, the pole is divided into several sections based on the layout spacing of the optical fiber sensor (5-10 cm); along the radial direction, considering the concrete wall thickness, the annular section is divided into an inner circle, a middle circle, an outer circle, etc., and each circle is further divided into several sectors. In this way, several quadrilateral or sector-shaped spatial grids are formed inside the entire concrete pole, and each grid is a control unit. The size of the control unit should match the sensor layout density to ensure that each control unit contains at least one sensor measurement point to obtain the hardening state data in the unit. By spatially discretizing the pole, the spatial distribution law of the hardening state can be analyzed more finely.

[0098] For each control unit, the hardening state level data of all sensor measuring points in the unit are extracted, the number of sites in the first hardening level, the second hardening level and the third hardening level are calculated, and then divided by the total number of detection sites in the unit to obtain the proportion of each hardening stage. This proportion reflects the overall hardening state distribution of the control unit. Since the hardening process in different areas may be inconsistent, the uniformity of hardening in each area can be quantitatively evaluated by calculating the proportion. The hardening state level distribution data of each control unit are arranged according to their spatial position to form a data matrix reflecting the hardening state distribution inside the entire concrete pole. For each characteristic moment, all adjacent control unit pairs are identified. The adjacent judgment principle is that the two control units have a common edge or face. For each pair of adjacent control units, the difference in their hardening state level proportions is calculated, that is, the difference in the proportion of the number of detection sites in the first hardening level, the second hardening level and the third hardening level. The proportion differences of all adjacent control unit pairs are summed and then divided by the number of adjacent control unit pairs to obtain the hardening non-uniformity index at the characteristic moment. The larger this index is, the greater the difference in hardening state between adjacent areas and the worse the overall uniformity. Since the hardening process evolves dynamically, it is one-sided to only examine the uniformity at a certain moment. Therefore, it is necessary to take the average of the hardening unevenness index at each characteristic moment to obtain a comprehensive hardening unevenness index. This index reflects the overall uniformity of the hardening inside the concrete pole. The smaller the index, the more uniform the hardening and the better the quality; the larger the index, the more uneven the hardening and the worse the quality, and measures need to be taken to improve it. By introducing the hardening unevenness index, the internal hardening quality of the concrete pole can be quantitatively evaluated, providing an objective basis for quality judgment.

[0099] It should be noted that in step S1310, the interior of the concrete pole is divided into two-dimensional grid control units along the length direction and radial direction. Assuming that the length direction is divided into m2 segments, the radial direction is divided into n2 circles, and each circle is further divided into p2 sectors, the total number of control units is: n1 = m2 × n2 × p2;

[0100] For each control unit, its adjacent control units include the following three categories:

[0101] (1) There are two adjacent control units along the length direction, and one control unit at each end;

[0102] (2) radially adjacent control units, with two in each internal control unit and one in each inner and outer ring control unit;

[0103] (3) For adjacent control units along the circumferential direction, except for the control units in the innermost circle and the outermost circle which have two adjacent units, the other control units should have four adjacent units (two in the clockwise direction and two in the counterclockwise direction).

[0104] Therefore, for the number of adjacent control units:

[0105] Internal control units: 2+2+4=8;

[0106] Internal control units at both ends: 1+2+4=7;

[0107] The middle control units of the inner and outer rings: 2+1+4=7;

[0108] End control units of the inner and outer rings: 1+1+4=6;

[0109] Four corner control units: 1+1+2=4;

[0110] To simplify the calculation, it can be approximately assumed that each control unit has an average of 7 adjacent control units. In this way, the number of adjacent control unit pairs can be estimated as: m = 7 × n1 / 2 = 3.5n1;

[0111] The approximate relationship between the number of adjacent control unit pairs m and the total number of control units n1 is: m≈3.5n1, that is, the number of adjacent control unit pairs is proportional to the total number of control units, and the proportionality coefficient is about 3.5.

[0112] It should be noted that the above derivation is based on some simplified assumptions. In actual situations, the relationship between the number of adjacent control unit pairs and the total number of control units may be more complicated. However, this approximate relationship can be used as a reference to facilitate the calculation of the hardening unevenness index. In practical applications, the number of adjacent control unit pairs can be accurately calculated by programming or other means according to the specific control unit division scheme to improve the accuracy of the hardening unevenness index calculation.

[0113] Step S1350, determining whether the comprehensive hardening non-uniformity index is greater than a preset non-uniformity threshold; if it is greater than the non-uniformity threshold, it indicates that the hardening inside the concrete pole is non-uniform, and a corresponding hardening homogenization scheme needs to be generated; if it is less than or equal to the non-uniformity threshold, it indicates that the hardening state inside the concrete pole is relatively uniform, and no additional hardening homogenization scheme needs to be generated;

[0114] Step S1360, adjusting the curing parameters of different control units according to the corresponding hardening homogenization scheme.

