A bamboo string sorting priority scheduling intelligent management method and system

Abstract

This invention relates to the field of bamboo processing automation and dynamic scheduling technology for industrial production, specifically a method and system for intelligent management of bamboo string sorting priority scheduling. The method includes: receiving and parsing an order queue containing multiple order tasks; obtaining real-time quality information of raw materials upstream of the production line; calculating the dynamic execution priority weight of the order task; and executing dynamic scheduling: when the quality matching degree of a first order task with high commercial priority is identified as being lower than a first preset threshold, and its remaining delivery time is greater than a second preset threshold, a resource optimization scheduling strategy is initiated to generate an adjusted task execution sequence; the remaining processing time of the first order task is monitored in real time, the resource optimization scheduling strategy is terminated, and the first order task is set to the highest urgency, generating a final task scheduling instruction. This invention effectively solves the problem of raw material mismatch and high loss caused by blind processing, significantly improving the overall material yield and production economic efficiency of the entire plant.

Description

Technical Field

[0001] This invention relates to the field of bamboo processing automation and industrial production dynamic scheduling technology, specifically to a method and system for intelligent management of bamboo string sorting priority scheduling. Background Technology

[0002] In the field of production management and scheduling in bamboo product processing and manufacturing, sorting systems need to allocate tasks for bamboo string raw materials with non-standard characteristics according to the commercial priority and quality constraints of different orders. At present, existing scheduling solutions generally adopt a fixed sequence management method based on static rules, which rigidly matches order delivery requirements with raw material supply.

[0003] Such solutions possess basic processing capabilities in ideal scenarios where raw material quality is highly consistent. However, in actual continuous production, the physical properties of bamboo, such as curvature and nodule distribution, exhibit natural random fluctuations, leading to a significant dynamic mismatch between the real-time distribution of raw material quality and order quality constraints. Existing technologies often struggle to perceive the probabilistic characteristics of upstream raw material quality in real time and lack a quantitative game-theoretic mechanism for balancing immediate execution benefits with delayed execution risks. This results in high-priority orders facing low-matching raw materials, where forced production could lead to severe resource waste and low efficiency. Conversely, simply suspending orders to wait for high-quality raw materials can easily result in uncontrolled delays and default risks due to a lack of dynamic monitoring of remaining delivery deadlines. Therefore, how to achieve a precise balance between immediate output benefits and long-term delivery risks by intelligently predicting and dynamically weighting the fluctuations in the quality of non-standard raw materials, while ensuring the bottom line of order delivery, has become a critical technical problem that needs to be solved to improve the level of bamboo string sorting and management. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent scheduling method and system for bamboo string sorting prioritization, aiming to solve the dynamic mismatch between raw material quality fluctuations and rigid order demands. By constructing a raw material quality distribution probability model in real time and dynamically calculating order execution priority weights, this invention can quantitatively balance immediate production benefits with the default risk of delayed execution. While improving the adaptability of the production system to non-standard raw materials, reducing material loss, and increasing the overall yield of the plant, it ensures the rigid constraint of the delivery bottom line by monitoring the remaining processing time limit in real time. Specifically, the technical solution of this invention is as follows:

[0005] A bamboo string sorting priority intelligent scheduling method, the method being executed by a computing device, includes: receiving and parsing an order queue containing multiple order tasks, wherein each order task has a corresponding business priority and quality constraints;

[0006] Obtain real-time quality information of raw materials upstream of the production line, and based on the real-time quality information, generate a probabilistic model to characterize the expected distribution of raw material quality grades over a future period of time.

[0007] For each order task in the order queue, the following operations are performed: based on the probability model and the quality constraints of the order task, the quality matching degree between the order task and the current raw materials is calculated; and based on the business priority, remaining delivery time and delay penalty coefficient of the order task, the dynamic execution priority weight of the order task is calculated through a preset decision model, wherein the decision model is used to weigh the benefits of immediate execution against the risks of delayed execution.

[0008] Execute dynamic scheduling: When the quality matching degree of the first order task with high commercial priority is identified as being lower than the first preset threshold and its remaining delivery time is greater than the second preset threshold, a resource optimization scheduling strategy is initiated. The strategy includes: temporarily reducing the scheduling order of the first order task and increasing the scheduling order of the second order task in the order queue that has the highest quality matching degree with the current raw materials, so as to generate an adjusted task execution sequence.

[0009] The remaining processing time of the first order task is monitored in real time. When the remaining processing time reaches the preset emergency processing threshold, the resource optimization scheduling strategy is terminated, the first order task is set to the highest urgency, and the final task scheduling instruction is generated.

