Automobile die manufacturing management system fused with situation awareness mechanism

Abstract

The invention discloses an automobile mold manufacturing management system fused with a situation awareness mechanism, and particularly relates to the technical field of automobile mold manufacturing management of the situation awareness mechanism, comprising a trajectory modeling module used for collecting historical manufacturing behavior data of an automobile mold, generating a normalized trajectory and executing trajectory clustering, constructing a reference track set and a behavior change rate set for subsequent comparison; the offset identification module is used for executing time normalization, derivative calculation and difference comparison on the current manufacturing behavior data to obtain a continuous offset section, and judging whether an offset state is formed or not based on the trajectory direction; a compensation control path and an update plan trigger condition are dynamically generated by constructing a normalized trajectory model and a change rate model of manufacturing behavior variables and based on a behavior difference identification result of a current manufacturing state and a historical reference trajectory; therefore, the core problem that an existing manufacturing system cannot recognize the machining condition deviation within the planned time is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile die manufacturing management mechanism of situation awareness, more particularly, the present application relates to a kind of automobile die manufacturing management system of fusion situation awareness mechanism. BACKGROUND

[0002] In the prior art of automobile die manufacturing management, the manufacturing process is strictly embedded in the execution of time-based pre-design plan, and the control system usually schedules processing instructions and parameter loading logic according to the time nodes in the process plan; for example, in the mold preheating stage, the system sets a fixed time threshold, and automatically switches to the finishing stage when the threshold is reached, while loading the corresponding tool compensation value, cooling parameters and thermal expansion correction model; The mechanism defaults that the time schedule can accurately reflect the process state evolution, ignoring the influence of environmental temperature, equipment load, material initial state and other factors on the process response rate in the actual manufacturing process;

[0003] When a disturbance occurs during the execution of the plan, such as fluctuations in environmental temperature and humidity, interruptions in preheating, equipment waiting, task switching, etc., although the triggering time of the processing instruction is still within the original plan range, the mold body state may not meet the requirements of the subsequent processing conditions; under this condition, continuing to execute the established parameter configuration will lead to problems such as thermal compensation error, cooling strategy deviation, and structural stress imbalance, resulting in irreversible microstructure damage or shape and position errors;

[0004] Since the control logic only judges "whether the time node is reached", it does not have the ability to perceive the actual execution state of the plan, so it cannot identify such process deviation processes, forming the problem of the manufacturing system lacking response to changes in processing conditions within the time planning mechanism;

[0005] Therefore, the system executes the correct processing decision within the planned time, but the process basis condition it relies on no longer exists, forming the problem of the control system lacking the ability to identify "in-plan processing abnormal state". SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a kind of automobile die manufacturing management system of fusion situation awareness mechanism, by constructing the normalized trajectory model and change rate model of manufacturing behavior variable, and based on the behavior difference identification result of current manufacturing state and historical reference trajectory, dynamically generate compensation control path and update plan trigger condition, to solve the core problem that the existing manufacturing system cannot identify the processing condition deviation within the planned time.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a kind of automobile die manufacturing management system of fusion situation awareness mechanism, including trajectory modeling module, deviation identification module, path compensation module, plan updating module;

[0008] The trajectory modeling module is used to collect historical manufacturing behavior data of automotive molds, generate normalized trajectories and perform trajectory clustering to build a set of baseline trajectories and a set of behavior change rates for subsequent comparison.

[0009] The offset recognition module is used to perform time normalization and derivative calculation on the current manufacturing behavior data, compare the differences to obtain continuous deviation segments, and determine whether an offset state is constituted based on the trajectory direction.

[0010] The path compensation module generates a compensation trajectory based on the offset state trajectory and matches it with the reference trajectory. It then performs path difference calculations to output the control parameter path, which is used to correct the current control behavior.

[0011] The plan update module is used to extract the final state quality variables and match them with the execution results of the control path to identify combinations of quality improvement intervals and update the plan trigger conditions.

[0012] In a preferred embodiment, the trajectory modeling module is used to collect and extract manufacturing behavior data from the historical manufacturing process of automotive molds, and construct a set of manufacturing behavior variables, which includes temperature trajectory, load trajectory, and cooling trajectory.

[0013] The set of manufacturing behavior variables is grouped according to the manufacturing stage, and time normalization is performed on each group of manufacturing behavior variables to generate stage normalized trajectories.

[0014] The extraction process involves slicing the manufacturing behavior data chronologically to extract the temperature change sequence, spindle load response sequence, and cooling flow record sequence for each time period. Then, for each sequence, extreme value difference, mean slope, and change amplitude calculations are performed over consecutive time periods. The calculation results are then categorized and combined according to the corresponding process segments to form a set of variables describing the temperature trajectory, load trajectory, and cooling trajectory. Finally, all variable sets are bound by stage numbers and time markers to form a set of manufacturing behavior variables.

