Intelligent pumping and distributing collaborative control system and method for pole production concrete

Abstract

The application relates to the technical field of cement product forming auxiliary control, and discloses a power pole production concrete intelligent pumping and distributing control system and method. The method comprises the following steps: dividing a power pole mold cavity into multiple axial distribution sections, determining an effective filling volume; obtaining a theoretical feeding quality based on the effective filling volume, correcting the theoretical feeding quality according to a concrete slump, a pumping pressure and a skeleton occupancy rate, and obtaining an allowed feeding amount; acquiring pumping operation parameters and concrete physical property parameters, calculating a flow resistance index and an segregation risk index, taking the allowed feeding amount as an upper limit to calculate target cumulative feeding qualities of the sections; calculating sectional quality deviations based on the target feeding qualities, combining pumping pressure overrun risks, segregation risks and centrifugal starting time limits to build boundary constraints, solving optimal control parameters and executing distribution, and correcting the control parameters based on actual feeding quality and pumping pressure trajectory feedback, so that the application improves the distribution control precision and forming quality stability.

Description

Technical Field

[0001] This application relates to the field of auxiliary control technology for cement product molding, and in particular to a collaborative control system and method for intelligent pumping and material placement in concrete production for utility poles. Background Technology

[0002] As a key supporting component of the power transmission and distribution system, the production quality of utility poles directly affects the safety and reliability of the power grid operation. Centrifugal casting technology, with its high-efficiency forming capability, has become the mainstream technical route for pole manufacturing. In the centrifugal casting process, pumping equipment delivers concrete to a placing equipment. The placing equipment moves along the mold cavity axis and quantitatively injects concrete into each axial placing section. The steel mold is then placed on a centrifuge and rotated at high speed, causing the concrete to be evenly distributed on the inner wall of the mold cavity under centrifugal force and compacted. Currently, the industry commonly uses a constant-speed pumping method combined with a fixed placing rhythm. While simple to operate, this method is difficult to adapt to the complex spatial distribution characteristics inside the pole mold cavity and the dynamic flow behavior of the concrete material. The pole mold cavity exhibits a significant conical structure, with large differences in cross-sectional dimensions at different axial positions. Simultaneously, the distribution density of the embedded steel reinforcement cage is significantly uneven across sections, resulting in substantial differences in the actual fillable space in each axial section. If the pumping equipment continuously supplies material at a uniform discharge rate, some sections will inevitably receive too much material while others will receive too little, ultimately leading to a systematic deviation in wall thickness distribution.

[0003] In addition, the flow characteristics of concrete during pumping and placement are affected by multiple factors such as mix design, type of admixture, mixing time and ambient temperature. Its viscosity and yield stress exhibit dynamic fluctuations. When the fluidity of concrete changes, if the original placement speed and residence time are maintained, local areas may be underfilled or segregated. Fluctuations in pumping pressure will further exacerbate this uncertainty.

[0004] Therefore, the key issue in the auxiliary control of centrifugal casting of electric poles is how to accurately control the pumping rate and the moving speed and residence time of the placing equipment based on the actual spatial volume difference of each section of the mold cavity and the real-time flow characteristics of concrete, so that the material quality obtained in each axial section meets the wall thickness requirements, avoids the risk of segregation caused by excessive accumulation, and ensures that all placing operations are completed within the time window of the centrifugal process. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a smart pumping and placement collaborative control system and method for concrete production of utility poles. This system is designed to solve the problems of wall thickness deviation and segregation defects caused by the conical structure of the mold cavity, uneven skeleton occupancy, and dynamic changes in concrete fluidity, thereby achieving precise and adaptive adjustment of placement control.

[0006] In a first aspect, this application provides a method for intelligent pumping and material placement collaborative control of concrete in power pole production, the method comprising: Step S1: Divide the pole mold cavity into multiple axial material distribution sections, obtain the geometric morphology parameters and internal occupancy structure parameters of each axial material distribution section, and determine the effective filling volume of each axial material distribution section. Step S2: Obtain the theoretical feed mass based on the effective filling volume, and correct the theoretical feed mass based on the fluidity characteristics of concrete, the pumping pressure of the pumping equipment, and the skeleton occupancy rate of each axial placement section to obtain the allowable feed amount of each axial placement section, and construct a state constraint set. Step S3: Obtain the pumping operation parameters of the pumping equipment and the physical property parameters of the concrete, calculate the flow resistance index and segregation risk index of the concrete during the transportation process, use the allowable feed rate as the upper limit constraint for the feed, and calculate the target cumulative feed mass of each axial distribution section. Step S4: Calculate the zonal quality deviation based on the target cumulative feed quality, construct boundary constraints using pump pressure over-limit risk, segregation risk and centrifugal start-up time limit, and solve for the optimal control parameters by combining the state constraint set, execute zonal quantitative material distribution, and simultaneously record the actual feed quality and pump pressure trajectory; Step S5: Calculate and predict the wall thickness deviation and the risk of zone segregation based on the actual feed quality, pump pressure trajectory and centrifugal speed plan. If the predicted wall thickness deviation or the risk of zone segregation exceeds the preset threshold, calculate the feed quality target for each axial distribution section of the subsequent batch based on the allowable feed amount in the state constraint set, and adjust the optimal control parameters.

[0007] Secondly, this application provides a collaborative control system for intelligent pumping and placing of concrete in the production of utility poles, the system comprising: The region division module is used to divide the pole mold cavity into multiple axial material distribution sections, obtain the geometric shape parameters and internal occupancy structure parameters of each axial material distribution section, and determine the effective filling volume of each axial material distribution section. The state constraint module is used to obtain the theoretical feed mass based on the effective filling volume, and to correct the theoretical feed mass based on the fluidity characteristics of concrete, the pumping pressure of the pumping equipment, and the skeleton occupancy rate of each axial placement section, so as to obtain the allowable feed amount of each axial placement section and construct the state constraint set. The preliminary calculation module is used to obtain the pumping operation parameters of the pumping equipment and the physical property parameters of concrete, calculate the flow resistance index of concrete during the transportation process and the segregation risk index of concrete, use the allowable feed rate as the upper limit constraint of the feed, and calculate the target cumulative feed mass of each axial distribution section. The parameter optimization module is used to calculate the zonal quality deviation based on the target cumulative feed quality, construct boundary constraints using pump pressure over-limit risk, segregation risk and centrifugal start-up time limit, and solve for the optimal control parameters in combination with the state constraint set, execute zonal quantitative material distribution, and simultaneously record the actual feed quality and pump pressure trajectory. The control and adjustment module is used to calculate and predict wall thickness deviation and zone segregation risk based on the actual feed quality, pump pressure trajectory and centrifugal speed plan. If the predicted wall thickness deviation or zone segregation risk exceeds the preset threshold, the feed quality target of each axial distribution section in the subsequent batch is calculated based on the allowable feed amount in the state constraint set, and the optimal control parameters are adjusted.

[0008] Compared with the prior art, the beneficial effects of the present invention are at least as follows: This application provides a smart pumping and placement collaborative control system and method for concrete in pole production. By incorporating the geometric boundaries of the mold cavity, the skeleton occupancy, and reserved holes into the volume calculation, the effective filling volume of each section after deducting internal occupancy is accurately quantified. This provides a spatial benchmark for determining the feed quality and differentiating the pumping displacement, avoiding wall thickness deviations caused by uneven mold cavity taper and internal occupancy. Through the introduction of a multi-level correction mechanism involving air content correction, pumping pressure adjustment, and skeleton occupancy rate safety margin compensation, the impact of concrete state fluctuations on the filling effect is transformed into the process boundary of the allowable feed volume. Combined with the flow resistance index and segregation risk index, the feed target is dynamically compensated, enabling the feed control to adapt to changes in the actual concrete conveying state and centrifugal process parameters. This solves the problems of insufficient feed and wall thickness deviations caused by neglecting flow resistance and centrifugal effects. Finally, through feedback correction driven by actual feed quality and pump pressure trajectory, the placement control method can identify quality risks and dynamically adjust control parameters before the centrifugal process begins, avoiding scrap losses and improving the stability and consistency of pole forming quality. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of a method for intelligent pumping and material placement collaborative control of concrete for pole production, as described in an embodiment of this application. Figure 2 This is a schematic diagram of the equipment connection relationship and the internal structure of the mold cavity in the concrete pumping and placing process according to an embodiment of this application. Figure 3This is a schematic diagram of the structure of a smart pumping and placing collaborative control system for producing concrete for utility poles, according to an embodiment of this application. Detailed Implementation

[0011] This application provides a collaborative control system and method for intelligent pumping and placing concrete in the production of utility poles. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0012] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 The present application embodiment of a method for intelligent pumping and material placement collaborative control of concrete for pole production includes: Step S1: Divide the pole mold cavity into multiple axial material distribution sections, obtain the geometric morphology parameters and internal occupancy structure parameters of each axial material distribution section, and determine the effective filling volume of each axial material distribution section.

[0013] Step S1 includes: obtaining the axial length of the pole; dividing the pole mold cavity into multiple axial material distribution sections based on the axial length; obtaining the geometric shape parameters and internal occupancy structure parameters of each axial material distribution section, wherein the geometric shape parameters include the inner mold dimensions within the axial material distribution section, and the internal occupancy structure parameters include the skeleton position data and reserved hole position data within the axial material distribution section; calculating the total annular space volume of each axial material distribution section based on the geometric shape parameters; calculating the skeleton occupancy volume and the reserved hole occupancy volume respectively based on the internal occupancy structure parameters; and subtracting the skeleton occupancy volume and the reserved hole occupancy volume from the total annular space volume to obtain the effective filling volume of each axial material distribution section.