[0115] Specifically, the preset unevenness threshold is determined based on the quality requirements and production experience of concrete poles. When the comprehensive hardening unevenness index exceeds the preset unevenness threshold, it means that the spatial variability of the hardening inside the concrete pole exceeds the acceptable range, the hardening quality does not meet the standard, and homogenization measures need to be taken. The generation of the hardening homogenization scheme needs to comprehensively consider the hardening state distribution map, the hardening unevenness index, and the factors affecting the hardening. First, it is necessary to identify areas where the hardening is severely delayed or abnormal. These areas may have problems such as improper proportioning, insufficient vibration, and large temperature gradient, which are the key to affecting the hardening uniformity. Secondly, the hardening unevenness index reflects the overall unevenness, and the corresponding homogenization scheme should focus on reducing the hardening differences between adjacent control units. Common measures include adjusting local curing conditions and optimizing molding processes. Finally, the formulation of the hardening homogenization scheme also needs to consider factors that affect hardening, such as raw material properties, admixture types, mold characteristics, demolding time, etc., and improve the hardening conditions by adjusting these factors.

[0116] The maintenance parameters include:

[0117] Curing temperature: By controlling the temperature of local areas, the hydration process of concrete can be accelerated or delayed; common measures include steam curing (increasing the temperature) and cooling measures (such as covering with an insulation layer or spraying water for cooling).

[0118] Curing humidity: The hardening of concrete depends on sufficient moisture. Adjusting the humidity can prevent early shrinkage and cracking. Humidity control measures include spraying water or covering with a wet cloth for curing.

[0119] Curing time: Control the curing time of different areas. For areas with delayed hardening, the curing time can be extended, and for areas with too fast early strength, the curing time can be shortened.

[0120] Maintenance method: Use different maintenance methods, such as steam maintenance, covering with insulation materials, natural maintenance, etc., and take corresponding measures according to the different hardening states of each area.

[0121] Curing Pressure: Under certain conditions, the application of pressure can promote the compaction of concrete and affect the rate of hardening.

[0122] The adjustment of maintenance parameters is an important means of controlling hardening homogenization. According to the differences in hardening states of various areas inside the concrete pole, targeted maintenance measures are taken. For areas where hardening is seriously delayed, steam curing can be used to increase the regional temperature and accelerate the hydration process; or an insulation layer can be covered locally to slow down heat loss. These measures can promote the strength development of the hardening delayed area so that it can catch up with other areas as soon as possible. For areas where early strength is too fast, the hardening process can be appropriately delayed by cooling, spraying, etc., waiting for other areas to catch up, so as to avoid excessive differences in hardening progress. By performing maintenance and regulation on different control units separately, the hardening differences can be effectively reduced and the internal hardening state can be homogenized. This is crucial to improving the overall quality of concrete poles. In addition to the adjustment of maintenance parameters, the implementation of the homogenization plan also requires the cooperation of production processes, such as optimizing the material distribution method during molding, adjusting the vibration time and frequency, etc., to reduce the generation of hardening differences from the source.

[0123] Step S1300 quantifies the hardening uniformity problem into a hardening unevenness index by spatially dividing the interior of the concrete pole, thus forming a complete set of hardening uniformity evaluation methods for the interior of the concrete pole. This method makes full use of the spatial resolution advantage of distributed fiber optic sensing to achieve refined monitoring of the hardening state inside the concrete pole. The introduction of the hardening unevenness index converts the qualitative uniformity problem into a quantitative indicator, providing an objective basis for quality judgment and process control. A hardening homogenization plan is generated, and targeted maintenance measures are taken for different areas to improve the effectiveness of homogenization control. In summary, the hardening uniformity evaluation system constructed in step S1300 can accurately diagnose the hardening quality problems inside the concrete pole, guide homogenization production, improve the mechanical properties and service life of the pole, and has good application prospects.

[0124] Step S1000 innovatively introduces distributed fiber optic sensing technology into the field of concrete pole production, establishes a method for evaluating the internal hardening state of concrete poles based on multi-parameter perception, and proposes a hardening homogenization control strategy on this basis. Traditional concrete pole production lacks effective internal hardening monitoring means, and quality control mainly relies on indirect indicators and empirical judgment, which is difficult to comprehensively evaluate and optimize. The technical solution of step S1000 takes advantage of the advantages of distributed fiber optic sensing to achieve high-precision and high-resolution monitoring of parameters such as temperature and strain inside the concrete pole, and obtains real-time data of the entire hardening process. By constructing a coupled model of temperature field and strain field and combining threshold criteria, a hierarchical evaluation standard for the internal hardening state is formed. The entire concrete pole is divided into spatial grids, and the hardening state differences of different grids are quantitatively analyzed to predict internal defects and uneven areas. Based on this, targeted maintenance and homogenization measures are formulated to improve the hardening conditions and the overall hardening quality of the concrete pole through local regulation. Step S1000 provides a new technical means for the intelligentization of concrete pole production equipment and the optimized control of production processes, which is of great significance for improving the mechanical properties and durability of concrete poles, and also provides a new idea for the refined quality control of other precast concrete components.

