Plate production order management system based on edge sealing and drilling

Abstract

The invention relates to the technical field of plate processing production management and industrial data processing, in particular to a plate production order management system based on edge sealing drilling, which comprises an order data acquisition step: constructing a holographic data input layer, and acquiring a process data set and real-time performance process data; a performance benchmark construction step: establishing a dual reference system, generating an ideal performance time benchmark, and generating theoretical performance delay evaluation data in combination with a process complexity parameter; a performance difference analysis step: separating the noise and the signal, and respectively calculating an actual performance deviation and a theoretical performance deviation only containing a process complexity factor; an order state judgment step: analyzing the distribution mode similarity of the deviation, if the distribution mode similarity is higher than a threshold value, judging that compliance process delay exists and the delivery time is adjusted, and if the distribution mode similarity is lower than the threshold value, judging that abnormal performance risk exists and generating a management instruction; according to the method, confusion of processing endogenous complexity and exogenous abnormity is successfully stripped, and fine attribution oriented management is realized.

Description

Technical Field

[0001] This invention relates to the field of sheet metal processing production management and industrial data processing technology, specifically a sheet metal production order management system based on edge banding drilling. Background Technology

[0002] In the flexible production scenario of customized furniture, the processing of boards involves multiple complex processes such as edge banding and drilling. The production line needs to process massive amounts of non-standard order flow data in real time. The accuracy of production scheduling and the fairness of performance evaluation largely depend on the order management system's ability to judge the attribution of deviations in fulfillment time.

[0003] In the traditional approach, order management mainly relies on extensive results-oriented assessment, which involves directly monitoring the actual production time and comparing it with the preset standard time. Once the time is exceeded, it is judged as abnormal or delayed. However, this approach ignores the intrinsic impact of the complexity of the board process on processing efficiency and makes it difficult to effectively distinguish between reasonable long time consumption caused by irregular edge banding or dense drilling and abnormal long time consumption caused by equipment failure or human negligence. As a result, the level of refinement in production management remains low. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides a board production order management system based on edge banding drilling. Specifically, the technical solution of the present invention includes:

[0005] The order data acquisition module is used to acquire the set of process data for the target production order and its real-time fulfillment process data;

[0006] The performance benchmark construction module is used to generate the ideal performance time benchmark for the order based on the process data set, and to generate the theoretical performance delay assessment data for the order by combining the preset process complexity parameters.

[0007] The performance deviation analysis module is used to calculate the first difference between the real-time performance process data and the ideal performance time benchmark to obtain the actual performance deviation, and to calculate the second difference between the theoretical performance delay assessment data and the ideal performance time benchmark to obtain the theoretical performance deviation.

[0008] The order status determination and scheduling module is used to analyze the similarity of the distribution patterns of the actual performance deviation and the theoretical performance deviation, and to make a logical determination of the order status based on the similarity.

[0009] If the similarity of the distribution pattern is higher than a preset threshold, the order status is determined to be a compliant process delay, and the promised delivery time of the order is dynamically adjusted accordingly; if the similarity of the distribution pattern is lower than the preset threshold, the order status is determined to be an abnormal performance risk, and a corresponding management decision instruction is generated.

[0010] Preferably, the order data acquisition module is specifically used for:

[0011] Extract the processing feature information of the order from the process data set. The processing feature information includes the drilling location and quantity, the edge banding path length and the material type of the board.

[0012] Order fulfillment progress information is extracted from real-time fulfillment process data. The fulfillment progress information includes the start and end timestamps of each process, equipment status logs, and the flow record of the sheet material on the production line.

[0013] Preferably, the performance benchmark construction module includes:

[0014] Process decomposition unit (PDU) is used to break down an order into multiple standard processing steps.

[0015] The baseline calculation unit is used to configure standard working hours for each standard processing unit and accumulate them to obtain the ideal fulfillment time baseline for completing the entire order.

[0016] Preferably, the performance benchmark construction module also includes:

[0017] The complexity mapping unit is used to map complex processing features to corresponding time adjustment coefficients based on historical order data or expert rules.

[0018] The delay assessment unit is used to apply the time adjustment factor to the ideal performance time benchmark, simulate and calculate to generate theoretical performance delay assessment data, which represents the expected time delay caused by the complexity of the order process.