[0010] Preferably, receiving and parsing an order queue containing multiple order tasks specifically includes: parsing each order task into a structured management object, wherein the management object includes the following attribute fields: remaining delivery time field, quality requirement standard field, order value density field, and delay penalty coefficient field;

[0011] Specifically, the urgency of order delivery is assessed based on the remaining delivery time field, and the minimum quality threshold of raw materials required to complete the order is defined based on the quality requirement standard field.

[0012] Preferably, generating a probability model based on the real-time quality information specifically includes: continuously acquiring raw material quality inspection data from the feed end as historical quality data;

[0013] Statistical analysis was performed on the historical quality data to determine the frequency of occurrence of raw materials of different quality grades in recent samples;

[0014] Based on the frequency of occurrence, an estimated probability distribution of the quality grade of raw materials arriving in the future is generated by extrapolation, which serves as the probability model.

[0015] Preferably, the dynamic execution priority weight is calculated through a preset decision model, specifically including: based on the probability model and the quality requirement standard, predicting the expected completion efficiency and raw material consumption rate of the order task under the current raw material supply conditions;

[0016] Based on the expected completion efficiency and the order value density, calculate the expected immediate execution revenue of the order task;

[0017] By combining the remaining delivery time with the delay penalty coefficient, calculate the estimated risk cost of the order if it is delayed.

[0018] The dynamic execution priority weight of the order task is determined based on the weighted calculation result of the expected immediate execution benefit and the estimated risk cost.

[0019] Preferably, dynamic scheduling is performed, which specifically includes: maintaining a task buffer queue to temporarily store order tasks that are delayed due to resource mismatch;

[0020] When the resource optimization scheduling strategy is activated, the first order task is temporarily placed in the task buffer queue.

[0021] From the currently executable order tasks, select the task with the highest matching degree with the current raw material quality determined according to the probability model and process it first, until the raw material quality distribution changes significantly or the emergency processing threshold is triggered.

[0022] Preferably, real-time monitoring of the remaining processing time of the first order task specifically includes: continuously comparing the remaining processing time of the first order task with the shortest operation cycle required to complete the order;

[0023] When it is determined that the remaining processing time is less than or equal to the sum of the shortest operation cycle and the preset safety margin, it is determined that the emergency processing threshold has been reached.

[0024] Preferably, it further includes: periodically updating the statistical sample of the historical quality data and recalculating the frequency of occurrence;

[0025] When the difference between the recalculated occurrence frequency and the historical frequency exceeds a set range, it is determined that the raw material quality distribution has changed significantly, and a recalculation of the probability model and the dynamic execution priority weights of all order tasks is triggered.

[0026] A bamboo string sorting priority intelligent scheduling system includes: an order management module, used to receive and parse the order queue, and construct each order task into a structured management object;

[0027] The quality analysis module is used to obtain real-time quality information of raw materials and to build and update a probabilistic model of raw material quality grades based on historical data.

[0028] The dynamic decision engine is used to calculate the quality matching degree and dynamic execution priority weight of each order task based on the probability model and order attributes, and to trigger resource optimization scheduling strategies when the conditions are met.

[0029] The scheduling and execution module is used to generate and adjust the task execution sequence and manage the task buffer queue based on the output of the dynamic decision engine.

[0030] The monitoring and arbitration module is used to monitor the remaining processing time of orders, and to forcibly terminate the optimization strategy and generate the final scheduling instruction when the emergency threshold is reached.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This invention improves raw material utilization and output efficiency: By constructing a probabilistic model of raw material quality distribution, the system achieves a shift from passive response to proactive prediction. This method can accurately quantify the matching degree between order demand and current raw materials, effectively solving the problems of raw material mismatch and high loss caused by blind processing; by temporarily suspending mismatched high-priority orders and using low-requirement orders to consume current raw materials, the overall material yield and production economic efficiency of the entire plant are significantly improved.

[0033] 2. This invention achieves production flexibility and optimized resource allocation: the resource optimization scheduling strategy breaks the rigid limitations of traditional scheduling; when a high-priority order is mismatched with the current raw material quality, the system can intelligently adjust its sequence and promote orders with higher matching degree. This gap-filling mechanism ensures that the production line is always in a high-efficiency operating state. This dynamic adjustment capability enables the system to flexibly respond to the quality fluctuations of non-standard raw materials, transforming potentially wasteful materials into qualified products, greatly enhancing the adaptability of the production system.

[0034] 3. This invention accurately balances commercial value and delivery risk: The decision-making model transforms qualitative rule judgments into quantitative mathematical games by comprehensively considering commercial priorities, delivery time, and penalties for delays. The system can scientifically find a balance between the benefits of immediate execution and the risks of delayed execution, ensuring that scheduling decisions fully consider potential default costs while pursuing profit maximization. This provides reliable economic support and logical interpretability for intelligent decision-making under complex working conditions.