[0015] The time normalization process maps the original trajectory to a unified time interval, performs interpolation at equally spaced nodes, and generates a normalized trajectory with consistent length and time index. The normalized trajectory is then used as the stage normalized trajectory corresponding to the current manufacturing stage.

[0016] In a preferred embodiment, the trajectory modeling module is also used to perform clustering operations on the stage-normalized trajectories to construct a set of baseline trajectories for situational awareness.

[0017] Perform a type of derivative operation on each trajectory in the baseline trajectory set to form a set of behavior change rates;

[0018] The clustering operation is performed by expanding each stage normalized trajectory into an equal-length vector sequence in time sequence; performing difference absolute value accumulation operation on each equal-length vector sequence at the corresponding time node to construct a full-quantity distance matrix between trajectories; classifying trajectories in the full-quantity distance matrix according to the average distance not exceeding the preset merging threshold, and classifying all trajectories satisfying the preset merging threshold as the same cluster group; in each cluster group, the average distance value of each trajectory and the remaining trajectories in the group is calculated, and the trajectory with an average distance value close to the average level of the group is selected as the representative trajectory; all representative trajectories are combined to form a reference trajectory set;

[0019] The first derivative operation is performed by performing subtraction operation on the variable values corresponding to adjacent two time indexes in each reference trajectory in time sequence, and then performing division operation on the difference value obtained after the subtraction operation and the corresponding time step to obtain the unit change value in the current time interval; the operation is repeated along the full length of the trajectory to obtain the continuous rate sequence of the trajectory; the calculation process of obtaining the trajectory continuous rate sequence is performed on all trajectories in the reference trajectory set to generate the corresponding rate sequence; all trajectory rate sequences are merged and arranged according to the trajectory number, and output as a behavior change rate set.

[0020] In a preferred embodiment, the offset identification module is used to collect manufacturing behavior data in the current manufacturing process of the automobile mold and extract the same variables as in the manufacturing behavior variable set to construct a current manufacturing behavior variable set.

[0021] The time normalization and second derivative operation are performed on the current manufacturing behavior variable set to generate a current behavior change rate set.

[0022] The second derivative operation is performed by calculating the difference between the variable values at adjacent time index positions and the ratio of the time step to generate a unit time change rate sequence for each normalized trajectory in the current trajectory set; all rate sequences are merged according to the trajectory number to form a current change rate set.

[0023] In a preferred embodiment, the offset identification module is also used to perform difference comparison between the current behavior change rate set and the behavior change rate set, and output a continuous deviation section.

[0024] The continuous deviation section is judged to determine whether there is a continuous deviation section, and whether the cooling trajectory and the temperature trajectory are opposite in direction, as two conditions for judgment, if both conditions meet the corresponding judgment conditions, the offset trajectory is output, otherwise the current trajectory state is maintained.

[0025] The difference comparison is performed by calculating the difference between the current behavior change rate set and the behavior change rate set aligned by time index for each index position; then the difference is generated into a fixed length time window by time sliding, and the number of data points whose difference exceeds the preset deviation threshold is counted in each time window; the time period corresponding to the window whose continuous data point number exceeds the preset threshold count standard is identified as the continuous deviation section.

[0026] In a preferred embodiment, the path compensation module performs state extraction on the current manufacturing behavior variable set based on when the offset trajectory is output, and constructs an offset state trajectory;

[0027] The offset state trajectory and the reference trajectory set are sequentially difference calculated, and a candidate compensation trajectory is output;

[0028] The state extraction is performed by locating the time period consistent with the time index marked by the offset trajectory in the current manufacturing behavior variable set; the temperature trajectory, the load trajectory and the cooling trajectory are sequentially subjected to time index cutting operation, and the variable value sequence of the corresponding interval is extracted; the cut variable value sequence is combined into a trajectory segment in time sequence; the temperature, load and cooling three trajectory segments are spliced in the same time index according to variable dimension, and output as an offset state trajectory.

[0029] In a preferred embodiment, the path compensation module is further used for judging the candidate compensation trajectory, judging whether there is a trajectory in which the cooling trajectory and the load trajectory are consistent in direction in the candidate compensation trajectory, if there is, taking the trajectory in which the cooling trajectory and the load trajectory are consistent in direction as the compensation trajectory, otherwise, selecting a trajectory in which the temperature trajectory hysteresis response amplitude is less than the preset temperature trajectory hysteresis response amplitude threshold from the candidate compensation trajectory as the compensation trajectory;

[0030] The compensation trajectory and the offset state trajectory are difference calculated, and a situation awareness driven control parameter path is output.

