Manufacturing method and system for optimized steel fiber anti-crack ultra-high performance concrete centrifugal electric pole

Abstract

The invention relates to the technical field of concrete electric pole manufacturing, and discloses an optimized steel fiber anti-crack ultra-high performance concrete centrifugal electric pole manufacturing method and system. The method comprises the following steps: dividing an electric pole manufacturing process into a plurality of manufacturing units according to production batches, and collecting material component data, process setting data and online detection data in each manufacturing unit to form a multi-source production data set; calculating an anti-cracking performance index and a strength performance index based on a multi-source production data set, and dynamically outputting an initial process correction value through a dual-threshold monitoring mechanism and a performance adjustment model; performing adaptive adjustment on the initial process correction value of each manufacturing unit according to the production correlation between the adjacent manufacturing units to generate an adjusted process correction value; and carrying out serialization processing on the adjusted process correction value, and outputting a production optimization instruction which is applied to a manufacturing execution process. According to the method, electric pole manufacturing whole-process data integration and dynamic process optimization are achieved, and the production efficiency and the product quality stability can be improved.

Description

Technical Field

[0001] This invention relates to the field of concrete pole manufacturing technology, specifically to an optimized method and system for manufacturing steel fiber crack-resistant ultra-high performance concrete centrifugal poles. Background Technology

[0002] In the infrastructure construction sector, steel fiber reinforced concrete centrifugal poles, known for their excellent crack resistance and strength, are widely used in power transmission and communication line construction. Currently, the manufacturing process of these poles largely relies on traditional batch production methods. However, the collection and utilization of data during production is significantly inadequate. Data is often recorded only for a single production stage, failing to create a complete dataset covering materials, processes, and testing. This fragmented data collection at different stages of production hinders comprehensive support for performance analysis and process optimization.

[0003] Existing manufacturing methods lack systematic design in performance index calculation and process adjustment. In evaluating crack resistance and strength, only a single parameter is often considered, such as judging crack resistance solely based on steel fiber content or measuring overall strength only by compressive strength. This ignores the correlation between material composition, process settings, and online monitoring data, making the calculated performance indicators unable to accurately reflect the actual quality state of the pole. Furthermore, process correction values ​​are often determined using fixed thresholds or empirical values, failing to consider the production correlation between adjacent manufacturing units, such as the impact of equipment operating status, production time intervals, and material type compatibility on process parameters. This results in a lack of targeted and dynamic adaptability in process adjustments.

[0004] When material batch fluctuations or equipment parameter drift occur during production, traditional methods struggle to make quick and accurate adjustments, leading to problems such as insufficient crack resistance and unstable strength in utility poles. This not only increases the scrap rate during production but may also pose safety hazards during subsequent use. Furthermore, existing manufacturing methods lack a mechanism for serializing process correction values, failing to systematically translate adjusted process parameters into production execution instructions. This hinders the effective implementation of process optimization solutions, impacting overall production efficiency and product quality stability, and ultimately failing to meet the current infrastructure construction demands for high-quality utility poles. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized method and system for manufacturing steel fiber-reinforced crack-resistant ultra-high performance concrete centrifugal poles, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an optimized method for manufacturing steel fiber-reinforced crack-resistant ultra-high performance concrete centrifugal poles, the method comprising: The pole manufacturing process is divided into multiple manufacturing units according to production batches. Material composition data, process setting data and online detection data of each manufacturing unit are collected to form a multi-source production dataset. Crack resistance and strength performance indicators are calculated based on multi-source production datasets, and initial process correction values ​​are dynamically output through a dual-threshold monitoring mechanism and performance adjustment model. The initial process correction value of each manufacturing unit is adaptively adjusted based on the production correlation between adjacent manufacturing units to generate the adjusted process correction value; the adjusted process correction value is serialized to output the production optimization instruction and apply it to the manufacturing execution process.

[0007] Preferably, the manufacturing unit is divided as follows: the manufacturing process is divided into multiple units according to the pole type and production time sequence, and each unit has a consistent material batch identifier and process parameter group, and the manufacturing unit is used as the basic processing unit.

[0008] Preferably, the calculation process of the crack resistance performance index includes: extracting the steel fiber content and water-cement ratio from the material composition data; obtaining the concrete cracking tendency parameter based on online detection data; calculating the crack resistance coefficient through a preset weight allocation scheme; generating the stability parameter according to the curing conditions in the process setting data; and generating the crack resistance performance index value by combining the crack resistance coefficient and the stability parameter.

[0009] Preferably, the calculation process for the strength performance index includes: extracting compressive strength and flexural strength from online testing data; normalizing the compressive strength and flexural strength; calculating the strength benchmark value based on the aggregate gradation and cement grade in the material composition data; combining the normalized compressive strength, flexural strength, and strength benchmark value into a strength performance index value through a weighted fusion method; and triggering an update of the strength performance index value when the water-cement ratio is higher than a set limit. The update rule is as follows: if the ratio of the number of consecutive water-cement ratio exceedances to the total number of monitoring times exceeds a threshold, the strength performance index value is reduced by a fixed proportion; otherwise, the strength performance index value is reduced by an increasing proportion based on the degree of water-cement ratio exceedance, with the upper limit of the reduction being a fixed proportion value.

[0010] Preferably, the derivation process of the initial process correction value includes: setting early warning limits for crack resistance performance index and early warning limits for strength performance index; if any performance index of a certain manufacturing unit is lower than the early warning limit, its initial process correction value is set to the default value; if all performance indexes are higher than the early warning limit, the initial process correction value is calculated based on the crack resistance performance index and strength performance index through an adjustment function.