[0014] Specifically, such as Figure 2The diagram shows the equipment connection relationship and internal structure of the mold cavity during the concrete pumping and placing process. In the centrifugal casting production of utility poles, after the concrete is prepared by the mixing equipment, it is pumped to the placing equipment through the conveying pipeline. The placing equipment moves along the axial direction of the mold cavity and injects concrete into each placing section in a metered manner. The three constitute a series link for material conveying and distribution. The utility pole mold cavity has a conical structure, with its inner diameter gradually shrinking from the large end to the small end. An inner mold is installed in the center of the mold cavity, and an annular filling space is formed between the outer surface of the inner mold and the inner wall of the mold cavity. A steel reinforcement skeleton and reserved holes are arranged in this space, resulting in significant differences in the actual fillable space at different axial positions. Therefore, it is necessary to divide the mold cavity into several independent placing sections along the axial direction, and quantify the effective filling volume of each section by deducting the volume occupied by the skeleton and holes in each section, so as to provide a spatial benchmark for subsequent differentiated placing control.

[0015] The axial length of the pole is obtained, which is the total distance from the large end face to the small end face. Based on this axial length, the conical mold cavity is divided into multiple axial material placement sections. During division, the mold cavity is divided into several preparatory sections of equal length to ensure regular section boundaries, facilitating the movement of the material placement equipment at fixed stroke intervals. After the section division is completed, the inner mold dimensions of each axial material placement section are obtained as geometric parameters. The inner mold refers to the cylindrical component installed at the center of the mold cavity. The inner mold dimensions specifically refer to the outer diameter of the inner mold. Its outer surface forms an annular space with the inner wall of the mold cavity, which is the filling area for the concrete material. The skeleton position data and reserved hole position data of each axial placement section are obtained as internal occupant structure parameters. The skeleton position data includes the number, diameter, axial start and end coordinates and radial distribution radius of the longitudinal main bars, as well as the diameter, axial spacing and radius position of the circumferential stirrups. These data record the spatial distribution and cross-sectional dimensions of the steel reinforcement skeleton in each section. The longitudinal main bars are arranged continuously along the pole axis, and the circumferential stirrups are fixed to the outside of the longitudinal main bars in a spiral or segmented ring manner. The two form an unfillable space occupant inside the mold cavity. The reserved hole position data includes the spatial coordinates and hole size of the pre-embedded bolt holes and lifting holes. These holes form local cavities during casting and also need to be deducted from the total volume.

[0016] For each axial material feeding section, the total volume of the annular space of that section is calculated based on the inner diameter of the mold cavity and the outer diameter of the inner mold corresponding to the starting and ending ends of the section, respectively, combined with the axial length of the section. The mold cavity has a conical structure, and its frustum volume is calculated by substituting the cross-sectional area determined by the inner diameter of the mold cavity at the starting and ending ends of the section and the axial length of the section into the formula for the volume of a frustum of a cone. The inner mold is a cylinder with a uniform cross-section, and its volume is obtained by multiplying the cross-sectional area determined by the outer diameter of the inner mold by the axial length of the section, i.e., the formula for the volume of a cylinder. Subtracting the volume of the inner mold cylinder from the volume of the frustum of the mold cavity yields the total volume of the annular space of that section. This total volume represents the maximum theoretical space between the mold cavity and the inner mold that can be filled with material.

[0017] Subsequently, the cross-sectional area of ​​the main longitudinal reinforcement bars is calculated based on the number and diameter of the longitudinal main reinforcement bars in the skeleton location data. Then, the volume occupied by the longitudinal main reinforcement bars is calculated according to the first formula, which is: ,in, This refers to the number of longitudinal main reinforcement bars. The cross-sectional area of ​​a single main reinforcement bar. The axial length of the section; the volume occupied by the circumferential stirrups is calculated according to the second formula, which is: ,in, This refers to the number of stirrup rings within the section. The cross-sectional area of ​​a single stirrup. The radius of the circle where the stirrups are located is given. The volume occupied by the longitudinal main reinforcement and the circumferential stirrups is added together to obtain the skeleton volume. If there is a very small volume overlap between the longitudinal main reinforcement and the circumferential stirrups at their intersection, and this overlap is less than the allowable error range for the total skeleton volume, it is ignored in this embodiment. For higher precision, Boolean intersection subtraction can be performed based on the spatial coordinates. The cross-sectional area of ​​the hole is calculated based on the hole diameter in the reserved hole location data, and multiplied by the hole depth to obtain the volume occupied by a single reserved hole. The volumes of all reserved holes in the same section are added together to obtain the total reserved hole volume. The total skeleton volume and the total reserved hole volume are subtracted sequentially from the total volume of the annular space to obtain the effective filling volume of each axial concrete placement section. This effective filling volume refers to the actual net space volume available for concrete filling.

[0018] By incorporating the geometric boundaries of the mold cavity, the skeleton, and the occupancy of holes into the volume calculation, the net filling space of each section can be accurately quantified, providing a spatial basis for determining the subsequent feed rate and the differentiated allocation of the discharge rate. This avoids wall thickness deviations caused by uneven mold cavity taper and skeleton occupancy, and improves the accuracy of material distribution control.

[0019] Step S2: Obtain the theoretical feed mass based on the effective filling volume. Correct the theoretical feed mass based on the fluidity characteristics of concrete, the pumping pressure of the pumping equipment, and the skeleton occupancy rate of each axial placement section to obtain the allowable feed amount of each axial placement section, and construct the state constraint set.

[0020] Step S2 includes: extracting the concrete design density, obtaining the theoretical feed mass based on the effective filling volume and the concrete design density; obtaining the concrete slump value as a flowability feature, collecting the pumping pressure of the pumping equipment in real time through a pressure sensor, extracting the longitudinal reinforcement distribution density and circumferential reinforcement distribution density in each axial feeding section, and calculating the skeleton occupancy rate of each axial feeding section based on the longitudinal reinforcement distribution density and circumferential reinforcement distribution density; determining the corresponding correction coefficient based on the slump value, pumping pressure, and skeleton occupancy rate, correcting the theoretical feed mass based on the correction coefficient, and determining the allowable feed amount for each axial feeding section; obtaining the maximum travel distance of the feeding equipment along the mold cavity axis as a feeding space constraint, obtaining the upper limit of the allowable time from the completion of feeding to the start of centrifugation as a process time constraint; and constructing a state constraint set for feeding optimization based on the allowable feed amount, feeding space constraint, and process time constraint.

[0021] Specifically, after obtaining the effective filling volume of each axial fabric section, in order to transform these static volume data into process constraints that can be used for fabric control, it is necessary to further introduce the actual impact of skeleton distribution on the filling process, and combine it with equipment capacity and process time constraints to form a complete constraint boundary for subsequent optimization solutions, so that the material feeding control of each section has both volume basis and process boundary.

[0022] The effective filling volume of the corresponding section is read from step S1. This effective filling volume is multiplied by the concrete design density to obtain the theoretical feed mass. This theoretical feed mass represents the feed mass when the concrete completely fills the effective filling volume. In actual concrete placement, the concrete may carry a certain proportion of air during pumping and placement, resulting in an actual filling density lower than the design density. To compensate for the air-carrying effect, the theoretical feed mass needs to be corrected. The correction factor is determined based on the concrete's fluidity and pumping pressure.

[0023] The fluidity of concrete is characterized by its slump value, which is obtained as follows: After concrete preparation and before pumping starts, a concrete sample is collected from the discharge port of the mixing equipment. The sample is placed into a standard slump cone, and the top is leveled. Then, the slump cone is lifted vertically upwards. The concrete inside the cone collapses under its own weight. The vertical height difference between the top plane of the cone and the top surface of the concrete sample is measured; this difference is the slump value. It should be noted that the slump of concrete gradually decreases as the cement hydration reaction progresses. During the continuous placement process (usually covering multiple axial sections, lasting from several minutes to tens of minutes), the slump value measured at the initial moment may deviate from the actual fluidity in the later stages of placement. Therefore, this initial slump value is only used to set the static upper limit boundary of the allowable feed rate and is not required to continuously and accurately reflect the fluidity of concrete throughout the entire placement process.

[0024] The air content correction factor is determined based on the slump value. A larger slump typically indicates a higher proportion of air entrained in the concrete during mixing and pumping, resulting in a greater deviation between the actual filling density and the design density. Therefore, a larger air content correction factor is required. Conversely, a smaller slump indicates a lower air content, and the correction factor is closer to 1. The air content correction factor is positively correlated with the slump value, and is greater than 1 and less than or equal to 1.5.

[0025] Pumping pressure is acquired in real time by a pressure sensor installed at the outlet of the pumping equipment. Its value reflects the strength of the squeezing action on the concrete in the delivery pipeline. This squeezing action helps to expel some of the air inside the concrete, thereby increasing the actual filling density. The pumping pressure is converted into a dimensionless pumping pressure adjustment coefficient as follows: when the pumping pressure is high, the adjustment is made downward based on the air content correction coefficient; when the pumping pressure is low, the air content correction coefficient remains unchanged. The physical meaning of this adjustment coefficient is to quantify the promoting effect of squeezing action on air escape, and its value range is greater than 0.95 and less than or equal to 1.

