An intelligent segment production scheduling system

Abstract

The present invention relates to the field of production scheduling technology, specifically an intelligent segment production scheduling system, the system comprising: a raw material analysis module, a material distribution control module, a scheduling and speed regulation module, a section judgment module, and a misalignment identification module. In the present invention, by quantifying the correlation between raw material parameter differences and processing time, precise control of task rhythm is achieved, the relationship between concrete expansion rate and fluidity response is utilized to optimize the discharge rate of the material distribution equipment, and the spatial mapping of the mold load center of gravity and the vehicle operation path is combined to improve the dynamic adaptability of the transportation process. By identifying the temperature distribution state of the mold surface, the accuracy of the judgment of the maintenance section switching is enhanced, and combined with the real-time judgment and compensation control of the mold slot offset, the response efficiency of production scheduling and the accuracy of path control are improved, bringing a highly adaptable scheduling response mechanism to segment production, enhancing the beat accuracy, resource coordination rationality and process continuity.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling, and in particular to an intelligent segment production scheduling system. Background Art

[0002] The field of production scheduling technology includes the orderly organization and coordination of production resources and tasks in the manufacturing system to ensure that each production link operates efficiently according to the set process flow. The core content of this technical field is to realize the formulation of production plans, task priority sorting, dynamic matching of work tasks and production resources, and critical path management. Production scheduling involves multiple aspects such as information collection and processing, process control, equipment operation coordination, personnel allocation and task issuance. Relying on data acquisition devices, central control units and execution terminals, it pushes tasks, provides status feedback and switches processes between production units through preset scheduling rules or control logic, and is applied to various industrial scenarios such as assembly manufacturing, building material prefabrication, electronic processing, and automobile manufacturing.

[0003] Among them, an intelligent segment production scheduling system refers to a scheduling control platform for concrete segment component production lines. The technical matters it targets include production task information management, segment mold placement sequence control, independent curing kiln temperature curve setting and partition switching, central control system command issuance, kiln in-and-out vehicle path scheduling, and automatic identification and positioning control of static positions. Specifically, it takes the central control system as the core, combined with the sequence control logic of the concrete placing boom to achieve uniform distribution of raw materials, uses the temperature control system to set the heating, constant temperature and cooling sections to control the steaming state, uses the path preset program to drive the transport vehicle to complete the mold transfer path switching, and uses video monitoring and sensor feedback to monitor and record the status of each key link in real time, and logically manages the task execution sequence in the production line and distributes scheduling commands.

[0004] Traditional segment production scheduling technology is based on static matching of task numbers and resource allocation rules, and relies on the central control platform to push tasks and feedback status according to rules. The data correlation between each link is weak, and the raw material status is only uniformly stored through batch coding. It is difficult to dynamically predict the duration of specific parameter differences. Material distribution control relies on fixed discharging procedures, and uneven material distribution is prone to occur when the concrete state is abnormal. The mold center of gravity control is mostly based on fixed paths and standard vehicle speeds, and lacks a feedback mechanism for real-time load status. A constant temperature control curve is used for partitioned operation during the curing stage, and the actual distribution of the thermal field is not involved in the scheduling judgment, resulting in over-curing or insufficient steaming of the mold. In the slot entry control, only the positioning sensor is relied upon to judge the mold in-place status, ignoring the micro-offset error caused by the change in mold posture, resulting in misalignment or secondary adjustment of the mold in the positioning area, increasing the overall cycle time, and reducing the accuracy of automated control and continuous operation efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent segment production scheduling system.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: an intelligent segment production scheduling system includes:

[0007] The raw material analysis module obtains cement strength grade, manufactured sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content data for each raw material batch, analyzes performance differences compared to preset benchmark values, predicts task processing time for each raw material batch, adjusts the task scheduling list, and obtains a production task list.

[0008] The distribution control module calls the production task list, uses an image acquisition device to identify the expansion speed of the concrete drop point in the concrete distribution equipment, adjusts the discharge rate by evaluating the fluidity of the concrete, and obtains a mold status list based on the mold number;

[0009] The scheduling and speed control module calls the mold status list and multiple weight sensors of the carrier to calculate the spatial offset direction and offset amplitude between the center of gravity of the mold and the center of the vehicle in real time after loading, and adjusts the speed parameters to obtain the speed control parameters;

[0010] Based on the speed control parameters, the section judgment module uses infrared imaging equipment to monitor the temperature distribution image of the mold in the maintenance area, including the heating zone, constant temperature zone, and cooling zone. It extracts the temperature distribution trend in the center area of ​​the image, the direction of the edge temperature gradient, and the change trend of the thermal field uniformity. It calculates the time point when the mold enters the next maintenance zone and obtains the hot zone switching record.

[0011] As a further solution of the present invention, the production task list includes the raw material batch number, the task processing time prediction value, and the task priority order; the mold status list includes the mold number, the concrete fluidity level, and the concrete distribution time record; the speed control parameters are specifically the offset direction vector value, the offset amplitude value, and the path speed correction coefficient; the hot zone switching record includes the center temperature change trend of the thermal map, the edge temperature difference distribution trend, and the maintenance zone switching time point.

[0012] As a further solution of the present invention, the raw material analysis module includes:

[0013] The performance difference submodule obtains the cement strength grade, machine-made sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content data of each raw material batch, extracts the performance difference between each parameter and the preset benchmark value, and generates a raw material parameter difference set;

[0014] The duration prediction submodule is based on the raw material parameter difference set and uses the formula:

[0015] ;

[0016] Calculate the predicted value of the task processing cycle for each raw material and obtain the predicted duration calculation result;

[0017] in, Represents the predicted value of task processing cycle, Indicates the benchmark duration of the task corresponding to the standard raw material parameter group. Indicates the current batch Raw material parameter values, Indicates the Standard reference value of each raw material, Indicates the The sensitivity coefficient of the item parameter to the processing time, Indicates the parameter number of the raw material currently being analyzed. Indicates the total number of raw material parameters involved in the processing;

[0018] The task scheduling submodule calls the predicted duration calculation result, adjusts the task scheduling list according to the required processing duration, and establishes a production task list.