[0011] Preferably, the specific implementation steps of adaptive adjustment include: randomly selecting a manufacturing unit as the target unit and obtaining the initial process correction values ​​of all its adjacent manufacturing units; generating a production tightness index value based on the equipment similarity and production time interval between the target unit and its adjacent manufacturing units; generating a material compatibility index value by comparing the material types of adjacent manufacturing units and the target unit; allocating adjustment factors according to the production tightness index value and the material compatibility index value; performing weighted adjustment on the initial process correction value of the target unit based on the adjustment factors and outputting the adjusted process correction value; traversing all manufacturing units to complete the adjustment and outputting the adjusted process correction value of all manufacturing units. The process of allocating adjustment factors includes: obtaining the standard specifications for pole manufacturing, setting production tightness weights and material compatibility weights according to the specifications, and calculating adjustment factors based on the set weights, production tightness index values, and material compatibility index values.

[0012] Preferably, the process of generating the production tightness index value includes: calculating the equipment matching degree based on the equipment similarity between the target unit and adjacent manufacturing units; calculating the time proximity based on the production time interval between the target unit and adjacent manufacturing units; obtaining the initial value of production tightness by weighted fusion of the equipment matching degree and time proximity, and setting compensation factors according to the production line type of adjacent manufacturing units; generating the production tightness index value by compensating the initial value of production tightness with the compensation factors; the compensation factor setting rule is: assigning a high compensation value if the production line types are the same, and assigning a low compensation value if the production line types are different.

[0013] Preferably, the process of generating the material compatibility index value includes: matching the dominant material type of adjacent manufacturing units with the material type of the target unit; if it belongs to a preset compatible material combination, then calculating the material compatibility index value according to each material type and its corresponding preset weight; if it does not belong to a compatible combination, then calculating the affinity index based on the material interaction specification, and obtaining the material compatibility index value by mapping the affinity index through a preset transformation function.

[0014] Preferably, the process of calculating the affinity index includes: statistically analyzing the co-occurrence frequency of the material types corresponding to the target unit and adjacent manufacturing units from historical production datasets; querying the initial functional dependency values ​​of the material types corresponding to the target unit and adjacent manufacturing units according to the material functional dependency table; calculating the compatibility coefficient between the target unit and adjacent manufacturing units according to the material compatibility rules; correcting the initial functional dependency value according to the compatibility coefficient to obtain the adjusted functional dependency value; and calculating the affinity index by combining the adjusted functional dependency value and the co-occurrence frequency.

[0015] Preferably, the present invention also includes an optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This optimized method for manufacturing steel fiber-reinforced, crack-resistant, ultra-high-performance concrete centrifugal poles integrates and aggregates data from the entire pole manufacturing process by dividing manufacturing into production batches and collecting multi-source production data. This data collection approach breaks down the fragmented data structure of traditional production processes, incorporating material composition data, process setting data, and online testing data into a unified dataset. This ensures that subsequent performance index calculations and process adjustments are based on a comprehensive and complete data foundation, avoiding analytical biases caused by incomplete data and guaranteeing accurate understanding of the pole production status.

[0017] In the performance index calculation and initial process correction value output stages, this method simultaneously focuses on crack resistance and strength performance indicators, and introduces a dual-threshold monitoring mechanism and a performance adjustment model. Through comprehensive evaluation of multi-dimensional performance indicators, it can more fully reflect the quality status of the poles, and compared to traditional single-parameter evaluation methods, it can more accurately identify potential quality risks during production. Furthermore, the combination of the dual-threshold monitoring mechanism and the performance adjustment model can dynamically output initial process correction values ​​based on the actual situation of different performance indicators, avoiding the limitations of fixed thresholds or empirical values, and making process adjustments more aligned with actual production needs.

[0018] The initial process correction values ​​are adaptively adjusted based on the production correlation between adjacent manufacturing units, fully considering factors such as equipment similarity, production time intervals, and material type matching. This adjustment method breaks through the limitations of traditional process adjustments that isolate individual manufacturing units. By analyzing the correlation between adjacent units, the process correction values ​​become more reasonable and targeted, effectively reducing process parameter deviations caused by ignoring production correlations and further improving the accuracy of process adjustments. Simultaneously, the adjusted process correction values ​​are serialized and output as production optimization instructions. This allows the optimized process parameters to be systematically transformed into directly executable production instructions, ensuring the smooth implementation of process optimization solutions. This achieves closed-loop management from data analysis and process adjustment to production execution, contributing to improved overall production efficiency and product quality stability.