[0125] Step S2000, obtaining historical production data of thin-walled concrete poles, extracting a reference inner mold demoulding scheme matching the current production conditions from the historical production data, and using the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; the initial inner mold demoulding parameter includes an initial inner mold demoulding time, an initial inner mold demoulding speed, and an initial inner mold demoulding force; correcting the initial inner mold demoulding parameter according to the hardening state level inside the concrete to obtain a second inner mold demoulding parameter;

[0126] Furthermore, step S2000 includes:

[0127] Step S2100, obtaining historical production data of thin-walled concrete poles, extracting a reference inner mold demoulding scheme matching the current production conditions from the historical production data, and using the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; the initial inner mold demoulding parameter includes an initial inner mold demoulding time, an initial inner mold demoulding speed, and an initial inner mold demoulding force;

[0128] Specifically, in the production of concrete poles, inner mold demoulding is a key process link, which directly affects the molding quality and production efficiency of the poles. Under different production conditions, the inner mold demoulding parameters need to be adjusted accordingly to adapt to the changes in the hardening state of concrete. Traditionally, the setting of inner mold demoulding parameters mainly relies on the experience of process personnel, which has certain blindness and uncertainty. Step S2100 uses historical production data to initialize the inner mold demoulding parameters, which can fully absorb the experience of previous successful cases and reduce the blindness of parameter setting. First, load the massive historical data accumulated by the concrete pole production line, including production condition parameters (such as raw material ratio, ambient temperature and humidity, centrifugal molding process parameters, etc.) and inner mold demoulding parameters (such as demoulding time, demoulding speed, demoulding force, etc.), and build a complete knowledge base. Then, use big data analysis techniques, such as association rule mining and cluster analysis, to deeply explore the intrinsic relationship between production condition parameters and inner mold demoulding parameters, and establish an association model. When there is a new production task, the current production conditions are input into the association model, and the inner mold demoulding scheme used in the past under similar conditions can be quickly found, and the demoulding parameters can be extracted as the initial values ​​for this production. This method based on intelligent initialization of historical data can inherit excellent production experience, while reducing the difficulty and workload of parameter setting, and providing a good starting point for the dynamic optimization of subsequent parameters. Of course, the initial parameters are not static, and further dynamic adjustment and optimization are required based on the concrete hardening data collected in real time. By combining this experience with real-time data, the adaptability and accuracy of the inner mold demoulding parameters can be continuously improved, thereby realizing the intelligent control of the inner mold demoulding process. The value of historical data is fully utilized, and the empirical knowledge is well inherited and sublimated, which reflects the important application of big data technology in the optimization of traditional processes.

[0129] Step S2200, calibrating the initial inner mold demoulding parameters according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameters.

[0130] The first hardening level is assigned to 1, the second hardening level is assigned to 2, and the third hardening level is assigned to 3. The divided n1 control units are used as basic units for calibrating the initial inner mold demolding parameters. The initial inner mold demolding parameters are calibrated according to the hardening state level of each detection point in each control unit to obtain the second inner mold demolding parameters, which include the second inner mold demolding time, the second inner mold demolding speed and the second inner mold demolding force.

[0131] The method for calibrating the initial inner mold demoulding parameters comprises:

[0132]

[0133] in:

[0134] The second inner mold demoulding parameter of the i"th control unit may be the second inner mold demoulding time, the second inner mold demoulding speed or the second inner mold demoulding force;

[0135] represents the initial inner mold demoulding parameter of the "i"th control unit, which may be the initial inner mold demoulding time, the initial inner mold demoulding speed or the initial inner mold demoulding force;

[0136] α: It is a scaling factor that controls the calibration amplitude and needs to be determined based on production experience;

[0137] L i”j” : represents the hardening state level value (1, 2 or 3) of the j'th detection site in the i'th control unit;

[0138] N i” : represents the total number of detection sites in the i-th control unit;

[0139] β: is the target value of the hardening state grade, reflecting the desired overall hardening degree;

[0140] γ: It is a shape parameter that controls the nonlinearity of the calibration curve and needs to be determined based on production data fitting.

[0141] Demolding parameters and Characterizes the time, speed, force and other process conditions of inner mold demoulding, which directly determines the quality and efficiency of demoulding. Extracted from historical production data, representing the parameters used in past successful production. Calibration value, i.e. the second inner mold demoulding parameter is On the basis of the current concrete hardening state, adjustments are made to adapt to real-time working conditions.

[0142] The scaling factor α reflects the tolerance of the production line to the deviation of the demoulding parameters from the initial value. When α is larger, it is more sensitive to the change of the hardening state and the calibration amplitude is larger; when α is smaller, it is more inclined to maintain the initial parameters. The value of α needs to balance production efficiency and quality risks and is determined through repeated production practice.

[0143] It represents the average value of the hardening state level in the i-th control unit. i”j” 1, 2, and 3 correspond to the first hardening level (plastic period), the second hardening level (setting period), and the third hardening level (strength development period) respectively. The average value reflects the overall hardening degree of the control unit. i” and L i”j” Obtained through real-time monitoring of concrete during the production process.