[0019] As a further solution of the present invention, the cloth control module includes:

[0020] The landing point monitoring submodule calls the production task list, uses the image acquisition device to analyze the expansion position of the concrete landing point in the mold in the continuous frame image, extracts the expansion radius data and corresponding timestamps of the current frame and the previous frame, and establishes the expansion speed change value;

[0021] The fluidity evaluation submodule calls the expansion speed change value, combines the standard fluidity rate and the benchmark discharge rate, and uses the formula:

[0022] ;

[0023] Calculate the discharge rate and establish the discharge rate control parameters;

[0024] in, To indicate the real-time discharge rate that the equipment should execute according to the current concrete fluidity, the unit is cubic meters per second. is the benchmark discharge rate, in cubic meters per second, indicating the target discharge rate set under standard fluidity conditions. The concrete expansion radius in the current image frame is in meters, indicating the boundary distance reached by the concrete from the landing point in the latest frame. is the concrete expansion radius in the previous image frame, in meters, indicating the boundary expansion distance of the concrete in the previous image frame. The timestamp corresponding to the current image frame, in seconds, indicates the time value when the system records the current frame image capture. The timestamp corresponding to the previous image frame, in seconds, indicates the time value when the system records the last frame of image capture. is the standard concrete diffusion rate, measured in meters per second, which represents the standard reference rate at which the concrete expansion radius changes with time under ideal conditions. is the liquidity response adjustment index, which is a dimensionless value;

[0025] The rhythm recording submodule calls the discharge rate control parameter, records the concrete spreading time of each mold, extracts the mold number of the completed concrete spreading, and establishes a mold status list.

[0026] As a further solution of the present invention, the scheduling and speed regulation module includes:

[0027] The center of gravity identification submodule calls the mold status list and multiple weight sensors of the carrier, extracts mass data at multiple positions and maps them to the vehicle chassis coordinate system, calculates the center of gravity position of the mold after loading, and obtains the center of gravity coordinate data;

[0028] The offset calculation submodule calls the center of gravity coordinate data and uses the formula:

[0029] ;

[0030] Calculate speed regulation control parameters;

[0031] in, is the speed adjustment control parameter, which is the adjusted running speed that the vehicle should execute in the current path section, in meters per second. The standard speed value of the section is the preset basic travel speed, in meters per second. is the coordinate value of the mold center of gravity along the transverse axis in the vehicle coordinate system, in meters. is the coordinate value of the mold center of gravity along the longitudinal axis in the vehicle coordinate system, in meters. is the coordinate value of the mold center of gravity along the vertical axis in the vehicle coordinate system, in meters. is the reference coordinate value of the center point of the vehicle track along the transverse axis in the vehicle coordinate system, in meters. is the reference coordinate value of the center point of the vehicle track along the longitudinal axis in the vehicle coordinate system, in meters. is the reference coordinate value of the center point of the vehicle track along the vertical axis in the vehicle coordinate system, in meters. is the boundary length of the mold in the vehicle coordinate system, in meters. is the boundary width of the mold in the vehicle coordinate system, in meters. is the boundary height of the mold in the vehicle coordinate system, in meters. is the adjustment weight coefficient of the proportion of the lateral offset difference, which is a dimensionless real number. is the adjustment weight coefficient of the proportion of the longitudinal offset difference, which is a dimensionless real number. is the adjustment weight coefficient of the proportion of vertical offset difference, which is a dimensionless real number;

[0032] The speed regulation submodule calls the speed regulation control parameters, sets the speed parameters of multiple positions in the parent-child vehicle path, including the running speeds of the turning section, the acceleration section and the parking section, and establishes the travel speed control parameters.

[0033] As a further solution of the present invention, the segment determination module includes:

[0034] The thermal image acquisition submodule calls the infrared imaging equipment of each curing sub-zone according to the speed control parameters, including the heating zone, the constant temperature zone, and the cooling zone, to collect the temperature distribution image of the mold surface and obtain the thermal image of the mold surface;

[0035] The thermal field trend identification submodule calls the mold surface thermal image, divides the image into multiple grids, extracts the temperature mean sequence of each grid in the center area of ​​the image, identifies the temperature distribution trend of the center area, combines the temperature in the grid in the edge area of ​​the image, calculates the edge temperature gradient direction, analyzes the change trend of the thermal field uniformity, and obtains the mold status analysis result;

[0036] The partition transfer determination submodule calls the mold state analysis result, combines the mold state required by each partition, calculates the time point when the mold enters the next maintenance period, and obtains the hot zone switching record.

[0037] As a further embodiment of the present invention, the system further comprises:

[0038] The misalignment recognition module calls the hot zone switching records and uses image acquisition equipment to identify the positions of the mold's two side edges and measure the angle between them and the track direction. This module detects positional deviations of the mold during slotting and adjusts the control parameters of the parent-child carriage based on the deviation direction to obtain segment production line management results.

[0039] The segment production line management results specifically refer to the mold misalignment angle, offset direction identification, and vehicle control adjustment instructions.

[0040] As a further solution of the present invention, the dislocation recognition module includes:

[0041] The image acquisition submodule calls the hot zone switching record and uses an image acquisition device to capture a top view image of the mold entry section. It locates the pixel points of the edge lines on both sides of the mold in the image coordinate system, converts them into actual space coordinates, constructs the mold edge direction vector, and measures the angle with the track center reference direction to obtain an edge angle data set.

[0042] The offset event determination submodule calls the edge angle data set to detect position offset events of the mold during the slotting process, including angle offset events, edge angle difference exceeding limit events, and slotting centerline offset events, and generates a slotting offset determination result;

[0043] The path adjustment execution submodule calls the slot entry offset judgment result, combines the offset angle direction, sets the corresponding mother-and-child vehicle's travel direction, driving speed and braking trigger value parameters, and obtains the segment production line management parameter set.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by quantifying the correlation between the differences in raw material parameters and the processing time, precise control of the task rhythm is achieved. The relationship between the concrete expansion rate and the fluidity response is used to optimize the discharge rate of the distribution equipment. The spatial mapping of the mold load center of gravity and the vehicle operation path is combined to improve the dynamic adaptability of the transportation process. By identifying the temperature distribution state of the mold surface, the accuracy of the judgment of the maintenance section switching is enhanced. Combined with the real-time judgment and compensation control of the mold slot offset, the response efficiency of the production scheduling and the accuracy of the path control are improved, bringing a highly adaptable scheduling response mechanism to the segment production, and enhancing the beat accuracy, resource coordination rationality and process continuity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 This is a flow chart of the raw material analysis module of the present invention;

[0048] Figure 3 This is a flow chart of the cloth control module of the present invention;

[0049] Figure 4 This is a flow chart of the scheduling and speed regulation module of the present invention;

[0050] Figure 5 This is a flow chart of the segment determination module of the present invention;

[0051] Figure 6 This is a flow chart of the dislocation identification module of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0054] See also Figure 1 , an intelligent segment production scheduling system includes:

[0055] The raw material analysis module obtains cement strength grade, manufactured sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content data for each raw material batch, analyzes performance differences compared to preset benchmark values, predicts task processing time for each raw material batch, adjusts the task scheduling list, and obtains a production task list.