[0019] Preferably, the performance discrepancy analysis module is specifically used for:

[0020] Calculate the deviation of real-time performance data from the ideal performance time baseline to generate the actual performance deviation that includes the influence of all factors;

[0021] The deviation of theoretical performance delay assessment data from the ideal performance time baseline is calculated to generate theoretical performance deviation that only includes process complexity factors.

[0022] Preferably, the order status determination and scheduling module is specifically used for:

[0023] The actual performance deviation and the theoretical performance deviation are respectively constructed as time series vectors;

[0024] Calculate the cosine similarity between two time series vectors and use this cosine similarity as the distribution pattern similarity.

[0025] Preferably, the order status determination and scheduling module also includes:

[0026] The scheduling update unit is used to respond to an order status being determined to be a compliant process delay, and to update the time nodes of the order and subsequent related orders in the overall production scheduling plan based on the total delay time corresponding to the actual performance deviation.

[0027] Preferably, the order status determination and scheduling module also includes:

[0028] The risk handling unit is used to respond to an order status being determined to be an abnormal performance risk, mark the order as requiring key attention, and push the key performance stage information that caused the deviation to the management port for manual review.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. This invention achieves digital reconstruction of the entire board production process by constructing a holographic data input layer and a dual performance reference system. It can effectively distinguish between compliance process delays caused by irregular edge banding or dense drilling and abnormal risks caused by equipment failures. By calculating the actual deviation between real-time performance process data and the ideal benchmark, as well as the theoretical deviation between theoretical evaluation data and the ideal benchmark, a precise difference analysis model is established, eliminating the influence of basic working hours. By introducing a distribution pattern similarity judgment mechanism, the actual delay curve and the theoretical prediction curve are compared to intelligently identify delay attributes, thereby transforming the extensive time assessment into a refined attribution analysis, significantly improving the fairness of performance evaluation and the accuracy of production scheduling.

[0031] 2. This invention establishes a high-precision database containing three-dimensional coordinates, contour vectors, and millisecond-level equipment status through a dual-track parallel fine-grained data extraction logic of the design domain and execution domain, combined with processing file parsing and real-time acquisition by the Industrial Internet of Things, ensuring accurate restoration of the life cycle of sheet metal in mixed-flow production lines; by using an atomized reconstruction method to decompose orders into standard process units and constructing a zero-fault ideal benchmark based on the assumption of physical limits, an objective efficiency measurement zero point can be obtained; by introducing a process complexity quantification simulation model based on process weights, physical characteristics such as edge banding curvature and hole density are mapped into time adjustment coefficients, effectively solving the problem of global coefficients diluting local processes, and realizing a logical closed loop from macro total time to micro process.

[0032] 3. This invention successfully separates the inherent process complexity and exogenous anomaly interference in the production process through a dual difference calculation strategy, providing a clean data foundation for differential analysis. By using vector alignment and log mapping technology based on standard process sequences, it solves the dimensional mismatch problem caused by rework or skipping sequences in actual production, ensuring the mathematical consistency of vector operations. By constructing deviation feature vectors and calculating cosine similarity, it can accurately identify the fingerprint features of compliance delays from the topological structure. This allows the system to determine compliance only when the actual slow node and the theoretically difficult node are highly consistent when facing non-standard order flow, greatly improving the sensitivity and robustness of anomaly identification.

[0033] 4. This invention utilizes a recursive translation algorithm with a circuit breaker mechanism to automatically perform resource conflict detection and scheduling updates when a compliant process delay is identified. This prevents a cascading change in production plans caused by a single order delay, ensuring the overall stability of the production cycle. Through the precise push mechanism of the risk handling unit, when an abnormal performance risk is identified, the specific process node with the largest deviation can be located through element-by-element difference analysis, and the key performance stage information can be pushed to the management port in real time. This targeted anomaly management mode enables rapid response and handling of production bottlenecks, avoids capacity waste caused by information lag, and significantly improves the management efficiency of the workshop. Attached Figure Description

[0034] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0035] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0037] Example 1:

[0038] Please see Figure 1 A board production order management system based on edge banding drilling includes:

[0039] The order data acquisition module is used to acquire the set of process data for the target production order and its real-time fulfillment process data;

[0040] The performance benchmark construction module is used to generate the ideal performance time benchmark for orders based on the process data set, and to generate theoretical performance delay assessment data for orders by combining preset process complexity parameters.