[0031] In a preferred embodiment, the plan updating module is used for performing mold detection data extraction on the compensation path, and constructing a final state quality variable set;

[0032] The final state quality variable set and the situation awareness driven control parameter path are response matched, and a quality feedback corresponding relationship is generated;

[0033] The mold detection data extraction is performed by collecting temperature field distribution data, residual stress distribution data and forming geometric deviation data of the final state along the mold surface and the forming area after the compensation path execution process is completed, performing spatial reconstruction, time alignment and variable discrete mapping operations on the collected data at the preset spatial points and detection time points, constructing a three-tuple set of spatial positions, time indexes and measurement values, and then dividing according to variable types and uniformly mapping into a quality dimension mapping index to integrate and form a final state quality variable set;

[0034] The response matching is performed by performing a normalized index matching operation on each quality variable in the final state quality variable set in the time dimension to locate the corresponding normalized time period in the manufacturing process; then performing a truncation operation on each control variable in the control parameter path in the located normalized time period to extract the corresponding control parameter sequence; then combining each quality variable with its corresponding control parameter sequence to form a candidate variable pair; performing variable slope consistency judgment, phase delay measurement and response sensitivity differential estimation on each candidate variable pair to screen out a variable pair set that meets the conditions of consistent direction, controllable lag and non-zero response; and mapping all variable pairs in the variable pair set that meet the conditions to a set of one-to-one response relationships, and outputting the quality feedback corresponding relationship as a whole.

[0035] In a preferred embodiment, the plan updating module is further used to judge the quality feedback corresponding relationship, judge whether there is an interval combination of quality improvement corresponding cooling trajectory and load trajectory change, if there is, extract the interval combination of quality improvement corresponding cooling trajectory and load trajectory change, if there is not, keep the original plan triggering condition;

[0036] The interval combination and the stage normalized trajectory are jointly processed to form the plan triggering condition.

[0037] Technical effects and advantages of the present application:

[0038] 1. The present application is based on the behavior change rate set and the difference comparison, which identifies whether the manufacturing behavior deviates from the reference trajectory, and avoids the mismatch between the plan time and the actual state;

[0039] 2. The reference trajectory set used for comparison is generated by manufacturing stage grouping, trajectory normalization and clustering, and the standard state expression is realized;

[0040] 3. The sliding difference window operation is performed on the current change rate set, and the trajectory direction relationship is combined to determine whether an offset state is formed, which improves the robustness and reliability of offset perception;

[0041] 4. In the offset state, the control parameter path containing temperature, load and cooling is output through candidate compensation trajectory screening, which ensures the continuous stability of the manufacturing process;

[0042] 5. The system combines the mold detection data with the control path to establish a response match, extracts a quality improvement interval combination, updates a plan trigger condition, and improves overall process management and adaptive level. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 A schematic diagram of the system module of the present application;

[0044] Figure 2 A flowchart of the system manufacturing behavior variable modeling execution in the present application;

[0045] Figure 3 A flowchart of the system offset trajectory identification and judgment execution in the present application;

[0046] Figure 4 A flowchart of the system compensation trajectory generation and control path construction execution in the present application;

[0047] Figure 5 A flowchart of the system quality feedback identification and plan trigger adjustment execution in the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0049] With reference to the drawings in the description Figures 1-5 An automobile mold manufacturing management system fusing a situation awareness mechanism according to an embodiment of the present application includes a trajectory modeling module, an offset identification module, a path compensation module, and a plan updating module.

[0050] The trajectory modeling module is used to collect automobile mold historical manufacturing behavior data, generate normalized trajectories, and perform trajectory clustering to construct a benchmark trajectory set and a behavior change rate set for subsequent comparison;

[0051] The offset identification module is used to perform time normalization and derivative calculation on current manufacturing behavior data, obtain continuous deviation sections through difference comparison, and determine whether an offset state is formed based on trajectory direction;

[0052] The path compensation module generates a compensation trajectory based on the offset state trajectory, matches the benchmark trajectory, performs path difference operation to output a control parameter path, and is used to correct the current control behavior;

[0053] The plan updating module is configured to extract the end state quality variable and perform response matching with the control path execution result, identify a quality improvement interval combination, and update a plan trigger condition.

[0054] It should be noted that for the formula structure involved in the present scheme, the dimensionless term can be used as a proportional or structural adjustment factor. When combined with quantities with units, it only plays a numerical scaling role and does not introduce new physical dimensions, so it will not change or confuse the unit system of the overall expression. Such a combination of "dimensionless term and quantity unit term" can be understood as a composite structure expression commonly used in mathematical and physical modeling, which complies with the principle of dimensional consistency and has a clear physical interpretation basis.

[0055] Secondly, in the formula structure of the present scheme, if it involves multiple variable terms with different physical units, including but not limited to time, mass, or energy variables, their joint occurrence is to express the cooperative modeling relationship of multiple physical mechanisms. Each variable can be mapped by a function, combined by a ratio, or normalized to form a unified structure. The unit is clear and the meaning is clear. The overall expression complies with the principle of dimensional consistency and the common norm of engineering modeling.