[0056] Machine-made sand particle gradation: Machine-made sand particle gradation refers to the mass percentage of particles of different particle sizes in machine-made sand. It is used to evaluate the rationality of the particle size distribution of machine-made sand. The percentage of each particle size is extracted through dry sieving test. The particle size combination is used to determine the gradation type of sand and its impact on the workability and strength of concrete.

[0057] The distribution control module calls the production task list and uses image acquisition equipment to identify the expansion speed of the concrete drop point in the concrete distribution equipment. It adjusts the discharge rate by evaluating the fluidity of the concrete and obtains the mold status list based on the mold number.

[0058] Concrete fluidity: This reflects the deformation capacity of fresh concrete under gravity and is an important parameter for concrete workability. The slump test is commonly used to measure this, with the unit being mm. In video monitoring of concrete distribution, the time function of the diffusion velocity and diffusion radius of the concrete drop point indirectly reflects its fluidity trend, thereby constructing its behavioral characteristic changes.

[0059] The scheduling and speed control module calls the mold status list and multiple weight sensors of the carrier to calculate the spatial offset direction and offset amplitude between the center of gravity of the mold and the vehicle center in real time after loading, and adjusts the speed parameters to obtain the speed control parameters.

[0060] The segment judgment module uses infrared imaging equipment to monitor the temperature distribution image of the mold within the curing area based on speed control parameters, including the heating zone, constant temperature zone, and cooling zone. It extracts the temperature distribution trend in the center of the image, the direction of the edge temperature gradient, and the trend of thermal field uniformity. It calculates the time point when the mold enters the next curing zone and obtains the hot zone switching record.

[0061] Thermal field uniformity trend: Thermal field uniformity assessment involves obtaining a temperature distribution map through infrared imaging and then performing multi-cycle trend extraction on indicators such as the temperature difference range between the center and edge areas and the temperature distribution density. This is used to determine whether the concrete component has completed the transition between heating, constant temperature, or cooling states, and serves as a reference signal for evaluating whether maintenance zones can be switched.

[0062] The misalignment recognition module calls the hot zone switching record and uses image acquisition equipment to identify the position of the side lines on both sides of the mold and measure the angle between them and the track direction. It detects the position offset event of the mold during the slotting process and adjusts the control parameters of the mother-and-child car according to the offset direction to obtain the management results of the pipe segment production line.

[0063] The production task list includes the raw material batch number, task processing time prediction value, and task priority order. The mold status list includes the mold number, concrete fluidity level, and concrete placement time record. The speed control parameters are specifically the offset direction vector value, offset amplitude value, and path speed correction coefficient. The hot zone switching record includes the center temperature change trend of the thermal map, the edge temperature difference distribution trend, and the maintenance zone switching time point. The segment production line management results specifically refer to the mold misalignment angle, offset direction mark, and vehicle control adjustment instructions.

[0064] See also Figure 2 , the raw material analysis module includes:

[0065] The performance difference submodule obtains the cement strength grade, machine-made sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content data of each raw material batch, extracts the performance difference between each parameter and the preset benchmark value, and generates a raw material parameter difference set;

[0066] The performance difference submodule collects four key raw material parameters from each raw material batch: cement strength grade, manufactured sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content. It then extracts the numerical differences between these parameters and the system-set benchmark reference values, thereby forming a raw material parameter difference set. During execution, a number label is first assigned to each batch of raw materials. For example, the current batch number is set to "B20240401." The cement strength grade of this batch is read as 52.5 MPa, and the difference between this and the system-set benchmark strength of 50.0 MPa is calculated to obtain 2. The deviation value of 5MPa is then read. The particle size distribution D60 value of the manufactured sand is 0.35mm, the standard reference value is 0.30mm, and the difference is 0.05mm. Then the fly ash water demand ratio is read as 0.68, the standard ratio is 0.65, and the difference is 0.03. The water content of the crushed stone is read as 1.9%, and the deviation is -0.1% by subtracting it from the standard value of 2.0%. The above four results are recorded as ΔC1=2.5, ΔC2=0.05, ΔC3=0.03, and ΔC4=-0.1, respectively, and form a parameter difference set in the form of a vector. This set is the data input directly called by the subsequent prediction processing time module. During the entire data acquisition process, all parameters are derived from the real-time detection data of the raw material delivery batch by the sensor sampling system or the fixed value data obtained by quantitative laboratory analysis, ensuring that the extracted differences are based on the authenticity of the raw materials and the processing status, providing an effective quantitative basis for further prediction.

[0067] The duration prediction submodule is based on the raw material parameter difference set and uses the formula:

[0068] ;

[0069] Calculate the predicted value of the task processing cycle for each raw material and obtain the predicted duration calculation result;

[0070] in, Represents the predicted value of task processing cycle, Indicates the benchmark duration of the task corresponding to the standard raw material parameter group. Indicates the current batch Raw material parameter values, Indicates the Standard reference value of each raw material, Indicates the The sensitivity coefficient of the item parameter to the processing time, Indicates the parameter number of the raw material currently being analyzed. Indicates the total number of raw material parameters involved in the processing;

