A Big Data-Based Production Planning and Management Method for Automotive Rubber Sealing Strips

Abstract

This invention discloses a big data-based production planning and management method for automotive rubber sealing strips, belonging to the field of order production planning and management technology. Specifically, it includes: integrating historical order and logistics data to construct a correlated dataset; establishing a logistics cost prediction network by analyzing production time requirements and logistics routes; generating an initial production sequence that meets all delivery deadlines based on the current pending production order pool; filtering orders with cost conflicts and forming a feature set by comparing planned delivery times with predicted low-cost windows in various regions; analyzing the matching relationship between adjustable production time windows and local low-cost windows for each conflicting order to generate a scheduling optimization parameter set; constructing a production sequence optimization model and solving for the optimal production sequence with the lowest overall logistics prediction cost. This invention, while ensuring delivery deadlines, proactively utilizes logistics price fluctuations through intelligent scheduling to reduce overall transportation costs.

Description

Technical Field

[0001] This invention relates to the field of order production planning and management technology, specifically to a big data-based method for production planning and management of automotive rubber sealing strips. Background Technology

[0002] In the automotive parts manufacturing sector, the production of rubber sealing strips typically employs an order-based production model. Companies receive orders from customers in different regions, each order containing specific delivery deadlines, product specifications, and quantities. Existing production planning and management methods generally focus on optimizing internal production resources and meeting delivery deadlines. A common practice is to prioritize orders based on the urgency of their delivery dates, such as using an earliest delivery date priority rule to arrange the production sequence, ensuring that each order is completed and shipped before its promised deadline.

[0003] However, this method only considers the constraints of the production process, neglecting the significant impact of logistics after the product leaves the factory. In actual operation, logistics costs are a crucial component of total costs, and their unit price is not fixed, exhibiting significant dynamic fluctuations over time (such as seasons, holidays, and fuel price fluctuations) and depending on the receiving region. Since the production sequence directly determines the completion and delivery time of each order, under the current prevalent "production complete, delivery complete" model, the predetermined production sequence locks orders into specific delivery points, thus passively bearing the logistics market prices on the delivery day. This leads to a prominent problem: a production plan arranged to meet delivery deadlines, while ensuring timeliness, may incur high transportation costs due to concentrating the delivery times of multiple orders during peak logistics periods.

[0004] Currently, there is a lack of effective technical means to incorporate dynamically changing downstream logistics cost factors into upstream production scheduling decisions in advance. Production management systems and logistics cost data systems are usually independent, and production scheduling decisions do not fully utilize historical and predicted big data on logistics prices. Therefore, designing a method that, while ensuring the hard constraint of on-time delivery of all orders, can intelligently adjust the production sequence to proactively optimize order delivery times, avoiding peak logistics periods and moving towards low-price periods, thereby achieving overall supply chain cost optimization, has become a pressing technical problem in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a big data-based method for production planning and management of automotive rubber sealing strips, addressing the following technical problems:

[0006] Existing production planning methods, while ensuring order delivery deadlines, fail to consider dynamically changing logistics costs. This can lead to a fixed production sequence that concentrates delivery times during periods of high logistics costs, resulting in excessively high overall transportation costs.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A big data-based method for production planning and management of automotive rubber sealing strips includes the following steps:

[0009] S1. Based on historical order data and logistics price data, extract order attributes and corresponding logistics cost records to establish an order-logistics related dataset;

[0010] S2, perform order production time demand analysis and logistics path reconstruction on the order-logistics related dataset, and construct a spatiotemporal network of logistics costs that includes each receiving area and its corresponding dynamic freight rate rules;

[0011] S3, In the current order pool to be scheduled for production, generate an initial production sequence based on the order delivery time limit, and calculate the estimated completion time of each order in the initial production sequence;

[0012] S4, compare the estimated completion time with the low-cost time window of the corresponding receiving area in the logistics cost spatiotemporal network, and filter out order samples with time conflicts to form a conflict order feature set;

[0013] S5, Based on the spatiotemporal network of logistics costs, identify the matching relationship between the production time window and the low-cost time window of the conflicting order feature set, and generate a scheduling optimization parameter set;

[0014] S6. Based on the scheduling optimization parameter set, construct and solve the production sequence optimization model, and arrange the production of all orders in the current pending production order pool according to the model solution results.

[0015] As a further aspect of the present invention: the specific process of establishing the order-logistics association dataset in S1 is as follows:

[0016] Collect basic information of historical orders and logistics settlement vouchers for corresponding shipment batches; extract the structured basic information of the orders to obtain the delivery address, product quantity, production completion time and promised delivery period for each order; parse the logistics settlement vouchers to extract the pricing period, destination and final freight for each transportation fee;

[0017] The production completion time of the order is time-aligned with the pricing cycle of the logistics settlement voucher, and the delivery address of the order is geospatially matched with the destination of the logistics settlement voucher. The successfully matched orders are then associated with the logistics records. All associated records are integrated to form a structured data set containing order attributes, logistics routes, time tags, and cost amounts, thus obtaining the order-logistics association dataset.

[0018] As a further aspect of the present invention: the specific construction process of the logistics cost spatiotemporal network in S2 is as follows:

[0019] The logistics path is reconstructed from the order-logistics association dataset, and the independent geographical area units involved in the historical shipping records are identified as basic nodes; based on the actual direction of goods movement in the logistics path, adjacent basic nodes with transportation flow associations are connected to construct the spatial topology of the network.

[0020] For each basic node, all cost records corresponding to it in the dataset are extracted and sorted by time label to form a cost sequence; time series analysis is performed based on the cost sequence and a mapping function from time to predicted cost value is constructed to obtain the dynamic freight rate pattern model of the node; combining the spatial topology and the dynamic freight rate pattern models of all basic nodes, the spatiotemporal network of logistics costs is obtained.

[0021] As a further aspect of the present invention: in step S3, the specific process for generating the initial production sequence is as follows:

[0022] For each order in the current pending production order pool, obtain the order delivery time limit recorded in the order-logistics association dataset, and extract the product specification parameters corresponding to the order; match the product specification parameters with the preset automotive rubber sealing strip production process parameter database to determine the unit production rate corresponding to the product specification; divide the unit production rate by the quantity of goods recorded in the order to obtain the production processing time required to complete the order.

[0023] Subtract the production processing time from the order delivery time limit to obtain the latest production start time for the order; calculate the latest production start time for each order, and arrange all orders in ascending order of the latest production start time to generate an initial production sequence.