[0056] In the present scheme, if a constant, weight, adjustment factor, threshold parameter, or proportion coefficient is designed, it is a tunable control parameter for different application environments. Its value depends on the target device configuration, data input characteristics, and performance optimization goals. In the implementation phase, it is set within a reasonable range through model verification, performance constraints, or engineering calibration. Although such parameters do not have a unique value, they have a clear adjustment logic and calculation path, and belong to the deterministic setting process in engineering implementation. The purpose of such setting is to ensure that the scheme has both general adaptability and reproducibility and operability, without affecting its technical clarity and implementability.

[0057] The trajectory modeling module is configured to collect manufacturing behavior data in the historical manufacturing process of the automobile mold and perform extraction processing, and construct a manufacturing behavior variable set. The manufacturing behavior variable set includes temperature trajectory, load trajectory, and cooling trajectory.

[0058] The manufacturing behavior variable set is grouped according to the manufacturing stage, and each group of manufacturing behavior variables is subjected to time normalization processing to generate stage-normalized trajectories. Grouping according to the manufacturing stage means that each trajectory data in the manufacturing behavior variable set is classified according to the specific manufacturing stage (such as rough machining, finishing, heat treatment, etc.) it belongs to, so that each group of data only corresponds to a single stage, to ensure that the subsequent normalization and modeling steps have comparability and logical consistency within the same manufacturing stage.

[0059] The extraction processing extracts the temperature value change sequence, the main shaft load response sequence and the cooling flow record sequence in each period by slicing the manufacturing behavior data in chronological order; then performs the extreme value difference calculation, the mean value slope calculation and the change amplitude calculation in each sequence; then classifies and combines the calculation results according to the corresponding process section to form a variable set for describing the temperature trajectory, the load trajectory and the cooling trajectory; and binds all variable sets according to the stage number and the time mark to form a manufacturing behavior variable set.

[0060] The time normalization processing generates normalized trajectories with consistent length and unified time index by performing interpolation on equidistant nodes after mapping the original trajectories to a unified time interval, and takes the normalized trajectories as the corresponding stage normalized trajectories in the current manufacturing stage.

[0061] The trajectory modeling module is also used to perform clustering operation on the stage normalized trajectories to construct a reference trajectory set for situation awareness.

[0062] The derivative operation is performed on each trajectory in the reference trajectory set to form a behavior change rate set.

[0063] The clustering operation expands each stage normalized trajectory into an equal-length vector sequence in chronological order; performs the difference absolute value accumulation operation on each equal-length vector sequence to construct a full-quantity distance matrix between trajectories; classifies the trajectories according to the average distance not exceeding the preset merging threshold in the full-quantity distance matrix, and classifies all trajectories satisfying the preset merging threshold as the same cluster group; in each cluster group, the average distance value of each trajectory and the remaining trajectories in the group is calculated, and the trajectory with an average distance value close to the average level of the group is selected as the representative trajectory; all representative trajectories are combined to form the reference trajectory set.

[0064] The derivative operation is performed on each trajectory in the reference trajectory set to form a behavior change rate set.

[0065] The offset identification module is used to collect the manufacturing behavior data in the current manufacturing process of the automobile mold and extract the same variables as in the manufacturing behavior variable set to construct a current manufacturing behavior variable set.

[0066] performing time normalization and second derivative operation on the current manufacturing behavior variable set to generate a current behavior change rate set;

[0067] defining the current behavior change rate set

[0068]

[0069] wherein the second derivative operation comprises:

[0070]

[0071] wherein is used to divide the difference between two adjacent time points by the time difference to obtain the unit change rate in each time interval; is used for direction judgment to extract the positive or negative characteristics of the variable trend;

[0072] wherein is the unit change rate of the manufacturing behavior variable v in the trajectory number i, time interval {t k , t k+1} on the normalized time scale; is the direction information of the unit change rate; i is the trajectory number, which is used to represent different trajectory instances of the same manufacturing behavior variable; v represents the manufacturing behavior variable, which is used to take values T (temperature trajectory), L (load trajectory), and C (cooling trajectory); t k is the normalized time scale value corresponding to the index number k; t is the time scale value of the normalized trajectory; k is the discrete time index number in the normalized time sequence; represents the normalized time scale value corresponding to the index number k of the trajectory number i in the manufacturing behavior variable set under the manufacturing behavior variable v; the symbol sign represents the encoding of the positive and negative directions of the logarithmic value;

[0073] The second derivative operation calculates the difference between the variable values and the time step at adjacent time index positions for each normalized trajectory in the current trajectory set to generate a unit time change rate sequence. The rate sequences are merged according to the trajectory number to form the current change rate set.