[0071] The duration prediction submodule obtains the raw material parameter difference set After that, multiply each difference by its corresponding processing time sensitivity coefficient to obtain the increase or decrease of the processing cycle for each item, and add the result to the benchmark duration to obtain the final predicted task processing cycle value. The specific steps are as follows: Set the benchmark duration For 120 minutes, the four parameters are substituted into the calculation. The treatment sensitivity coefficient of the first cement strength is set to 2.0min / MPa. Calculation is performed. The second parameter, machine-made sand grading sensitivity coefficient, is 30.0 min / mm. The third item, fly ash water demand, has a sensitivity coefficient of 25.0min. ; The fourth item, the sensitivity coefficient of crushed stone moisture content, is 15.0min / %, calculated , then add all the adjustment values ​​to the benchmark task duration to form the complete calculation expression:

[0072] ;

[0073] Bring in data to perform specific operations:

[0074] ;

[0075] The predicted value of the task processing cycle refers to the system's dynamic prediction of the task completion time based on the difference in raw material parameters. The result shows that under the current parameter conditions, the predicted value of the processing cycle of a single task is 125.75 minutes. This result will be used as the basis for determining whether to reschedule the task list in the next stage of adjustment. The formula takes the task benchmark processing time corresponding to the standard raw material parameter group as the starting point, and uses each difference between the current batch of raw material parameters and the standard parameters as an influencing factor for cumulative correction. The weight of each difference item is determined by its corresponding time sensitivity coefficient, reflecting its response intensity to the processing time. By multiplying the differences of all participating parameters by their sensitivity coefficients and summing them, a set of total time adjustment values ​​is formed, which is superimposed on the benchmark time. Finally, the predicted processing cycle of the batch of tasks is calculated. Its essence is to cumulatively map the time changes caused by the deviation of raw materials from the standard ratio to the task scheduling cycle to quantify the impact of raw material performance fluctuations on the production line processing rhythm.

[0076] The task scheduling submodule calls the predicted duration calculation result, adjusts the task scheduling list according to the required processing duration, and establishes a production task list;

[0077] The task scheduling submodule receives the predicted processing time of 125.75 minutes from the previous module, and combines it with the initial set benchmark task time of 120 minutes for the current task number "T002" in the task list. It performs a difference comparison between the predicted value and the benchmark value. The tolerance threshold is set to ±3 minutes. The current difference is 5.75 minutes, which exceeds the threshold limit. Therefore, it is judged as "needing to be rescheduled". When executing the adjustment process, the current task is first extracted from the original position to determine whether the conditions for the replacement of the previous and next task numbers are met. If so, the task is "re-scheduled". "T002" is postponed to the next two task positions. For example, if the original order is "T001→T002→T003", it is updated to "T001→T003→T002". Then the "Task Duration" field of the "T002" task is updated to "125.75", its status field is updated to "Time-consuming Task", and the postponement record is inserted into the scheduling record table. The system also provides the adjusted task list and prediction period field to subsequent sub-modules (such as the fabric control module) for the speed regulation and scheduling linkage module to call and process.

[0078] Table 1 Calculation table of raw material differences and task scheduling

[0079]

[0080] As shown in Table 1, the current raw material batch has different degrees of deviation compared with the benchmark in various parameters, and the corresponding processing time sensitivity coefficient is converted into a single time increase or decrease value, resulting in a total predicted processing cycle of 125.75 minutes, which exceeds the threshold standard and triggers a task adjustment. Task number "T002" is re-included at the end of the task list and the status is updated.

[0081] See also Figure 3 , the cloth control module includes:

[0082] The landing point monitoring submodule calls the production task list and uses image acquisition equipment to analyze the expansion position of the concrete landing point in the mold in consecutive frame images, extracts the expansion radius data and corresponding timestamps of the current frame and the previous frame, and establishes the expansion speed change value;

[0083] The landing point monitoring submodule calls the production task list generated in the previous order, and performs real-time image acquisition on the mold corresponding to the current task number in the list. The industrial camera arranged just above the concrete placing equipment collects concrete landing point images at a frequency of 5 frames per second. The system parses each frame of the image, identifies the edge contour of the concrete landing point in the image, and extracts its maximum expansion radius information in the bottom area of ​​the mold. During the execution process, the system reads the boundary position of the concrete expansion in the current frame (such as the nth frame) and calculates the distance L2 from the center of the circle to the farthest point on the edge. For example, if the timestamp of the nth frame image is 8.2 seconds and the expansion radius is 0.38 meters, the reading Take the previous frame image (n-1 frame) with a timestamp of 8.0 seconds and an expansion radius of 0.35 meters. The system subtracts the radius values ​​and timestamps of the two frames, and calculates the expansion radius increment ΔL=0.38-0.35=0.03 meters and the time interval ΔT=8.2-8.0=0.2 seconds. The system then calculates the expansion speed v=0.03 / 0.2=0.15 meters per second through the expansion increment and the time difference, and records this speed value as the expansion speed change indicator at the current time point. The system stores the above calculation results in the data structure corresponding to the task and continuously iterates to update the expansion speed data set of subsequent image frames in the mold.

[0084] The liquidity assessment submodule calls the extended speed change value, combines the standard liquidity rate with the benchmark discharge rate, and uses the formula:

[0085] ;

[0086] Calculate the discharge rate and establish the discharge rate control parameters;

[0087] in, To indicate the real-time discharge rate that the equipment should execute according to the current concrete fluidity, the unit is cubic meters per second. is the benchmark discharge rate, in cubic meters per second, indicating the target discharge rate set under standard fluidity conditions. The concrete expansion radius in the current image frame is in meters, indicating the boundary distance reached by the concrete from the landing point in the latest frame. is the concrete expansion radius in the previous image frame, in meters, indicating the boundary expansion distance of the concrete in the previous image frame. The timestamp corresponding to the current image frame, in seconds, indicates the time value when the system records the current frame image capture. The timestamp corresponding to the previous image frame, in seconds, indicates the time value when the system records the last frame of image capture. is the standard concrete diffusion rate, measured in meters per second, which represents the standard reference rate at which the concrete expansion radius changes with time under ideal conditions. is the liquidity response adjustment index, which is a dimensionless value;

[0088] After obtaining the expansion radius increment ΔL and time interval ΔT between consecutive frames, the fluidity assessment submodule calculates the concrete discharge rate control parameter Qo at the current moment, combining the preset standard concrete diffusion rate and the system benchmark discharge rate. In this step, the system first reads the standard concrete diffusion rate Vs value as 0.05 meters per second, sets the system benchmark discharge rate Qb to 0.15 cubic meters per second, and sets the fluidity response adjustment index α to 1.2 dimensionless. Based on the measured expansion radius increment of 0.03 meters and the time interval of 0.2 seconds, substitute into the formula:

[0089] ;

[0090] in, , , , , , α=1.2, substitute it into the formula to perform the calculation:

[0091] ;

[0092] The result shows that the current concrete fluidity has significantly improved, and the system should perform an adjustment to increase the discharge rate. The adjustment value is 0.5606 cubic meters per second, which exceeds the Qb±20% threshold. The system determines that the distribution rate setting of the rhythm recording module needs to be updated. This formula calculates the current expansion speed by measuring the difference in expansion radius and time between concrete landing points in consecutive image frames. The ratio is then normalized by the standard diffusion rate to form a dimensionless relative fluidity index. This ratio represents the degree of deviation between the current concrete fluidity and the standard state. Nonlinear adjustment is then performed using a set exponential parameter to enhance or suppress the impact of this deviation on the discharge rate. The adjusted result is multiplied by the system's set baseline discharge rate to determine the actual discharge rate to be executed, enabling dynamic control of the concrete distribution equipment. The formula constructs a fluidity response mechanism based on visual feedback. By detecting the speed of concrete expansion within the mold, the system perceives the concrete's working performance in real time and, based on the comparison with the standard diffusion behavior, intelligently adjusts the discharge rate to ensure rapid distribution in high-fluidity conditions while appropriately slowing down discharge when fluidity is poor to prevent accumulation and unevenness. This effectively enhances the system's adaptability to changes in concrete state and enables intelligent scheduling and precise control.

[0093] The rhythm recording submodule calls the discharge rate control parameters, records the concrete spreading time of each mold, extracts the mold number of the completed concrete spreading, and establishes a mold status list;

[0094] The rhythm recording submodule receives the calculated discharge rate control parameter Qo=0.5606 cubic meters / second and binds it to the material distribution process record of the current mold number "F011". During the recording process, the system counts the time it takes for the F011 mold to be filled with concrete from the start of the first discharge to the time when the concrete covers the entire mold cavity area. The specific process is as follows: assuming the material distribution start time is 8.0 seconds, the system detects that the time it takes for the concrete in the current frame image to cover the mold outline boundary is 9.4 seconds, then the material distribution time is 1.4 seconds. This value is the material distribution rhythm recording result corresponding to the mold. The system writes this time value, the corresponding mold number, and the Qo value into the mold status list. Subsequent modules will use this to determine whether to enter the scheduling and speed regulation link. The complete data format of the record is shown in the following table:

[0095] Table 2 Mould fabric status parameter record table

[0096]

[0097] As shown in Table 2, the mold F011 needs to increase its discharge rate to 0.5606 cubic meters per second due to an abnormal increase in expansion rate in the current production task, and its total feeding time is 1.4 seconds. Based on this list, the system makes coordinated adjustments to the speed and rhythm of downstream tasks.

[0098] See also Figure 4 , the scheduling and speed regulation module includes:

[0099] The center of gravity identification submodule calls the mold status list and multiple weight sensors of the carrier, extracts mass data from multiple locations and maps them to the vehicle chassis coordinate system, calculates the center of gravity position after the mold is loaded, and obtains the center of gravity coordinate data;

[0100] The center of gravity identification submodule obtains the mold number marked as "fabric completed" in the current task based on the mold status list generated by the previous module, and corresponds to the mother-and-child car binding positioning module, and assigns the mold position to the vehicle loading platform. The four sets of weight sensors arranged at the four corners of the vehicle chassis by the mother-and-child car collect the instantaneous load-bearing data on the four support points. Assuming that the mold numbered F017 is currently in the center area of ​​the vehicle platform, the detection values ​​of the front left, front right, rear left, and rear right sensors are 260kg, 255kg, and 270kg respectively. kg, 265kg. The system sets the origin of the vehicle coordinate system at the center of the platform as the benchmark, and sets the positions of each sensor to (-0.75, 1.0, 0.0), (0.75, 1.0, 0.0), (-0.75, -1.0, 0.0), (0.75, -1.0, 0.0). Then, according to the three-dimensional moment balance formula, the overall mass of the mold is calculated to be 260+255+270+265=1050kg. The center of gravity coordinates are calculated for the X, Y, and Z axes. The center of gravity in the X direction is Meters, Y direction is The Z direction is initially defined as 0.95 meters because the sensors are in the same plane. The coordinates of the center of gravity of the mold in the coordinate system of the parent-child car are obtained through the above calculation as (−0.0071m, −0.0190m, 0.95m). This coordinate is the center of gravity position result of the current mold loading state.

[0101] The offset calculation submodule calls the center of gravity coordinate data and uses the formula:

[0102] ;

[0103] Calculate speed regulation control parameters;

[0104] in, is the speed adjustment control parameter, which is the adjusted running speed that the vehicle should execute in the current path section, in meters per second. The standard speed value of the section is the preset basic travel speed, in meters per second. is the coordinate value of the mold center of gravity along the transverse axis in the vehicle coordinate system, in meters. is the coordinate value of the mold center of gravity along the longitudinal axis in the vehicle coordinate system, in meters. is the coordinate value of the mold center of gravity along the vertical axis in the vehicle coordinate system, in meters. is the reference coordinate value of the center point of the vehicle track along the transverse axis in the vehicle coordinate system, in meters. is the reference coordinate value of the center point of the vehicle track along the longitudinal axis in the vehicle coordinate system, in meters. is the reference coordinate value of the center point of the vehicle track along the vertical axis in the vehicle coordinate system, in meters. is the boundary length of the mold in the vehicle coordinate system, in meters. is the boundary width of the mold in the vehicle coordinate system, in meters. is the boundary height of the mold in the vehicle coordinate system, in meters. is the adjustment weight coefficient of the proportion of the lateral offset difference, which is a dimensionless real number. is the adjustment weight coefficient of the proportion of the longitudinal offset difference, which is a dimensionless real number. is the adjustment weight coefficient of the proportion of vertical offset difference, which is a dimensionless real number;

[0105] The offset calculation submodule obtains the center of gravity coordinate data 、 、 Then, combined with the reference value of the vehicle reference track center point in the coordinate system 、 、 , according to the formula:

[0106] ;