[0026] Subsequently, the skeleton occupancy rate is calculated. The longitudinal and circumferential reinforcement distribution densities for each axial fabric section are extracted from the skeleton location data. Based on these densities, the skeleton occupancy rate for each axial fabric section is calculated. First, the cross-sectional area of ​​a single main reinforcement bar is calculated based on its diameter. This area is then multiplied by the number of main reinforcement bars to obtain the total cross-sectional area of ​​the longitudinal main reinforcement bars in that section. This total cross-sectional area is then divided by the circumferential cross-sectional area of ​​that section to obtain the longitudinal reinforcement cross-sectional occupancy rate. Since the axial length of the same axial fabric section is a constant L, and the longitudinal main reinforcement bars are arranged along the entire axial length, the volumetric occupancy rate of the longitudinal main reinforcement bars is numerically equal to their cross-sectional occupancy rate (both contain the same length factor L, which cancels each other out in the ratio calculation). This allows the longitudinal reinforcement cross-sectional occupancy rate to be added to the subsequent circumferential reinforcement volumetric occupancy rate with a unified dimension, resulting in the dimensionless skeleton occupancy rate for that section.

[0027] Secondly, calculate the volume occupancy ratio of the circumferential reinforcement. Calculate the number of stirrup turns within the section based on the stirrup spacing. The number of stirrup turns equals the axial length of the section divided by the stirrup spacing. Calculate the cross-sectional area of ​​a single stirrup based on its diameter. Calculate the circumference of the circle based on the radius of the stirrup's location. The volume of a single stirrup equals its cross-sectional area multiplied by the circumference. Multiply the volume of a single stirrup by the number of stirrup turns to obtain the total volume of the circumferential reinforcement within the section. Then, divide this total volume of the circumferential reinforcement by the volume of the annular space within the section to obtain the volume occupancy ratio of the circumferential reinforcement. This ratio reflects the proportion of volume occupied by the circumferential reinforcement within the annular space of the section. The longitudinal reinforcement volume occupancy rate is then added to the circumferential reinforcement volume occupancy rate to obtain the skeleton occupancy rate of the axial placement section. The skeleton occupancy rate is a dimensionless ratio representing the volume proportion occupied by the skeleton in the section. The larger the ratio, the denser the skeleton distribution, the higher the skeleton occupancy rate, the greater the concrete flow resistance in the skeleton gaps, and the more difficult it is to ensure the filling density, requiring additional feed to compensate. Therefore, a safety margin coefficient is determined based on the skeleton occupancy rate. The skeleton occupancy rate and the safety margin coefficient are positively correlated; the denser the skeleton, the larger the margin. The safety margin coefficient ranges from greater than or equal to 1 to less than or equal to 1.2, used to compensate for the influence of filling resistance. The air content correction coefficient and the safety margin coefficient have a product coupling relationship. The total correction coefficient after multiplying the two should not exceed the preset total correction upper limit, such as 1.5. When the product result exceeds this upper limit, the control system prioritizes proportional reduction of the two based on real-time pumping pressure feedback to avoid excessive deviation of the allowable feed volume from the quality benchmark corresponding to the theoretical volume.

[0028] The theoretical feed mass is multiplied sequentially by the air content correction factor, pumping pressure adjustment factor, and safety margin factor to obtain the allowable feed volume for the axial placement section. This allowable feed volume represents the maximum concrete mass allowed to be filled in this section under the conditions of meeting the design wall thickness and skeleton occupancy constraints. The allowable feed volume is greater than the theoretical feed mass, which is the ideal value under the condition that the concrete completely fills the space without any material loss. In the actual pumping process, the air carried by the concrete will occupy part of the volume and leave pores after hardening. In areas with dense skeletons, the flow of concrete is obstructed, which can easily lead to local incomplete filling. Therefore, by amplifying and correcting the theoretical feed mass, the volume loss after the air escapes and the mass deficiency caused by the filling resistance of the skeleton gaps are compensated to ensure that the final wall thickness is consistent with the design value. This upper limit constraint and the real-time density measured by the online densitometer used in step S3 are at different levels of the control architecture. The latter is a dynamic feedback quantity used to improve the accuracy of the conversion from volume dimension to mass dimension for the current batch. The combination of the two can achieve accurate quantification of dynamic target values ​​while ensuring that the process safety is not compromised.

[0029] The maximum travel distance of the fabric placement equipment along the mold cavity axis is read from the technical parameter document of the fabric placement equipment. This maximum travel distance is determined by the effective length of the guide rail of the traveling mechanism. The effective length of the guide rail is equal to the total length of the guide rail minus the length of the safety buffer at both ends. This maximum travel distance value serves as a constraint on the fabric placement space, which determines the range of axial sections that the fabric placement equipment can cover. If the axial length of the pole exceeds this constraint, a segmented clamping processing branch needs to be added to the subsequent control strategy.

[0030] The upper limit of the allowable time from the completion of material placement to the start of centrifugation is read from the process design document. This upper limit is determined by the cement type, admixture type and ambient temperature in the concrete mix proportion. This upper limit value serves as a process aging constraint. If the total material placement time exceeds this upper limit, the fluidity of the concrete will decrease, which will affect the centrifugal molding quality and lead to uneven wall thickness or insufficient density.

[0031] The allowable feed amount is stored in the state constraint set using the axial fabric section number as an index. This, along with the fabric space constraint and process aging constraint, constitutes the state constraint set for fabric optimization. During the construction of the state constraint set, the validity of the allowable feed amount is verified. If the allowable feed amount for a certain section is zero or negative, the calculation process of the effective filling volume or skeleton occupancy rate is backtracked and checked. The fabric space constraint is compared with the pole's axial length. If the fabric space constraint is less than the pole's axial length, a segment clamping identifier is recorded in the state constraint set. After completing the above processing, the state constraint set is stored in the control system's memory for subsequent use by the fabric control module.

[0032] Step S3: Obtain the pumping operation parameters of the pumping equipment and the physical property parameters of the concrete, calculate the flow resistance index and segregation risk index of the concrete during the conveying process, use the allowable feed rate as the upper limit constraint for the feed, and calculate the target cumulative feed mass of each axial distribution section.

[0033] The step S3, which calculates the flow resistance index and segregation risk index of concrete during transportation, includes: collecting the pumping pressure and instantaneous flow rate of the pumping equipment at the current pumping cycle; obtaining the temperature of the conveying pipe, the mixing power of the mixing equipment, and the location of the placing equipment during concrete transportation; calculating the rheological parameters of the concrete based on the pumping pressure, instantaneous flow rate, and conveying pipe temperature; generating the flow resistance index based on the rheological parameters, mixing power, and the location of the placing equipment; calculating the fluctuation variance of the rheological parameters and the gradient change value of the instantaneous flow rate; and generating the segregation risk index based on the fluctuation variance and gradient change value.

[0034] Specifically, after obtaining the state constraint set, in order to link the cavity space constraints with the real-time flow state of concrete to determine the feed target of each section under actual pumping conditions, it is necessary to collect the operating parameters of the pumping equipment and the physical property parameters of the concrete, calculate the flow resistance index reflecting the ease of concrete flow and the segregation risk index reflecting the tendency of aggregate separation, so that the setting of the feed target can be dynamically adjusted with the change of concrete state.

[0035] First, after the pumping equipment enters a stable operating state after the start-up acceleration phase, the pumping pressure signal sequence and instantaneous flow rate signal sequence are collected under the current pumping cycle. The instantaneous flow rate is obtained through a flow meter installed on the delivery pipeline. To eliminate sensor measurement noise and instantaneous disturbances caused by pipeline vibration, the collected pumping pressure signal sequence and instantaneous flow rate signal sequence are filtered using moving average filtering. The filtering window length is set to one pumping cycle period; that is, the average pressure value of all sampling points within the calculation window is used as the filtered pressure value, and the average flow rate value of all sampling points within the calculation window is used as the filtered instantaneous flow rate value. The filtered pumping pressure value and instantaneous flow rate value represent the actual delivery capacity under that pumping cycle.

[0036] Simultaneously, the temperature of the conveying pipe when the concrete flows in the conveying pipe is obtained by measuring the temperature through a temperature sensor placed on the outer wall of the conveying pipe. The mixing power consumed by the mixing equipment during the concrete preparation process is obtained by reading the mixing power from the electrical control system of the mixing equipment. The current coordinate position of the placing equipment in the axial direction of the mold cavity is obtained as the position of the placing equipment, which is obtained by measuring the position of the placing equipment through a displacement sensor installed on the traveling mechanism of the placing equipment.

[0037] After obtaining the pumping pressure, instantaneous flow rate, and delivery pipe temperature, the rheological parameters of the concrete are calculated. The rheological parameters include yield stress and plastic viscosity. Yield stress reflects the minimum shear stress that concrete needs to overcome to start flowing from a static state, while plastic viscosity reflects the proportional relationship between shear stress and shear rate in the flowing state. Together, they describe the actual flow behavior of concrete in the delivery pipe. Specifically, based on the Bingham fluid model, the pressure drop and flow rate relationship of concrete flow in the pipeline are used for back-calculation. At least two sets of flow rate and pumping pressure data under different pumping capacities are collected. The pumping pressure is considered as the pressure drop due to friction along the pipeline, and the instantaneous flow rate is converted into the average velocity of the pipeline cross-section. Combining the pipe diameter and length, the shear stress and shear rate at the pipe wall are solved using least-squares fitting or iteration to obtain the yield stress and plastic viscosity under the basic conditions. Based on this, the plastic viscosity is temperature-corrected according to the temperature of the delivery pipe: when the delivery pipe temperature is higher than the reference temperature, the thermal motion of the concrete particles intensifies, the flow resistance decreases, and the plastic viscosity is adjusted downwards according to the temperature correction factor; when the delivery pipe temperature is lower than the reference temperature, the concrete viscosity increases, and the plastic viscosity is adjusted upwards according to the temperature correction factor. The temperature correction factor is determined based on the difference between the delivery pipe temperature and the reference temperature. When the difference is positive, the correction factor is less than one; when the difference is negative, the correction factor is greater than one. The basic plastic viscosity is multiplied by the temperature correction factor to obtain the final plastic viscosity. The yield stress is less affected by temperature and remains unchanged based on the fitted value. After temperature correction, the final yield stress and final plastic viscosity are obtained and used as inputs for generating the flow resistance index. The corrected plastic viscosity can more accurately reflect the actual flow resistance of concrete at the delivery temperature.