[0035] 4. This invention strengthens the bottom line for delivery and guarantees commercial reputation: The real-time monitoring and emergency handling threshold mechanism sets an insurmountable safety red line for the system; by continuously comparing the remaining time limit with the shortest physical operation cycle, the system can ensure that order delivery does not exceed the contract bottom line under any resource optimization behavior; This arbitration logic with a safety valve function ensures the rigid constraints of production delivery while pursuing the ultimate resource utilization, effectively maintaining the company's commercial reputation. Attached Figure Description

[0036] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0037] Figure 1 This is a flowchart of the method of the present invention;

[0038] Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0039] 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.

[0040] Example 1:

[0041] Please see Figure 1 A method for intelligent scheduling of bamboo string sorting priority, the method being executed by a computing device, includes: receiving and parsing an order queue containing multiple order tasks, wherein each order task has a corresponding business priority and quality constraints; acquiring real-time quality information of raw materials upstream of the production line, and generating a probabilistic model based on the real-time quality information to characterize the expected distribution of raw material quality grades over a future period of time;

[0042] For each order task in the order queue, the following operations are performed: based on the probability model and the quality constraints of the order task, the quality matching degree between the order task and the current raw materials is calculated; and based on the business priority, remaining delivery time and delay penalty coefficient of the order task, the dynamic execution priority weight of the order task is calculated through a preset decision model, wherein the decision model is used to weigh the benefits of immediate execution against the risks of delayed execution.

[0043] Execute dynamic scheduling: In response to identifying that the quality matching degree of a first order task with high commercial priority is lower than a first preset threshold and its remaining delivery time is greater than a second preset threshold, a resource optimization scheduling strategy is initiated. The strategy includes: temporarily reducing the scheduling order of the first order task and increasing the scheduling order of the second order task in the order queue that has the highest quality matching degree with the current raw materials, so as to generate an adjusted task execution sequence; monitoring the remaining processing time limit of the first order task in real time, and in response to the remaining processing time limit reaching a preset emergency processing threshold, terminating the resource optimization scheduling strategy, setting the first order task to the highest urgency, and generating a final task scheduling instruction.

[0044] This embodiment details the execution flow of the intelligent scheduling method for bamboo string sorting priority, aiming to solve the dynamic mismatch between rigid order delivery requirements and flexible raw material quality distribution. The system executes the order queue receiving and parsing steps, treating order tasks as dynamic objects carrying multi-dimensional attributes, including business priority. The quality constraints are established by the static levels preset by the business departments. This strictly defines the physical standards for bamboo strings, such as the range of bending and the tolerance for knots;

[0045] The specific mapping logic is as follows: The system maintains a preset raw material grade mapping table. The physical standard of the bamboo string is quantified into a feature vector. in, Indicates curvature, Indicates the number of nodules; if and Then it is mapped to a level. Superior grade; if and Then it is mapped to a level. Good quality; and so on, by using logical judgment operators, the continuous physical attribute space is divided into a discrete set of raw material grades. Thus achieving With level The system employs quantitative benchmarking; it acquires real-time quality information of raw materials through a visual sensor array deployed upstream of the production line. Instead of assuming constant raw material quality, it uses a sliding time window mechanism to collect visual inspection data, identify the quality grade of each bamboo string, and generate a probabilistic model based on the sampled data to characterize the expected distribution of raw material quality grades over a future period. The model is constructed as a discrete probability distribution vector;

[0046] Based on this, for each order task in the queue The system calculates the quality matching degree. The specific calculation formula is as follows:

[0047] ;

[0048] in, This indicates that, under the current probability distribution of raw materials, the order can be fulfilled. The expected percentage of raw materials meeting quality requirements; Indicates time Raw material grade is The probability of its occurrence; As an indicator function, when the quantified raw material grade Satisfying order constraints The value is 1 if the condition is met, and 0 otherwise. Mathematically, this indicator is equivalent to the expected output rate of the current raw materials for this order. It is used to quantify the degree of overlap between the order constraints and the raw material probability model, and to calculate the dynamic execution priority weight. This weight is calculated by the decision model based on the game between the benefits of immediate execution and the risks of delayed execution; the system executes dynamic scheduling, and the core logic lies in quality-based reverse inhibition;

[0049] When the system identifies the first order task with high business priority The quality matching degree of the current raw materials is lower than the first preset threshold. Its emergency treatment threshold is denoted as First preset threshold The preferred value range is... In this embodiment, it is preferably set to This is used to define the critical point of severe raw material mismatch; and the remaining delivery time is greater than the second preset threshold. When the system is determined to be in a state of high loss and low efficiency, an optimization strategy is immediately initiated to forcibly suppress the problem. The system executes the task and suspends it; simultaneously, it scans the order queue to find the second order task that best matches the current low-quality raw materials. And improve its scheduling order, generating an adjusted task execution sequence, thereby improving the efficiency of task execution. In other words, it is the raw material of waste products. Superior-quality products are processed; the system monitors suspended products in real time. If the remaining processing time limit is reached, and the system determines that the time risk exceeds the cost of raw material loss, it will immediately terminate the resource optimization scheduling strategy. Set to the highest urgency level and force the production line to switch to handling it. .