[0024] As a further aspect of the present invention: in step S3, the specific calculation process for the estimated completion time of each order is as follows:

[0025] Define the current pointer of the production timeline as the start time, and process each order sequentially according to the initial production sequence. For the first order in the initial production sequence, compare the current pointer's time value with its own latest production start time, and determine the smaller time value as the actual production start time of the order. Add the actual production start time of the order to the production processing time, and update the current pointer with the sum. Repeat the above process until all orders in the initial production sequence have been processed, and obtain the actual production start time of each order.

[0026] As a further aspect of the present invention: in step S4, the specific process for generating the conflict order feature set is as follows:

[0027] Read the estimated completion time of each order in the initial production sequence; for each order, locate the target basic node in the logistics cost spatiotemporal network according to its delivery address, obtain the predicted cost time series curve by calling the dynamic freight rate law model of the node, calculate the average cost value of the curve and identify the continuous time period when the predicted cost value is lower than the average cost value, thereby extracting the corresponding low cost time window;

[0028] The estimated completion time of each order is compared with its low-cost time window, and orders whose estimated completion time does not fall within their own low-cost time window are selected to form a conflict order sample set. The production processing time and the latest production start time are read from each order in this set as production time parameters, and the delivery address is read as destination parameters. The production time parameters and destination parameters of each order are associated and bound, and the conflict order feature set is generated after summarizing.

[0029] As a further aspect of the present invention: in step S5, the specific process for generating the scheduling optimization parameter set is as follows:

[0030] For each order record in the conflicting order feature set, the corresponding dynamic freight rate model is matched according to its destination parameter, and the production time window of the order is determined according to the latest production start time and production processing time in its production time parameter.

[0031] The dynamic freight rate model is invoked to obtain the predicted cost values ​​at each time point within the production time window, calculate their average value, and identify continuous sub-time periods with predicted cost values ​​lower than the average value as local low-cost time windows.

[0032] Obtain the lowest predicted cost value within the local low-cost time window, and combine the destination parameters, production time parameters, dynamic freight rate pattern model, start and end times of the local low-cost time window, and lowest predicted cost value corresponding to each order to generate the scheduling optimization parameter set.

[0033] As a further aspect of the present invention: the specific process of arranging the production of all orders in the current pending production order pool in step S6 is as follows:

[0034] Based on the production time parameters of each order, enumerate all production sequence arrangements that satisfy the latest production start time constraint; for each arrangement, determine the actual production start time and production completion time of each order according to its order order and its respective production processing time.

[0035] Based on the production completion time of each order, the dynamic freight rate law model corresponding to that order in the scheduling optimization parameter set is invoked to obtain the predicted cost value for that production completion time. This predicted cost value is then subtracted from the lowest predicted cost value recorded for that order in the scheduling optimization parameter set to obtain the scheduling cost for that order. The scheduling costs of all orders under this arrangement are summed to obtain the total scheduling cost. The total scheduling costs of all enumerated arrangements are compared, and the arrangement with the minimum total scheduling cost is selected as the optimal production sequence and output as the final production arrangement instruction.

[0036] The beneficial effects of this invention are:

[0037] 1) Integrating dynamic logistics costs directly into production sequencing decisions, achieving cost savings through time window optimization. This method constructs a spatiotemporal network of logistics costs to predict future freight rate trends across different time periods and delivery areas, forming a clear "low-cost time window." When scheduling production, it considers not only the production duration and final delivery deadline of each order, but more importantly, proactively aligning the ideal delivery time (i.e., production completion time) of each order with the predicted low-cost time window. Since the quantity of goods and delivery address of an order remain constant, its transportation cost is solely determined by the predicted unit price at its delivery time. Therefore, by consciously adjusting the production sequence and changing the completion and delivery times of each order, more orders can be delivered during the low-cost period at their destination, thereby directly reducing logistics costs at the source.

[0038] 2) Establish a "cost-flexible optimization framework under rigid delivery constraints" to ensure that the scheduling scheme is feasible and cost-optimal. Understandably, the first priority is to ensure that each order is produced and shipped before its promised delivery date. To this end, the latest time when production must begin is calculated based on the latest delivery date of each order, generating an initial production sequence that ensures no orders are delayed. This constitutes the safety boundary for all subsequent optimizations. Within this rigid constraint framework, the method introduces cost optimization flexibility: by comparing the planned delivery time of each order in the initial sequence with the predicted low-price window of the corresponding destination, orders that "can be delivered on time, but whose delivery time coincides with a period of high logistics costs" are accurately identified—i.e., conflicting orders with optimization potential. Subsequently, for these orders, within their own allowed production time range (defined by the latest start time and production duration), local time windows with lower predicted freight rates are found and locked as optimization targets. This process ensures that all optimization adjustments are made within the feasible solution space of "not delaying any order," enabling cost savings and delivery guarantee requirements to be achieved synergistically without conflict.

[0039] 3) Employing a global enumeration and comprehensive evaluation strategy, the system automatically solves for the lowest total cost production sequence that satisfies the constraints. Optimization adjustments to a single order can have a cascading effect on the production time of other orders; therefore, the optimal solution must be sought from a holistic perspective. Based on the production time constraints of all orders and their respective cost optimization parameters (such as the target low-price window), the system automatically enumerates all possible production sequences that do not cause delays. For each sequence, the system rigorously simulates the continuous process of "production completed and then shipped," calculating the exact shipping time for each order under this sequence. Based on the delivery address, the system calls the prediction model to retrieve the specific predicted logistics unit price at that time. Combining this with the quantity of goods in the order, the total logistics cost for all orders under this sequence can be accurately calculated. By systematically traversing and comparing the total costs of all feasible sequences, the method can automatically select the optimal production sequence that minimizes the overall logistics cost while ensuring 100% on-time delivery. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings.

[0041] Figure 1 This is a schematic diagram of a production planning and management method for automotive rubber sealing strips based on big data, according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 As shown, this invention is a production planning and management method for automotive rubber sealing strips based on big data, comprising the following steps:

[0044] S1. Based on historical order data and logistics price data, extract order attributes and corresponding logistics cost records to establish an order-logistics related dataset;

[0045] First, export historical order execution records from the Enterprise Resource Planning (ERP) system. These records include the order number, product specifications, quantity, delivery address (accurate to the prefecture-level administrative division), the customer's promised delivery date, and the actual completion time of the order on the production line. Simultaneously, export all logistics cost settlement statements for the same historical period from the Transportation Management System (TMS) or Financial Settlement System. These statements include the waybill number, shipping time, destination, carrier, chargeable weight or volume, and the final settled freight amount.