[0019] This method, through meticulous management and dynamic adjustment throughout the entire process, can effectively address uncertainties such as material batch fluctuations and equipment parameter drift during production, reducing scrap rates and lowering production costs. Simultaneously, stable product quality enhances the reliability of utility poles during subsequent use, reduces safety hazards, and supports the stable progress of infrastructure construction. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the working principle of the optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method described in this invention. Figure 2 A flowchart illustrating the manufacturing unit division method; Figure 3 This is a flowchart of the strength performance index calculation process. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1This invention provides an optimized method and system for manufacturing ultra-high performance concrete centrifugal poles with crack resistance using steel fiber reinforcement. The method includes batch-based segmentation and data analysis. The manufacturing process is first divided into multiple manufacturing units, each representing an independent production batch and possessing a material batch identifier and a set of process parameters. Within each manufacturing unit, the system collects material composition data, process setting data, and online monitoring data to form a multi-source production dataset. The material composition data includes parameters such as steel fiber content, water-cement ratio, aggregate gradation, and cement grade. The process setting data covers operating conditions such as centrifugation speed, curing temperature, and curing time. The online monitoring data involves real-time measurements of concrete workability, compressive strength, and flexural strength. Based on the multi-source production dataset, the system calculates crack resistance and strength performance indicators. The crack resistance indicator is derived by comprehensively considering steel fiber content, water-cement ratio, cracking tendency parameters, and curing stability. The strength performance indicator is weighted and fused based on the normalized values ​​of compressive strength and flexural strength, as well as benchmark values ​​derived from aggregate gradation and cement grade. A dual-threshold monitoring mechanism is employed. The system sets early warning limits for both crack resistance and strength performance indicators. If either indicator falls below its warning limit, a default process correction is triggered. If all indicators exceed their warning limits, an initial process correction value is dynamically output through an adjustment function in the performance adjustment model. This initial process correction value is further adaptively adjusted based on the production correlation between adjacent manufacturing units. This adjustment process involves calculating production density and material compatibility indicators, assigning adjustment factors based on these indicators, and weighting the initial value to generate the adjusted process correction value. The system serializes the adjusted process correction value, transforming it into executable production optimization instructions, which are then applied to the manufacturing process, such as adjusting centrifugation parameters or curing conditions, thereby achieving closed-loop optimization of the production process.

[0023] Example 1: See Figure 2The manufacturing unit division strictly follows the logical framework of pole type and production time sequence, cutting the complete manufacturing process into a series of discrete manufacturing units. Each unit is assigned a unique material batch identifier and a complete set of process parameters, serving as the basic processing unit for data acquisition and process optimization. Pole types are distinguished according to the product catalog; for example, ring-shaped prestressed poles and ordinary reinforced concrete poles are considered different types. The production time sequence precisely records the start and end timestamps of each batch, ensuring that units are continuous and non-overlapping in the time dimension. The material batch identifier is bound to the purchase receipt number, tracking the entire process from raw material acceptance to material feeding. The process parameter set systematically includes all equipment setting parameters within the unit, such as the centrifuge speed curve, mold vibration frequency, and temperature and pressure setpoints for steam curing. This division method makes each manufacturing unit an independent data container, with highly consistent multi-source production data within it. The data acquisition system synchronously records all relevant material composition information, equipment operating parameters, and online sensor readings within the boundaries of this unit.

[0024] The calculation of crack resistance performance indicators is a multi-step comprehensive analysis process. It begins with the precise extraction of key variables from material composition data, primarily the steel fiber content and the water-cement ratio. The steel fiber content is expressed as the volume or mass percentage of steel fiber per cubic meter of concrete; this parameter directly affects the toughness of the concrete and its ability to inhibit crack propagation. The water-cement ratio, the ratio of water to the total mass of cementitious materials (including cement, fly ash, and mineral powder), is one of the core factors controlling concrete shrinkage and cracking sensitivity. Cracking tendency parameters of concrete are obtained based on online monitoring data. This data comes from embedded sensors or non-contact measuring devices, which monitor the strain changes, temperature history, and surface humidity distribution of concrete in real time during the plastic stage and early hardening stage. Algorithm models then convert these physical quantities into quantitative parameters characterizing cracking risk. The crack resistance coefficient is calculated using a pre-defined weighting scheme, which is determined in advance through multiple regression analysis of a large amount of historical production data and expert experience. Usually, the steel fiber content is given a higher weight because it contributes most directly to crack resistance performance, followed by the water-cement ratio. The cracking tendency parameter is given a dynamically adjusted weight based on its correlation with the final cracking morphology.

[0025] Stability parameters are generated based on detailed curing condition records in the process setup data. These curing conditions primarily include the accuracy of temperature control, humidity maintenance level, and duration of each curing stage (e.g., static shutdown, heating, constant temperature, cooling). Stability parameters are comprehensively evaluated by calculating the deviation of the volatility (e.g., variance or standard deviation) of these curing parameters over time from their target values. A stable and controllable curing process corresponds to a higher stability parameter value. Finally, the system integrates the calculated crack resistance coefficient with the stability parameters to generate the final crack resistance performance index value. This integration process typically uses a linear weighted summation method, but the weighting configuration may vary slightly depending on the specific type of pole and application scenario. This ensures that the index value simultaneously reflects the material's inherent crack resistance and the impact of the production process on its stability. The system automates the process from data acquisition to index output, with index values ​​updated in real time and compared against preset warning limits.

[0026] The division of manufacturing units not only considers the orderliness of production but also the granularity of data management, making each unit the smallest operable object for process optimization. When a production line produces multiple specifications of poles simultaneously, the system automatically groups them according to the pole type code. Several pole components of the same type and with adjacent production times are grouped into the same manufacturing unit for batch analysis. This ensures consistency of processes within the unit and improves data processing efficiency. The introduction of material batch identification enables full lifecycle traceability of raw materials. If a problem occurs in a batch of materials, the affected manufacturing unit can be quickly located, allowing for targeted measures. The completeness of the process parameter group is crucial. It covers all key process parameters from concrete mixing, placement, centrifugal molding to steam curing. These parameters are centrally stored in a relational database table, with each parameter having a timestamp and equipment number, facilitating correlation queries and trend analysis.