[0074] The offset identification module is also used to perform difference comparison between the current behavior change rate set and the behavior change rate set, and output a continuous deviation section;

[0075] The continuous deviation section is judged to determine whether there is a continuous deviation section and whether the directions of the cooling trajectory and the temperature trajectory are opposite, as two conditions for judgment. If both conditions meet their respective judgment conditions, the offset trajectory is output, otherwise the current trajectory state is maintained;

[0076] The difference value comparison is performed by aligning the current behavior change rate set with the behavior change rate set by time index, calculating the difference value of both at each index position; then generating a fixed length time window by time sliding of the difference value, counting the number of data points whose difference value exceeds the preset deviation threshold in each time window; identifying the time period corresponding to the window whose continuous data point number exceeds the preset threshold count standard as the continuous deviation section.

[0077] The path compensation module performs state extraction on the current manufacturing behavior variable set when outputting the offset trajectory, and constructs an offset state trajectory;

[0078] The offset state trajectory and the reference trajectory set are subjected to piecewise difference value operation to output a candidate compensation trajectory; the candidate compensation trajectory is defined as Wherein And The offset state trajectory and the reference trajectory set are subjected to piecewise difference value operation;

[0079]

[0080] Wherein g s is the candidate compensation trajectory of trajectory number s; E s is the difference value matching error between the candidate compensation trajectory of trajectory number s and the offset state trajectory; s represents the number of one of all candidate compensation trajectories; j is the trajectory number of traversing all candidate compensation trajectories; E j is the difference value matching error between the candidate compensation trajectory of trajectory number j and the offset state trajectory; ∈ represents an error neighborhood threshold value, which is used to control the range of candidate trajectories; the symbol is a minimization operator; s (T) (t k ) is the normalized time scale value of the temperature trajectory in the offset state trajectory corresponding to index number k; s (L) (t k ) is the normalized time scale value of the load trajectory in the offset state trajectory corresponding to index number k; s (C) (t k ) is the normalized time scale value of the cooling trajectory in the offset state trajectory corresponding to index number k; is the normalized time scale value of the temperature trajectory in the reference trajectory set corresponding to index number k; is the normalized time scale value of the load trajectory in the reference trajectory set corresponding to index number k; is the normalized time scale value of the cooling trajectory in the reference trajectory set corresponding to index number k;

[0081] The state extraction is performed by locating a time period consistent with the marked time index of the offset trajectory in the current manufacturing behavior variable set; performing time index cutting operation on the temperature trajectory, the load trajectory and the cooling trajectory in sequence to extract the variable value sequence of the corresponding interval; combining the cut variable value sequences in time sequence into trajectory segments; splicing the three trajectory segments of temperature, load and cooling in the same time index according to the variable dimension, and outputting as the offset state trajectory.

[0082] The path compensation module is further configured to judge the candidate compensation trajectory, judge whether there is a trajectory in which the cooling trajectory and the load trajectory are consistent in direction in the candidate compensation trajectory, if there is, take the trajectory in which the cooling trajectory and the load trajectory are consistent in direction as the compensation trajectory, otherwise, select a trajectory in which the lag response amplitude of the temperature trajectory is less than a preset temperature trajectory lag response amplitude threshold from the candidate compensation trajectory as the compensation trajectory;

[0083] The compensation trajectory and the offset state trajectory are subjected to difference calculation, and a situation awareness driven control parameter path is output;

[0084] The situation awareness driven control parameter path is defined Wherein c (v) (t k ) represents the difference calculation of the compensation trajectory and the offset state trajectory; (v) (t k )

[0085]

[0086] P (v) (t k )=(c (v) (t k )-s (v) (t k ))·θ (v) (t k )

[0087]

[0088] Wherein P (v) (t k ) is the normalized time scale value of the manufacturing behavior variable v corresponding to the index number k; P (T) (t k ) is the normalized time scale value of the temperature trajectory corresponding to the index number k; P (L) (t k ) is the normalized time scale value of the load trajectory corresponding to the index number k; P (C) (t k ) is the normalized time scale value of the cooling trajectory corresponding to the index number k; c (v) (t k) is a normalized time scale value corresponding to the index number k in the candidate compensation trajectory; s (v) (t k ) represents a normalized time scale value corresponding to the index number k in the offset state trajectory; θ (v) (t k ) is an adjustment factor, which represents the degree of response of the manufacturing behavior variable v to the control intensity; θ (C) (t k ) is a normalized adjustment factor of the temperature change rate to the cooling change rate, which represents the dependence weight of the cooling behavior on the temperature behavior; d (T) (t k ) is a normalized time scale value corresponding to the index number k in the temperature trajectory; d (C) (t k ) is a normalized time scale value corresponding to the index number k in the cooling trajectory.