[0050] Example 2:

[0051] Receiving and parsing an order queue containing multiple order tasks specifically includes: parsing each order task into a structured management object, wherein the management object includes the following attribute fields: remaining delivery time field, quality requirement standard field, order value density field, and delay penalty coefficient field; wherein, the urgency of order delivery is assessed based on the remaining delivery time field, and the minimum quality threshold of raw materials required to complete the order is defined based on the quality requirement standard field.

[0052] This embodiment details the structured parsing process of the order queue. The system introduces the concept of structured management objects, transforming unstructured ERP orders into computer-processable structured management objects. The construction of this object depends on the precise definition and assignment of the following core attribute fields:

[0053] Remaining delivery time field The source is the time difference between the current system time and the order contract deadline, and its physical meaning is the time urgency of order delivery, with units of hours or minutes;

[0054] Quality requirement standard fields The source is the feature extraction from the order's technical specifications. Its physical meaning is the order's tolerance threshold for raw material defects such as mold and insect holes, which is represented as a Boolean vector or feature vector.

[0055] Order value density field The source is pricing data from the financial system, and the physical meaning is the commercial value corresponding to a unit of finished bamboo string, with the unit being yuan / thousand pieces;

[0056] Delay penalty coefficient field The source is the parameterized transformation of contract default clauses, and the physical meaning is the nonlinear risk function parameter that increases with the delay time;

[0057] The system dynamically assesses the urgency of order delivery based on the remaining delivery time field, using it as a time benchmark for calculating risk costs. At the same time, it defines the minimum quality threshold of raw materials required to complete the order based on the quality requirement standard field, which serves as a filter for subsequent matching calculations.

[0058] The structured management object construction scheme described in this embodiment, in the context of intelligent manufacturing information processing, transforms fuzzy commercial order attributes into standardized holographic feature vectors, providing a quantitative computational basis for subsequent complex mathematical games. In particular, the parameterization of the delay penalty coefficient and value density enables the scheduling system to move away from qualitative rule judgments and instead seek the scheduling solution that maximizes economic benefits in a multi-dimensional numerical space, ensuring the interpretability of the decision-making logic at the commercial value level.

[0059] Example 3:

[0060] The probability model generated based on the real-time quality information specifically includes: continuously acquiring raw material quality inspection data from the feed end as historical quality data; performing statistical analysis on the historical quality data to determine the frequency of occurrence of raw materials of different quality grades in recent samples; and extrapolating the frequency of occurrence to generate an estimated probability distribution of the quality grade of raw materials arriving in the future, which serves as the probability model.

[0061] This embodiment details the process of generating a probabilistic model based on real-time quality information, which constitutes the system's perception and prediction layer. The system continuously acquires images of bamboo skewers at the feed end using a high-speed industrial camera, identifies surface defects using a deep learning algorithm to generate a quality inspection data stream, and stores it in a limited-capacity first-in-first-out queue as historical quality data. The system performs real-time histogram statistics on the samples in this queue, targeting the raw material quality grade set. Each level Statistics in recent Number of samples Calculate the frequency of occurrence Based on this, the system introduces trend correction logic to overcome the lag of simple frequency statistics, and to prevent mathematical anomalies such as negative probability values ​​caused by linear extrapolation, the following modified calculation formula is adopted:

[0062] ;

[0063] The current sliding window period Internally, the quality grade is The frequency of occurrence of raw materials; The quality level at the previous moment was The frequency of occurrence of raw materials;

[0064] This formula compensates for the fluctuation trend of raw material quality distribution by introducing a first derivative term, thus ensuring the foresight of the prediction model.

[0065] in, Indicates the current sliding window period Internal level is The frequency of occurrence of raw materials This represents the extrapolated prediction of the frequency of the next cycle, which compensates for trend fluctuations by introducing a first derivative term. The preset trend sensitivity coefficient ranges from 0.1 to 0.5; it is used to detect differences in raw material distribution. Exceeding the set range During the initial batch switching phase, the system automatically... Reduced to 0.1 to filter out instantaneous prediction noise caused by quality abrupt changes, ensuring the convergence of the probabilistic model; The update step size of the sliding window ranges from 1 second to 60 seconds, with 5 seconds being preferred. The rectifier function is used to ensure that the estimated probability value is non-negative; the system... Perform normalization:

[0066] ;