[0046] Subsequently, key fields were aligned and correlated between the two batches of data. Using "shipping time" as the time anchor, the "shipping time" in the logistics settlement document was matched with the "actual production completion time" in the order record, allowing a reasonable time buffer (e.g., within 24 hours) to correlate production line completion and logistics commencement records. Simultaneously, using "destination" as the spatial anchor, the "receiving address" of the order was geocoded and matched with the "destination" of the logistics settlement document, ensuring both point to the same region. For example, the order's receiving address "Beilun District, Ningbo City, Zhejiang Province" and the logistics destination "Ningbo Port" should be successfully correlated after address standardization.

[0047] After successful matching, the order's "order number," "product specifications," "quantity," "promised delivery date," and "delivery address" are integrated with the corresponding logistics "freight amount" and "delivery time" to form a structured order-logistics related data entry, which is then stored in a dedicated database. For example, a record might be: "Order ID: DD20231027001, Specification: Door sealing strip - L-type, Quantity: 500 sets, Delivery date: 2023-11-05, Delivery location: Wuhan City, Logistics freight: 2250 yuan, Delivery time: 2023-10-30 14:20."

[0048] S2, perform order production time demand analysis and logistics path reconstruction on the order-logistics related dataset, and construct a spatiotemporal network of logistics costs that includes each receiving area and its corresponding dynamic freight rate rules;

[0049] First, analyze the production time requirements. For each record in the dataset, calculate the "production lead time," which is the time required to go back from the "actual production completion time" to the start of production. This can be estimated using the historical average production efficiency or standard time quota for the same product specification.

[0050] Next, the logistics routes are reconstructed. Based on the "origin-destination" pairs in historical logistics data, all frequently occurring and stable transportation flows are identified. For example, the data might show that there are three high-frequency routes from the "Shanghai factory" to "Wuhan," "Guangzhou," and "Xi'an." The starting and ending cities (or regional logistics hubs) of each route are defined as "basic nodes" in the network, and node pairs with direct or stable transit relationships are connected to form the spatial topology of the network. For example, Shanghai, Wuhan, Guangzhou, and Xi'an constitute four nodes, with connecting lines between Shanghai and each of Wuhan, Guangzhou, and Xi'an.

[0051] Then, for each destination region node in the network (e.g., "Wuhan"), extract all historical order-logistics data destined for that node. Arrange this data in order of "shipping time" to form a one-dimensional time series data for that region, where each data point is (shipping time, unit freight rate), and the unit freight rate is calculated by dividing "logistics freight cost" by standardized factors such as "quantity of goods".

[0052] Finally, this time series is modeled. Time series analysis methods (such as seasonal decomposition (STL) or ARIMA models) are used to identify and quantify the patterns of freight rate changes over time, including long-term trends, seasonal cycles (such as quarterly fluctuations and monthly fluctuations), and possible holiday effects. A prediction model is trained for each regional node, which can take a future date as input and output the predicted unit freight rate for shipments to that region on that date. Combining the "spatial topology" (nodes and connections) with the "dynamic freight rate prediction model" attached to each node completes the spatiotemporal network of logistics costs. For example, inputting "2024-06-15" and the node "Wuhan" can return a predicted freight rate, such as "3.8 yuan / kg".

[0053] S3, In the current order pool to be scheduled for production, generate an initial production sequence based on the order delivery time limit, and calculate the estimated completion time of each order in the initial production sequence;

[0054] Retrieve all orders that currently require production scheduling (i.e., the order pool). For each order in the pool, first query the standard production time database based on its product specifications to determine the production time per unit, then multiply by the order quantity to obtain the total production processing time for that order. Subtract the total production processing time from the order's promised delivery date to obtain the latest time when production must begin for that order.

[0055] Sort all orders from earliest to latest according to their "latest start time". Starting from the production plan start time (e.g., 8:00 AM on the plan start date), schedule production for each order in this order. The scheduling rule is: the actual start time of the current order is the earlier of the "earliest time when current production resources are available" and the "latest time that the order itself must start". After scheduling the previous order, its completion time (start time + processing time) is used as the "earliest time when current production resources are available" for the next order. This process continues, generating an initial production sequence that ensures no orders are delayed in delivery.

[0056] Based on this initial sequence and the actual start time and production processing time of each order, the estimated completion time (i.e., the theoretical time point for future shipment) of each order can be calculated. For example, if order A is scheduled to start at 9:00 on October 25th and requires 4 hours of production, then its estimated completion / shipment time is 13:00 on October 25th.

[0057] S4, compare the estimated completion time with the low-cost time window of the corresponding receiving area in the logistics cost spatiotemporal network, and filter out order samples with time conflicts to form a conflict order feature set;

[0058] For each order in the initial sequence, perform the following operations: First, locate the corresponding target region node in the spatiotemporal network of logistics costs constructed in S2 based on its "delivery address". Then, call the dynamic freight rate prediction model attached to that node to predict the daily or hourly freight rate for that region over a future period (covering the time before and after the order's expected delivery time), forming a predicted freight rate curve.

[0059] Analyzing this forecast freight rate curve, we identify continuous intervals where freight rates are significantly lower than adjacent periods or lower than the average level for that period, defining these as low-cost time windows. For example, for orders shipped to "Wuhan," the forecast curve shows that November 1st to 3rd is a low-cost period (3.5 yuan / kg), while October 30th-31st and after November 4th are high-cost periods (above 4.2 yuan / kg). Therefore, "November 1st to 3rd" is identified as a low-cost window.

[0060] Next, the estimated completion time of the order (e.g., 13:00 on October 25th for order A in the previous example) is compared with its calculated low-cost time window (November 1st to 3rd). If the estimated completion time falls outside its low-cost window (e.g., October 25th is much earlier than November 1st), then the current production schedule for the order is considered to have a "time conflict," meaning it is scheduled to be shipped at a time with higher logistics costs.

[0061] All orders with such conflicts are filtered out to form a conflict order sample set. For each order in the set, its key parameters are extracted: production time parameters (including production processing time and the latest required start time) and destination parameters (receiving address). These parameters are then structured and integrated to form a conflict order feature set.

[0062] S5, Based on the spatiotemporal network of logistics costs, identify the matching relationship between the production time window and the low-cost time window of the conflicting order feature set, and generate a scheduling optimization parameter set;

[0063] For each order in the conflict order feature set, a deep analysis is conducted. First, based on its "latest start time" and "production processing time," the production time window for which production can be flexibly scheduled is determined. For example, if an order must start no later than October 30th and requires 2 days of production, its earliest possible start time is limited by production resources, but its completion time must be before November 1st (assuming it starts on October 30th and is completed on November 1st).