[0041] The performance deviation analysis module is used to calculate the first difference between real-time performance process data and ideal performance time benchmark to obtain the actual performance deviation, and to calculate the second difference between theoretical performance delay assessment data and ideal performance time benchmark to obtain the theoretical performance deviation.

[0042] The order status determination and scheduling module is used to analyze the similarity of the distribution patterns of actual performance deviation and theoretical performance deviation, and to make logical determinations on the order status based on the similarity.

[0043] If the distribution pattern similarity is higher than a preset threshold, the order status is determined to be a compliant process delay, and the promised delivery time of the order is dynamically adjusted accordingly; if the distribution pattern similarity is lower than a preset threshold, the order status is determined to be an abnormal performance risk, and corresponding management decision instructions are generated.

[0044] This embodiment provides a board production order management system based on edge banding and drilling. The system aims to solve the technical pain point in customized furniture production where it is difficult to distinguish between reasonable long time consumption caused by board processes such as irregular edge banding and dense drilling and abnormal long time consumption caused by equipment failure or human negligence.

[0045] The order data acquisition module constructs a holographic data input layer, which not only connects to... Production Execution System (MES) obtains static data and The document also connects to equipment such as edge banding machines and six-sided drills via industrial IoT interfaces. The controller collects production flow data in real time at the millisecond level;

[0046] The performance benchmark construction module establishes a dual reference system, namely the physical limit benchmark and the process simulation benchmark. This module is based on... Geometric data calculation assumes an ideal delivery time benchmark under the condition of no equipment failure and no waiting, i.e., a zero-failure model. Combined with preset process complexity parameters such as hole density coefficient and edge curvature factor, reasonable process consumption time is superimposed on the ideal benchmark to generate theoretical delivery delay assessment data.

[0047] This step is essentially a feedforward simulation, predicting the shape of the production curve if only the sheet metal is difficult to produce.

[0048] The performance difference analysis module separates noise and signal. This module calculates the first difference between real-time performance process data and ideal performance time benchmark to obtain the actual performance deviation that includes all factors, namely process difficulty, failure and idleness. At the same time, it calculates the second difference between theoretical performance delay assessment data and ideal performance time benchmark to obtain the theoretical performance deviation that only includes process complexity factors.

[0049] The order status determination and scheduling module makes intelligent decisions through pattern matching. This module does not directly compare the magnitude of the duration values, but analyzes the similarity of the distribution patterns of the actual performance deviation and the theoretical performance deviation in the time series or feature vector.

[0050] If the similarity of the distribution pattern exceeds a preset threshold, meaning that the actual delay curve closely matches the delay curve caused by the difficulty in theoretical simulation, the system determines that the order status is a compliant process delay and dynamically adjusts the promised delivery time of the order accordingly. Revise the efficiency evaluation criteria for the corresponding process.

[0051] If the similarity of the distribution pattern is lower than the preset threshold, it means that the actual delay presents a form that the theoretical model did not predict, such as a sudden long-term stagnation. The system determines that the order status is an abnormal fulfillment risk and generates corresponding management decision instructions, such as triggering an audible and visual alarm or suspending the work order.

[0052] In this embodiment, under the flexible production scenario of customized furniture, the concept of synthetic analysis is introduced and the similarity comparison with double bias is used to successfully separate the confusion between endogenous complexity and exogenous anomalies in board processing. This enables production management to shift from a rough result-oriented approach of penalizing overtime to a refined attribution-oriented approach, effectively improving the accuracy of production scheduling and the fairness of performance evaluation.

[0053] The order data acquisition module is specifically used for:

[0054] Extract the processing feature information of the order from the process data set. The processing feature information includes the drilling location and quantity, the edge banding path length and the material type of the board.

[0055] Order fulfillment progress information is extracted from real-time fulfillment process data. The fulfillment progress information includes the start and end timestamps of each process, equipment status logs, and the flow record of the sheet material on the production line.

[0056] This embodiment refines the data extraction logic of the order data acquisition module, following the principle of parallel design and execution domains.