[0027] In calculating crack resistance performance indicators, the accuracy of steel fiber content needs to be ensured through regular calibration of the weighing system, while the calculation of the water-cement ratio relies on precise readings from level gauges and flow meters, as well as real-time metering data from the cementitious material silo. Obtaining parameters related to concrete cracking tendency depends on advanced sensing technologies. For example, distributed fiber optic sensors are used to monitor changes in the strain field within the concrete, or digital image correlation technology is used to non-contactly measure micro-deformations on the component surface. The application of these high-tech methods allows for a shift from qualitative to quantitative assessment of cracking risk. The weighting scheme is not static; the system allows process engineers to periodically review and fine-tune the weights based on newly generated production data. A monitoring network for curing conditions is deployed throughout the curing pits and steam curing kilns, with temperature and humidity sensors arranged at a specific spatial density to ensure the capture of spatial non-uniformity in the curing environment.

[0028] Example 2: See Figure 3 The calculation of strength performance indicators begins with extracting core mechanical parameters from the online testing data stream, primarily the measured values ​​of compressive strength and flexural strength. These data originate from sensors installed on compression and flexural testing machines, which transmit the data back immediately after destructive testing of sampled specimens during production. The compressive strength value reflects the ultimate compressive strength of concrete, while the flexural strength characterizes its resistance to bending deformation. Normalizing the compressive and flexural strengths is an indispensable step. This normalization is not a simple linear scaling process, but rather a dynamic benchmark interval established for each strength parameter based on the strength value distribution range accumulated from historical production data of this type of pole. The current measured value is mapped to a relative level within this benchmark interval, thus eliminating the comparison difficulties caused by differences in absolute numerical units. A theoretical benchmark strength value is calculated based on the aggregate gradation curve recorded in the material composition data and the strength grade of the cement. The aggregate gradation is calculated as an index characterizing the bulk density by analyzing the distribution of aggregates of various particle sizes. The cement grade is converted into an inherent strength contribution factor according to national standards. The two are combined and the expected strength value that can be achieved under ideal process conditions is derived through an empirical model.

[0029] The normalized compressive strength and flexural strength are combined with the calculated strength benchmark value using a weighted fusion method to form a single strength performance index value. The weighting coefficients used are not fixed but dynamically adjusted according to the design requirements of the pole. For example, for poles subjected to large bending moments, the weight of flexural strength is appropriately increased, while for foundation piles mainly subjected to compressive stress, more emphasis is placed on compressive strength. The strength benchmark value, as a theoretical assessment of the material's inherent potential, is usually set to a low and stable value for calibration and reference. When the monitoring system detects that the water-cement ratio is higher than the preset process limit, it triggers the strength performance index value update mechanism. The update logic includes branch judgment: the system counts the number of production batches whose water-cement ratio continuously exceeds the limit within a sliding time window and calculates the ratio of this number to the total number of production batches within the window. If this ratio exceeds a certain set threshold, it is determined to be a systematic deviation, and a fixed proportion is applied downward adjustment to the strength performance index value.

[0030] If the continuous exceedance ratio does not reach the threshold but occasional exceedances occur, the level is determined based on the specific extent to which the measured water-cement ratio exceeds the limit in a single instance. Different levels correspond to different adjustment ratios; the larger the exceedance, the higher the adjustment ratio. However, there is a preset upper limit for the adjustment range in all cases to prevent distortion of the index value due to a single severe deviation. The level classification of the water-cement ratio exceedance is based on the allowable fluctuation range in the process specifications. For example, the exceedance range is divided into three levels: slight, moderate, and significant, with each level corresponding to an increasing penalty coefficient. The entire calculation and updating process of the strength performance index is automated, and the results are fed back to the subsequent monitoring module in real time. The derivation process of the initial process correction value is based on a dual-threshold monitoring mechanism. This system sets independent early warning limits for crack resistance and strength performance indices. These limits are derived from the product technical specifications and the statistical quantiles of historical qualified products, providing clear engineering basis. The monitoring logic first checks the crack resistance and strength performance values ​​calculated by the current manufacturing unit. If the value of either of these values ​​falls below its corresponding warning limit, the system determines that the production status of the unit is abnormal. At this time, the initial process correction value of the unit will be directly set to a preset default value. This default value usually corresponds to a set of conservative process parameter adjustment schemes, such as slightly increasing the curing temperature or extending the static stop time, in order to quickly bring the production process back to a stable range.

[0031] If, after assessment, both performance index values ​​are above their respective warning limits, it indicates that the current production status is within an acceptable range. The system will then activate the adjustment function in the performance adjustment model to calculate the initial process correction value. This adjustment function uses the current crack resistance and strength performance indices as input variables. Its internal mapping relationship is a multivariate response surface model trained based on a large amount of historical optimization data. This model can capture the optimal process parameter adjustment direction and magnitude corresponding to minor changes in performance indices. The calculated initial process correction value covers key process parameters, such as the adjustment amount of centrifugal speed and changes in the temperature setpoint of the curing regime. These values ​​serve as preliminary optimization suggestions, providing a basic input for subsequent adaptive adjustments. The entire derivation process emphasizes real-time performance and automation, ensuring that production anomalies or optimization opportunities can be quickly identified and transformed into specific process intervention instructions. The quality of online detection data directly affects the reliability of the strength performance indices; therefore, regular calibration and maintenance of the testing machine are fundamental. Simultaneously, the system will perform outlier removal and smoothing filtering on the collected raw strength data to eliminate the interference of random errors. The historical data baseline interval upon which normalization processing relies needs to be updated regularly to reflect the impact of raw material performance fluctuations and long-term changes in the production system, maintaining the timeliness of the assessment benchmark. The calculation model for the strength benchmark value will be iteratively optimized by incorporating the latest materials science research results, such as introducing more accurate aggregate interference effect coefficients or cement hydration kinetic parameters, to make theoretical predictions closer to reality.