[0107] Calculate the speed control parameters and set the standard speed for the vehicle m / s, the maximum length, width and height of the mold that can be accommodated by the vehicle chassis are 、 、 , and set the offset direction adjustment weights to 、 、 , substitute all parameters into the formula for calculation:

[0108] ;

[0109] The results of the speed adjustment control parameters show that the current mold load causes a slight offset between the vehicle's center of gravity and the track center, and the travel speed needs to be slightly reduced from the standard speed of 1.2m / s to 1.1959m / s to ensure the stability of the mold center of gravity. The main function of the speed adjustment control parameters is to dynamically adjust the speed of the transport vehicle according to the mold loading status and the real-time operation status of the vehicle to ensure safety and accuracy in the production process. The system monitors the center of gravity position of the mold and its offset relative to the vehicle center, and automatically adjusts the speed of the vehicle in different path sections (such as turning sections, acceleration sections, and dwell sections) in combination with the transportation needs of each section. This adaptive speed adjustment mechanism can effectively reduce stability problems caused by center of gravity offset or road section changes, reduce accident risks, ensure the stability and position accuracy of the mold during transportation, and improve the operation efficiency and product quality of the entire production line.

[0110] The speed control submodule calls the speed adjustment control parameters to set the speed parameters of multiple positions in the parent-child vehicle path, including the running speed of the turning section, acceleration section and parking section, and establishes the travel speed control parameters;

[0111] The speed control submodule reads the speed adjustment control parameter value m / s, and call the path segment information provided by the segment recording module in the current vehicle path, and divide the segment into m / s, section B is the main running section, which is equal to the Vc value, i.e. 1.1959 m / s, and section C is the stop section, with the speed set to 0. The system eventually writes the speed parameters of the three sections into the vehicle path control table, marking the start and end coordinates of each section and the bound speed value for the vehicle main control system to synchronously read and execute instructions, forming a complete travel speed control parameter. The data record is shown in the table below:

[0112] Table 3. Control parameters of the speed regulation of the shuttle bus

[0113]

[0114] As shown in Table 3, when the mother-and-child vehicle is carrying mold F017, the adjusted running speed parameters are calculated based on the offset difference between the current load center of gravity and the vehicle center position and refined to each section of the path for subsequent precise control of the vehicle speed regulation rhythm.

[0115] See also Figure 5 , the segment judgment module includes:

[0116] The thermal image acquisition submodule calls the infrared imaging equipment of each curing sub-zone according to the speed control parameters, including the heating zone, constant temperature zone, and cooling zone, to collect the temperature distribution image of the mold surface and obtain the thermal image of the mold surface;

[0117] After obtaining the vehicle speed adjustment parameter Vc, the thermal map acquisition submodule triggers the mold's current position identification process based on this parameter. The mold is located in the maintenance zone subsegment and a thermal imaging scan is performed by calling the infrared imaging device corresponding to the current segment. The infrared device collects a thermal distribution image every 2 seconds, using a 5×5 grid to cover the entire mold surface area. For example, mold F021 is currently in the constant temperature zone. The thermal map distribution collected by the infrared device is shown in the table. Each grid represents the average temperature per unit area of ​​a local area on the mold surface, in degrees Celsius. The temperature in the central area of ​​the image is concentrated between 65°C and 66.5°C, while the edge areas fluctuate slightly and have individual high and low temperature differences. For example, the temperature at row 1, column 4 is 68.05°C, and the temperature at row 3, column 4 is 61.17°C. The system records the acquisition timestamp and mold number of the current sampled image and stores the acquisition result as "Mold F021-Constant Temperature Zone-Image 1". This numbered image sequence is continuously updated during mold movement and stored in the thermal map storage module as the input image dataset for subsequent thermal field trend identification. This process obtains thermal image data of the mold surface.

[0118] The thermal field trend identification submodule uses the mold surface thermal image, divides the image into multiple grids, extracts the temperature mean sequence of each grid in the center area of ​​the image, identifies the temperature distribution trend in the center area, combines the temperature in the grids in the edge area of ​​the image, calculates the edge temperature gradient direction, analyzes the change trend of the thermal field uniformity, and obtains the mold status analysis results;

[0119] The thermal field trend recognition submodule calls the mold surface thermal image data, divides each image into 25 5×5 grid areas, and sets the central area as nine grid areas from row 2 to row 4 and column 2 to column 4. The temperature values ​​of each grid in the central area are extracted in sequence, which are 68.16℃, 66.53℃, 64.06℃, 64.07℃, 65.48℃, 61.17℃, 62.97℃, 65.63℃, and 63.18℃, respectively. The mean sequence trend of the nine values ​​is calculated, and the sequence standard deviation is used to judge the temperature fluctuation trend and identify whether it tends to be uniform. The current standard deviation of the central area is approximately 1.97°C. If the system sets the standard deviation fluctuation threshold to 2.5°C, the center of the thermal field is judged to be "stable". At the same time, the average temperature value of 16 grids around the edge of the image is extracted to be 64.18°C. Compared with the average value of 64.94°C in the central area, the temperature gradient from the edge to the center is calculated to be -0.76°C. Analysis shows that the direction of the gradient points to the center point of the image, indicating that the heat converges from the edge to the center and the thermal field uniformity is stabilizing. Finally, the judgment result is stored in the mold state analysis field and the label is set as "stable temperature - mid-term state of constant temperature zone".

[0120] Table 4 Mold heat distribution grid table

[0121]

[0122] Table 4 shows thermal image data collected for mold F021 at a specific moment in the constant temperature zone. The center grid refers to the nine regions formed by rows 2 through 4 and columns 2 through 4, while the edge grids are the 16 outer grids. The data in this table is precisely aligned to the image coordinates.