[0057] This module has a built-in function for extracting processing feature information from orders. The file parser directly reads the board material. or Processing documents;

[0058] Specifically, for the location and number of holes, the three-dimensional coordinates and hole depth of each hole are extracted to calculate the tool path travel and the number of tool changes;

[0059] For the edge banding path length, analyze the board outline vector diagram and calculate the physical length of straight edge banding and curved edge banding;

[0060] For different types of boards, the module extracts material metadata, such as particleboard and high-density fiberboard, because different materials have different upper limits for feed speed. Simultaneously, when extracting order fulfillment progress information, this module... Agreement to subscribe to the tag points of production line equipment;

[0061] Specifically, this includes recording high-precision timestamps of the board entering the edge banding machine's pressure rollers and leaving the discharge port; collecting equipment status logs such as spindle load rate, glue pot temperature, and servo motor current; and... Alternatively, a QR code scanner can be used to record the flow of sheet metal between different workstations and the queuing time.

[0062] This embodiment extracts the processing features that determine how long the work should last and the actual progress of the contract fulfillment in a fine-grained manner, providing a solid data foundation for building a high-precision physical benchmark model. This all-dimensional information collection method avoids the blind spots in analysis caused by a single data dimension, ensuring that the life cycle of each board can be accurately restored even on a complex mixed-flow production line.

[0063] The performance benchmark construction module includes:

[0064] Process decomposition unit (PDU) is used to break down an order into multiple standard processing steps.

[0065] The baseline calculation unit is used to configure standard working hours for each standard processing unit and accumulate them to obtain the ideal fulfillment time baseline for completing the entire order.

[0066] This embodiment uses an atomized reconstruction method to construct the baseline;

[0067] The process decomposition unit breaks down an order into multiple standard processing steps, where a standard processing step is the smallest indivisible unit of motion in the production process, such as drilling a 5mm diameter hole. Hole opening, edge banding machine preheating, and board flipping;

[0068] The benchmark calculation unit is based on the physical limit assumption, namely that the equipment is in optimal condition and there is no human intervention delay, to calculate the ideal performance time benchmark. Considering the differences in kinematic characteristics between different processing steps such as edge banding and drilling, this embodiment calculates the path time separately, and the calculation formula is as follows:

[0069] in, Ideal delivery time benchmark, derived from calculation, physical meaning is the theoretical fastest time under zero failure conditions;

[0070] The total number of types of standard processing operation units, derived from operation decomposition units;

[0071] : No. The number of process units is derived from the order data acquisition module;

[0072] : No. The standard working hours for a type of process unit do not include long-distance displacement and are derived from a preset database of equipment physical parameters.

[0073] : Total time spent on dynamic path, of which Indicates the path type, such as edge sealing cutting path, drill bit idle movement path, For the first Total length of classpath This improvement ensures that the constant feed rate of the edge banding machine and the point movement of the CNC drill are calculated accurately, corresponding to the theoretical maximum feed rate or rapid traverse speed of the corresponding equipment.

[0074] The theoretical minimum number of tool changes is calculated based on differences in borehole diameter and edge banding type, and the data source is the order data acquisition module.

[0075] : The standard cycle time for a single tool change of the equipment, the value of which is derived from the equipment's factory parameters;

[0076] Theoretically, the total material unloading time is calculated by multiplying the total number of sheets in the order by the standard cycle of a single action of the automated robotic arm.

[0077] This embodiment constructs a physical limit model that eliminates inefficient factors, thereby obtaining an absolutely objective zero point for the system. Any delay in actual production is a positive deviation relative to this zero point, thus transforming the complex workshop management problem into a mathematical deviation analysis problem and providing a unified metric for subsequent quantitative evaluation.

[0078] Example 2:

[0079] The performance benchmark building module also includes:

[0080] The complexity mapping unit is used to map complex processing features to corresponding time adjustment coefficients based on historical order data or expert rules.

[0081] The delay assessment unit is used to apply the time adjustment factor to the ideal performance time benchmark, simulate and calculate to generate theoretical performance delay assessment data, which represents the expected time delay caused by the complexity of the order process.

[0082] This embodiment further introduces a process complexity quantification simulation model based on process weights to solve the problem of global coefficients diluting or exaggerating local processes;

[0083] Complexity mapping units perform hierarchical computation:

[0084] Constructing process weight vectors: Obtaining the ideal time consumption for each process type from the baseline calculation unit. And the Prime Minister wanted to spend time Calculate weights Here, to clarify the parameter definitions in the formula, the system defines the standard time percentage of the edge banding process as... The standard time consumption percentage of the drilling process is defined as ,Right now and The weights derived from the above calculations The portion corresponding to a specific process;

[0085] Calculate the local strength factor: edge sealing strength factor To accurately reflect the difference in difficulty between straight and curved edge sealing, a piecewise weighted algorithm is adopted; prior to this, the system performs boundary checks: if the total edge sealing path length... If it is 0, then let ;like Then perform the following calculation:

[0086] in, This represents the total path length for edge sealing. For the total length of a curve segment with a radius of curvature less than a preset threshold, such as 50mm, this threshold is set based on the physical radius of the edge banding machine's pressure roller. When the radius of curvature of the edge banding trajectory is less than this physical limit, the equipment must be forced to slow down to ensure bonding quality.