[0032] The weighted fusion strategy can be customized according to the specific requirements of different engineering projects, which increases the flexibility of the method. The water-cement ratio monitoring and index update mechanism constitutes an important feedback loop, which enables the strength performance index to not only reflect the current mechanical properties, but also to sensitively capture deviations in the mix proportion execution, playing a role in early warning. The setting of the early warning limit needs to balance sensitivity and stability to avoid frequent false alarms caused by normal fluctuations in the production process. It is usually set scientifically using the control chart principle in statistical process control. The selection of the default process correction value relies on the expert knowledge base, which stores standardized response plans for different typical anomaly modes. The adjustment function is the core of the performance tuning model, and its accuracy determines the optimization effect. Therefore, it needs to be continuously trained and verified using high-quality data accumulated from long-term operation of the production line. The initial process correction value is finally output in a structured data format, including parameter codes, adjustment direction, and adjustment amount, which facilitates parsing and execution by the manufacturing execution system.

[0033] Example 3: The specific implementation steps of adaptive adjustment begin with randomly selecting a unit from the set of manufacturing units that have completed the initial process correction value calculation as the target unit to be processed. This random selection mechanism helps avoid systematic deviations that may be caused by the processing order. The system then identifies and obtains the initial process correction values ​​of all adjacent manufacturing units of the target unit in the manufacturing unit relationship network. The adjacency relationship is defined based on the physical proximity of production, including units with sequential time on the same production line, parallel processing units sharing the same set of core production equipment, or logically grouped units that complete production within the same workday. The obtained initial process correction values ​​of these adjacent units constitute a reference information pool for context-aware adjustment of the target unit.

[0034] Based on quantifiable attributes between the target unit and each adjacent manufacturing unit, a production tightness index is generated. This index characterizes the degree of mutual influence between units due to production equipment and time correlation. Equipment similarity is assessed by comparing the model codes, key performance parameters (such as centrifuge maximum speed, curing kiln temperature control accuracy), and equipment maintenance records of the main production equipment used by the target unit and adjacent units, calculating a scalar value of equipment matching degree between 0 and 1. Production time intervals are calculated in hours or shifts, measuring the absolute difference between the target unit's production start time and the adjacent unit's production start time, and converting this difference into a scalar value representing time proximity using a decay function; the shorter the interval, the higher the proximity. These two scalar values—equipment matching degree and time proximity—are assigned predefined weights and fused to generate an initial production tightness value. The system also checks whether adjacent units belong to the same production line type as the target unit (e.g., fully automated or semi-automated production line) and applies a compensation factor based on the check results for final adjustment, thus obtaining the production tightness index value used for subsequent calculations.

[0036] By systematically comparing the dominant material types used in the target unit and its adjacent units, a material compatibility index value is generated. This index is used to assess the potential process interaction strength between units due to material characteristics. Dominant material types mainly include cement type and grade, steel fiber material and shape characteristics, and admixture chemical type. The system matches the material type list of adjacent units with the material type list of the target unit. If the material combination is found to belong to a preset compatibility matrix (which defines material combinations that have been experimentally verified to coexist well), a weighted material compatibility index value is calculated directly based on the preset weights assigned to the importance of each material in the formulation. If the material combination does not belong to the preset compatible combination, a rule-based affinity index calculation process needs to be initiated. This process first queries the historical production database for the co-occurrence frequency of the corresponding material types of the target unit and adjacent units over a certain period of time, i.e., the statistical probability of their simultaneous occurrence in the same batch or adjacent batches. It then queries a material function dependency table, which stores known physicochemical interaction relationships between different materials (such as strength promotion effect and shrinkage inhibition effect), to obtain an initial function dependency value. Then, a compatibility coefficient is calculated based on a material compatibility rule base (containing knowledge about alkali-aggregate reaction, chloride ion attack risk, etc.). This compatibility coefficient is used to correct the initial value of functional dependence, and then combined with the co-occurrence frequency, an affinity index is calculated through a mapping function. Finally, this affinity index is converted into a material compatibility index value.

[0037] After obtaining the production compactness index and material compatibility index values ​​for each adjacent unit, the system assigns an adjustment factor to each pair of "target unit-adjacent unit" relationships according to a predetermined strategy. The allocation process considers both general standards and specifications for pole manufacturing and the company's specific process procedures. These documents indicate the relative importance of equipment consistency and material compatibility to process stability, and production compactness weights and material compatibility weights are set accordingly. The adjustment factor is a weighted sum of the production compactness index value and the material compatibility index value, quantifying the influence of that specific adjacent unit on the process correction value of the target unit. After processing the current target unit, the system iterates through the next unprocessed unit in the manufacturing unit set, repeating all the steps from random selection (or sequential selection) to weighted adjustment until all manufacturing units in the set have obtained adaptively adjusted process correction values. Finally, the system outputs a complete list containing all manufacturing units and their corresponding adjusted process correction values. This list reflects the comprehensive optimization scheme after considering the interrelationships between production units, providing precise input for generating the final production optimization instructions.