[0144] The target value β represents the expected overall hardening state of concrete. It is usually hoped that most areas are in the late stage of setting to the early stage of strength development, that is, β is between 2 and 2.5. The selection of β must take into account both production efficiency and product quality. Too large a value will extend the production cycle, and too small a value may cause demoulding defects.

[0145] The shape parameter γ controls the nonlinear characteristics of the calibration curve. When γ is large, the curve changes gently near β, changes drastically at both ends, and is more sensitive to the hardening state deviating from the target value; when γ is small, the curve tends to be linear as a whole. The value of γ needs to fit the actual production data to reflect the response characteristics of the production line to changes in the hardening state.

[0146] The formula quantitatively characterizes the influence of the hardening state of concrete on the demoulding parameters, and provides a mathematical tool for refined production control. By making full use of the real-time monitoring data obtained by distributed optical fiber sensing technology, the spatial distribution unevenness of the hardening state can be dynamically responded to, and the local demoulding scheme can be adjusted in a targeted manner. The target value of the hardening state level is introduced to achieve the best match between the production process and the performance of concrete materials, shortening the production cycle while ensuring quality. The power function form is adopted, and the nonlinear characteristics of the calibration curve can be adjusted by parameters to better adapt to the process requirements of different production lines. When the overall hardening state of concrete is close to the target value β, the calibration coefficient is close to 1, and the demoulding parameters remain near the initial value. When the hardening is seriously delayed or too fast, the calibration coefficient deviates significantly from 1, and the demoulding parameters are greatly adjusted. As γ increases, the calibration curve becomes steeper and more sensitive to changes in the hardening state. In short, the formula realizes the intelligent closed-loop control of the inner mold demoulding process by comprehensively considering the monitoring data of the concrete hardening state and the production process parameters, and has broad application prospects in improving production efficiency and product quality.

[0147] Step S2200 uses this method of dynamic calibration of partitions to fully consider the unevenness of the hardening of concrete poles, optimize the inner mold demoulding parameters according to local conditions, and improve the demoulding efficiency and reduce the wear of the inner mold while ensuring the quality of pole molding. This method breaks through the traditional "one-size-fits-all" parameter setting mode, embodies the concept of refined and personalized control, and is of great significance for improving the production level of concrete poles.

[0148] Step S3000, controlling the inner mold demoulding process according to the second inner mold demoulding parameter, and monitoring the demoulding process in real time to perform closed-loop optimization control.

[0149] Furthermore, step S3000 includes:

[0150] Step S2310, starting the inner mold demoulding program according to the second inner mold demoulding parameter; during the inner mold demoulding process, collecting the second concrete hardening data in real time, and judging whether the inner mold of each control unit is fully separated from the concrete according to the preset inner mold separation evaluation model;

[0151] Step S2320: If the inner mold of the i'th control unit is not fully separated from the concrete, adjust the inner mold demolding speed and inner mold demolding force of the i'th control unit until the inner mold of the i'th control unit is fully separated from the concrete; if the inner mold of each control unit is fully separated from the concrete, maintain the current inner mold demolding speed and inner mold demolding force until the inner mold is completely separated.

[0152] Specifically, inner mold demoulding is the process of removing a concrete pole that has been cured to a certain degree from the inner mold using a specific method. Reasonable control of the inner mold demoulding process is crucial to ensuring the quality and efficiency of pole demoulding. Using the second inner mold demoulding parameters obtained through partition dynamic calibration as execution instructions, differentiated demoulding strategies can be adopted for different control units. Inner mold demoulding usually achieves relative displacement between the inner mold and concrete by controlling the axial movement and radial contraction of the inner mold until the two are completely separated. In order to monitor the inner mold demoulding process in real time, it is necessary to pre-install sensors such as pressure sensors and displacement sensors on the inner mold surface and the inner wall of the concrete pole to collect the second concrete hardening data. These data reflect the stress state and degree of separation between the inner mold and concrete, and are the basis for evaluating and optimizing the demoulding process. According to the inner mold detachment evaluation model, it can be judged whether the mold in each area has been fully separated from the concrete. The inner mold detachment evaluation model takes into account multiple influencing factors such as the contact pressure, relative displacement, and demoulding speed between the inner mold and concrete, and is obtained through theoretical analysis and experimental data training. When the evaluation results show that the mold and concrete in a certain area are not fully separated, the information is fed back in time to adjust the demoulding speed and demoulding force of the area. By reducing the demoulding speed, increasing the demoulding force and other measures, the inner mold and concrete are fully separated in the control unit to avoid affecting the quality of pole forming due to insufficient local demoulding. When each control unit reaches a fully separated state, the optimized demoulding parameters can be maintained to complete the entire inner mold demoulding process smoothly and orderly. This demoulding control strategy of real-time monitoring, dynamic evaluation, and closed-loop optimization is a typical intelligent feedback control method. Its basic principle is to continuously collect the state data of the inner mold demoulding process through sensors, and compare it with the preset target state. According to the size and direction of the deviation, the control amount is automatically adjusted so that the demoulding state continuously approaches the target value until it reaches the optimal value. In the past, the inner mold demoulding process usually adopts open-loop control, and the actual demoulding effect has a large deviation from the expected effect, which is easy to cause problems such as pole damage and inner mold wear. The closed-loop optimization control method proposed in this step can adaptively adjust the parameters of the inner mold demoulding process, so that the demoulding process is always in the best state, greatly improving the accuracy and efficiency of concrete pole demoulding. This method integrates multiple functions such as automatic detection, intelligent analysis, and precise execution. It reflects the in-depth application of intelligent manufacturing technology in the production of concrete poles and has a demonstrative significance for promoting technological progress in the industry.