[0064] Then, combining the low-cost time window for the order's destination identified in S4, we analyze the overlap between these two windows. The goal is to find whether there exists a production start time arrangement within the "production time window" that allows its completion time to fall within the "low-cost time window." For example, an order's production time window allows it to start production between October 28th and 30th, and its low-cost shipping window is November 1st to 3rd. Calculations show that if production is scheduled to start on October 30th (2 days of processing), it can be completed and shipped on November 1st, perfectly falling within the low-cost window. Therefore, October 30th is selected as a preferred production start time for this order, and the corresponding predicted minimum logistics cost is recorded.

[0065] For each conflicting order, based on the above analysis, its adjustable production time range, target low-cost window, corresponding preferred start time, and expected minimum cost are integrated into an optimization instruction parameter. The set of optimization instruction parameters for all conflicting orders constitutes the scheduling optimization parameter set, which specifies how the production sequence and time of which orders can be adjusted to reduce logistics costs.

[0066] S6. Based on the scheduling optimization parameter set, construct and solve the production sequence optimization model, and arrange the production of all orders in the current pending production order pool according to the model solution results.

[0067] First, based on the preferred adjustment scheme provided for each conflicting order in the scheduling optimization parameter set—including an adjustable production time window, a corresponding target low-cost time window, a preferred production start time, and an expected minimum cost—a global optimization model with production sequence as the core decision variable is constructed. The objective function of this model is to minimize the predicted total logistics cost of all orders, where the logistics cost of any order is calculated from its actual delivery time determined according to the production sequence and the dynamic freight rate model corresponding to its delivery address constructed in S2.

[0068] The main constraints of the model include: First, the actual completion time of each order must not be later than its promised delivery date, which is an inviolable hard constraint; Second, production resources operate continuously during the planning period, and orders are processed sequentially according to the arranged sequence without interruption; Third, when searching for feasible solutions, the model will use the preferred solutions provided by the scheduling optimization parameter set as high-quality initial solutions or optimization guidance, but the final sequence arrangement is still weighed with the global optimum as the goal.

[0069] Subsequently, heuristic optimization algorithms such as genetic algorithms and simulated annealing are used to solve the above model. This solution process essentially involves systematically searching, evaluating, and comparing the solution space comprised of all feasible production arrangements that satisfy delivery deadlines and production continuity. For each evaluated production sequence, the system simulates its continuous production flow, accurately calculates the actual delivery time for each order, and accordingly calculates the total logistics forecast cost.

[0070] After the solution is completed, the optimal production sequence that minimizes the total logistics forecast cost is output. The production scheduling system will directly generate the final production instructions based on this sequence and its corresponding time arrangement, and arrange and execute the production of all orders in the current pending order pool. For example, the solution result may show that order A, which was originally planned to be produced earlier and shipped to Wuhan, and order B, which was planned to be produced later and shipped to Guangzhou, are swapped in terms of production order. Although this adjustment slightly increases the delivery time pressure of order A, it still ensures that it will be completed before the deadline; the key benefit is that the delivery time of both orders after the adjustment falls within the low-cost time window predicted for their respective destinations, thereby achieving a significant reduction in overall logistics costs.

[0071] In a preferred embodiment of the present invention, the specific process of establishing the order-logistics association dataset in step S1 is as follows:

[0072] Collect basic information of historical orders and logistics settlement vouchers for corresponding shipment batches; extract the structured basic information of the orders to obtain the delivery address, product quantity, production completion time and promised delivery period for each order; parse the logistics settlement vouchers to extract the pricing period, destination and final freight for each transportation fee;

[0073] The production completion time of the order is time-aligned with the pricing cycle of the logistics settlement voucher, and the delivery address of the order is geospatially matched with the destination of the logistics settlement voucher. The successfully matched orders are then associated with the logistics records. All associated records are integrated to form a structured data set containing order attributes, logistics routes, time tags, and cost amounts, thus obtaining the order-logistics association dataset.

[0074] In another preferred embodiment of the present invention, the specific construction process of the logistics cost spatiotemporal network in step S2 is as follows:

[0075] The logistics path is reconstructed from the order-logistics association dataset, and the independent geographical area units involved in the historical shipping records are identified as basic nodes; based on the actual direction of goods movement in the logistics path, adjacent basic nodes with transportation flow associations are connected to construct the spatial topology of the network.

[0076] For each basic node, all cost records corresponding to it in the dataset are extracted and sorted by time label to form a cost sequence; time series analysis is performed based on the cost sequence and a mapping function from time to predicted cost value is constructed to obtain the dynamic freight rate pattern model of the node; combining the spatial topology and the dynamic freight rate pattern models of all basic nodes, the spatiotemporal network of logistics costs is obtained.

[0077] First, the logistics path is reconstructed from the order-logistics association dataset. This is based on the principle that goods with the same delivery address are typically transported via the same or similar logistics hubs and trunk routes. For example, by analyzing all historical shipping documents, it was found that orders with a delivery address in "Wuhan, Hubei Province" show a high degree of consistency in the origin, transit, and destination information recorded in their shipping documents, often exhibiting a fixed pattern of "Shanghai warehouse → Wuhan distribution center." Therefore, "Wuhan" can be identified as a stable logistics service endpoint and defined as an independent basic node. Subsequently, based on the actual direction of goods movement in the logistics path, the physical connectivity between nodes is analyzed. This is based on the principle of stable freight flow. A stable transportation flow implies the existence of mature transportation services and cost relationships between two points. For example, data analysis shows that goods are frequently and stably transported from the "Shanghai" node to the "Wuhan" node, and also from the "Shanghai" node to the "Guangzhou" node, but rarely directly from the "Wuhan" node to the "Guangzhou" node. Therefore, adjacent nodes with such stable transportation flow relationships (such as Shanghai and Wuhan, Shanghai and Guangzhou) are connected to construct a spatial topology that characterizes the actual geographical connectivity of logistics. Next, for each basic node, all corresponding cost records in the dataset are extracted and sorted by time label. The principle is that the pattern of freight rate changes over time is hidden in the historical price sequence arranged by time. For example, by compiling all historical freight records destined for the "Wuhan" node and arranging them strictly according to the year, month, and day of shipment, a cost sequence reflecting how freight rates in that location have changed over time is obtained. Time series analysis based on this cost sequence is based on the principle that logistics freight rates are often affected by systematic factors such as seasons, holidays, and market cycles, thus exhibiting identifiable and quantifiable repetitive or trending fluctuation patterns over time. By analyzing this sequence, inherent patterns such as "freight rates generally rise in the third quarter of each year" and "freight rates peak two weeks before holidays" can be identified. Based on these patterns, a system can be constructed that accepts a future date as input and outputs... The mathematical mapping function for predicting freight rates on that date is derived, and this function is the dynamic freight rate pattern model for that node. Finally, the spatial topology describing "where goods can be transported from and to" is combined with the dynamic freight rate pattern model of each node describing "how much it will cost to transport to a certain place at a certain time." The principle is that complete logistics cost decisions require simultaneous knowledge of spatial accessibility and time cost. What is obtained is an integrated network model with prediction and query capabilities in both spatiotemporal dimensions, which can not only answer "can goods be transported from Shanghai to Wuhan?", but also "if it is planned to ship goods from Shanghai to Wuhan on May 10th next year, what is the approximate freight rate per unit?", namely the spatiotemporal network of logistics costs.The benefit and purpose of this approach is that it transforms the originally scattered, static, and reactive logistics cost information into a unified decision-making knowledge base that covers all service areas, can predict the future, and has a consistent structure and patterns. Its core purpose is to provide crucial cost predictability for subsequent production scheduling decisions, enabling the system to know in advance the logistics prices that may correspond to different delivery times when arranging production sequences. This lays an indispensable and quantitative data foundation for achieving the ultimate goal of "proactively adjusting order delivery times to the lower-price window." As the "cost map" and "prediction engine" of the entire method, this network directly transforms cost-optimized production scheduling from experience and intuition into calculable and optimizable scientific decision-making.