[0038] The traversal processing mechanism ensures the inherent consistency of the entire production batch. The allocation logic of adjustment factors incorporates domain knowledge, enabling process modifications not only based on the performance indicators of the unit itself but also intelligently absorbing effective information from closely related production units. The calculation of the production tightness index integrates hardware and timing factors, while the material compatibility index focuses on the chemical and physical compatibility of the formulation. Together, they form the basis for evaluating the dimensions of influence between units. Weighting coefficients... , and time constant The specific values ​​typically need to be calibrated based on the characteristics of the specific production line and historical data to accurately reflect the actual impact of equipment and time factors in a particular production environment. The entire adaptive adjustment process, as a computationally intensive task, is usually executed on the production line's servers or edge computing nodes to meet real-time requirements.

[0039] Example 4: The generation of production tightness index values ​​relies on a refined measurement of the physical relationships between manufacturing units. The calculation begins with an assessment of the equipment similarity between the target unit and specific adjacent manufacturing units. Assume the target unit is Unit A, which is producing Φ190×12m ring-shaped prestressed poles. Its adjacent units include Unit B, which was produced on the same production line the previous day, and Unit C, which was produced on a similar production line two days ago. Assessing equipment similarity first requires comparing the models and key parameters of the core production equipment. For example, Unit A uses an LWL-250 centrifuge with a maximum speed of 800 rpm, while Unit B uses the same model centrifuge but its maintenance records show slight belt wear. Unit C uses an LWL-250A centrifuge with a maximum speed of 850 rpm. The calculation of equipment matching degree comprehensively considers the consistency of equipment models, differences in key performance parameters, and equipment status monitoring data. For example, by querying equipment ledgers and operating logs, higher matching degree scores are assigned to equipment with completely identical models, parameter deviations within allowable ranges, and good condition.

[0040] The production time interval is calculated based on the timestamps recorded by the production scheduling system. The production start time interval between Unit A and Unit B is 18 hours, and the interval between Unit A and Unit C is 45 hours. Time proximity is typically calculated using a decay model; the shorter the time interval, the higher the proximity value, reflecting the stronger reference significance of recent production conditions for the current unit. For example, an 18-hour interval might correspond to a higher proximity value, while a 45-hour interval would correspond to a significantly lower value. Integrating equipment matching degree and time proximity to generate an initial value for production tightness requires a weighting scheme. This scheme is usually set by process engineers based on experience, for example, assigning a higher weight to equipment matching degree because equipment stability is the basis for process reproducibility. Assuming the calculated equipment matching degree for Unit A and Unit B is 0.85 and the time proximity degree is 0.75, assigning a weight of 0.6 to equipment matching degree and 0.4 to time proximity degree, the initial production tightness value is...

[0041] The system checks whether the production line type of adjacent manufacturing units is the same as that of the target unit. Production line types can be categorized based on automation level, equipment layout, and control system, such as "fully automated high-speed line" and "semi-automated standard line." If adjacent units have the same production line type as the target unit, a compensation factor greater than 1 (e.g., 1.1) is assigned, indicating that experience from the same production line is more valuable. If the types are different, a compensation factor equal to or less than 1 (e.g., 0.9) is assigned. In this example, units A and B are both "fully automated high-speed lines," so the compensation factor is 1.1; units A and C are different production line types, so the compensation factor is 0.9. Multiplying the initial production density value by the corresponding compensation factor yields the final production density index value. The index values ​​for units A and B are... The index values ​​of units A and C are (assuming their initial value is 0.7). The process of generating material compatibility index values ​​focuses on the chemical and physical interactions of the formulation composition. The system first identifies the dominant material types of the target unit and its neighboring units. For example, target unit A uses P.O52.5 cement, hooked steel fibers, and polycarboxylate superplasticizer; neighboring unit B uses P.O52.5 cement, straight steel fibers, and naphthalene-based superplasticizer; and neighboring unit C uses PSA42.5 cement, hooked steel fibers, and polycarboxylate superplasticizer. The system matches the material type lists of units B and C with those of unit A and queries a preset compatibility matrix. If the material combination belongs to a preset compatible combination (e.g., the matrix defines "P.O52.5 cement and naphthalene-based superplasticizer" as a compatible combination), the system assigns preset weights to each material based on their relative importance in the formulation (e.g., cement has the highest weight, admixtures the second highest, and fiber type the third highest), and then calculates the weighted material compatibility index value.

[0042] If the combination is not a preset compatible combination, for example, the combination of PSA42.5 cement used in unit C and P.O52.5 cement in unit A is not explicitly defined in the compatibility matrix, then an affinity index needs to be calculated. The system will statistically analyze the frequency of "P.O52.5 cement" and "PSA42.5 cement" appearing in adjacent batches over the past year from historical production data. Simultaneously, it will query the material function dependency table to obtain the initial value of the function dependency when these two types of cement are used together. This value may stem from differences in their heat of hydration and alkali content. Next, a compatibility coefficient is calculated based on the material compatibility rule base, which may contain clauses such as "the hydration rate matching should be considered when mixing different types of cement." This coefficient is used to correct the initial value of the function dependency, and combined with the co-occurrence frequency, an affinity index is obtained through a transformation function. Finally, this index is converted into a material compatibility index value. Refer to Table 1, which illustrates the process of calculating the production density index value and material compatibility index value for target unit A and its two adjacent units B and C, as well as the final factor calculation used for adjustment (assuming fixed weights).