[0153] For example, a concrete pole production enterprise adopts the method of the present invention to intelligently control inner mold demoulding and achieves good results. Through mining and analysis of historical production data, the enterprise has established a set of intelligent initialization methods for inner mold demoulding parameters, which can quickly generate demoulding schemes according to production conditions, reducing the blindness of manual settings. During the production process, distributed optical fiber sensing technology is used to collect temperature field and strain field data inside the concrete pole in real time, evaluate the distribution characteristics of the hardening state, and dynamically calibrate the inner mold demoulding parameters based on the partitions, and adopt differentiated demoulding strategies for different regions. At the same time, the stress state and displacement state of the demoulding process are monitored in real time, and the inner mold demoulding speed and strength are dynamically optimized through closed-loop feedback control. After a period of application, the qualified rate of the company's concrete poles increased from 92% to 98%, the service life of the inner mold was extended by 20%, the production efficiency was increased by 15%, and the comprehensive cost was reduced by 10%. This fully proves the important value of the method of the present invention in improving the production level of concrete poles and promoting the intelligent development of the industry.

[0154] "Fully detached" means that there is no obvious adhesion between the inner mold and the concrete, and the inner mold can be detached from the concrete pole smoothly and unhindered without adversely affecting the demoulding process and the quality of the pole.

[0155] In order to judge whether the inner form is fully separated from the concrete, the criteria for "sufficient separation" can be defined from the following aspects:

[0156] (1) The adhesion between the inner mold and the concrete is reduced to below the set threshold. The resistance of the inner mold during demolding can be monitored by sensors. When the resistance is less than the set threshold, it is considered that the inner mold has been fully detached.

[0157] (2) During the process of removing the inner mold, there are no obvious defects such as scratches, cracks, and collapse on the surface of the concrete pole; machine vision technology can be used to perform real-time detection and analysis of the pole surface.

[0158] (3) The inner mold release speed is stable, and the deviation from the set second inner mold release speed is within the allowable range. A sharp fluctuation in speed may mean that the release process is not smooth.

[0159] (4) The real-time collected concrete hardening data, such as the internal temperature and strain of the concrete, meet the preset "sufficient detachment" condition, that is, the concrete has reached a certain strength and stiffness.

[0160] Only when a control unit meets all or part of the above conditions can it be determined that the inner mold of the control unit has been fully separated from the concrete. If the inner molds of each control unit have been fully separated, the entire inner mold demoulding process has come to an end and can be smoothly completed.

[0161] Example 2

[0162] This embodiment provides a thin-walled concrete pole intelligent production system based on the first embodiment. Figure 4 As shown, including:

[0163] Homogenization control module: used to collect the first concrete hardening data of n detection sites related to the internal hardening state of thin-walled concrete poles in real time; construct a concrete internal hardening state evaluation model, and obtain the hardening state level of the concrete according to the first concrete hardening data and the concrete internal hardening state evaluation model; divide the interior of the concrete pole into n1 control units, calculate the hardening unevenness index according to the hardening state level of the concrete, and generate a corresponding hardening homogenization plan, where n and n1 are both positive integers;

[0164] Demolding parameter calculation module: used to obtain historical production data of thin-walled concrete poles, extract a reference inner mold demoulding scheme that matches the current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; calibrate the initial inner mold demoulding parameter according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameter; the initial inner mold demoulding parameter includes initial inner mold demoulding time, initial inner mold demoulding speed and initial inner mold demoulding force;

[0165] Closed-loop optimization module: used to control the inner mold demoulding process according to the second inner mold demoulding parameters, and monitor the demoulding process in real time to perform closed-loop optimization control.

[0166] The homogenization control module comprises:

[0167] A hardening data acquisition unit: used for real-time acquisition of first concrete hardening data of n detection sites related to the hardening state of the pole; the first concrete hardening data includes strain and temperature inside the concrete;

[0168] A hardening state evaluation unit is used to construct a concrete internal hardening state evaluation model, and obtain the concrete internal hardening state grade according to the first concrete hardening data and the concrete internal hardening state evaluation model;

[0169] Index calculation unit: used to divide the interior of the concrete pole into n1 control units, calculate the hardening unevenness index according to the internal hardening state level of the concrete, and generate a corresponding hardening homogenization plan.