[0038] When generating the flow resistance index, the basic resistance coefficient is first calculated based on the yield stress and plastic viscosity. Before calculating the basic resistance coefficient, the yield stress and plastic viscosity are normalized. The basic resistance coefficient is positively correlated with the yield stress and plastic viscosity. The basic resistance coefficient is calculated by multiplying the normalized yield stress value by the first weighting coefficient and adding the normalized plastic viscosity value by the second weighting coefficient. The values ​​of the first and second weighting coefficients are determined according to the layout of the pipeline. When the pipeline is mainly composed of horizontal pipe sections, the contribution of yield stress to the flow resistance is relatively significant, and the value of the first weighting coefficient is greater than that of the second weighting coefficient. When the pipeline is mainly composed of vertical pipe sections or bends, the contribution of plastic viscosity to the flow resistance is relatively significant, and the value of the second weighting coefficient is greater than that of the first weighting coefficient.

[0039] After obtaining the basic resistance coefficient, the mixing power is divided by the rated power of the mixing equipment to obtain the mixing power ratio. This mixing power ratio reflects the load level of the mixing equipment under the current working conditions. The rated power of the mixing equipment refers to the maximum power that its drive motor can continuously output under rated working conditions. This value is provided by the mixing equipment manufacturer and marked on the equipment nameplate. The higher the mixing power ratio, the greater the frictional resistance between the particles inside the concrete, and the greater the corresponding flow resistance. The mixing power ratio is multiplied by the mixing power influence coefficient to obtain the mixing power correction term. The mixing power influence coefficient is a dimensionless empirical correction coefficient used to quantify the degree of correction of the concrete flow resistance by the change of mixing power. This coefficient is obtained by regression after conducting rheological tests and synchronously measuring the mixing power for concrete with the same mix proportion under laboratory conditions. It reflects the response amplitude of the flow resistance when the mixing power increases by one unit. The mixing power correction term is added to the basic resistance coefficient to obtain the corrected resistance coefficient.

[0040] After obtaining the corrected resistance coefficient, the length of the pipeline through which concrete flows from the pump outlet to the placing boom outlet is calculated based on the location of the placing boom. This pipeline length is determined by the current coordinate position of the placing boom in the axial direction of the mold cavity. The conveying distance is shorter when the placing boom is near the large end of the mold cavity, and increases accordingly when the placing boom moves to the small end of the mold cavity. The path resistance correction value is obtained by multiplying the pipeline length by the path resistance coefficient. The path resistance coefficient is determined based on the number of bends in the pipeline and the roughness of the pipeline inner wall. The more bends there are, the larger the path resistance coefficient value is; the larger the roughness of the pipeline inner wall, the larger the path resistance coefficient value is. The path resistance correction value is superimposed on the corrected resistance coefficient to obtain the flow resistance index. This flow resistance index characterizes the magnitude of the frictional and viscous resistance encountered by the concrete material when flowing in the conveying pipeline. This index directly affects the pressure demand and flow stability of the pumping equipment.

[0041] After generating the flow resistance index, the variance of rheological parameters and the gradient change value of instantaneous flow rate are calculated. A segregation risk index is then generated based on the variance and gradient change values. During continuous operation of the pumping equipment, multiple consecutive time windows are set. The length of each time window is set to the time required for the pumping equipment to complete at least three complete pumping cycles (i.e., the plunger or screw to complete three complete pushing cycles). Multiple sets of pumping pressure and instantaneous flow rate data under different pumping cycles are collected within each time window. Linear regression is performed on the collected data within that time window to obtain a set of yield stress and plastic viscosity values ​​corresponding to that time window. This collection and regression process is repeated for the next time window until multiple sets of rheological parameter data covering the entire pumping period are obtained. The variances of yield stress and plastic viscosity are calculated separately. Each variance is divided by the square of the corresponding mean to obtain the coefficient of variation. The weighted sum of the yield stress coefficient of variation and the plastic viscosity coefficient of variation yields the rheological parameter variance index. The instantaneous flow gradient change value is obtained by calculating the time derivative of the instantaneous flow signal sequence to obtain the flow gradient of each sampling point, and statistically analyzing the proportion of the number of sampling points whose flow gradient exceeds a preset threshold to the total number of sampling points. This proportion is used as the instantaneous flow gradient change value index. The dimension of the preset threshold is the first-order rate of change of volumetric flow rate with respect to time. The specific value is determined based on the maximum allowable flow fluctuation amplitude under the rated operating conditions of the pumping equipment.

[0042] After obtaining the rheological parameter fluctuation variance index and the instantaneous flow gradient change value index, the instantaneous flow gradient change value index is normalized by dividing it by a preset maximum reference value for the flow gradient. This normalized index ensures its value range is on the same order of magnitude as the rheological parameter fluctuation variance index. The preset maximum reference value for the flow gradient is determined based on the maximum allowable flow fluctuation amplitude of the pumping equipment under rated operating conditions; this value is provided by the pumping equipment manufacturer or obtained through historical operating data. The rheological parameter fluctuation variance index and the normalized instantaneous flow gradient change value index are then weighted and summed to obtain the segregation risk index. The weights are determined based on the relative contents of coarse and fine aggregates in the concrete mix design. A higher coarse aggregate content results in a larger weight for the rheological parameter fluctuation variance index, while a higher fine aggregate content results in a larger weight for the flow gradient change value. The segregation risk index characterizes the tendency of aggregates and paste to separate in concrete. Segregation can lead to uneven composition of concrete, affecting the strength uniformity and durability of the pole after molding.

[0043] By collecting pumping pressure and instantaneous flow rate, yield stress and plastic viscosity are calculated. Combined with mixing power and conveying distance, a flow resistance index is generated, which enables the feed quality to compensate for the filling loss caused by flow resistance. A segregation risk index is generated by the variance of rheological parameter fluctuations and the change value of flow gradient, which enables the feed quality to compensate for the segregation effect during centrifugation, avoiding feed deviation and uneven wall thickness caused by fluctuations in concrete state.

[0044] Step S3 calculates the target cumulative feed mass for each axial feeding section, including: extracting the effective filling volume of each axial feeding section; calculating the resistance compensation of the effective filling volume based on the flow resistance index to obtain the compensation volume; obtaining a preset centrifugal speed plan, which is a preset curve showing the change of the mold cavity speed of the electric pole over time during the centrifugation process; correcting the centrifugal distribution of the compensation volume based on the segregation risk index and the centrifugal speed plan to obtain the corrected volume; obtaining the real-time density of the concrete; multiplying the corrected volume by the real-time density of the concrete to obtain the target cumulative feed mass; and verifying and adjusting the target cumulative feed mass based on the allowable feed amount until the target cumulative feed mass is less than or equal to the allowable feed amount.

[0045] Specifically, in the centrifugal casting production of utility poles, the actual fillable space at different positions along the axial direction of the mold cavity varies due to the conical structure and the space occupied by the internal skeleton. At the same time, when the concrete flows in the pumping pipeline, it is subject to frictional resistance and viscous resistance, resulting in pressure loss. This causes the actual amount of material entering each section to be lower than the theoretical requirement. In addition, the concrete may experience density stratification between aggregate and paste due to centrifugal force during the subsequent centrifugation process. These factors together make it impossible to guarantee the uniformity of wall thickness after centrifugation when the amount of material is determined solely based on the effective filling volume.

[0046] To address the aforementioned issues, this step is based on the effective filling volume of each axial material placement section determined in step S1. This effective filling volume is the net space volume obtained by subtracting the volume occupied by the skeleton and the reserved holes from the total volume of the annular space, representing the theoretical space available for concrete filling in each section under static conditions. The effective filling volume is multiplied by the resistance compensation coefficient corresponding to the flow resistance index to obtain the compensation volume. The resistance compensation coefficient is determined based on the flow resistance index, and the two are positively correlated; that is, the larger the flow resistance index, the larger the resistance compensation coefficient. Furthermore, the resistance compensation coefficient is always greater than one. The resistance compensation coefficient is determined according to the relative position of the current flow resistance index with respect to the historical fluctuation range, in order to compensate for the material loss caused by flow resistance.

[0047] After obtaining the compensation volume, a centrifugal speed plan is acquired, and the compensation volume is corrected for centrifugal distribution based on the segregation risk index and the centrifugal speed plan to obtain the corrected volume. The centrifugal speed plan is a preset curve showing the change of the mold cavity speed over time during the centrifugation process. This curve includes the maximum speed parameter, where the maximum speed determines the maximum centrifugal force experienced by the concrete during centrifugation. The segregation risk index reflects the tendency of aggregate and paste to separate during pumping and placement of concrete. A larger index indicates a more significant tendency for concrete segregation, and a higher risk of uneven wall thickness distribution due to density stratification during centrifugation. The centrifugal distribution correction coefficient is determined jointly based on the segregation risk index and the centrifugal speed plan. A larger segregation risk index results in a larger correction coefficient, and a higher maximum speed also results in a larger correction coefficient, with the correction coefficient always being greater than one.