[0087] The irregular shape correction factor is set to 0.4. It is obtained based on statistical regression of historical production data. Specifically, at least 1,000 order data containing irregular edge banding processes are collected within the historical period. The actual time consumption of the irregular edge banding process is compared with the theoretical time consumption, and the average time increment ratio is calculated. That is, the actual time consumption is 1.4 times the ideal time consumption. This means that each unit of standard time of curve processing will generate an additional 0.4 units of time cost.

[0088] Based on this, significance testing of the sample data is performed. Test, and obtain its A value less than 0.05 confirms the presence of the irregularity correction factor. There is a significant linear correlation between this parameter and the actual time increment, ensuring its stability and persuasiveness across different batches of production;

[0089] In this formula, As a weighting coefficient and length Multiplication, in its physical sense, is used to calculate the average unit time increment ratio of the entire edge sealing path; that is... Ultimately, this represents the average delay strength of the edge banding process of the order relative to the standard speed, thus accurately converting the 1.4 times physical increment relationship into a dimensionless strength coefficient;

[0090] The linear baseline factor is set to 0.1. This value is derived from the acceleration and deceleration performance curves provided by the equipment manufacturer and represents the 10% basic process loss caused by start-stop acceleration and deceleration.

[0091] Drilling strength factor ;in, This is the pore density value, calculated as the total number of pores. Divide by the surface area of ​​the board The unit of measurement is units / ;

[0092] The density sensitivity coefficient is a regression model based on historical data, and its physical dimensions are defined as follows: Or it can be expressed as the area occupied per unit quantity, used to offset the pore density. Dimensions Thus ensuring It is a dimensionless value;

[0093] The method for obtaining this data is as follows: Select historical samples of orders with no failures, using the hole density value as the independent variable. The dependent variable is the ratio of actual drilling time to standard drilling time minus 1. The slope value obtained by performing least squares linear regression;

[0094] Synthesized global adjustment coefficients: The time adjustment coefficients are generated using a weighted summation method. :

[0095] This formula uses the local intensity factor With process weight Multiply by the product to calculate the weighted incremental contribution of the edge banding process to the total time, ensuring that The physical increment logic it represents is correctly inherited and expressed in the global calculation;

[0096] The delay assessment unit generates theoretical performance delay assessment data. This data not only provides a theoretical upper limit for the total amount, but its components are also cached in shared memory as the base components for subsequent vector construction.

[0097] This embodiment introduces weights. This corrects the problem of physical distortion caused by simple multiplication in the traditional model, clarifies the conversion relationship between the correction factor and physical velocity, and realizes a logical closed loop from macroscopic total time to microscopic process.

[0098] The performance discrepancy analysis module is specifically used for:

[0099] Calculate the deviation of real-time performance data from the ideal performance time baseline to generate the actual performance deviation that includes the influence of all factors;

[0100] The deviation of theoretical performance delay assessment data from the ideal performance time baseline is calculated to generate theoretical performance deviation that only includes process complexity factors.

[0101] This embodiment performs a double difference calculation to separate the bias components;

[0102] The performance discrepancy analysis module calculates the actual completion time in the real-time performance process data. With ideal performance time benchmark The difference generates the actual performance deviation. The calculation formula is: This deviation includes the total delay caused by both the difficulty in making the sheet material (internal factor) and machine failure (external factor).

[0103] This module calculates theoretical performance delay assessment data. With ideal performance time benchmark The difference generates the theoretical performance deviation. The calculation formula is: This deviation only includes the theoretical delay caused by the difficulty in manufacturing the sheet material, i.e., the internal factor.

[0104] In this embodiment, through these two differential calculations, the system prepares two sets of data: one set is the actual deviation, and the other set is the theoretically predicted deviation. This differential design cleverly eliminates the influence of the base working hours, allowing subsequent analysis to focus on the nature of the incremental part, thereby more sensitively capturing the signals of production anomalies.