[0170] In the hardening state evaluation unit, the step of constructing a concrete internal hardening state evaluation model and obtaining the concrete internal hardening state grade according to the first concrete hardening data and the concrete internal hardening state evaluation model comprises:

[0171] Step S1210, establishing a concrete internal temperature field evolution model and a concrete internal strain field evolution model;

[0172] Step S1220, coupling the concrete internal temperature field evolution model and the concrete internal strain field evolution model to obtain a concrete internal hardening state assessment model;

[0173] Step S1230, inputting the first concrete hardening data into the concrete internal hardening state assessment model to obtain the temperature time history curve and the strain time history curve of n detection points;

[0174] Step S1240, selecting m1 characteristic moments, extracting the temperature and strain of n detection points at the m1 characteristic moments from the temperature-time curve and the strain-time curve; m1 is a positive integer;

[0175] Step S1250: calculate the temperature T of each detection point at each characteristic moment. i,j and strain E i,j The first temperature threshold T1, the second temperature threshold T2, the first strain threshold E1 and the second strain threshold E2 are compared to determine the hardening state level of each detection site at each characteristic moment; i is the index of the detection site, 1≤i≤n; j is the index of the characteristic moment, 1≤j≤m1; T i,j represents the temperature of the i-th detection point at the j-th characteristic moment; E i,j represents the strain at the i-th detection site and the j-th characteristic moment.

[0176] The method for determining the hardening state level of each detection site at each characteristic moment includes:

[0177] When T i,j <T1, if E i,j <E1, the concrete is in the plastic stage and is marked as the first hardening grade; if E i,j ≥E1, the concrete is in an abnormal state and further analysis of the cause is required;

[0178] When T1≤T i,j ≤T2, if E1≤E i,j ≤E2, the concrete is in the setting stage and is marked as the second hardening grade; otherwise, the concrete is in an abnormal state and further analysis of the cause is required;

[0179] When T i,j >T2, if E i,j >E2, the concrete is in the strength development stage and is marked as the third hardening grade; otherwise, the concrete is in an abnormal state and further analysis of the cause is required.

[0180] In the index calculation unit, the interior of the concrete pole is divided into n1 control units, and the hardening unevenness index is calculated according to the hardening state level inside the concrete, and the corresponding hardening homogenization scheme is generated, including:

[0181] Step S1310, dividing the interior of the concrete pole into n1 control units according to the pole length direction and radial direction, each control unit including at least one detection site;

[0182] Step S1320, counting the number of detection sites at the first hardening level, the second hardening level, and the third hardening level in each control unit at different characteristic moments, and calculating the proportion of the number of detection sites at the three hardening levels in the number of all detection sites in the control unit;

[0183] Step S1330, calculating the absolute value of the proportion difference of the number of hardening state level detection sites between adjacent control units at different characteristic moments, summing up the absolute value of the proportion difference and dividing it by the number of adjacent control unit pairs to obtain the hardening non-uniformity index at the characteristic moment; the number of adjacent control unit pairs is determined by the number of divided control units;

[0184] Step S1340, taking the average of the hardening non-uniformity index at each characteristic moment to obtain a comprehensive hardening non-uniformity index;

[0185] Step S1350, determining whether the comprehensive hardening non-uniformity index is greater than a preset non-uniformity threshold; if it is greater than the non-uniformity threshold, it indicates that the hardening inside the concrete pole is non-uniform, and a corresponding hardening homogenization scheme needs to be generated;

[0186] Step S1360, adjusting the curing parameters of different control units according to the corresponding hardening homogenization scheme.

[0187] The demoulding parameter calculation module includes:

[0188] Parameter initialization unit: used to obtain historical production data of thin-walled concrete poles, extract a reference inner mold demoulding scheme matching the current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; the initial inner mold demoulding parameter includes initial inner mold demoulding time, initial inner mold demoulding speed and initial inner mold demoulding force;

[0189] Parameter calibration unit: used to calibrate the initial inner mold demoulding parameters according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameters.

[0190] In the parameter calibration unit, the calibrating of the initial inner mold demoulding parameters according to the hardening state level inside the concrete includes:

[0191] The first hardening level is assigned to 1, the second hardening level is assigned to 2, and the third hardening level is assigned to 3. The divided n1 control units are used as basic units for calibrating the initial inner mold demolding parameters. The initial inner mold demolding parameters are calibrated according to the hardening state level of each detection point in each control unit to obtain the second inner mold demolding parameters, which include the second inner mold demolding time, the second inner mold demolding speed and the second inner mold demolding force.

[0192] The closed-loop optimization module includes:

[0193] The judging unit is used to start the inner mold demoulding program according to the second inner mold demoulding parameter; during the inner mold demoulding process, the second concrete hardening data is collected in real time, and according to the preset inner mold detachment evaluation model, it is judged whether the inner mold of each control unit is fully separated from the concrete; if the inner mold of the i"th control unit is not fully separated from the concrete, the inner mold demoulding speed and the inner mold demoulding force of the i"th control unit are adjusted until the inner mold of the i"th control unit is fully separated from the concrete; if the inner mold of each control unit is fully separated from the concrete, the current inner mold demoulding speed and the inner mold demoulding force are maintained until the inner mold is completely separated.