[0067] in, This indicates that the data was generated based on historical data extrapolation, targeting a quality level of [missing information]. The original predicted frequency of raw materials, To prevent the use of minute quantities with a denominator of zero, an extrapolation is performed to generate a predicted probability distribution of the quality grade of raw materials arriving in the future; this predicted probability distribution is established as a probabilistic model. The model quantifies the proportion of each quality grade in the next batch of bamboo strings;

[0068] The probability model generation mechanism described in this embodiment, in the scenario of online monitoring of raw material flow, transforms lagging detection data into a forward-looking probability distribution by introducing a first-order derivative correction algorithm with non-negative constraints, thus solving the time delay problem of traditional feedback control. This mechanism enables the scheduling system to have predictive capabilities and make decisions based on the mathematical expectation of future raw material flow attributes, thereby avoiding raw material mismatch caused by blind processing and significantly improving the adaptability of the production system to fluctuations in non-standard raw materials.

[0069] Example 4:

[0070] The dynamic execution priority weight is calculated through a preset decision model, specifically including: based on the probability model and the quality requirement standard, predicting the expected completion efficiency and raw material consumption rate of the order task under the current raw material supply conditions;

[0071] By combining the expected completion efficiency and the order value density, the expected immediate execution benefit of the order task is calculated; by combining the remaining delivery time and the delay penalty coefficient, the estimated risk cost of the order task if it is delayed is calculated; and based on the weighted calculation result of the expected immediate execution benefit and the estimated risk cost, the dynamic execution priority weight of the order task is determined.

[0072] This embodiment details the core algorithm logic for calculating dynamic execution priority weights in the decision model; the system introduces the expected output rate. To quantify production performance, the calculation formula is as follows:

[0073] ;

[0074] in: The source is a weighted sum of probability model and order demand; its physical meaning is the production order under current raw material conditions. The expected output rate, dimensionless; The source is the output of the probability model, and its physical meaning is the current time step (i.e., the nth time step). The probability of obtaining graded raw materials; The source is a logical judgment, and the physical meaning is a matching indicator function. It is 1 when the raw material grade meets the order requirements, and 0 otherwise.

[0075] Meanwhile, the system derives the raw material consumption rate based on the expected output rate. Defined as ,in It is a very small positive number, such as 1e-6, used to prevent the denominator from being zero, and is used to characterize the number of raw material input units required to produce one unit of qualified product;

[0076] Calculate the expected immediate return based on the output rate. :

[0077] ;

[0078] in: The source is the product of output rate and value. Its physical meaning is the value output per unit time of executing the order at the current moment, and the unit is yuan / second. The source is the order attribute, and the physical meaning is the order value density unit is yuan / thousand pieces. Dividing by 1000 in the formula is to unify the dimensions. The source is equipment parameters, and the physical meaning is the standard physical speed of the production line, with the unit being pieces per second;

[0079] Simultaneously, the system calculates the estimated risk cost by incorporating the time dimension; prior to this, the system must first estimate the required operation time under the current output rate. The calculation formula is:

[0080] ;

[0081] in, This refers to the remaining quantity of the order that has not yet been fulfilled. It is also used to prevent computational overflow when the output rate approaches 0; based on this calculation, risk costs are estimated. :

[0082] ;

[0083] in, For orders The delay penalty coefficient, For orders The required operation time, For orders The remaining delivery time, The preset risk sensitivity factor has the physical dimension of frequency and the unit of measurement as . , need to be with and The time unit is seconds. Maintaining the reciprocal relationship to ensure the exponent term It is a dimensionless pure number, and its range of values ​​is . Used to control the slope at which the risk value increases as delivery approaches; The upper limit of the risk cost benchmark preset for the system, such as taking 10 times the order value density, is used to prevent the exponential term from overflowing in extreme delay scenarios, which could lead to computer memory calculation errors or the failure of sorting weights.

[0084] The dynamic execution priority weights are determined through weighted calculation. To address the issue of integrating different units of measurement and prevent normalization division-to-zero errors in the case of a single order, a modified Min-Max standardization method is adopted: Let the maximum values ​​of each indicator in the current queue be respectively... , The minimum values ​​are respectively , Before performing normalization calculations, the system executes boundary check logic: first, it obtains the total number of tasks in the current pending queue. ;like Then the normalization result is defined directly. and ;like However, the entire queue was found to have the same metrics, i.e. Then assign a uniform value Only when Only when the following Min-Max normalization formula is used will the weight fusion logic be mathematically stable under any queue size:

[0085] when and When using this method, the following standardized formula should be employed:

[0086] ;

[0087] The final weight formula is obtained as follows:

[0088] ;

[0089] in: The value is derived from the management strategy setting, ranging from 0.1 to 0.9. Its physical meaning is the strategy preference coefficient, which is used to adjust the system's tendency between pursuing profits and avoiding default.

[0090] System basis When sorting all orders, the weight of an order naturally decreases when extremely poor raw materials result in low profits and ample time leads to low risk.