[0123] The partition transfer determination submodule calls the mold status analysis results, combines the mold status required by each partition, calculates the time point when the mold enters the next maintenance period, and obtains the hot zone switching record;

[0124] After receiving the mold status analysis results, the partition transfer judgment submodule compares the mold status according to the stage requirements of each maintenance zone. The temperature in the heating zone needs to detect a rapid temperature rise and a central fluctuation amplitude greater than 3°C. The constant temperature zone requires the central fluctuation standard deviation to be less than 2.5°C and maintained for 2 minutes. The cooling zone requires the central temperature drop rate to be less than -1°C / min for two consecutive frames. The current mold F021 has a central area temperature fluctuation of less than 1.5°C for two consecutive frames in the constant temperature zone, and the edge gradient is slowing down, meeting the constant temperature stability judgment condition. At the same time, the system obtains the image acquisition timestamp from the historical record. The current image number timestamp is 14:08:00, and the previous image number timestamp is 14:06:00, indicating that the mold has been in the constant temperature stability section for more than 2 minutes. The system confirms that the mold meets the conditions for entering the next stage cooling zone, records the transfer time point as 14:08:00, establishes a hot zone switching record item and stores it as "F021-constant temperature zone→cooling zone", completing the partition switching data labeling.

[0125] See also Figure 6 , the dislocation recognition module includes:

[0126] The image acquisition submodule calls the hot zone switching record and uses the image acquisition device to capture the top view of the mold entry section. It locates the pixel points of the mold edge lines on both sides in the image coordinate system and converts them into actual space coordinates. It constructs the mold edge direction vector and measures the angle with the track center reference direction to obtain the edge angle data set.

[0127] The image acquisition submodule selects the image acquisition period of the corresponding mold during the slot entry process based on the mold number and time node in the hot zone switching record, and calls the industrial vision system arranged above the slot entry area to obtain the top view image of the mold. The system locates the pixel coordinates of the left and right edge points of the mold through the image processing program. For example, the left edge pixel coordinates of the mold numbered F024 in the image are (122, 295) and the right edge pixel coordinates are (523, 297). By presetting the image pixel to actual space conversion coefficient of 1 pixel = 0.25 mm, the coordinates of the left edge point after conversion are (30.5 mm, 73.75 mm) and the right edge point coordinates are (130.75 mm, 74.25 mm). The system regards these two points as the starting point and end point of the mold edge direction vector. According to the two-point vector construction method, the direction vector is (100.25 mm, 0.5 mm). Then the reference direction vector of the track centerline (100 mm, 0 mm) is called to calculate the direction angles of the two in the horizontal coordinate system. The mold edge direction angle is , the orbital direction angle is , the angle between the two is 0.29°. The system adds the angle value to the edge angle data set, records the mold number, image number, edge point spatial coordinates and calculated angle, and uses it as the input data source for subsequent offset event judgment.

[0128] The offset event determination submodule calls the edge angle data set to detect the position offset events of the mold during the slotting process, including angle offset events, edge angle difference exceeding the limit events, and slotting centerline offset events, and generates the slotting offset determination results;

[0129] The offset event judgment submodule extracts the edge line angle value and mold direction angle information of each mold from the edge line angle data set, and performs three offset judgments by comparing with the preset offset judgment threshold. The first is the angle offset event judgment. If the absolute value of the deviation between the edge line direction angle and the track direction angle is greater than 2°, it is judged as an angle offset event. The angle of the current mold number F024 is 1.15°, which is less than 2° and is not judged as an angle offset event; the second is the edge line angle difference over-limit event. The system calculates the difference between the left and right edge line extension directions. If it exceeds 3°, it is over-limit. The edge line direction angle of the current mold image is only 0.29° after detection, and the difference over-limit is not triggered; the third is the slot centerline offset event. The coordinates of the midpoint of the mold edge line and the coordinates of the center point of the track are called to calculate the lateral offset distance of the midpoint. If it exceeds the set tolerance of ±10mm, it is recorded as a centerline offset event. The midpoint position of the F024 mold is calculated as , the track center is set to 80mm, the offset is 0.625mm, which is lower than the 10mm tolerance. The system records the offset as "normal". The final output result is: mold F024 has no offset abnormality, and the generated slot offset judgment result is "Offset status: none".

[0130] The path adjustment execution submodule calls the slot entry offset determination result, combines the offset angle direction, sets the corresponding vehicle's travel direction, speed, and brake trigger value parameters, and obtains the segment production line management parameter set.

[0131] After reading the slotting offset determination result, the path adjustment execution submodule sets the path control parameters for the shuttle car based on the offset status and angle deviation direction in the result field. If the mold status is "offset direction to the left," the shuttle car's travel angle is set to offset 0.5° to the right for compensation. If the offset angle is greater than 3°, the deceleration control mechanism is automatically triggered and the brake trigger value is recorded. The current mold F024 is determined to be "no offset." The system sets the standard travel path angle to 0° and maintains the speed at the standard speed value of 1.2m / s. The brake trigger value is set to 10%, indicating that if the angle deviation of subsequent images exceeds 1°, the speed will be immediately reduced by 10%. Finally, the system generates a set of segment production line management parameter sets, with the record fields being "mold number, travel angle, correction direction, speed value, and brake threshold." The data details are shown in the table:

[0132] Table 5 Segment entry offset and path management parameters

[0133]

[0134] As shown in Table 5, the system combined image detection and track comparison analysis to confirm that mold F024 had no deviation abnormality. The path control system maintained the standard set parameters and activated the lowest level warning mechanism to enable continuous tracking and adjustment of the control strategy in subsequent sections.