[0078] In another preferred embodiment of the present invention, the specific process for generating the initial production sequence in step S3 is as follows:

[0079] For each order in the current pending production order pool, obtain the order delivery time limit recorded in the order-logistics association dataset, and extract the product specification parameters corresponding to the order; match the product specification parameters with the preset automotive rubber sealing strip production process parameter database to determine the unit production rate corresponding to the product specification; divide the unit production rate by the quantity of goods recorded in the order to obtain the production processing time required to complete the order.

[0080] Subtract the production processing time from the order delivery time limit to obtain the latest production start time for the order; calculate the latest production start time for each order, and arrange all orders in ascending order of the latest production start time to generate an initial production sequence.

[0081] For each order in the current pending production order pool, for example, order A requires the production of 500 sets of "Window Guide Channel - Type B" products and must be delivered before November 10th, the system first obtains the order delivery deadline defined in the historical data mode, which is November 10th, and simultaneously extracts its product specification parameter "Window Guide Channel - Type B". Next, the specification parameter "Window Guide Channel - Type B" is matched with a preset database of automotive rubber sealing strip production process parameters. The principle is that different types and specifications of sealing strips have different materials, structures, and vulcanization times, so their production line cycle time or output per unit time is relatively stable and measurable. The database pre-stores correspondences such as "Window Guide Channel - Type B: Unit Production Rate = 50 sets / hour". By matching, the unit production rate corresponding to this product specification can be determined. Then, the unit production rate (50 sets / hour) is divided by the quantity of goods recorded in order A (500 sets). The principle is that the total working hours required to complete the order equals the total order quantity divided by the production efficiency per unit time, thus calculating the time required to complete the order. The production processing time is 10 hours. Then, the production processing time (10 hours) is subtracted from the order delivery deadline (November 10th, 24:00). The principle is that to ensure on-time delivery, production must be completed no later than the last moment before the delivery deadline. Therefore, the latest production start time for this order is calculated backwards to determine the last point in time when production must begin. This gives us the latest production start time for this order as November 10th, 14:00. This calculation is performed on all orders in the current pending production order pool. For example, the latest start time for order B is November 9th. At 18:00, order C is scheduled for 9:00 on November 11th. Finally, the latest production start time of each order is arranged in chronological order. The principle is that the most intuitive and absolutely reliable production scheduling strategy is to prioritize the most urgent orders with the least buffer time. According to this principle, order B (18:00 on November 9th) is the most urgent and is ranked first, followed by order A (14:00 on November 10th), and finally order C (9:00 on November 11th), thus generating the initial production sequence "B->A->C".

[0082] First, by strictly adhering to the most rigid constraint in commercial contracts—the delivery date—it ensures that any subsequent optimizations and adjustments will not cross the "order delay" red line, giving the entire scheduling scheme basic legitimacy for application in actual production. Second, it determines the urgency of production through scientific calculations (based on specification-based rate matching and time backward calculation) rather than subjective experience, making the sequencing basis objective and reliable. For the ultimate goal of the scheme—reducing total logistics costs—this step constructs a crucial "safety boundary" and "search starting point." All subsequent sequencing adjustments aimed at reducing costs will be explored and optimized within the timeframe defined by this initial sequence of "delivery on time no matter what," thus ensuring that the pursuit of cost optimization will not come at the expense of customer delivery commitments, making the entire method both cost-aggressive and delivery-stable.

[0083] In another preferred embodiment of the present invention, the specific calculation process for the estimated completion time of each order in step S3 is as follows:

[0084] Define the current pointer of the production timeline as the start time, and process each order sequentially according to the initial production sequence. For the first order in the initial production sequence, compare the current pointer's time value with its own latest production start time, and determine the smaller time value as the actual production start time of the order. Add the actual production start time of the order to the production processing time, and update the current pointer with the sum. Repeat the above process until all orders in the initial production sequence have been processed, and obtain the actual production start time of each order.

[0085] In another preferred embodiment of the present invention, the specific process of generating the conflicting order feature set in step S4 is as follows:

[0086] Read the estimated completion time of each order in the initial production sequence; for each order, locate the target basic node in the logistics cost spatiotemporal network according to its delivery address, obtain the predicted cost time series curve by calling the dynamic freight rate law model of the node, calculate the average cost value of the curve and identify the continuous time period when the predicted cost value is lower than the average cost value, thereby extracting the corresponding low cost time window;

[0087] The estimated completion time of each order is compared with its low-cost time window, and orders whose estimated completion time does not fall within their own low-cost time window are selected to form a conflict order sample set. The production processing time and the latest production start time are read from each order in this set as production time parameters, and the delivery address is read as destination parameters. The production time parameters and destination parameters of each order are associated and bound, and the conflict order feature set is generated after summarizing.