[0091] Example 5:

[0092] The dynamic scheduling process includes: maintaining a task buffer queue to temporarily store order tasks that are delayed due to resource mismatch; when the resource optimization scheduling strategy is activated, placing the first order task into the task buffer queue for temporary storage; selecting the task with the highest matching degree with the current raw material quality as determined by the probability model from the currently executable order tasks and processing it first, until the raw material quality distribution changes significantly or the emergency processing threshold is triggered.

[0093] This embodiment specifically illustrates the queue management mechanism during dynamic scheduling. The system allocates a region in memory as a task buffer queue, specifically for storing order tasks with high commercial priority but low output efficiency due to insufficient raw material compatibility. When the resource optimization scheduling strategy is activated, i.e., when it is determined that a high-priority order is not suitable for the current raw material, the system executes temporary storage logic, removing the first order task from the main execution queue and placing it in the task buffer queue, putting it in a logically suspended state without occupying physical production line resources. During this period, the system executes opportunistic selection logic, scanning the remaining executable orders in real time, and using a probability model to calculate the matching degree between each candidate order and the current raw material, selecting the task with the highest matching degree for priority processing. For example, when the raw materials are mostly short materials, the system automatically selects the order for producing short bamboo skewers. This filling process continues until the raw material quality distribution changes significantly, causing the matching degree of high-priority orders to recover, or triggering an emergency processing threshold that forces the tasks in the buffer queue to be executed.

[0094] The task buffer queue mechanism described in this embodiment constructs a flexible time-resource exchange pool in a continuous flow production scenario. By suspending temporarily mismatched high-priority orders and using low-priority orders as a waste disposal pool, this mechanism effectively smooths out the production shocks caused by fluctuations in raw material quality, transforms the waste that might otherwise be generated into raw materials for low-end products, and greatly improves the overall material yield of the entire plant.

[0095] Example 6:

[0096] Real-time monitoring of the remaining processing time of the first order task specifically includes: continuously comparing the remaining processing time of the first order task with the shortest operation cycle required to complete the order; when it is determined that the remaining processing time is less than or equal to the sum of the shortest operation cycle and the preset safety margin, it is determined that the emergency processing threshold has been reached.

[0097] This embodiment details the mechanism for real-time monitoring of remaining processing time to prevent breach of contract. A daemon process runs in the background to monitor the status of high-priority orders at millisecond-level frequency; the system calculates the shortest job cycle. The formula is as follows:

[0098] ;

[0099] in, The source is the real-time status of the order, and the physical meaning is the remaining order quantity;

[0100] The source is equipment parameters, and the physical meaning is the maximum physical speed of the production line;

[0101] The source is the system statistics module, which sorts the actual yield data of similar orders in the past 72 hours in ascending order and uses the 5th percentile as the dynamic lower bound; if the system is in the cold start phase and has not been running for 72 hours, then a forced setting is applied. This is the theoretical yield of this product category. To provide the most conservative time redundancy guarantee; this parameter, as a conservative estimation factor, is used to define the maximum physical time required to complete the order under the most severe operating conditions with the most drastic fluctuations in raw material quality; it is used to define the most severe operating conditions; for scenarios where the system has accumulated less than 72 hours of data in the early stages of operation, the system uses 50% of the historical experience yield average of the product category to which the order belongs as... The initial default value is used until the real-time sample size meets the statistical confidence requirement;

[0102] The source is the aforementioned physical limit calculation, and the physical meaning is the minimum physical time required to complete the remaining quantity of the order, in seconds;

[0103] The system sets a safety margin. Its value is usually set based on the average changeover time of the production line, and is preferably... Used to handle uncontrollable factors such as equipment failure or line changeover time; the system determines the emergency handling threshold and continuously compares the remaining processing time. The sum of the operation period and margin, when satisfying the inequality When the emergency threshold is reached, the system immediately determines that the emergency handling threshold has been reached. In response to this determination, the system triggers the highest level interrupt command, forcing production regardless of raw material costs, which means that the physical time window is about to close.

[0104] The threshold monitoring mechanism described in this embodiment acts as a system-level safety valve in aggressive resource optimization strategies. By reverse-calculating the physical limit time, it establishes an inviolable delivery red line, ensuring that no matter how the system postpones orders to save raw materials, it will never break the bottom line of contract delivery, thereby guaranteeing the security of the scheduling strategy at the level of commercial reputation.

[0105] Example 7:

[0106] This method also includes: periodically updating the statistical samples of the historical quality data and recalculating the occurrence frequency; when the difference between the recalculated occurrence frequency and the historical frequency exceeds a set range, it is determined that the raw material quality distribution has changed significantly, and the recalculation of the probability model and the dynamic execution priority weights of all order tasks is triggered.