[0089] The planning update module is used to perform mold detection data extraction on the compensation path, and to construct a set of final state quality variables;

[0090] The set of final state quality variables is matched in response to the situation awareness driven control parameter path to generate a quality feedback corresponding relationship;

[0091] The mold detection data extraction is performed by collecting temperature field distribution data, residual stress distribution data and forming geometric deviation data of the final state along the mold surface and the forming area after the compensation path execution process, performing spatial reconstruction, time alignment and variable discrete mapping operations on the collected data at the preset spatial points and detection time points, constructing a set of triplets of spatial positions, time indexes and measurement values, and then dividing and uniformly mapping into the quality dimension mapping index according to the variable type to integrate and form the set of final state quality variables;

[0092] The response matching is performed by performing a normalized index matching operation on each quality variable in the set of final state quality variables in the time dimension to locate its corresponding normalized time period in the manufacturing process; then performing a cutting operation on each control variable in the control parameter path within the located normalized time period to extract the corresponding control parameter sequence; then combining each quality variable with its corresponding control parameter sequence to form a candidate variable pair; performing variable slope consistency judgment, phase delay measurement and response sensitivity differential estimation on each candidate variable pair to screen out a set of variable pairs that meet the conditions of consistent direction, controllable lag and non-zero response; mapping all variable pairs in the set of variable pairs that meet the conditions to a set of one-to-one response relationships, and the overall output is the quality feedback corresponding relationship.

[0093] The plan updating module is further configured to judge the quality feedback correspondence, judge whether there is a quality improvement corresponding cooling trajectory and load trajectory change interval combination, if there is, extract the quality improvement corresponding cooling trajectory and load trajectory change interval combination, if not, keep the original plan triggering condition, wherein the quality improvement corresponding cooling trajectory and load trajectory change interval combination refers to time alignment of the change interval of the quality feedback variable and the change interval of the cooling trajectory and the load trajectory; then for each overlapping time period after time alignment, judge whether the value of the quality feedback variable is within the system preset improvement amplitude threshold range, and judge whether the change of the cooling trajectory and the load trajectory satisfies the direction consistency or linkage condition; if all three satisfy at the same time, it is determined that the time period constitutes a quality improvement interval combination;

[0094] The interval combination and the stage normalized trajectory are jointly processed to constitute a plan triggering condition, replacing the original plan triggering condition;

[0095] The plan triggering condition is defined

[0096]

[0097] Wherein [t a ,t b ] is a continuous time interval in the target evaluation stage; R ij (t) is the dynamic value of the feedback response intensity at the time scale value t of the normalized trajectory; S (C) (t) is the change rate of the cooling trajectory at the time scale value t of the normalized trajectory; D (L) (t) is the change direction of the load trajectory at the time scale value t of the normalized trajectory; G z ′(t) is the disturbance derivative of the stage normalized trajectory, which represents the dynamic degree of disturbance in the global manufacturing stage; η, ζ are system thresholds for defining control response and disturbance intensity; the symbol ∧ represents "and" or "and" simultaneously satisfied, and the overall logical judgment is established.

[0098] It needs to be overall stated that the traditional automobile mold manufacturing process is based on static time plan control logic, that is, parameter loading and process switching are automatically triggered according to the preset time schedule; this mechanism assumes that the manufacturing process is stable, but ignores the actual process response lag caused by the fluctuation of on-site environment temperature, the change of equipment load, the difference of material state, etc.

[0099] When the plan is executed on time, the mold state may not be preheated, not fully cooled or not load balanced, resulting in subsequent finishing precision decline or mold thermal expansion behavior abnormality, structure damage or geometric deviation; such "in-plan process mismatch" problem is an important hidden danger difficult to identify in time-oriented manufacturing system;

[0100] Therefore, it is necessary to construct a manufacturing management scheme of fusion situation awareness mechanism, so that the system can realize real-time awareness of manufacturing state deviation before executing the plan, dynamically correct the control path according to the deviation, and finally feed back the correction result to optimize the future plan;

[0101] The scheme includes a trajectory modeling stage:

[0102] By collecting historical manufacturing process data, three types of manufacturing behavior variables of temperature, load and cooling are extracted, classified according to manufacturing stages and time normalized to form stage normalized trajectories. Then, clustering analysis is performed on the stage normalized trajectories to construct a benchmark trajectory set reflecting the evolution characteristics of standard working conditions. Then, derivative operation is performed on the trajectories in the benchmark trajectory set to extract the dynamic behavior of each variable in the trajectory with time to form a behavior change rate set.

[0103] This stage is used to construct a multi-dimensional manufacturing behavior trajectory data basis with consistent structure, unified scale and strong comparability, which supports subsequent deviation identification and state judgment.

[0104] The scheme includes a deviation identification stage:

[0105] By collecting current manufacturing process data and extracting three types of variables corresponding to the historical variable set, a current manufacturing behavior variable set is generated. The current manufacturing behavior variable set is first time-normalized and derivative-calculated to generate a current behavior change rate set, and then compared with the behavior change rate set according to the time index to extract the rate difference continuous threshold time window to form a continuous deviation section. If the current behavior trajectory has a continuous deviation section and the cooling trajectory and the temperature trajectory are opposite in direction, it is judged as a deviation trajectory state.