[0088] In another preferred embodiment of the present invention, the specific process of generating the conflict order feature set in S4 is as follows: First, the estimated completion time of each order in the initial production sequence is read. This time is calculated by continuously accumulating according to the production order in S3. For example, the first order A in the initial sequence is expected to be completed and shipped at 3 pm on October 28th. Next, for each order, the target basic node is located in the previously constructed logistics cost spatiotemporal network based on its delivery address. The principle is that the logistics cost spatiotemporal network has abstracted geographical areas into nodes. For example, the delivery address of order A, "Zhengzhou City", corresponds to a basic node named "Zhengzhou" in the network. After location, the "Zhengzhou" node is called. The dynamic freight rate model attached to the node takes a future date range as input and outputs the predicted unit freight rate for each day to Zhengzhou within that period. Connecting these predicted values ​​in chronological order forms a time-series curve of predicted costs for the region over a future period. Then, the average of all predicted values ​​on this curve is calculated. This average serves as a benchmark for measuring freight rates, as the curve reflects the model's overall expectation of future freight rates to Zhengzhou. Next, all time periods on the curve where the predicted cost value is consistently lower than this average are identified. For example, the curve shows that the predicted prices for the periods from October 30th to November 1st and from November 5th to November 7th are consistently low. On average, these consecutive low-price periods were extracted as low-cost time windows for shipments to "Zhengzhou". Subsequently, the estimated completion time of order A (3 PM on October 28th) was compared with its corresponding low-cost time window (after October 30th). It was found that October 28th did not fall within any consecutive low-price period starting from October 30th; that is, the estimated completion time did not fall within its own low-cost time window. This indicates that according to the current plan, order A will be shipped during a time when shipping costs are higher, leaving room for optimization. Therefore, order A was filtered out. The above comparison was performed on all orders. All orders like order A with the conflict of "planned shipping time not falling within the predicted low-price period" constituted the conflict order sample set. Then, from each order in this set, key planning information is read, including the production processing time required to complete it (e.g., 8 hours) and its latest production start time (e.g., 12:00 noon on October 27th). This information collectively defines the boundaries that the order can be flexibly arranged on the production timeline, and these are used as production time parameters. At the same time, its delivery address is read as the destination parameter. Finally, these production time parameters and destination parameters of each conflicting order are associated and bound together, for example, forming a record "Production time parameter: (processing 8 hours, latest start time 12:00 noon on October 27th), destination parameter: Zhengzhou City". By summarizing all such records, the conflicting order feature set is generated.

[0089] By precisely identifying orders from all pending production orders where current production scheduling doesn't align with logistics cost optimization opportunities, limited optimization resources are focused on the most likely areas to generate benefits. By combining the production plan derived from delivery deadlines with the cost window derived from the forecasting model, it accurately identifies the contradiction of "on-time delivery possible, but delivery timing uneconomical." This allows subsequent optimization steps (S5 and S6) to focus on resolving these specific problem orders, rather than dealing with complex permutations of all orders. The goal is to adjust their production positions to bring their delivery times into the lower-cost window, ensuring they are not delayed. This plays a crucial role in achieving the ultimate goal of reducing total logistics costs through focused targeting and efficiency improvement. It ensures that the entire optimization method seeks solutions to specific problems, significantly improving the method's operability and solution efficiency, making cost savings in complex order environments concrete and actionable.

[0090] In another preferred embodiment of the present invention, the specific process for generating the scheduling optimization parameter set in step S5 is as follows:

[0091] For each order record in the conflicting order feature set, the corresponding dynamic freight rate model is matched according to its destination parameter, and the production time window of the order is determined according to the latest production start time and production processing time in its production time parameter.

[0092] The dynamic freight rate model is invoked to obtain the predicted cost values ​​at each time point within the production time window, calculate their average value, and identify continuous sub-time periods with predicted cost values ​​lower than the average value as local low-cost time windows.

[0093] Obtain the lowest predicted cost value within the local low-cost time window, and combine the destination parameters, production time parameters, dynamic freight rate pattern model, start and end times of the local low-cost time window, and lowest predicted cost value corresponding to each order to generate the scheduling optimization parameter set.

[0094] For each order record in the conflicting order feature set, for example, an order A record contains production time parameters (the latest production start time is 14:00 on November 10, and the production processing time is 10 hours) and destination parameters (Zhengzhou City), firstly, based on its destination parameter "Zhengzhou City", the corresponding dynamic freight rate pattern model is matched in the logistics cost spatiotemporal network. This model is a mathematical relationship that was previously trained in S2 specifically for the "Zhengzhou" node and can predict the freight rate to Zhengzhou on any future date. Next, based on the latest production start time (14:00 on November 10th) and production processing time (10 hours) in its production time parameters, the production time window for this order is determined. The principle is that the production start time of an order cannot be later than its latest start time, and once started, it must be carried out continuously until completion. Therefore, the range of completion times corresponding to all possible production arrangement schemes (i.e., different start times) constitutes a time period from the "earliest possible completion time" to the "latest mandatory completion time" (i.e., delivery deadline). This time period is the complete set of possibilities for the order to be scheduled for shipment, i.e., the production time window. For example, assuming that order A can start production immediately, its earliest completion time is 10 hours after start, and its latest completion time is the delivery deadline of 24:00 on November 10th. Therefore, its production time window may be a completion time point between 0:00 and 24:00 on November 10th. Subsequently, the dynamic freight rate pattern model of the matched "Zhengzhou" node is invoked. Each possible time point within this production time window (e.g., in hourly units) is input into the model, and the model outputs the predicted unit freight rate for each time point, thus obtaining a list of correspondences between a series of time points and predicted cost values ​​within the window. The arithmetic mean of these predicted cost values ​​is calculated, using this average as the overall benchmark for the freight rate level of the order within its allowed delivery time range. Then, within this production time window, all sub-time periods where the predicted cost value is continuously lower than this average are identified as local low-cost time windows. The principle is that, relative to the average freight rate over the entire deliverable period of the order, these local time periods offer more cost-effective specific delivery opportunities. Next, within these identified local low-cost time windows, the point with the lowest predicted cost value is found and recorded, i.e., the lowest predicted cost value within the local low-cost time window is obtained. For example, it is found that within the production time window of order A, the freight rate is lowest during the sub-time period from 18:00 to 20:00 on November 10th, with the predicted cost at 19:00 being the lowest for the entire window.Finally, all the key information corresponding to each order—namely, its destination parameters (e.g., Zhengzhou City), production time parameters (latest start time and production processing time), the matched dynamic freight rate model itself, the start and end times of the identified local low-cost time window (e.g., 6 PM to 8 PM), and the lowest predicted cost value within that window (e.g., the cost value corresponding to 7 PM)—is combined and packaged to form a complete optimization guidance parameter package for that order. Integrating such parameter packages from all conflicting orders generates the aforementioned scheduling optimization parameter set.