[0107] This embodiment involves the dynamic maintenance and adaptive updating of the probability model. The system sets a fixed update period, such as every 30 seconds, and recalculates the frequency distribution of the current sample at the end of each period. and the frequency distribution of the previous period The comparison is performed; in this process, the system uses the divergence, or the squared Euclidean distance, to measure the difference between the two distributions. The formula is as follows:

[0108] ;

[0109] For the recalculated number Frequency of occurrence of raw materials of different quality grades; For the first period of the previous cycle Frequency of occurrence of raw materials of different quality grades;

[0110] The degree of drift in raw material quality distribution is quantified by calculating the Euclidean distance between adjacent period frequency distribution vectors.

[0111] in, The source is the calculation of distribution differences, and the physical meaning is the degree of drift in the distribution of raw material quality;

[0112] Response to difference value Exceeding the set range The system determines that a significant change has occurred in the raw material quality distribution, i.e., ConceptDrift; a range is set. The 3-Sigma principle, based on historical stable period data, is used to determine the process, i.e., calculating under normal production conditions. Standard deviation of values ,make When the distribution drift exceeds this statistical threshold, a global reconfiguration of the decision engine is automatically triggered. Once a significant change occurs in the judgment, the system immediately triggers a global recalculation mechanism to update the probability model and force a dynamic re-evaluation of the execution priority weights for all order tasks. Recalculate to ensure that the scheduling strategy keeps pace with the latest changes in raw materials;

[0113] The adaptive update mechanism described in this embodiment enables the system to react quickly to quality fluctuations in non-steady-state raw material supply scenarios. By capturing the drift signal of quality distribution in real time and triggering global recalculation, this mechanism effectively prevents the system from making incorrect scheduling decisions by using outdated probability models, ensuring that the scheduling logic can still quickly converge to the new optimal solution when raw material batches are switched instantly.

[0114] Example 8:

[0115] Please see Figure 2 A bamboo string sorting priority intelligent scheduling system includes: an order management module for receiving and parsing order queues and constructing each order task as a structured management object; a quality analysis module for acquiring real-time quality information of raw materials and constructing and updating a probability model of raw material quality grades based on historical data; a dynamic decision engine for calculating the quality matching degree and dynamic execution priority weight of each order task based on the probability model and order attributes, and triggering a resource optimization scheduling strategy when conditions are met; a scheduling execution module for generating and adjusting the task execution sequence and managing the task buffer queue according to the output of the dynamic decision engine; and a monitoring and arbitration module for monitoring the remaining processing time of orders, forcibly terminating the optimization strategy and generating a final scheduling instruction when an emergency threshold is reached.

[0116] This embodiment provides a bamboo string sorting priority intelligent scheduling system that implements the above method. The system is based on a distributed computing architecture. The order management module, as the system's input interface, is responsible for connecting to the enterprise's ERP or MES system. It includes a parser to map received JSON or XML format order data into structured management objects. Simultaneously, the quality analysis module connects the industrial camera at the feeding end to the edge computing unit, internally running statistical analysis algorithms to output probability models in real time. It possesses high concurrency processing capabilities; the core dynamic decision engine incorporates a decision model algorithm, periodically retrieving order objects and probability models, calculating the dynamic weight of each task, and outputting sorting suggestions; for task sequences exceeding a preset scale, such as more than 5,000 task sequences, the dynamic decision engine adopts an incremental calculation strategy, recalculating the weights only for order tasks whose remaining delivery time has changed significantly or are affected by raw material distribution shifts, to ensure the real-time performance of scheduling instructions; the scheduling execution module, as the execution mechanism, directly controls the PLC controller of the sorting machine and maintains the main execution queue and task buffer queue, dynamically adjusting the queue order according to the sorting suggestions; the monitoring and arbitration module, as the independent supervisory layer of the system, runs countdown monitoring logic and has the highest authority. Once an emergency threshold is detected, it can directly send the highest urgency interrupt signal to the scheduling execution module, overriding any optimization strategy;

[0117] The system architecture described in this embodiment, in the context of industrial automation integration, achieves closed-loop management from data perception and decision analysis to execution control through the decoupling design of functional modules. The collaborative work of each module translates the complex dynamic probability scheduling algorithm into a highly reliable industrial control system. In particular, the independent setting of the monitoring and arbitration module ensures the rigid constraints of production safety and delivery bottom line from the architectural level, and has extremely high applicability to industrial sites.