[0105] The order status determination and scheduling module is specifically used for:

[0106] The actual performance deviation and the theoretical performance deviation are respectively constructed as time series vectors;

[0107] Calculate the cosine similarity between two time series vectors and use this cosine similarity as the distribution pattern similarity.

[0108] This embodiment achieves pattern recognition of delay properties by constructing a deviation feature vector and calculating cosine similarity. This recognition process deconstructs production time deviations into quantifiable technical attribution features by calculating the geometric similarity between the actual physical execution deviation vector and the theoretical process deviation vector. The specific steps are as follows:

[0109] Data Alignment and Vectorization: To ensure consistency in the mathematical dimensions of vector operations, this step does not use the variable-length actual execution flow as a benchmark, but rather the standard process sequence generated by the process decomposition unit. To unify the standards; among which, According to The total number of theoretical atomic actions parsed from the document, i.e., the ordered set of actions such as drilling, edge sealing, and grooving, is used to establish a fixed... 3D vector space;

[0110] Constructing the actual performance deviation vector Execution Log - Process Projection Mapping; This step aims to solve the problem of attribution under multiple concurrent processes and data noise. The specific logic is as follows:

[0111] Traversal sequence Each standard node in Matching and aggregation: in Retrieval and node data in real-time performance process Matching records;

[0112] To ensure matching accuracy, the system employs a dual verification mechanism: matching process Secondly, verify whether the log timestamp falls within the valid production time window of the order batch to eliminate interference from historical cached data; if there are multiple records that meet the conditions, such as drilling rework due to quality inspection failure, then accumulate their time consumption as the actual total time consumption of that node. ;

[0113] Missing records handling: If no corresponding record is found, for example, if the process is skipped, then... ;

[0114] Deviation calculation: Calculation ,in, This represents the standard working hours for that node; ultimately forming a vector. This vector represents the actual time deviation distribution on the standard process topology;

[0115] Constructing a theoretical performance deviation vector Standard sequences based on micro-feature mapping method Each node Theoretical predictions are assigned to reflect the expected delays;

[0116] The computational logic here strictly follows the process complexity model established in Example 1, applying it from macroscopic statistics to microscopic nodes: if node For the edge sealing action, call the geometric analysis result: if the segment contains a radius of curvature The curve is then modified by applying the anomaly correction factor. The definition is given in Example 1. Calculation ;

[0117] Use directly here Multiplying by the standard working time is because, for a single atomic node, the intensity factor is... It can be considered as a curve segment with a length of 100%, and the calculated result... This is the theoretical man-hour increment caused by process complexity as defined in the model of Example 1, thus ensuring the consistency between vector construction and macroscopic evaluation in a physical sense;

[0118] If it is a straight line, calculate... ;

[0119] If node For drilling operations, calculate the local density at the borehole location. To ensure computational efficiency, the system pre-constructs a system based on the two-dimensional coordinates of the holes, which may contain a large number of holes in the order. - The spatial index structure uses the geometric coordinates of the board plane as the search dimension;

[0120] During computation, a fast range query, i.e., a K-dimensional tree space index, is performed using this index; the role of this index structure in this invention is to transform the traditional Global distance search complexity reduced to This enables the system to extract local hole topological features in real time and efficiently under complex working conditions of large-scale dense drilling, thereby supporting theoretical delay fingerprint prediction at the millisecond level.

[0121] During calculation, a fast range query is performed using this index to retrieve data centered at the current drilling point with a radius equal to a preset threshold. For example, 100 Number of holes in the neighborhood And calculate the local pore density. The radius value is 100. The determination is based on the minimum physical distance between adjacent drill bits in the drilling kit and the safe range of local stress concentration in the plate. It is used to characterize the heat dissipation waiting or tool retraction that may be caused by continuous drilling within this range, and applies the density sensitivity coefficient. ,calculate The resulting vector This constitutes the theoretical delay fingerprint of the order under the standard process sequence, and its peaks precisely correspond to the processing difficulties in the geometry of the sheet metal;

[0122] If node As an auxiliary process, This generates a vector. Its peaks and troughs precisely correspond to the difficult-to-process points in the geometry of the board;

[0123] Similarity determination: Zero vector verification is performed before calculation.