[0135] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent segment production scheduling system, characterized in that: The system comprises: The raw material analysis module obtains cement strength grade, manufactured sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content data for each raw material batch, analyzes performance differences compared to preset benchmark values, predicts task processing time for each raw material batch, adjusts the task scheduling list, and obtains a production task list. The distribution control module calls the production task list, uses an image acquisition device to identify the expansion speed of the concrete drop point in the concrete distribution equipment, adjusts the discharge rate by evaluating the fluidity of the concrete, and obtains a mold status list based on the mold number; The cloth control module includes: The landing point monitoring submodule calls the production task list and uses an industrial camera placed directly above the concrete placing equipment to identify the edge contour of the concrete landing point in the image, analyze the expansion position of the concrete landing point in the mold in consecutive frame images, extract the expansion radius data and corresponding timestamps of the current frame and the previous frame, and establish the expansion speed change value; The fluidity evaluation submodule calls the expansion speed change value, combines the standard fluidity rate and the benchmark discharge rate, and uses the formula: ； Calculate the discharge rate and establish the discharge rate control parameters; in, To indicate the real-time discharge rate that the equipment should execute according to the current concrete fluidity, is the base discharge rate, is the concrete expansion radius in the current image frame, is the concrete expansion radius in the previous image frame, is the timestamp corresponding to the current image frame, is the timestamp corresponding to the previous image frame, is the standard concrete diffusion rate, is the liquidity response adjustment index; The rhythm recording submodule calls the discharge rate control parameters, records the concrete spreading time of each mold, extracts the mold number of the completed concrete spreading, and establishes a mold status list; The scheduling and speed control module calls the mold status list and multiple weight sensors of the carrier to calculate the spatial offset direction and offset amplitude between the center of gravity of the mold and the center of the vehicle in real time after loading, and adjusts the speed parameters to obtain the speed control parameters; The section judgment module uses infrared imaging equipment to monitor the temperature distribution image of the mold in the curing area based on the speed control parameters, including the heating zone, constant temperature zone, and cooling zone. It extracts the temperature distribution trend in the center area of ​​the image, the direction of the edge temperature gradient, and the trend of the change in thermal field uniformity. It calculates the time point when the mold enters the next curing zone and obtains the hot zone switching record. The segment judgment module includes: The thermal image acquisition submodule calls the infrared imaging equipment of each curing sub-zone according to the speed control parameters, including the heating zone, the constant temperature zone, and the cooling zone, to collect the temperature distribution image of the mold surface and obtain the thermal image of the mold surface; The thermal field trend identification submodule calls the mold surface thermal image, divides the image into multiple grids, extracts the temperature mean sequence of each grid in the center area of ​​the image, identifies the temperature distribution trend of the center area, combines the temperature in the grid in the edge area of ​​the image, calculates the edge temperature gradient direction, analyzes the change trend of the thermal field uniformity, and obtains the mold status analysis result; The partition transfer determination submodule calls the mold state analysis result, combines the mold state required by each partition, calculates the time point when the mold enters the next maintenance period, and obtains the hot zone switching record.

2. The intelligent segment production scheduling system according to claim 1, characterized in that: The production task list includes the raw material batch number, the predicted value of the task processing time, and the task priority order; the mold status list includes the mold number, the concrete fluidity level, and the concrete distribution time record; the speed control parameters are specifically the offset direction vector value, the offset amplitude value, and the path speed correction coefficient; the hot zone switching record includes the center temperature change trend of the heat map, the edge temperature difference distribution trend, and the maintenance zone switching time point.

3. The intelligent segment production scheduling system according to claim 1, characterized in that: The raw material analysis module includes: The performance difference submodule obtains the cement strength grade, machine-made sand particle size distribution, fly ash water requirement ratio, and crushed stone moisture content data of each raw material batch, extracts the performance difference between each parameter and the preset benchmark value, and generates a raw material parameter difference set; The duration prediction submodule is based on the raw material parameter difference set and uses the formula: ； Calculate the predicted value of the task processing cycle for each raw material and obtain the predicted duration calculation result; in, Represents the predicted value of task processing cycle, Indicates the benchmark duration of the task corresponding to the standard raw material parameter group. Indicates the current batch Raw material parameter values, Indicates the Standard reference value of each raw material, Indicates the The sensitivity coefficient of the item parameter to the processing time, Indicates the parameter number of the raw material currently being analyzed. Indicates the total number of raw material parameters involved in the processing; The task scheduling submodule calls the predicted duration calculation result, adjusts the task scheduling list according to the required processing duration, and establishes a production task list.

4. The intelligent segment production scheduling system according to claim 1, characterized in that: The scheduling and speed regulation module includes: The center of gravity identification submodule calls the mold status list and multiple weight sensors of the carrier, extracts mass data at multiple positions and maps them to the vehicle chassis coordinate system, calculates the center of gravity position of the mold after loading, and obtains the center of gravity coordinate data; The offset calculation submodule calls the center of gravity coordinate data and uses the formula: ； Calculate speed regulation control parameters; in, is the speed regulation control parameter, is the standard speed value of the section, is the coordinate value of the mold center of gravity along the transverse axis in the vehicle coordinate system, is the coordinate value of the mold center of gravity along the longitudinal axis in the vehicle coordinate system, is the coordinate value of the mold center of gravity along the vertical axis in the vehicle coordinate system, is the reference coordinate value of the vehicle track center point along the transverse axis in the vehicle coordinate system, is the reference coordinate value of the vehicle track center point along the longitudinal axis in the vehicle coordinate system, is the reference coordinate value of the vehicle track center point along the vertical axis in the vehicle coordinate system, is the boundary length of the mold in the vehicle coordinate system, is the boundary width of the mold in the vehicle coordinate system, is the boundary height value of the mold in the vehicle coordinate system, is the adjustment weight coefficient of the proportion of the lateral offset difference, is the adjustment weight coefficient of the proportion of the longitudinal offset difference, is the adjustment weight coefficient of the proportion of vertical offset difference; The speed regulation submodule calls the speed regulation control parameters, sets the speed parameters of multiple positions in the parent-child vehicle path, including the running speeds of the turning section, the acceleration section and the parking section, and establishes the travel speed control parameters.

5. The intelligent segment production scheduling system according to claim 1, characterized in that: The system further comprises: The misalignment recognition module calls the hot zone switching records and uses image acquisition equipment to identify the positions of the mold's two side edges and measure the angle between them and the track direction. This module detects positional deviations of the mold during slotting and adjusts the control parameters of the parent-child carriage based on the deviation direction to obtain segment production line management results. The segment production line management results specifically refer to the mold misalignment angle, offset direction identification, and vehicle control adjustment instructions.

6. The intelligent segment production scheduling system according to claim 5, characterized in that: The dislocation recognition module includes: The image acquisition submodule calls the hot zone switching record and uses an image acquisition device to capture a top view image of the mold entry section. It locates the pixel points of the edge lines on both sides of the mold in the image coordinate system, converts them into actual space coordinates, constructs the mold edge direction vector, and measures the angle with the track center reference direction to obtain an edge angle data set. The offset event determination submodule calls the edge angle data set to detect position offset events of the mold during the slotting process, including angle offset events, edge angle difference exceeding limit events, and slotting centerline offset events, and generates a slotting offset determination result; The path adjustment execution submodule calls the slot entry offset judgment result, combines the offset angle direction, sets the corresponding mother-and-child vehicle's travel direction, driving speed and braking trigger value parameters, and obtains the segment production line management parameter set.

Images