[0048] After obtaining the corrected volume, the real-time density of the concrete is acquired. This real-time density is continuously measured using an online density meter installed at the outlet of the mixing equipment or the inlet of the pumping equipment. The corrected volume is multiplied by the real-time density to convert the volume dimension to the mass dimension, yielding the initial value of the target cumulative feed mass. This initial value is compared with the allowable feed amount for the corresponding section in step S2. The allowable feed amount is the maximum mass of concrete material allowed to be filled in that section while meeting the design wall thickness and skeleton occupancy constraints. If the initial value is less than or equal to the allowable feed amount, this initial value is used as the final target cumulative feed mass for that section. If the initial value is greater than the allowable feed amount, the resistance compensation coefficient is gradually reduced. Each reduction recalculates the compensation volume, corrected volume, and target cumulative feed mass until the target cumulative feed mass is less than or equal to the allowable feed amount. If the resistance compensation coefficient is reduced to one and still greater than the allowable feed amount, the centrifugal distribution correction coefficient is gradually reduced until the upper limit constraint is met. If both adjustments fail to meet the constraint, the allowable feed amount is used as the target cumulative feed mass for that section. The target cumulative feed quality, after upper limit verification and adjustment, is stored in the control system for subsequent step S4 to retrieve.

[0049] By introducing resistance compensation and centrifugal distribution correction into the effective filling volume, the target cumulative feed quality can simultaneously reflect the pressure loss effect during pumping and the density stratification effect during centrifugation. The calculation results are verified by the upper limit of the allowable feed volume to ensure that they do not exceed the allowable range of the process. This solves the problem of wall thickness deviation caused by ignoring flow resistance and centrifugal effect when determining the feed volume based solely on the effective filling volume.

[0050] Step S4: Calculate the zone quality deviation based on the target cumulative feed quality, construct boundary constraints using pump pressure over-limit risk, segregation risk, and centrifugal start-up time limit, and solve for the optimal control parameters by combining the state constraint set, execute zone quantitative material distribution, and simultaneously record the actual feed quality and pump pressure trajectory.

[0051] In step S4, constructing boundary constraints and solving for optimal control parameters includes: obtaining the current pumping displacement, residence time, and real-time concrete density; inputting the preset prediction model to obtain the predicted feed quality; calculating the deviation between the target cumulative feed quality and the predicted feed quality to obtain the zonal quality deviation; the upper boundary of the zonal quality deviation is limited by the allowable feed amount in the state constraint set, and the predicted feed quality must not exceed the allowable feed amount; establishing a control variable space including pumping displacement, placing equipment moving speed, residence time, and switching sequence; setting the pump pressure over-limit risk, segregation risk, and centrifugal start-up time limit as boundary constraints of the control variable space; combining the boundary constraints and the state constraint set, with minimizing the zonal quality deviation as the objective function, solving for the optimal pumping displacement, placing equipment moving speed, residence time, and switching sequence to obtain the optimal control parameters.

[0052] Specifically, before optimization, a feasible set of switching sequences is pre-determined based on the comparison between the maximum travel distance of the material placing device along the mold cavity axis and the axial length of the pole. If the single clamping travel of the material placing device can cover the entire axial material placing section, the feasible set of switching sequences includes two schemes: accessing segments from the large end to the small end and accessing segments from the small end to the large end. If the single clamping travel of the material placing device cannot cover the entire axial material placing section, the entire section is divided into several groups according to the reach of the material placing device, ensuring that the segments within each group are continuously arranged and the total length does not exceed the maximum travel distance of the material placing device. The switching sequence within a group is determined to be accessing segments from the end closest to the clamping start point to the end furthest away. Switching between groups is achieved through segmented clamping. The feasible set of switching sequences is jointly determined by the grouping scheme and the segmented access direction within each group. This feasible set of switching sequences is fixed in the candidate space of discrete decision variables before the optimization iteration begins and is not used as a continuous variation dimension of the optimization algorithm during the iteration process.

[0053] The optimization algorithm uses pumping capacity and concrete placing equipment speed as continuous optimization variables, and the switching order of each section as discrete optimization variables. It iteratively solves the problem with the objective function of minimizing the quality deviation of each section. For any candidate combination of pumping capacity and concrete placing equipment speed, it is successively substituted into each feasible switching order scheme for evaluation. After the switching order of each section is determined, the dwell time of each section does not directly participate in random search or independent variation, but is determined by the target cumulative feed mass, current pumping capacity, and real-time concrete density of that section according to the following formula: ,in, Let be the dwell time of the i-th axial fabric segment. Let the target cumulative feed mass be the fabric delivery mass for the i-th axial fabric delivery segment. For the i-th section of the fabric, the pumping displacement of the pumping equipment is... The real-time density of concrete during the i-th section of the concrete distribution is determined because once the pumping displacement is determined, the residence time of each section is uniquely determined.

[0054] The currently selected pumping rate and residence time, along with the real-time concrete density, are input into the prediction model. The prediction model is a mathematical calculation model. This model outputs the predicted feed mass according to the calculation logic of pumping rate multiplied by residence time multiplied by real-time concrete density. The predicted feed mass represents the theoretically achievable concrete mass value for the corresponding placement section under the current candidate control parameter combination. The difference between the target cumulative feed mass and the predicted feed mass is used as the partition quality deviation. The partition quality deviation reflects the degree of deviation of the predicted feed quantity from the target value under the current candidate combination. The smaller the absolute value of the deviation, the higher the placement accuracy of the control parameters. During the optimization iteration process, the target cumulative feed mass remains constant, while the predicted feed mass changes with the currently attempted pumping rate and residence time. The two are equal only at optimization convergence. This deviation drives the optimization algorithm to continuously search for the optimal control parameters.

[0055] The upper boundary of the zone quality deviation is limited by the allowable feed rate in the state constraint set. If the predicted feed quality exceeds the allowable feed rate, the set of control parameters is marked as an infeasible solution and excluded in subsequent iterations. Pump pressure over-limit risk, segregation risk, and centrifugal start-up time limit are set as boundary constraints in the control variable space. Pump pressure over-limit risk requires that the pumping pressure must not exceed a preset safe pressure threshold, which is set based on the rated working pressure of the pumping equipment. The pumping equipment manufacturer provides the rated working pressure value at the time of equipment delivery, which is marked on the equipment nameplate and technical parameter document, representing the highest pressure value at which the equipment can operate safely and continuously under normal conditions. Segregation risk requires that the segregation risk index must not exceed a preset segregation risk threshold. The segregation risk threshold is obtained by pre-calibrating a segregation test on concrete with the current mix proportion. The test method involves vibrating the concrete sample on a standard vibration table for a specific duration, measuring the difference in mass distribution of coarse aggregate at the bottom and top of the sample, and determining the maximum allowable degree of segregation for the mix proportion without significant segregation, quantifying it as the segregation risk threshold value.

[0056] The centrifugal start-up time limit refers to the longest time interval between the moment the placing equipment completes the placing operation of all axial placing sections and the moment the centrifugal equipment starts rotating. The upper limit of this time interval is determined by a combination of factors including the type of cement, admixtures, and ambient temperature in the concrete mix design. Shorter cement setting time results in a shorter time limit; higher ambient temperature leads to a faster hydration reaction rate and a correspondingly shorter time limit; and higher retarder admixture content allows for a longer time limit. The reason for setting this time limit is that cement in concrete undergoes a continuous hydration reaction after contact with water, causing the concrete's fluidity to gradually decrease over time. If the waiting time after placing is too long, the concrete may have partially lost its fluidity before the centrifugal equipment starts, preventing it from fully migrating and compacting to the inner wall of the mold cavity under centrifugal force during centrifugation, ultimately resulting in uneven wall thickness distribution or insufficient density. The centrifugal start-up time limit serves as a boundary constraint condition in the control variable space. The constraint method is that the sum of the dwell times of all axial placing sections plus the sum of the moving times of the placing equipment between sections must not exceed this time limit.

[0057] Under the combined constraints of boundary conditions and state constraint sets, the algorithm iteratively solves the problem with the objective function of minimizing the zonal quality deviation. In each iteration, the optimization algorithm selects a set of unevaluated candidate control parameter combinations from the control variable space. This combination includes the values ​​of four variables: pumping displacement, placing equipment moving speed, residence time of each axial placing section, and section switching order. The pumping displacement and residence time from the current candidate combination, along with the measured real-time concrete density from step S3, are input into the prediction model. The prediction model outputs the predicted feed quality under this combination. The difference between the target cumulative feed quality and the predicted feed quality is calculated to obtain the zonal quality deviation.

[0058] Simultaneously, it is determined whether the pumping pressure under the candidate combination exceeds the safe pressure threshold, whether the segregation risk index exceeds the segregation risk threshold, whether the sum of the residence time of all sections plus the sum of the movement time of the placing equipment between sections exceeds the centrifugal start-up time limit, whether the predicted feed quality exceeds the allowable feed amount in the state constraint set, and whether the single clamping stroke of the placing equipment can cover the axial range of all sections in the switching sequence. If any constraint condition is not met, the control parameter set is marked as an infeasible solution and excluded in subsequent iterations. If all constraints are met, the partition quality deviation corresponding to the control parameter set is recorded. Through iterative search, the control parameter combination with the smallest partition quality deviation among all feasible solutions is finally selected as the optimal control parameter output. If no feasible solution satisfying all constraints is found within the preset number of iterations, the candidate solution closest to the feasible domain boundary and with the smallest deviation in the current generation is output as the optimal control parameter.