[0106] This stage is used to perceive whether the manufacturing behavior evolution deviates from the standard evolution path, trigger the deviation state identification, and provide the basis for judgment for the compensation logic.

[0107] The scheme includes a path compensation stage:

[0108] From the current manufacturing behavior variables, the trajectory data in the deviation period is extracted to form a deviation state trajectory. The deviation state trajectory and the benchmark trajectory set are difference-operated to generate a candidate compensation trajectory set. In the candidate set, if there is a trajectory with consistent cooling and load direction, it is preferred. Otherwise, the trajectory with a lag response amplitude less than a threshold is selected as the compensation trajectory. Finally, the compensation trajectory and the deviation trajectory are point-by-point difference-operated to construct a situation awareness driven control parameter path.

[0109] This stage generates a multi-variable control path based on the trajectory deviation state to form a dynamic control response to ensure real-time adjustment of the manufacturing process.

[0110] The scheme includes a plan updating stage:

[0111] After the control path execution is completed, mold detection data is collected, a spatial variable distribution is reconstructed, and a final state quality variable set is formed; the final state quality variable set is matched with the control parameter path execution process in response to generate a quality feedback corresponding relationship; if there is a control variable change and a quality variable improvement direction consistent time period combination, and the improvement amplitude reaches a threshold, then the quality improvement interval combination is extracted; the quality improvement interval combination is analyzed in combination with the normalized trajectory to form a new plan triggering condition to replace the original static time node;

[0112] This stage uses control execution feedback to form a reverse perception mechanism to realize active adjustment of the manufacturing state to the plan logic and solve the "in-plan mismatch" problem from the root.

[0113] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. An automotive mold manufacturing management system integrating situational awareness mechanisms, comprising a trajectory modeling module, an offset recognition module, a path compensation module, and a plan update module, characterized in that: The trajectory modeling module is used to collect historical manufacturing behavior data of automotive molds, generate normalized trajectories and perform trajectory clustering to build a set of baseline trajectories and a set of behavior change rates for subsequent comparison. The offset recognition module is used to perform time normalization and derivative calculation on the current manufacturing behavior data, compare the differences to obtain continuous deviation segments, and determine whether an offset state is constituted based on the trajectory direction. The path compensation module generates a compensation trajectory based on the offset state trajectory and matches it with the reference trajectory. It then performs path difference calculations to output the control parameter path, which is used to correct the current control behavior. The plan update module is used to extract the final state quality variables and match them with the execution results of the control path to identify combinations of quality improvement intervals and update the plan trigger conditions.

2. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 1, characterized in that: The trajectory modeling module is used to collect and extract manufacturing behavior data from the historical manufacturing process of automotive molds, and to construct a set of manufacturing behavior variables, which includes temperature trajectory, load trajectory, and cooling trajectory. The set of manufacturing behavior variables is grouped according to the manufacturing stage, and time normalization is performed on each group of manufacturing behavior variables to generate stage normalized trajectories. The extraction process involves slicing the manufacturing behavior data in chronological order to extract the temperature value change sequence, spindle load response sequence, and cooling flow record sequence within each time period. Then, for each sequence, the extreme value difference, mean slope, and change amplitude within a continuous time period are calculated. Finally, the calculation results are categorized and combined according to the corresponding process segment to form a set of variables used to describe the temperature trajectory, load trajectory, and cooling trajectory. The entire set of variables is bound to a stage number and a time stamp to form a set of manufacturing behavior variables; The time normalization process maps the original trajectory to a unified time interval, performs interpolation at equally spaced nodes, and generates a normalized trajectory with consistent length and time index. The normalized trajectory is then used as the stage normalized trajectory corresponding to the current manufacturing stage.

3. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 2, characterized in that: The trajectory modeling module is also used to perform clustering operations on stage-normalized trajectories to construct a set of baseline trajectories for situational awareness; Perform a type of derivative operation on each trajectory in the baseline trajectory set to form a set of behavior change rates; The clustering operation expands each stage normalized trajectory into an equal-length vector sequence in chronological order; performs an accumulation operation on the absolute value of the difference at the corresponding time node for each equal-length vector sequence to construct a full distance matrix between trajectories; and classifies trajectories in the full distance matrix according to the standard that the average distance does not exceed a preset merging threshold, and groups all trajectories that meet the preset merging threshold into the same cluster group. Within each cluster, the average distance between each trajectory and the other trajectories in the group is calculated, and the trajectory whose average distance is close to the average level of its group is selected as the representative trajectory. Combine all representative trajectories to form a baseline trajectory set; The aforementioned derivative operation involves subtracting the variable values ​​corresponding to two adjacent time indices in chronological order within each baseline trajectory, then dividing the difference obtained after the subtraction operation by the corresponding time step to obtain the unit change value within the current time interval. This operation is repeated segment by segment along the entire length of the trajectory to obtain the continuous rate sequence of the trajectory. The calculation process for obtaining the continuous rate sequence of the trajectory is performed on all trajectories in the baseline trajectory set, generating the corresponding rate sequence for each trajectory. The rate sequences of all trajectories are merged and arranged according to the trajectory number, and the output is a set of behavioral change rates.

4. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 3, characterized in that: The offset recognition module is used to collect manufacturing behavior data in the current manufacturing process of automotive molds and extract the same variables as the manufacturing behavior variable set to construct the current manufacturing behavior variable set. Perform time normalization and second derivative operations on the current set of manufacturing behavior variables to generate a set of current behavior change rates; The second type of derivative operation generates a unit time rate of change sequence by calculating the ratio of the difference of variable values ​​to the time step at adjacent time index positions for each normalized trajectory in the current trajectory set; all rate sequences are merged according to trajectory number to form the current rate of change set.

5. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 4, characterized in that: The offset recognition module is also used to compare the difference between the current set of behavior change rates and the set of behavior change rates, and output the continuous deviation segment; The system judges whether there are continuous deviation sections and whether the cooling trajectory and temperature trajectory are opposite. These are the two conditions for judgment. If both conditions are met, the deviation trajectory is output; otherwise, the current trajectory state is maintained. The difference comparison is performed by aligning the current set of behavior change rates with the set of behavior change rates by time index, and calculating the difference between the two at each index position; Then, the difference is slid over time to generate a fixed-length time window. Within each time window, the number of data points whose difference exceeds a preset deviation threshold is counted. The time period corresponding to the window where the number of consecutive data points exceeds the preset threshold counting standard is identified as a continuous deviation segment.

6. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 5, characterized in that: The path compensation module extracts the state of the current manufacturing behavior variable set when the offset trajectory is output, and constructs the offset state trajectory. Perform a line-by-line difference calculation between the offset trajectory and the set of reference trajectories, and output candidate compensation trajectories; The state extraction is achieved by locating the time period that matches the time index marked on the offset trajectory in the current set of manufacturing behavior variables; performing time index truncation operations on the temperature trajectory, load trajectory, and cooling trajectory in sequence to extract the variable value sequence of the corresponding interval; combining the truncated variable value sequence into trajectory segments in chronological order; and concatenating the three trajectory segments of temperature, load, and cooling under the same time index according to the variable dimension to output the offset state trajectory.

7. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 6, characterized in that: The path compensation module is also used to judge the candidate compensation trajectory. It determines whether there is a trajectory in the candidate compensation trajectory whose cooling trajectory is in the same direction as the load trajectory. If there is, the trajectory whose cooling trajectory is in the same direction as the load trajectory is used as the compensation trajectory. Otherwise, the trajectory whose temperature trajectory hysteresis response amplitude is less than the preset temperature trajectory hysteresis response amplitude threshold is selected from the candidate compensation trajectories as the compensation trajectory. The difference between the compensated trajectory and the offset state trajectory is calculated, and the control parameter path driven by situational awareness is output.

8. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 7, characterized in that: The plan update module is used to extract mold inspection data from the compensation path and construct a set of final-state quality variables; The final state quality variable set is matched with the control parameter path driven by situational awareness to generate a quality feedback correspondence. The mold detection data extraction process involves collecting final-state temperature field distribution data, residual stress distribution data, and forming geometric deviation data along the mold surface and forming area after the compensation path execution process is completed. Spatial reconstruction, time alignment, and variable discretization mapping operations are performed on the collected data at preset spatial points and detection time points. Spatial location, time index, and measured value are combined into a set of triplets, which are then classified according to variable type and uniformly categorized into the quality dimension mapping index to form a final-state quality variable set. The response matching process involves performing a normalized index matching operation on each quality variable in the final state quality variable set in the time dimension to locate its corresponding normalized time period in the manufacturing process; then, within the located normalized time period, performing a truncation operation on each control variable in the control parameter path to extract the corresponding control parameter sequence; subsequently, combining each quality variable with its corresponding control parameter sequence to form a candidate variable pair; and performing slope consistency judgment, fluctuation phase delay measurement, and response sensitivity differential estimation on each candidate variable pair to select a set of variable pairs that meet the requirements of consistent direction, controllable hysteresis, and non-zero response. Map all variable pairs that meet the conditions in the variable pair set to a one-to-one correspondence of response relationships, and the overall output is the quality feedback correspondence.

9. The automotive mold manufacturing management system integrating situational awareness mechanism according to claim 7, characterized in that: The plan update module is also used to judge the correspondence between quality feedback and determine whether there is a combination of intervals for changes in cooling trajectory and load trajectory corresponding to quality improvement. If there is, the combination of intervals for changes in cooling trajectory and load trajectory corresponding to quality improvement is extracted. If there is no, the original plan triggering conditions are maintained. The combined processing of interval combinations and stage-normalized trajectories constitutes the plan triggering conditions.

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