[0095] For each order identified as having cost conflicts, a specific and quantifiable "optimization action guide" is tailored, transforming the vague "needs optimization" into a clear "how to optimize." It goes beyond simply pointing out that a particular order's planned delivery time is unfavorable; it delves deeper into all possible, non-delaying alternative delivery options for that order, precisely identifying the more cost-effective timeframes and target cost values. This provides high-quality "clues" and "road signs" for the subsequent global optimization model. The goal is to decompose the complex global ranking problem, first finding the optimal solution direction for each local problem (adjustment of individual orders), thereby significantly constraining and narrowing the search scope of the global optimization model, guiding it towards the most likely correct direction to find the optimal combination. This step plays a crucial role in connecting the preceding and following steps and providing precise guidance for the final goal of reducing total logistics costs. It inherits the problems selected in S4 and provides vital structured input for the global solution in S6—not only telling the model which orders can be adjusted, but also what the ideal target for each order's adjustment is (aiming at which low-cost window) and how large the adjustment potential is (the potential gap between the lowest cost value and the current planned cost value). This enables the final optimization model to perform informed, efficient, and intelligent searches, rather than blind enumeration, thereby significantly increasing the likelihood of finding the global optimum and improving computational efficiency. This ensures that cost savings can be achieved through precise and executable scheduling instructions.

[0096] In another preferred embodiment of the present invention, the specific process of arranging the production of all orders in the current pending production order pool in step S6 is as follows:

[0097] Based on the production time parameters of each order, enumerate all production sequence arrangements that satisfy the latest production start time constraint; for each arrangement, determine the actual production start time and production completion time of each order according to its order order and its respective production processing time.

[0098] Based on the production completion time of each order, the dynamic freight rate law model corresponding to that order in the scheduling optimization parameter set is invoked to obtain the predicted cost value for that production completion time. This predicted cost value is then subtracted from the lowest predicted cost value recorded for that order in the scheduling optimization parameter set to obtain the scheduling cost for that order. The scheduling costs of all orders under this arrangement are summed to obtain the total scheduling cost. The total scheduling costs of all enumerated arrangements are compared, and the arrangement with the minimum total scheduling cost is selected as the optimal production sequence and output as the final production arrangement instruction.

[0099] The specific process for arranging production for all orders in the current pending production order pool is as follows: First, based on the production time parameters of each order, especially the known "latest production start time" for each order, the system automatically lists all possible order processing sequences. The principle is that different production sequences will result in different start and end times for each order, thus affecting its delivery time and logistics costs. Therefore, all possible sequences must be systematically examined. For example, for pending production orders A, B, and C, the system will list all permutations and combinations such as "A->B->C", "A->C->B", "B->A->C", "B->C->A", "C->A->B", and "C->B->A". However, during this process, invalid permutations that obviously cause an order to start only after its latest start time will be immediately eliminated. For example, if a certain sequence forces order C to start later than its latest start time during evaluation, that permutation is discarded, and only all feasible permutations are retained. Next, for each remaining feasible permutation, the system simulates a continuous production line operation process based on the order of orders in that permutation and the production processing time required for each order. This determines the specific start and finish times for each order during actual execution. The principle is that production resources are continuously occupied. For example, for the permutation "A->B->C", assuming order A requires 5 hours, order B requires 3 hours, and order C requires 4 hours, the simulation process is as follows: order A starts production at midnight (the start time of the planning period) and finishes at 5:00 AM; order B follows immediately, starting at 5:00 AM and finishing at 8:00 AM; order C then starts at 8:00 AM and finishes at 12:00 PM. This yields the actual production start and finish times for each order under that permutation. Then, based on the production completion time (i.e., the shipping time) of each order, the system automatically calls the dynamic freight rate pattern model corresponding to its destination, which has been matched and stored in the scheduling optimization parameter set generated by S5 for that order. The specific time point is input into the model, and the model outputs the predicted unit freight rate for shipping at that exact time. At the same time, the system reads the "lowest predicted cost value" recorded for that order in the same parameter set. This value represents the theoretical lowest freight rate that the order can achieve within its own possible time range. The difference between the calculated actual time point predicted cost value and this theoretical minimum value is defined as the scheduling cost of the order under this specific production sequence. The principle of this difference is that it quantifies "the additional cost that the order is forced to bear because the production sequence arrangement caused it to fail to ship at its most ideal low price time". The smaller the difference, the closer the current arrangement is to the ideal shipping state of the order; zero means that the lowest price point has been perfectly hit.Subsequently, the scheduling costs calculated for all orders under this permutation are summed to obtain the total scheduling cost corresponding to this permutation. The principle is that the total scheduling cost reflects the total cost corresponding to the total deviation of the overall delivery timing of the entire order pool from its ideal timing under the current production sequence. Finally, the system automatically compares the total scheduling costs calculated for all enumerated and evaluated feasible permutations. The principle is that the permutation with the minimum total scheduling cost means that this sequence makes the overall delivery time combination closest to the low-price ideal point of each order, thereby minimizing the total logistics cost while meeting delivery requirements. This permutation with the minimum total cost is selected as the optimal production sequence and output as an explicit production instruction, such as "produce in the order of B->A->C", to the production execution system.

[0100] Under the complex conditions of hard constraints on delivery, it is understandable that by automating global search and quantitative evaluation, the absolutely cost-optimal production sequence can be found, transforming complex trade-offs that are difficult to achieve manually into deterministic and optimal instructions. Its fundamental purpose is to achieve the ultimate goal pursued by the method—minimizing total logistics costs. This step is the decision-making center and value realization point of the entire solution. It does not make isolated adjustments to individual orders, but treats all orders as a whole system. Through rigorous simulation calculations and cost comparisons, it intelligently selects the delivery plan with the lowest total cost from all feasible options. The advantage of this approach is that it completely solves the coupling problem of "a change in one part affects the whole" in multi-order scheduling, ensuring that any adjustment to a single order is evaluated from a global perspective. The final output is not optimal for a single order, but optimal for the entire order pool. This is crucial for the solution to ultimately reduce total logistics costs. It brings together all the data preparation, network construction, conflict identification, and parameterization of the preceding steps and transforms them into an executable optimal solution. This ensures that the closed loop from cost forecasting to scheduling decisions is rigorous, automated, and pursues global optimization. Ultimately, it translates the idea of ​​"reducing costs by leveraging price fluctuations" into precise, efficient, and verifiable production scheduling instructions.