[0043] Table 1: Calculation of correlation indicators between target unit A and adjacent units B and C

[0044] After obtaining the production tightness index and material compatibility index values ​​between each adjacent unit and the target unit A, the system calculates adjustment factors based on preset weights (e.g., in standard specifications, production tightness and material compatibility may be equally important at this stage, hence each with a weight of 0.5). These adjustment factors are used to weight and adjust the initial process correction values ​​of unit A, ensuring that information provided by unit B, which is more closely related to the current unit A in terms of equipment, time, and materials, carries greater weight in the final decision. The entire calculation process embodies intelligent decision-making logic based on data correlation, making process optimization no longer an isolated event, but a continuous adjustment process that considers the inherent connections within the production system. The material compatibility rule base needs to be continuously updated to cover knowledge of new material combinations, the accuracy of equipment matching calculations depends on the completeness of the digital equipment archives, and the parameters of the time proximity decay model need to be calibrated according to the specific production rhythm to ensure that it can truly reflect the timeliness of production experience.

[0045] Example 5: The calculation of the affinity index begins with statistical operations on historical production datasets to determine the frequency of co-occurrence of key material types used by the target manufacturing unit and its adjacent units being evaluated. Assume that target unit A, currently being evaluated, uses P.O52.5 grade cement and incorporates S95 grade mineral powder, while its adjacent unit B, serving as a reference, uses P.O42.5 grade cement and also incorporates S95 grade mineral powder. The system scans all production records within a defined historical period, such as data from the past 24 months or the most recent 1000 production batches, counting the number of times the material combinations "P.O52.5 cement and S95 mineral powder" and "P.O42.5 cement and S95 mineral powder" co-occur in defined related manufacturing units (such as units in continuous production or units using the same mixing equipment). This co-occurrence frequency is usually normalized, for example, by converting it into a proportion relative to the total number of possible co-occurrences, or by using a logarithmic function to smooth out the effects of extreme values, thus obtaining a quantitative co-occurrence intensity index that reflects the degree to which the two material combinations have been used together in past production practices.

[0046] The next crucial step is to look up the initial functional dependencies of the target cell and its adjacent cells in the material functional dependency table. The material functional dependency table is a structured knowledge base whose content may originate from laboratory studies, material supplier technical data sheets, and industry standards and specifications. This table records the mutual influence relationships of different material pairings across specific performance dimensions. For example, for the combination of "P.O52.5 cement" and "S95 mineral powder," the table might indicate a positive synergistic effect in improving the later-stage strength of concrete, assigning a high initial functional dependency value, such as 0.8 (assuming a value range of 0 to 1). For the combination of "P.O42.5 cement" and "S95 mineral powder," the table might show a standard synergistic effect, assigning a moderate initial value, such as 0.6. The initial functional dependency value focuses on the theoretical, inherent potential for chemical and physical interactions between the materials.

[0047] Calculating the compatibility coefficient between the target unit and adjacent units based on material compatibility rules is a dynamically adjusted step. Material compatibility rules are a set of logical judgment conditions based on materials science principles and engineering experience. These rules consider potential negative interactions between materials. For example, one rule might check whether the alkali content of cement matches the activity of mineral admixtures to avoid potential alkali-aggregate reaction risks; another rule might assess whether there is incompatibility between chemical admixtures from different sources, leading to flocculation or water-reducing agent failure. The system inputs the actual material parameters of the target unit and adjacent units into these rules for matching and calculation. Assuming the rule engine determines that the combination of "P.O52.5 cement and S95 mineral powder" meets all safety rules and has bonuses in certain aspects of enhanced durability, it might output a compatibility coefficient slightly greater than 1, such as 1.05. However, if a certain characteristic of "P.O42.5 cement" has a slight mismatch with the requirements of S95 mineral powder in terms of rapid hydration, but does not reach the safety threshold, it might output a coefficient equal to or slightly less than 1, such as 0.98.

[0048] The initial functional dependency value obtained from the previous query is corrected based on the calculated compatibility coefficient to obtain the adjusted functional dependency value. This correction process is typically a multiplicative relationship. For the material combination of elements A and B, For another hypothetical combination of units A and C (such as involving different admixtures), if its initial functional dependency is 0.7 and its compatibility coefficient is 0.98, then This adjustment step makes the theoretical functional dependency relationship closer to the actual expectation under the current specific material configuration. The final summary stage is to calculate the affinity index by combining the adjusted functional dependency value with the previously obtained co-occurrence frequency. The synthesis method needs to be predefined and may use geometric mean, weighted average, or other fusion algorithms. Geometric mean can take both aspects into account; a low value in either aspect will significantly lower the final index. For example, assuming the co-occurrence frequency of the material combination of target unit A and the adjacent material combination of unit B is calculated to be 0.75 (indicating frequent historical co-occurrence), while the adjusted functional dependency value is 0.84, then its affinity index might be... If the co-occurrence frequency with unit C is very low (e.g., 0.30), and the adjusted functional dependency value is only 0.686, then the affinity index may be... This affinity index is a comprehensive scalar that takes into account both empirical evidence of historical use (co-occurrence frequency) and functional dependencies supported by materials science theory.