[0124] like This means that there is no deviation in actual production. At this point, it is necessary to further distinguish the theoretical vector state:

[0125] like If there is theoretically no expected deviation, it indicates that the actual performance is completely consistent with the theoretical benchmark, i.e., a perfect state with zero delay. At this point, let the similarity... This is to determine if the calculation is in compliance and to prevent calculation errors where the denominator is zero.

[0126] like Then let ;

[0127] like and That is, standard boards, without technological difficulties but with actual delays, then the similarity is... Because any delay cannot be explained by the process;

[0128] like and Then, perform cosine similarity calculation: This computational logic ensures that the calculation is only performed when the actual slow part happens to be the theoretically difficult part. Only then will it approach 1;

[0129] The preferred range for the preset threshold is 0.85 to 0.95. The specific method for determining the threshold is as follows: collect historical order samples that have been manually verified as compliant process delays within a historical period, such as the last three months; calculate the similarity of the distribution patterns of all orders in the sample set; and select the lower 5% quantile of its probability density function as the preset threshold to ensure that the judgment model has a confidence level of more than 95%.

[0130] This embodiment solves the dimensional mismatch problem caused by rework or skipping sequences in actual production by using vector alignment based on standard sequences, enabling the system to accurately identify the fingerprint features of compliance delays caused by process complexity from the topology.

[0131] The order status determination and scheduling module also includes:

[0132] The scheduling update unit is used to respond to an order status being determined to be a compliant process delay, and to update the time nodes of the order and subsequent related orders in the overall production scheduling plan based on the total delay time corresponding to the actual performance deviation.

[0133] In this embodiment, the order status determination and scheduling module includes a scheduling update unit, which aims to achieve adaptive adjustment of the production plan;

[0134] In response to an order status being determined to be a compliant process delay, the scheduling update unit executes the following strict resource conflict detection and migration logic:

[0135] Extract the total delay time corresponding to the actual performance deviation. ;

[0136] Lock the current order Key equipment resources occupied For example, edge sealing lines, and obtaining the original end time of the order in the APS system. ;

[0137] Conflict detection: Check time window Internal resources Has it been assigned to the next order? ;

[0138] Recursive translation and circuit breaker verification: If a time window conflict is detected, the following steps are performed:

[0139] Overlap Calculation: Calculate the duration of conflict overlap. ,Will The earliest start time has been updated to a tentative time. ,in, For orders The earliest start time before the update / adjustment;

[0140] Calendar constraint validation: Read the factory production calendar; if... Falling into non-working hours Then correct ;

[0141] Recursive Propagation and Termination Check: with Modified Based on this, recursively check subsequent orders. ;

[0142] To prevent infinite recursion (deadlock) caused by an overloaded schedule, the system sets a maximum recursion depth. For example, a 50-layer threshold is set based on the maximum disturbance propagation tolerance in the production management logic. This means that to prevent a single order delay from causing a cascading change in the entire production schedule, the system must limit the length of the order chain affected by the adjustment to maintain the overall stability of the production cycle and the scheduling cutoff horizon. For example, T+7 days, this time threshold is determined based on the factory's standard production cycle and the maximum allowable dwell time of work-in-process, to ensure that scheduling adjustments do not exceed the actual controllable production scope;

[0143] If the recursion process reaches Or the calculated time point exceeds If this happens, a scheduling overflow exception will be triggered, automatic shifting will be stopped, and affected subsequent orders will be pushed into a queue awaiting manual intervention, while resource usage will be locked.

[0144] Update Release: If the circuit breaker is not triggered, the updated time points will be written back to the overall production schedule.

[0145] This embodiment introduces a recursive translation algorithm with a circuit breaker mechanism to achieve flexible self-healing of the production plan. This avoids the overall scheduling chaos caused by the bullwhip effect and prevents the risk of the algorithm going into a dead loop under extreme capacity load, thus ensuring the industrial-grade availability of the system.

[0146] The order status determination and scheduling module also includes:

[0147] The risk handling unit is used to respond to an order status being determined to be an abnormal performance risk, mark the order as requiring key attention, and push the key performance stage information that caused the deviation to the management port for manual review.

[0148] In this embodiment, the order status determination and scheduling module includes a risk handling unit, which aims to achieve accurate push notifications for anomalies;

[0149] In response to an order status being determined to be at risk of abnormal fulfillment, the risk handling unit... The order is highlighted in red on the dashboard and requires manual intervention.