[0118] 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 method for intelligent scheduling of bamboo string sorting based on priority, characterized in that, The method is executed by a computing device and includes: Receive and parse an order queue containing multiple order tasks, where each order task has a corresponding business priority and quality constraints; Obtain real-time quality information of raw materials upstream of the production line, and based on the real-time quality information, generate a probabilistic model to characterize the expected distribution of raw material quality grades over a future period of time. For each order task in the order queue, the following operations are performed: based on the probability model and the quality constraints of the order task, the quality matching degree between the order task and the current raw materials is calculated; and based on the business priority, remaining delivery time and delay penalty coefficient of the order task, the dynamic execution priority weight of the order task is calculated through a preset decision model, wherein the decision model is used to weigh the benefits of immediate execution against the risks of delayed execution. Execute dynamic scheduling: When the quality matching degree of the first order task with high commercial priority is identified as being lower than the first preset threshold and its remaining delivery time is greater than the second preset threshold, a resource optimization scheduling strategy is initiated. The strategy includes: temporarily reducing the scheduling order of the first order task and increasing the scheduling order of the second order task in the order queue that has the highest quality matching degree with the current raw materials, so as to generate an adjusted task execution sequence. The remaining processing time of the first order task is monitored in real time. When the remaining processing time reaches the preset emergency processing threshold, the resource optimization scheduling strategy is terminated, the first order task is set to the highest urgency, and the final task scheduling instruction is generated.

2. The intelligent scheduling method for bamboo string sorting priority according to claim 1, characterized in that, The process of receiving and parsing an order queue containing multiple order tasks specifically includes: Each order task is parsed into a structured management object, which includes the following attribute fields: remaining delivery time field, quality requirement standard field, order value density field, and delay penalty coefficient field; Specifically, the urgency of order delivery is assessed based on the remaining delivery time field, and the minimum quality threshold of raw materials required to complete the order is defined based on the quality requirement standard field.

3. The intelligent scheduling method for bamboo string sorting priority according to claim 1, characterized in that, The generation of the probability model based on the real-time quality information specifically includes: Continuously acquire raw material quality inspection data from the infeed end as historical quality data; Statistical analysis was performed on the historical quality data to determine the frequency of occurrence of raw materials of different quality grades in recent samples; Based on the frequency of occurrence, an estimated probability distribution of the quality grade of raw materials arriving in the future is generated by extrapolation, which serves as the probability model.

4. The intelligent scheduling method for bamboo string sorting priority according to claim 2, characterized in that, The calculation of dynamic execution priority weights through a preset decision model specifically includes: Based on the probability model and the quality requirement standard fields, the expected completion efficiency and raw material consumption rate of the order task under the current raw material supply conditions are predicted. By combining the expected completion efficiency with the order value density field, the expected immediate execution benefit of the order task is calculated; By combining the remaining delivery time with the delay penalty coefficient, calculate the estimated risk cost of the order if it is delayed. The dynamic execution priority weight of the order task is determined based on the weighted calculation result of the expected immediate execution benefit and the estimated risk cost.

5. The intelligent scheduling method for bamboo string sorting priority according to claim 1, characterized in that, The execution dynamic scheduling specifically includes: Maintain a task buffer queue to temporarily store order tasks that are delayed due to resource mismatch; When the resource optimization scheduling strategy is activated, the first order task is temporarily placed in the task buffer queue. From the currently executable order tasks, select the task with the highest matching degree with the current raw material quality determined according to the probability model and process it first, until the raw material quality distribution changes significantly or the emergency processing threshold is triggered.

6. The intelligent scheduling method for bamboo string sorting priority according to claim 1, characterized in that, The real-time monitoring of the remaining processing time for the first order task specifically includes: Continuously compare the remaining processing time of the first order task with the shortest job cycle required to complete the order; When it is determined that the remaining processing time is less than or equal to the sum of the shortest operation cycle and the preset safety margin, it is determined that the emergency processing threshold has been reached.

7. The intelligent scheduling method for bamboo string sorting priority according to claim 3, characterized in that, Also includes: The statistical sample of the historical quality data is periodically updated, and the frequency of occurrence is recalculated. When the difference between the recalculated occurrence frequency and the historical frequency exceeds a set range, it is determined that the raw material quality distribution has changed significantly, and a recalculation of the probability model and the dynamic execution priority weights of all order tasks is triggered.

8. A bamboo string sorting priority intelligent scheduling system, applied to the bamboo string sorting priority intelligent scheduling method according to any one of claims 1 to 7, characterized in that, include: The order management module is used to receive and parse the order queue, and to build each order task into a structured management object; The quality analysis module is used to obtain real-time quality information of raw materials and to build and update a probabilistic model of raw material quality grades based on historical data. The dynamic decision engine is used to calculate the quality matching degree and dynamic execution priority weight of each order task based on the probability model and order attributes, and to trigger resource optimization scheduling strategies when the conditions are met. The scheduling and execution module is used to generate and adjust the task execution sequence and manage the task buffer queue based on the output of the dynamic decision engine. The monitoring and arbitration module is used to monitor the remaining processing time of orders, and to forcibly terminate the optimization strategy and generate the final scheduling instruction when the emergency threshold is reached.

Images