[0049] The calculated affinity index is mapped to the final material compatibility index value by a pre-defined transformation function. This transformation function could be a simple linear scaling, mapping the affinity index from its original range (e.g., 0 to 1) to another specified range (e.g., 0.3 to 0.95), or it could be a non-linear piecewise function to more sensitively reflect differences within a specific interval. For example, an affinity index of 0.794 might be mapped to a material compatibility index value of 0.82, while an affinity index of 0.454 might be mapped to 0.50. The coverage and quality of historical datasets directly affect the statistical reliability of co-occurrence frequencies. The material function dependency table needs to be regularly reviewed and updated by materials experts to incorporate the latest research findings. The completeness and accuracy of the material compatibility rule base determine the scientific validity and engineering practicality of the compatibility coefficient calculation.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An optimized method for manufacturing steel fiber reinforced, crack-resistant, ultra-high performance concrete centrifugal poles, characterized in that, include: The pole manufacturing process is divided into multiple manufacturing units according to production batches. Material composition data, process setting data and online detection data of each manufacturing unit are collected to form a multi-source production dataset. Crack resistance and strength performance indicators are calculated based on multi-source production datasets, and initial process correction values ​​are dynamically output through a dual-threshold monitoring mechanism and performance adjustment model. The initial process correction value of each manufacturing unit is adaptively adjusted based on the production correlation between adjacent manufacturing units to generate the adjusted process correction value. The adjusted process correction values ​​are serialized to output production optimization instructions, which are then applied to the manufacturing execution process.

2. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 1, characterized in that, The manufacturing unit is divided as follows: the manufacturing process is divided into multiple units according to the pole type and production time sequence. Each unit has a consistent material batch identifier and process parameter group, and the manufacturing unit is used as the basic processing unit.

3. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 1, characterized in that, The calculation process of crack resistance index includes: extracting steel fiber content and water-cement ratio from material composition data; obtaining concrete cracking tendency parameters based on online detection data; calculating crack resistance coefficient through a preset weight allocation scheme; generating stability parameters based on curing conditions in process setting data; and generating crack resistance index value by combining crack resistance coefficient and stability parameters.

4. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 1, characterized in that, The calculation process for strength performance indicators includes: extracting compressive strength and flexural strength from online testing data; normalizing the compressive strength and flexural strength; calculating the strength benchmark value based on the aggregate gradation and cement grade in the material composition data; combining the normalized compressive strength, flexural strength, and strength benchmark value into a strength performance indicator value through a weighted fusion method; and triggering an update of the strength performance indicator value when the water-cement ratio exceeds a set limit. The update rule is as follows: if the ratio of consecutive water-cement ratio exceedances to the total number of monitoring times exceeds a threshold, the strength performance indicator value is reduced by a fixed proportion; otherwise, the strength performance indicator value is reduced by an increasing proportion based on the degree of water-cement ratio exceedance, with the upper limit of the reduction being a fixed proportion value.

5. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 1, characterized in that, The derivation process of the initial process correction value includes: setting early warning limits for crack resistance performance index and early warning limits for strength performance index; if any performance index of a manufacturing unit is lower than the early warning limit, its initial process correction value is set to the default value; if all performance indexes are higher than the early warning limit, the initial process correction value is calculated based on the crack resistance performance index and strength performance index through an adjustment function.

6. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 1, characterized in that, The specific implementation steps of adaptive adjustment include: randomly selecting a manufacturing unit as the target unit and obtaining the initial process correction values ​​of all its adjacent manufacturing units; generating a production tightness index value based on the equipment similarity and production time interval between the target unit and its adjacent manufacturing units; generating a material compatibility index value by comparing the material types of adjacent manufacturing units and the target unit; allocating adjustment factors according to the production tightness index value and the material compatibility index value; weighting the initial process correction value of the target unit based on the adjustment factors and outputting the adjusted process correction value; traversing all manufacturing units to complete the adjustment and outputting the adjusted process correction value of all manufacturing units. The process of allocating adjustment factors includes: obtaining the standard specifications for pole manufacturing, setting production tightness weights and material compatibility weights according to the specifications, and calculating adjustment factors based on the set weights, production tightness index values, and material compatibility index values.

7. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 6, characterized in that, The process of generating the production tightness index value includes: calculating the equipment matching degree based on the equipment similarity between the target unit and adjacent manufacturing units; calculating the time proximity based on the production time interval between the target unit and adjacent manufacturing units; obtaining the initial value of production tightness by weighted fusion of the equipment matching degree and time proximity, and setting compensation factors according to the production line type of adjacent manufacturing units; generating the production tightness index value by compensating the initial value of production tightness with the compensation factors; the compensation factor setting rule is: assign a high compensation value if the production line types are the same, and assign a low compensation value if the production line types are different.

8. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 6, characterized in that, The process of generating material compatibility index values ​​includes: matching the dominant material type of adjacent manufacturing units with the material type of the target unit; if they belong to a preset compatible material combination, calculating the material compatibility index value based on each material type and its corresponding preset weight; if they do not belong to a compatible combination, calculating the affinity index based on the material interaction specification, and obtaining the material compatibility index value by mapping the affinity index through a preset transformation function.

9. The optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method as described in claim 8, characterized in that, The process of calculating the affinity index includes: statistically analyzing the co-occurrence frequency of the material types corresponding to the target unit and adjacent manufacturing units from historical production datasets; querying the initial functional dependency values ​​of the material types corresponding to the target unit and adjacent manufacturing units according to the material functional dependency table; calculating the compatibility coefficient between the target unit and adjacent manufacturing units according to the material compatibility rules; correcting the initial functional dependency value according to the compatibility coefficient to obtain the adjusted functional dependency value; and calculating the affinity index by combining the adjusted functional dependency value and the co-occurrence frequency.

10. A manufacturing system for optimized steel fiber-reinforced crack-resistant ultra-high performance concrete centrifugal poles, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the optimized steel fiber crack-resistant ultra-high performance concrete centrifugal pole manufacturing method according to any one of claims 1 to 9.

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