[0150] Calculate vectors and Element-wise interpolation to identify the dimension with the largest difference. That is, the first The calculation formula for each process is as follows:

[0151] in The range of values ​​is to ,and Indicates the total number of process nodes;

[0152] : indicates the first Actual performance deviation at each process node;

[0153] : indicates the first Theoretical performance deviation at each process node;

[0154] The first Key performance information for each process, such as the actual time taken for the edge banding process being 20 minutes longer than the theoretical process time, and the equipment load being normal, is packaged and pushed to the workshop director's management port or handheld terminal.

[0155] This embodiment achieves targeted anomaly management. It not only informs managers that a problem has occurred, but also accurately locates the problematic link by comparing the differences between theory and practice. This mechanism greatly shortens the time for anomaly investigation and handling, enabling managers to respond quickly and resolve production bottlenecks.

[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A board production order management system based on edge banding drilling, characterized in that, include: The order data acquisition module is used to acquire the set of process data for the target production order and its real-time fulfillment process data; The performance benchmark construction module is used to generate the ideal performance time benchmark for the order based on the process data set, and to generate the theoretical performance delay assessment data for the order by combining the preset process complexity parameters. The performance deviation analysis module is used to calculate the first difference between the real-time performance process data and the ideal performance time benchmark to obtain the actual performance deviation, and to calculate the second difference between the theoretical performance delay assessment data and the ideal performance time benchmark to obtain the theoretical performance deviation. The order status determination and scheduling module is used to analyze the similarity of the distribution patterns of the actual performance deviation and the theoretical performance deviation, and to make a logical determination of the order status based on the similarity. If the similarity of the distribution pattern is higher than a preset threshold, the order status is determined to be a compliant process delay, and the promised delivery time of the order is dynamically adjusted accordingly; if the similarity of the distribution pattern is lower than the preset threshold, the order status is determined to be an abnormal performance risk, and a corresponding management decision instruction is generated.

2. The board production order management system based on edge banding drilling according to claim 1, characterized in that, The order data acquisition module is specifically used for: The processing feature information of the order is extracted from the process data set, including the drilling location and quantity, the edge banding path length, and the material type of the board. Order fulfillment progress information is extracted from the real-time fulfillment process data. The fulfillment progress information includes the start and end timestamps of each process, equipment status logs, and the flow record of the sheet material on the production line.

3. The board production order management system based on edge banding drilling according to claim 1, characterized in that, The performance benchmark construction module includes: The process decomposition unit is used to break down the order into multiple standard processing process units; A benchmark calculation unit is used to configure standard working hours for each of the standard processing steps and accumulate them to obtain the ideal fulfillment time benchmark for completing the entire order.

4. The board production order management system based on edge banding drilling according to claim 3, characterized in that, The performance benchmark construction module also includes: The complexity mapping unit is used to map complex processing features to corresponding time adjustment coefficients based on historical order data or expert rules. The delay assessment unit is used to apply the working time adjustment coefficient to the ideal performance time benchmark and simulate and calculate to generate the theoretical performance delay assessment data, which represents the expected time delay caused by the complexity of the order process.

5. A sheet metal production order management system based on edge banding drilling according to claim 1, characterized in that, The performance discrepancy analysis module is specifically used for: The deviation of the real-time performance process data from the ideal performance time benchmark is calculated to generate the actual performance deviation that includes the influence of all factors; The deviation of the theoretical performance delay assessment data from the ideal performance time benchmark is calculated to generate the theoretical performance deviation that only includes process complexity factors.

6. The board production order management system based on edge banding drilling according to claim 1, characterized in that, The order status determination and scheduling module is specifically used for: The actual performance deviation and the theoretical performance deviation are respectively constructed as time series vectors; Calculate the cosine similarity between the two time series vectors, and use the cosine similarity as the distribution pattern similarity.

7. A board production order management system based on edge banding drilling according to claim 1, characterized in that, The order status determination and scheduling module also includes: The scheduling update unit is used to update the time nodes of the order and subsequent related orders in the overall production scheduling plan in response to the order status being determined as a compliant process delay, based on the total delay time corresponding to the actual performance deviation.

8. A sheet metal production order management system based on edge banding and drilling according to claim 1, characterized in that, The order status determination and scheduling module also includes: The risk handling unit is used to mark the order as requiring special attention when the order status is determined to be an abnormal performance risk, and to push the key performance stage information that caused the deviation to the management port for manual review.

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