[0194] Example 3

[0195] This embodiment discloses an electronic device, which may include one or more processors and one or more memories, wherein the memories store computer-readable codes, which, when executed by the one or more processors, may execute the above-mentioned intelligent production method for thin-walled concrete poles.

[0196] The method or system according to the embodiment of the present application can also be implemented with the help of the architecture of an electronic device. The electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, can store the intelligent production method of thin-walled concrete poles provided in the present application. The intelligent production method of thin-walled concrete poles may, for example, include: real-time collection of first concrete hardening data of n detection sites related to the internal hardening state of the thin-walled concrete pole; constructing a concrete internal hardening state evaluation model, and obtaining the hardening state level of the concrete according to the first concrete hardening data and the concrete internal hardening state evaluation model; dividing the interior of the concrete pole into n1 control units, calculating the hardening unevenness index according to the hardening state level of the concrete, and generating a corresponding hardening homogenization scheme, wherein n and n1 are both positive integers; obtaining historical production data of thin-walled concrete poles, extracting a reference inner mold demolding scheme matching the current production conditions from the historical production data, and using the first inner mold demolding parameter in the reference inner mold demolding scheme as the initial inner mold demolding parameter; calibrating the initial inner mold demolding parameter according to the hardening state level of the concrete to obtain the second inner mold demolding parameter; the initial inner mold demolding parameter includes the initial inner mold demolding time, the initial inner mold demolding speed and the initial inner mold demolding force; controlling the inner mold demolding process according to the second inner mold demolding parameter, and monitoring the demolding process in real time to perform closed-loop optimization control.

[0197] Furthermore, the electronic device may also include a user interface. Of course, the architecture is only exemplary, and when implementing different devices, one or more components in the electronic device may be omitted according to actual needs.

[0198] Example 4

[0199] This embodiment discloses a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the thin-walled concrete pole intelligent production method according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0200] In addition, according to the implementation mode of the present application, the process described in the above reference flow chart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium stores machine-readable instructions, the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, for example: real-time acquisition of first concrete hardening data of n detection sites related to the hardening state inside the thin-walled concrete pole; building a concrete internal hardening state evaluation model, and obtaining the hardening state level of the concrete according to the first concrete hardening data and the concrete internal hardening state evaluation model; dividing the interior of the concrete pole into n1 control units, and calculating the hardening unevenness according to the hardening state level of the concrete. Index, generate the corresponding hardening homogenization scheme, where n and n1 are both positive integers; obtain the historical production data of thin-walled concrete poles, extract the reference inner mold demoulding scheme that matches the current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; according to the hardening state level inside the concrete, calibrate the initial inner mold demoulding parameter to obtain the second inner mold demoulding parameter; the initial inner mold demoulding parameter includes the initial inner mold demoulding time, the initial inner mold demoulding speed and the initial inner mold demoulding force; control the inner mold demoulding process according to the second inner mold demoulding parameter, and monitor the demoulding process in real time to perform closed-loop optimization control. When the computer program is executed by the central processing unit (CPU), the above functions defined in the method of this application are executed.

[0201] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0202] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0203] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Intelligent production method of thin-walled concrete poles, characterized in that: The method comprises: The first concrete hardening data of n detection sites related to the hardening state of the inside of the thin-walled concrete pole are collected in real time; a concrete internal hardening state evaluation model is constructed, and the hardening state level of the inside of the concrete is obtained according to the first concrete hardening data and the concrete internal hardening state evaluation model; the inside of the concrete pole is divided into n1 control units, and the hardening non-uniformity index is calculated according to the hardening state level of the inside of the concrete, and a corresponding hardening homogenization scheme is generated, wherein n and n1 are both positive integers; Obtain historical production data of thin-walled concrete poles, extract a reference inner mold demoulding scheme matching current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; calibrate the initial inner mold demoulding parameter according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameter; the initial inner mold demoulding parameter includes an initial inner mold demoulding time, an initial inner mold demoulding speed, and an initial inner mold demoulding force; The inner mold demoulding process is controlled according to the second inner mold demoulding parameter, and the demoulding process is monitored in real time to perform closed-loop optimization control.

2. The intelligent production method of thin-walled concrete poles according to claim 1 is characterized in that: The step of constructing a concrete internal hardening state evaluation model and obtaining the concrete internal hardening state grade according to the first concrete hardening data and the concrete internal hardening state evaluation model comprises: Establish the evolution model of concrete internal temperature field and concrete internal strain field; The concrete internal temperature field evolution model and the concrete internal strain field evolution model are coupled to obtain the concrete internal hardening state evaluation model; Inputting the first concrete hardening data into the concrete internal hardening state evaluation model to obtain the temperature time history curve and the strain time history curve of n detection points; Select m1 characteristic moments, and extract the temperature and strain of n detection points at m1 characteristic moments from the temperature-time curve and the strain-time curve; m1 is a positive integer; The temperature T of each detection point at each characteristic moment i,j and strain E i,j The hardening state level of each detection site at each characteristic moment is determined by comparing with the preset first temperature threshold T1, second temperature threshold T2, first strain threshold E1 and second strain threshold E2; i is the index of the detection site, 1≤i≤n; j is the index of the characteristic moment, 1≤j≤m1; E i,j represents the strain at the i-th detection site and the j-th characteristic moment.