[0059] In step S4, the execution of zoned quantitative material distribution and simultaneous recording of actual feed mass and pump pressure trajectory includes: driving the material distribution equipment to move to the target axial distribution section according to the switching sequence, and locking the position of the material distribution equipment after it has moved into place; adjusting the output displacement of the pumping equipment according to the pumping displacement corresponding to the current axial distribution section, and opening the valve assembly corresponding to the current axial distribution section to inject concrete into the current axial distribution section; after the dwell time of the current axial distribution section is reached, closing the valve assembly corresponding to the current axial distribution section, and driving the material distribution equipment to move to the next axial distribution section, until the material distribution of all axial distribution sections is completed; during the material distribution process, the actual feed mass of each axial distribution section is measured in real time by a mass flow meter, and the pump pressure data of the pumping equipment is collected in real time by a pressure sensor to generate a pump pressure trajectory.

[0060] Specifically, after obtaining the optimal control parameters, the execution phase begins. Following the switching sequence in the optimal control parameters, the fabric placement equipment is driven to move to the target axial placement section. The traveling mechanism of the fabric placement equipment receives the target position coordinates and the set travel speed and then initiates the drive to slide along the guide rail. During the movement, displacement sensors mounted on the traveling mechanism monitor the current position of the fabric placement equipment in real time. When the current position reaches the error range of the target position, the traveling mechanism stops driving and locks the position, ensuring that the fabric placement equipment does not experience axial displacement during the placement process. The error range is determined based on the axial length of the pole and the fabric placement accuracy requirements.

[0061] After the concrete placing equipment reaches the target section and locks its position, the output displacement of the pumping equipment is adjusted according to the pumping displacement corresponding to the current axial placing section. This adjustment is achieved by regulating the driving power or speed of the pumping equipment. For hydraulically driven pumping equipment, the displacement is controlled by adjusting the flow rate or pressure of the hydraulic pump; for electrically driven pumping equipment, the displacement is controlled by adjusting the motor speed or frequency converter frequency. After the displacement adjustment is complete, the valve assembly corresponding to the current axial placing section is opened, allowing concrete to be injected into the current section through the delivery pipeline. The valve assembly consists of multiple electric valves, each corresponding to one axial placing section. The opening and closing of the valves are automatically scheduled by the control system based on the switching sequence and dwell time. The switching logic is to first close the valve of the current section and then open the valve of the next section, with an interval in between to ensure the reliability of valve operation and avoid uncontrolled flow distribution caused by multiple valves opening simultaneously.

[0062] After the dwell time for the current axial placing section is reached, the valve assembly corresponding to the current section is closed, stopping the injection of concrete into that section. The dwell time is controlled by a timer in the control system. The timer starts counting down when the valve is opened, and sends a switching command to the valve assembly and the traveling mechanism when the countdown reaches zero. After closing the valve, the placing equipment is driven to move to the next axial placing section, repeating the above operations of positioning, displacement adjustment, valve opening, dwelling placing, and valve closing until placing concrete in all axial placing sections is completed.

[0063] During the concrete placement process, a mass flow meter installed at the outlet of the conveying pipeline or placing equipment measures the mass of concrete flowing through that location in real time. The mass flow meter uses the Coriolis effect or a measurement principle based on a weighing sensor to directly output the mass flow rate value. The control system accumulates and integrates the real-time feed mass of each section. The accumulated value is equal to the time integral of the mass flow meter measurement value during the residence time of that section, obtaining the actual feed mass of each axial placing section, which is stored in the control system database. A pressure sensor installed at the pumping equipment outlet collects pumping pressure values ​​at a fixed sampling frequency. The pressure values ​​are stored chronologically, forming a pump pressure trajectory that records the pressure fluctuation characteristics of the pumping process, including average pressure level, peak pressure, and pressure fluctuation amplitude. The actual feed mass and pump pressure trajectory serve as input data for the subsequent step S5 to calculate and predict wall thickness deviation and zonal segregation risk.

[0064] Through the above execution and recording process, the optimal control parameters are transformed into coordinated actions of the pumping equipment, the material distribution equipment, and the valve assembly, ensuring that the feed rate in each section is executed in accordance with the target requirements. At the same time, the measured data obtained by the mass flow meter and pressure sensor provide a basis for subsequent quality assessment and feedback adjustment.

[0065] Step S5: Calculate and predict the wall thickness deviation and the risk of zone segregation based on the actual feed quality, pump pressure trajectory and centrifugal speed plan. If the predicted wall thickness deviation or the risk of zone segregation exceeds the preset threshold, calculate the feed quality target for each axial distribution section of the subsequent batch based on the allowable feed amount in the state constraint set, and adjust the optimal control parameters.

[0066] The calculation of predicted wall thickness deviation and zonal segregation risk in step S5 includes: dividing the actual feed mass of each axial placement section by the real-time density of concrete to obtain the actual feed volume of each axial placement section; calculating the radial displacement of concrete under centrifugal force based on the maximum rotation speed in the centrifugal rotation speed plan; calculating the predicted wall thickness of each axial placement section; calculating the difference between the predicted wall thickness and the preset target wall thickness to obtain the predicted wall thickness deviation; performing frequency domain analysis on the pump pressure trajectory to extract high-frequency fluctuation components; calculating high-frequency fluctuation characteristic values ​​based on the high-frequency fluctuation components; inputting them into a preset segregation degree mapping model to obtain the segregation degree benchmark value of each axial placement section; and correcting the segregation degree benchmark value based on the actual feed mass of each axial placement section to obtain the zonal segregation risk.

[0067] Specifically, after the fabric is laid and before the centrifugation process is started, the wall thickness deviation and the risk of segregation in different zones are calculated and predicted based on the actual feed quality, pump pressure trajectory and centrifugation speed, so as to determine in advance whether the quality of the current batch of fabric meets the design requirements.

[0068] The calculation process for predicting the wall thickness is as follows: The cumulative mass flow rate of each axial placement section is read from the control system's database to obtain the actual feed mass of each section. The actual feed mass of each section is divided by the real-time concrete density of that section to obtain the actual feed volume of each section. The actual feed volume of each section is considered as the volume of the annular filling space after centrifugation. An equation is established based on the principle of volume conservation, with the wall thickness as the unknown quantity, and the feed volume equal to the annular space volume. The inner radius of the annular space is the outer radius of the inner mold, and the outer radius is the outer radius of the inner mold plus the wall thickness. Based on this, the radial displacement calculated according to the centrifugal speed plan is introduced to correct the outer radius of the annular space. Specifically, the centrifugal rotation speed plan refers to a pre-set process curve showing the change in the rotational speed of the mold cavity over time during the centrifugal forming process. This curve includes a start-up phase, an acceleration phase, a constant speed phase, and a deceleration phase. The maximum rotational speed is the highest rotational speed reached by the mold cavity during centrifugation. Based on the maximum rotational speed, the radial displacement of the concrete under centrifugal force is calculated. The maximum rotational speed is converted from revolutions per minute (rpm) to angular velocity. The centrifugal acceleration is equal to the square of the angular velocity multiplied by the radius of rotation, which is the average radius of the inner wall of the mold cavity in that section. Under centrifugal force, the concrete migrates radially outward. The radial displacement is positively correlated with the centrifugal acceleration; that is, the greater the centrifugal acceleration, the greater the radial displacement. The specific value is obtained by multiplying the centrifugal acceleration by a proportional coefficient calibrated in advance for centrifugal tests on concrete with the same mix proportion. The radial displacement represents the distance the concrete migrates radially outward under centrifugal force. This displacement increases the outer radius of the annular space; that is, the corrected outer radius is equal to the inner mold outer radius plus the wall thickness plus the radial displacement. Substituting the corrected outer radius into the formula for the volume of the annular space, and setting it equal to the actual feed volume, the wall thickness obtained by solving this equation is the predicted wall thickness. This predicted wall thickness reflects the estimated wall thickness that can actually be formed on the inner wall of the mold cavity after centrifugation, considering the feed volume and centrifugal migration effect. Subtracting the predicted wall thickness from the target wall thickness yields the predicted wall thickness deviation.

[0069] The predicted wall thickness of each section is subtracted from the preset target wall thickness to obtain the predicted wall thickness deviation. The target wall thickness refers to the concrete wall thickness required by the design of each axial section of the pole. It comes from the pole strength design document and usually shows a gradual change in thickness from the large end to the small end along the axial direction. It is used to determine the theoretical filling thickness of each section and to check the rationality of the volume calculation results. The positive and negative values ​​of the predicted wall thickness deviation indicate whether the wall thickness exceeds the standard or is insufficient.

[0070] The calculation method for the risk of segregation is as follows: Frequency domain analysis is performed on the pump pressure trajectory recorded in step S4 to extract high-frequency fluctuation components higher than the pumping cycle frequency. In frequency domain analysis, the time-domain pressure signal is converted into a frequency domain spectrum through Fourier transform. A frequency range more than twice the pumping cycle frequency is extracted from the spectrum. The amplitudes of each frequency component within this range are summed to obtain the high-frequency fluctuation characteristic value. This high-frequency fluctuation characteristic value reflects the degree of instability of the pressure signal during pumping. This instability is related to the uneven distribution of aggregate and slurry in the pipeline when concrete segregates, leading to local differences in flow resistance within the pipeline. When concrete segregates, the coarse aggregate and slurry move relative to each other due to density differences, resulting in uneven material distribution within the pipeline cross-section. This causes differences in flow resistance at different locations on the inner wall of the pipeline, thereby causing high-frequency fluctuations in pumping pressure. The amplitude of the high-frequency fluctuation characteristic value is positively correlated with the degree of segregation.