[0101] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A big data-based automobile rubber sealing strip production planning management method, characterized in that, Includes the following steps: S1. Based on historical order data and logistics price data, extract order attributes and corresponding logistics cost records to establish an order-logistics related dataset. S2, perform order production time demand analysis and logistics path reconstruction on the order-logistics related dataset, and construct a spatiotemporal network of logistics costs that includes each receiving area and its corresponding dynamic freight rate rules; The specific construction process of the logistics cost spatiotemporal network is as follows: The logistics path is reconstructed from the order-logistics association dataset, and the independent geographical area units involved in the historical shipping records are identified as basic nodes; based on the actual direction of goods movement in the logistics path, adjacent basic nodes with transportation flow associations are connected to construct the spatial topology of the network. For each basic node, all cost records corresponding to it in the dataset are extracted and sorted by time label to form a cost sequence; based on the cost sequence, time series analysis is performed and a mapping function from time to predicted cost value is constructed to obtain the dynamic freight rate pattern model of the node; combining the spatial topology and the dynamic freight rate pattern models of all basic nodes, the spatiotemporal network of logistics costs is obtained. S3, In the current order pool to be scheduled for production, generate an initial production sequence based on the order delivery time limit, and calculate the estimated completion time of each order in the initial production sequence; S4, compare the estimated completion time with the low-cost time window of the corresponding receiving area in the logistics cost spatiotemporal network, and filter out order samples with time conflicts to form a conflict order feature set; S5, Based on the spatiotemporal network of logistics costs, identify the matching relationship between the production time window and the low-cost time window of the conflicting order feature set, and generate a scheduling optimization parameter set; The specific process for generating the scheduling optimization parameter set is as follows: For each order record in the conflicting order feature set, the corresponding dynamic freight rate model is matched according to its destination parameter, and the production time window of the order is determined according to the latest production start time and production processing time in its production time parameter. The dynamic freight rate model is invoked to obtain the predicted cost values ​​at each time point within the production time window, calculate their average value, and identify continuous sub-time periods with predicted cost values ​​lower than the average value as local low-cost time windows. Obtain the lowest predicted cost value within the local low-cost time window, and combine the destination parameters, production time parameters, dynamic freight rate pattern model, start and end times of the local low-cost time window, and lowest predicted cost value corresponding to each order to generate the scheduling optimization parameter set; S6. Based on the scheduling optimization parameter set, construct and solve the production sequence optimization model, and arrange the production of all orders in the current pending production order pool according to the model solution results.

2. The big data-based production planning and management method for automobile rubber sealing strips according to claim 1, characterized in that, In S1, the specific process of establishing the order-logistics association dataset is as follows: Collect basic information on historical orders and logistics settlement vouchers for corresponding shipment batches, and extract the basic information on the orders in a structured manner to obtain the delivery address, product quantity, production completion time and promised delivery period for each order; The logistics settlement vouchers are parsed to extract the pricing period, destination, and final freight cost for each transportation fee. The production completion time of the order is time-aligned with the pricing cycle of the logistics settlement voucher, and the delivery address of the order is geospatially matched with the destination of the logistics settlement voucher. The successfully matched orders are then associated and bound with the logistics records. By integrating all associated and bound records, a structured data set containing order attributes, logistics routes, time tags, and cost amounts is formed, resulting in the order-logistics associated dataset.

3. The big data-based production planning and management method for automobile rubber sealing strips according to claim 1, characterized in that, In S3, the specific process for generating the initial production sequence is as follows: For each order in the current pending production order pool, obtain the order delivery time limit recorded in the order-logistics association dataset, and extract the product specification parameters corresponding to the order; match the product specification parameters with the preset automotive rubber sealing strip production process parameter database to determine the unit production rate corresponding to the product specification; The production processing time required to complete the order is obtained by dividing the unit production rate by the quantity of goods recorded in the order. Subtracting the production processing time from the order delivery time limit yields the latest production start time for that order. The latest production start time for each order is calculated, and all orders are arranged in ascending order of their latest production start time to generate an initial production sequence.

4. The big data-based automobile rubber sealing strip production planning management method according to claim 3, characterized in that, In step S3, the specific calculation process for the estimated completion time of each order is as follows: Define the current pointer of the production timeline as the start time, and process each order sequentially according to the initial production sequence; for the first order in the initial production sequence, compare the time value of the current pointer with its own latest production start time, and determine the time value with the smaller value as the actual production start time of the order; add the actual production start time of the order to the production processing time, and update the current pointer with the time value obtained by the addition; Repeat the above process until all orders in the initial production sequence have been processed, and obtain the actual production start time for each order.

5. The big data-based automobile rubber sealing strip production planning management method according to claim 4, characterized in that, In step S4, the specific process for generating the conflict order feature set is as follows: Read the estimated completion time of each order in the initial production sequence; for each order, locate the target basic node in the logistics cost spatiotemporal network according to its delivery address, obtain the predicted cost time series curve by calling the dynamic freight rate law model of the node, calculate the average cost value of the curve and identify the continuous time period when the predicted cost value is lower than the average cost value, thereby extracting the corresponding low cost time window; The estimated completion time of each order is compared with its low-cost time window, and orders whose estimated completion time does not fall within their own low-cost time window are selected to form a conflict order sample set. The production processing time and the latest production start time are read from each order in this set as production time parameters, and the delivery address is read as destination parameters. The production time parameters and destination parameters of each order are associated and bound, and the conflict order feature set is generated after summarizing.

6. The method for production planning and management of automotive rubber sealing strips based on big data according to claim 1, characterized in that, In step S6, the specific process of arranging the production of all orders in the current pending production order pool is as follows: Based on the production time parameters of each order, enumerate all production sequence arrangements that satisfy the latest production start time constraint; for each arrangement, determine the actual production start time and production completion time of each order according to its order order and its respective production processing time. Based on the production completion time of each order, the dynamic freight rate law model corresponding to that order in the scheduling optimization parameter set is invoked to obtain the predicted cost value for that production completion time. This predicted cost value is then subtracted from the lowest predicted cost value recorded for that order in the scheduling optimization parameter set to obtain the scheduling cost for that order. The scheduling costs of all orders under this arrangement are summed to obtain the total scheduling cost. The total scheduling costs of all enumerated arrangements are compared, and the arrangement with the minimum total scheduling cost is selected as the optimal production sequence and output as the final production arrangement instruction.

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