3. The intelligent production method of thin-walled concrete poles according to claim 2 is characterized in that: The method for determining the hardening state level of each detection site at each characteristic moment includes: When T i,j <T1, if E i,j <E1, the concrete is in the plastic stage, marked as the first hardening grade; When T1≤T i,j ≤T2, if E1≤E i,j ≤E2, the concrete is in the setting stage and is marked as the second hardening grade; When T i,j >T2, if E i,j >E2, the concrete is in the strength development stage and is marked as the third hardening grade.

4. The intelligent production method of thin-walled concrete poles according to claim 1 is characterized in that: The control unit is divided according to the length direction and radial direction of the pole, and each control unit contains at least one detection site; The calculation of the hardening unevenness index according to the hardening state level inside the concrete includes: Count the number of detection sites at the first hardening level, the second hardening level, and the third hardening level in each control unit at different characteristic moments, and calculate the proportion of the number of detection sites at the three hardening levels in the number of all detection sites in the control unit respectively; Calculate the absolute value of the difference in the number of hardening state level detection sites between adjacent control units at different characteristic moments, sum the absolute values ​​of the difference in the proportions and divide them by the number of adjacent control unit pairs to obtain the hardening non-uniformity index at the characteristic moment; the number of adjacent control unit pairs is determined by the number of divided control units; The average of the hardening non-uniformity index at each characteristic moment is taken to obtain a comprehensive hardening non-uniformity index.

5. The intelligent production method of thin-walled concrete poles according to claim 1 is characterized in that: The generating of the corresponding hardening homogenization scheme comprises: Determine whether the comprehensive hardening unevenness index is greater than the preset unevenness threshold; if it is greater than the unevenness threshold, it indicates that the hardening inside the concrete pole is uneven, and a corresponding hardening homogenization plan needs to be generated; according to the corresponding hardening homogenization plan, adjust the maintenance parameters of different control units.

6. The intelligent production method of thin-walled concrete poles according to claim 1 is characterized in that: The calibration of the initial inner mold demoulding parameters according to the hardening state level inside the concrete includes: The first hardening level is assigned to 1, the second hardening level is assigned to 2, and the third hardening level is assigned to 3. The divided n1 control units are used as basic units for calibrating the initial inner mold demolding parameters. The initial inner mold demolding parameters are calibrated according to the hardening state level of each detection point in each control unit to obtain the second inner mold demolding parameters, which include the second inner mold demolding time, the second inner mold demolding speed and the second inner mold demolding force.

7. The intelligent production method of thin-walled concrete poles according to claim 1 is characterized in that: The real-time monitoring of the demoulding process and the closed-loop optimization control include: According to the second inner mold demoulding parameter, the inner mold demoulding program is started; during the inner mold demoulding process, the second concrete hardening data is collected in real time, and according to the preset inner mold detachment evaluation model, it is judged whether the inner mold of each control unit is fully detached from the concrete; If the inner mold of the i'th control unit is not fully separated from the concrete, adjust the inner mold demoulding speed and inner mold demoulding force of the i'th control unit until the inner mold of the i'th control unit is fully separated from the concrete; if the inner mold of each control unit is fully separated from the concrete, maintain the current inner mold demoulding speed and inner mold demoulding force until the inner mold is completely separated.

8. An intelligent production system for thin-walled concrete poles, which is used to implement the intelligent production method for thin-walled concrete poles according to any one of claims 1 to 7, characterized in that: The system comprises: Homogenization control module: used to collect the first concrete hardening data of n detection sites related to the internal hardening state of thin-walled concrete poles in real time; construct a concrete internal hardening state evaluation model, and obtain the hardening state level of the concrete according to the first concrete hardening data and the concrete internal hardening state evaluation model; divide the interior of the concrete pole into n1 control units, calculate the hardening unevenness index according to the hardening state level of the concrete, and generate a corresponding hardening homogenization plan, where n and n1 are both positive integers; Demolding parameter calculation module: used to obtain historical production data of thin-walled concrete poles, extract a reference inner mold demoulding scheme that matches the current production conditions from the historical production data, and use the first inner mold demoulding parameter in the reference inner mold demoulding scheme as the initial inner mold demoulding parameter; calibrate the initial inner mold demoulding parameter according to the hardening state level inside the concrete to obtain the second inner mold demoulding parameter; the initial inner mold demoulding parameter includes initial inner mold demoulding time, initial inner mold demoulding speed and initial inner mold demoulding force; Closed-loop optimization module: used to control the inner mold demoulding process according to the second inner mold demoulding parameters, and monitor the demoulding process in real time to perform closed-loop optimization control.

9. An electronic device comprising a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, characterized in that: When the central processing unit executes the computer program, the intelligent production method of thin-walled concrete poles described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the intelligent production method for thin-walled concrete poles according to any one of claims 1 to 7 is implemented.

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