[0071] The high-frequency fluctuation characteristic values ​​are input into a pre-defined segregation degree mapping model, which is a regression model: ,in, Let i be the baseline value for the degree of segregation in the i-th segment. Let i be the high-frequency fluctuation characteristic value corresponding to the i-th segment. For regression coefficients, Using the intercept as the independent variable, the high-frequency fluctuation characteristic values ​​of each section in historical production batches are used as the independent variable, and the difference in the area ratio of the inner and outer half-layer coarse aggregates in the slices after centrifugation of the corresponding section is used as the dependent variable. The result is obtained by fitting using the least squares method. and The larger the amplitude of the high-frequency fluctuation characteristic value, the more intense the high-frequency fluctuation of the pump pressure, and the more severe the degree of concrete segregation. The benchmark value of the degree of segregation increases accordingly. The benchmark value of the degree of segregation characterizes the severity of concrete segregation based on the characteristics of pump pressure fluctuation.

[0072] Since high-frequency fluctuation characteristic values ​​reflect the overall fluctuation level of the pumping process, and the actual feed quality varies in different axial distribution sections, even if the segregation degree benchmark value is the same, the impact of segregation on wall thickness uniformity is more significant in sections with higher actual feed quality. Therefore, the segregation degree benchmark value needs to be corrected based on the actual feed quality of each section. The correction method is to multiply the segregation degree benchmark value of each section by the ratio of the actual feed quality of that section to the average actual feed quality of all sections. After correction, the segregation risk increases for sections with actual feed quality higher than the average level, and decreases for sections with actual feed quality lower than the average level, thus obtaining the zonal segregation risk for each section.

[0073] In step S5, adjusting the optimal control parameters includes: determining whether the predicted wall thickness deviation, pump pressure peak value, and segregation risk of each axial fabric placement section exceed their respective preset thresholds; if the predicted wall thickness deviation, pump pressure peak value, or segregation risk of the axial fabric placement section exceeds the corresponding preset threshold, the parameter adjustment process is triggered; obtaining the pre-stored safety process formula, which includes conservative pumping displacement settings, fabric placement equipment moving speed settings, and switching sequence schemes; determining the feed quality correction amount for each axial fabric placement section in subsequent batches based on the deviation direction and magnitude of the actual feed quality of each section in the current batch from the target cumulative feed quality, using the allowable feed amount in the state constraint set as the upper limit of the feed quality of each section in subsequent batches, dynamically correcting the pumping displacement, fabric placement equipment moving speed, and switching sequence in the safety process formula, and generating corrected control parameters; and outputting the corrected control parameters to the formula library of the control system as preset control parameters for subsequent batches of pole fabric placement.

[0074] Specifically, after the material is laid and before the centrifugation process starts, the cumulative mass flow rate of each axial material laying section is read from the database of the control system to obtain the actual feed mass of each axial material laying section in the current batch. The actual feed mass of each axial material laying section is compared with the target cumulative feed mass calculated in step S3 one by one, and the difference between the actual feed mass and the target cumulative feed mass of each section is calculated to obtain the feed deviation value and deviation direction of each axial material laying section. At the same time, the pump pressure peak value is read from the pump pressure trajectory, and the predicted wall thickness deviation and segregation risk of each axial material laying section are obtained from the calculation in step S5.

[0075] The system determines whether the absolute value of the predicted wall thickness deviation for each axial fabric section exceeds a preset wall thickness deviation threshold, whether the peak pump pressure exceeds a preset peak pump pressure threshold, and whether the risk of segregation in each axial fabric section exceeds a preset segregation risk threshold. If the absolute value of the predicted wall thickness deviation for a certain axial fabric section exceeds the wall thickness deviation threshold, or the peak pump pressure exceeds the peak pump pressure threshold, or the risk of segregation in a certain axial fabric section exceeds the segregation risk threshold, the parameter adjustment process is triggered, the current batch of fabric is marked as substandard, and the number and type of the substandard section are recorded.

[0076] After triggering the parameter adjustment process, the pre-stored safety process formula is retrieved from the control system formula library. The safety process formula is a pre-designed combination of alternative control parameters for this pole specification, including conservative pump displacement and fabric placement equipment movement speed settings, as well as a validated switching sequence scheme. The pump displacement setting is lower than the normal operating displacement value, and the fabric placement equipment movement speed setting is lower than the normal operating speed value, used to reduce pumping pressure and segregation risk in the event of quality risks. Based on the recorded direction and magnitude of the feed deviation, the feed quality correction amount for each axial fabric placement section in subsequent batches is determined: for sections where the actual feed quality is higher than the target cumulative feed quality, it indicates that the feed in that section is excessive, and the target cumulative feed quality for that section is corrected downwards in subsequent batches, with the correction amount determined based on the ratio of the actual deviation value to the target value; for sections where the actual feed quality is lower than the target cumulative feed quality, it indicates that the feed in that section is insufficient, and the target cumulative feed quality for that section is corrected upwards in subsequent batches, with the correction amount also determined based on the ratio of the actual deviation value to the target value.

[0077] The corrected feed quality target for each axial placement section is compared with the allowable feed amount in the state constraint set. If the corrected feed quality target for a certain section exceeds the allowable feed amount, the feed quality target for that section is lowered to the allowable feed amount. The dwell time for each axial placement section is recalculated based on the corrected feed quality target and the pumping displacement in the safety process formula. The dwell time is equal to the corrected feed quality target divided by the pumping displacement and then by the real-time concrete density. The recalculated dwell time, together with the moving speed and switching sequence of the placement equipment in the safety process formula, constitutes the corrected control parameters. These corrected control parameters are output to the control system formula library as preset control parameters for subsequent batches of poles of the same specification. By converting the feed deviation information of the current batch into the feed quality correction amount for subsequent batches, iterative optimization of control parameters from the current batch to subsequent batches is achieved, enabling subsequent batches to avoid similar deviations and gradually improve the consistency of pole forming quality.

[0078] The above describes a method for intelligent pumping and placing of concrete in power pole production according to an embodiment of this application. The following describes a method for intelligent pumping and placing of concrete in power pole production according to an embodiment of this application. Please refer to [link to relevant documentation]. Figure 3 The intelligent pumping and placing collaborative control system for producing concrete for utility poles, as described in this application embodiment, includes: The region division module is used to divide the pole mold cavity into multiple axial material distribution sections, obtain the geometric shape parameters and internal occupancy structure parameters of each axial material distribution section, and determine the effective filling volume of each axial material distribution section. The state constraint module is used to obtain the theoretical feed mass based on the effective filling volume, and to correct the theoretical feed mass based on the fluidity characteristics of concrete, the pumping pressure of the pumping equipment, and the skeleton occupancy rate of each axial placement section, so as to obtain the allowable feed amount of each axial placement section and construct the state constraint set. The preliminary calculation module is used to obtain the pumping operation parameters of the pumping equipment and the physical property parameters of concrete, calculate the flow resistance index of concrete during the transportation process and the segregation risk index of concrete, use the allowable feed rate as the upper limit constraint of the feed, and calculate the target cumulative feed mass of each axial distribution section. The parameter optimization module is used to calculate the zonal quality deviation based on the target cumulative feed quality, construct boundary constraints using pump pressure over-limit risk, segregation risk and centrifugal start-up time limit, and solve for the optimal control parameters in combination with the state constraint set, execute zonal quantitative material distribution, and simultaneously record the actual feed quality and pump pressure trajectory. The control and adjustment module is used to calculate and predict wall thickness deviation and zone segregation risk based on the actual feed quality, pump pressure trajectory and centrifugal speed plan. If the predicted wall thickness deviation or zone segregation risk exceeds the preset threshold, the feed quality target of each axial distribution section in the subsequent batch is calculated based on the allowable feed amount in the state constraint set, and the optimal control parameters are adjusted.

[0079] In summary, this application combines the quantification of the mold cavity space with real-time rheological sensing of concrete. It uses a state constraint set to define the upper limit of material feed, equipment stroke, and centrifugal aging boundary, and uses the flow resistance index and segregation risk index to compensate for pumping losses and centrifugal stratification effects. After execution, it dynamically adjusts the control parameters through a feedback correction mechanism driven by the actual material feed quality and pump pressure trajectory. This constructs a complete control loop from volume benchmark establishment, process boundary setting, dynamic target calculation, optimized execution to feedback correction. It effectively solves the problems of wall thickness deviation and segregation defects caused by mold cavity taper, skeleton occupancy, and concrete state fluctuations in the centrifugal casting of electric poles, and improves the accuracy and adaptability of material placement control.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0081] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent pumping and material placement collaborative control of concrete in power pole production, characterized in that, The method includes: Step S1: Divide the pole mold cavity into multiple axial material distribution sections, obtain the geometric morphology parameters and internal occupancy structure parameters of each axial material distribution section, and determine the effective filling volume of each axial material distribution section. Step S2: Obtain the theoretical feed mass based on the effective filling volume, and correct the theoretical feed mass based on the fluidity characteristics of concrete, the pumping pressure of the pumping equipment, and the skeleton occupancy rate of each axial placement section to obtain the allowable feed amount of each axial placement section, and construct a state constraint set. Step S3: Obtain the pumping operation parameters of the pumping equipment and the physical property parameters of the concrete, calculate the flow resistance index and segregation risk index of the concrete during the transportation process, use the allowable feed rate as the upper limit constraint for the feed, and calculate the target cumulative feed mass of each axial distribution section. Step S4: Calculate the zonal quality deviation based on the target cumulative feed quality, construct boundary constraints using pump pressure over-limit risk, segregation risk and centrifugal start-up time limit, and solve for the optimal control parameters by combining the state constraint set, execute zonal quantitative material distribution, and simultaneously record the actual feed quality and pump pressure trajectory; Step S5: Calculate and predict the wall thickness deviation and the risk of zone segregation based on the actual feed quality, pump pressure trajectory and centrifugal speed plan. If the predicted wall thickness deviation or the risk of zone segregation exceeds the preset threshold, calculate the feed quality target for each axial distribution section of the subsequent batch based on the allowable feed amount in the state constraint set, and adjust the optimal control parameters.

2. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S1 includes: Obtain the axial length of the pole, and divide the pole mold cavity into multiple axial material laying sections according to the axial length; Obtain the geometric shape parameters and internal occupancy structure parameters of each axial fabric section, wherein the geometric shape parameters include the inner mold dimensions within the axial fabric section, and the internal occupancy structure parameters include the skeleton position data and reserved hole position data within the axial fabric section; The total volume of the annular space of each axial fabric section is calculated based on the geometric morphology parameters, and the volume occupied by the skeleton and the volume occupied by the reserved hole are calculated based on the internal occupancy structure parameters respectively. The effective filling volume of each axial fabric section is obtained by subtracting the volume occupied by the skeleton and the volume occupied by the reserved hole from the total volume of the annular space.

3. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S2 includes: Extract the concrete design density, and obtain the theoretical feed mass based on the effective filling volume and the concrete design density; The slump value of concrete is obtained as a fluidity characteristic. The pumping pressure of the pumping equipment is collected in real time by a pressure sensor. The longitudinal reinforcement distribution density and the circumferential reinforcement distribution density in each axial placement section are extracted. The skeleton occupancy rate of each axial placement section is calculated based on the longitudinal reinforcement distribution density and the circumferential reinforcement distribution density. Based on the slump value, pumping pressure, and skeleton occupancy rate, a corresponding correction coefficient is determined. Based on the correction coefficient, the theoretical feed quality is corrected to determine the allowable feed amount for each axial material distribution section. The maximum travel distance of the fabric feeding device along the mold cavity axis is obtained as the fabric feeding space constraint. The upper limit of the allowed time from the completion of fabric feeding to the start of centrifugal centrifugation is obtained as the process time constraint. Based on the allowed feed amount, the fabric feeding space constraint and the process time constraint, a set of state constraints for fabric feeding optimization is constructed.

4. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S3 involves calculating the flow resistance index and segregation risk index of concrete during transportation, including: Collect the pumping pressure and instantaneous flow rate of the pumping equipment at the current pumping cycle, and obtain the temperature of the conveying pipe, the mixing power of the mixing equipment, and the equipment position of the placing equipment during the conveying of concrete material. The rheological parameters of the concrete are calculated based on the pumping pressure, the instantaneous flow rate, and the temperature of the delivery pipe. A flow resistance index is generated based on the rheological parameters, the stirring power, and the position of the fabric distribution device; Calculate the fluctuation variance of the rheological parameters and the gradient change value of the instantaneous flow rate, and generate a segregation risk index based on the fluctuation variance and the gradient change value.

5. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 4, characterized in that, Step S3 calculates the target cumulative feed mass for each axial fabric section, including: Extract the effective filling volume of each axial fabric section, and calculate the resistance compensation of the effective filling volume according to the flow resistance index to obtain the compensation volume; A preset centrifugal speed plan is obtained, which is a preset curve of the change of the rotational speed of the electric rod in the mold cavity over time during the centrifugation process. The compensation volume is then corrected for centrifugal distribution based on the segregation risk index and the centrifugal speed plan to obtain the corrected volume. The real-time density of the concrete is obtained, and the corrected volume is multiplied by the real-time density of the concrete to obtain the target cumulative feed mass. The target cumulative feed mass is then verified and adjusted in conjunction with the allowable feed amount until the target cumulative feed mass is less than or equal to the allowable feed amount.

6. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S4 involves constructing boundary constraints and solving for the optimal control parameters, including: Obtain the current pumping displacement, residence time, and real-time concrete density of the current attempt; input the preset prediction model to obtain the predicted feed quality; calculate the deviation between the target cumulative feed quality and the predicted feed quality to obtain the zone quality deviation; the upper boundary of the zone quality deviation is limited by the allowable feed amount in the state constraint set; the predicted feed quality must not exceed the allowable feed amount. Establish a control variable space that includes pumping displacement, fabric spreading equipment moving speed, dwell time and switching sequence, and set the pump pressure over-limit risk, segregation risk and centrifugal start-up time limit as boundary constraints of the control variable space. By combining the boundary constraints and the set of state constraints, and taking the minimization of the partition quality deviation as the objective function, the optimal pumping displacement, the moving speed of the fabric spreading equipment, the dwell time, and the switching sequence are solved to obtain the optimal control parameters.

7. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S4 involves performing zoned quantitative material distribution and simultaneously recording the actual feed quality and pump pressure trajectory, including: Drive the fabric placement equipment to the target axial fabric placement section according to the switching sequence, and lock the position of the fabric placement equipment after it has moved into place; Adjust the output displacement of the pumping equipment according to the pumping displacement corresponding to the current axial placement section, and open the valve assembly corresponding to the current axial placement section to inject concrete into the current axial placement section. After the dwell time of the current axial feeding section is reached, the valve assembly corresponding to the current axial feeding section is closed, and the feeding equipment is driven to move to the next axial feeding section until feeding is completed in all axial feeding sections. During the fabric application process, the actual feed mass of each axial fabric application section is measured in real time using a mass flow meter, and the pump pressure data of the pumping equipment is collected in real time using a pressure sensor to generate a pump pressure trajectory.

8. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S5 calculates the predicted wall thickness deviation and the risk of zonal segregation, including: Divide the actual feed mass of each axial feeding section by the real-time density of the concrete to obtain the actual feed volume of each axial feeding section. Calculate the radial displacement of the concrete under centrifugal force based on the maximum rotation speed in the centrifugal rotation speed plan. Calculate the predicted wall thickness of each axial feeding section. Calculate the difference between the predicted wall thickness and the preset target wall thickness to obtain the predicted wall thickness deviation. Frequency domain analysis is performed on the pump pressure trajectory to extract high-frequency fluctuation components. High-frequency fluctuation characteristic values ​​are calculated based on the high-frequency fluctuation components and input into a preset segregation degree mapping model to obtain the segregation degree benchmark value for each axial material distribution section. The segregation degree benchmark value is then corrected based on the actual feed quality of each axial material distribution section to obtain the zonal segregation risk.

9. The intelligent pumping and material placement collaborative control method for concrete production in utility pole manufacturing according to claim 1, characterized in that, Step S5 involves adjusting the optimal control parameters, including: Determine whether the predicted wall thickness deviation, pump pressure peak value, and risk of segregation in each axial fabric section exceed their respective preset thresholds; If the predicted wall thickness deviation, the peak pump pressure, or the risk of zonal segregation in the axial fabric section exceeds the corresponding preset threshold, the parameter adjustment process is triggered. Obtain a pre-stored safety process formula, which includes a conservative pumping displacement setting, a fabric spreading equipment moving speed setting, and a switching sequence scheme. Based on the deviation direction and magnitude of the actual feed quality of each section in the current batch from the target cumulative feed quality, the feed quality correction amount for each axial laying section in subsequent batches is determined. The allowable feed amount in the state constraint set is used as the upper limit of the feed quality of each section in subsequent batches. The pumping displacement, laying equipment moving speed and switching sequence in the safety process formula are dynamically corrected to generate corrected control parameters. The corrected control parameters are output to the formula library of the control system as the preset control parameters for the laying of poles in subsequent batches. The corrected control parameters are output to the pumping and spreading equipment, replacing the current control parameters, and the remaining spreading operations are continued.

10. A collaborative control system for intelligent pumping and placing of concrete in power pole production, used to implement the collaborative control method for intelligent pumping and placing of concrete in power pole production as described in any one of claims 1-9, characterized in that, The system includes: The region division module is used to divide the pole mold cavity into multiple axial material distribution sections, obtain the geometric shape parameters and internal occupancy structure parameters of each axial material distribution section, and determine the effective filling volume of each axial material distribution section. The state constraint module is used to obtain the theoretical feed mass based on the effective filling volume, and to correct the theoretical feed mass based on the fluidity characteristics of concrete, the pumping pressure of the pumping equipment, and the skeleton occupancy rate of each axial placement section, so as to obtain the allowable feed amount of each axial placement section and construct the state constraint set. The preliminary calculation module is used to obtain the pumping operation parameters of the pumping equipment and the physical property parameters of concrete, calculate the flow resistance index of concrete during the transportation process and the segregation risk index of concrete, use the allowable feed rate as the upper limit constraint of the feed, and calculate the target cumulative feed mass of each axial distribution section. The parameter optimization module is used to calculate the zonal quality deviation based on the target cumulative feed quality, construct boundary constraints using pump pressure over-limit risk, segregation risk and centrifugal start-up time limit, and solve for the optimal control parameters in combination with the state constraint set, execute zonal quantitative material distribution, and simultaneously record the actual feed quality and pump pressure trajectory. The control and adjustment module is used to calculate and predict wall thickness deviation and zone segregation risk based on the actual feed quality, pump pressure trajectory and centrifugal speed plan. If the predicted wall thickness deviation or zone segregation risk exceeds the preset threshold, the feed quality target of each axial distribution section in the subsequent batch is calculated based on the allowable feed amount in the state constraint set, and the optimal control parameters are adjusted.

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