A customized furniture dynamic coupling picking method for full-set delivery

Abstract

This invention discloses a dynamic coupling picking method for customized furniture aimed at complete set delivery, relating to the fields of intelligent warehousing and production scheduling management. This method collects customer order data and production progress information; collects warehouse inventory status, work-in-process processing progress, and inbound time and location data; calculates the number of key components already in place, the average remaining processing time of work-in-process, and the spatial distance of key components; further calculates the complete set coupling coefficient to generate a picking strategy; performs path planning based on component coordinates and dependency chains, calculates the total path length, number of point switching times, and key component arrival status, generates a dynamic path reconstruction index, and executes path optimization strategies; and calculates the complete set completion probability index through inventory matching, time series forecasting, and historical backtracking analysis to guide order picking priorities and production coordination strategies. This method can achieve dynamic optimization of order picking and production processes, improving the complete set completion rate and warehousing efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and production scheduling management technology, specifically a dynamic coupling picking method for customized furniture oriented towards complete set delivery. Background Technology

[0002] Customized furniture production has gradually replaced traditional mass production in recent years, exhibiting significant characteristics of diverse varieties, small batches, and flexibility. This type of production typically requires processing and assembling different components based on customers' personalized orders. In this process, the ability to deliver complete sets has become a key factor affecting production efficiency and customer satisfaction. Complete set delivery not only requires all components in the order to arrive within the specified time, but also requires the picking sequence and path to dynamically adapt to changes in inventory and production schedule.

[0003] Existing furniture manufacturing and warehouse picking management systems mainly rely on manual experience or static rules for picking and dispatching, which has the following shortcomings:

[0004] Lack of detailed modeling of kit dependencies: Traditional methods often manage only based on the parts list, ignoring the functional dependencies and assembly order between furniture parts. This leads to picking being initiated even when critical parts are missing, increasing rework and waiting time.

[0005] The separation of inventory and work-in-process information: Currently, most companies' ERP, MES and warehouse management systems have not achieved data linkage, making it impossible to conduct integrated analysis of real-time inventory, work-in-process processing progress and warehousing time, and making it difficult to determine in a timely manner whether an order has all the necessary conditions.

[0006] Insufficient picking route planning: Traditional picking usually uses fixed or heuristic routes, without considering the priority of kitting dependency chains and key components, resulting in long picking routes, frequent switching, and reduced operational efficiency.

[0007] Lack of risk warning and strategy: When the order completeness is insufficient or the parts are delayed in warehousing, the existing methods often lack dynamic risk assessment and early warning mechanisms, and cannot adjust the picking order or production schedule according to the real-time status, which can easily lead to production interruption and delivery delay. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a dynamic coupling picking method for customized furniture oriented towards complete set delivery, thereby solving the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a dynamic coupling picking method for customized furniture oriented towards complete set delivery, characterized by comprising the following steps:

[0010] Step 1: Collect customer order data and production progress information, parse the order parts list, transform the strong coupling relationship between parts into a graph model, extract the dependency path of key parts, generate a kit dependency chain, and establish a standard kit dataset.

[0011] Step 2: By collecting real-time data on warehouse inventory status, work-in-process processing progress, and component entry time and location; after unified timeline processing and anomaly removal, the actual available quantity of components, remaining time of work-in-process, and warehouse coordinates are extracted based on the kitting dependency chain, and a picking perception set is constructed to form a picking status vector.

[0012] Step 3: Based on the picking awareness set, use database query and keyword matching algorithms to compare the kitting dependency chain with the inventory status, obtain the number of key components that have arrived and the total number of components required for the order, and calculate the average remaining processing time of work-in-process and the spatial distance between key components and the current picking position through the average value algorithm. Further calculate the kitting coupling coefficient CCI and compare it with the kitting coupling threshold Cth to determine whether the picking execution of the current order meets the kitting delivery requirements. If not, give an appropriate strategy.

[0013] Step 4: Based on the in-stock coordinates of components and the kitting dependency chain, use graph traversal and Dijkstra's algorithm to perform path planning, calculate the total walking path length, analyze the number of pick-up point jumps through sequence statistics, and combine knowledge graph reasoning and dependency comparison to verify whether the key components have met the kitting requirements. Calculate and obtain the dynamic path reconstruction index DPI and compare it with the dynamic path reconstruction threshold Dth to determine whether the picking path is in an acceptable state. If not, give an appropriate strategy.

[0014] Step 5: Through inventory matching, time series forecasting, and historical backtracking analysis, obtain the real-time inventory satisfaction rate, the probability of on-time warehousing of work-in-process, and the historical order completeness rate, respectively. Calculate the completeness probability index GPI and compare it with the completeness probability threshold Gth to determine whether the order completeness probability is qualified. If it is not qualified, give appropriate strategies.

[0015] This invention provides a dynamically coupled picking method for customized furniture oriented towards complete set delivery. It has the following beneficial effects:

[0016] 1. This is a dynamic coupling picking method for customized furniture oriented to complete set delivery. By constructing complete set dependency chains, extracting key component paths and establishing a standard complete set dataset, it can accurately analyze the component dependency relationship of orders, effectively identify key components and ensure that picking execution meets the complete set delivery requirements, and reduce delivery delays caused by missing or incorrect components.

[0017] 2. This is a dynamic coupling picking method for customized furniture oriented to complete set delivery. It uses graph traversal and Dijkstra's algorithm to plan the path of component coordinates and dependency chains. At the same time, it counts the number of jumps at picking points and verifies the arrival status of key components. It calculates the dynamic path reconstruction index (DPI) to realize dynamic optimization of picking path, improve picking efficiency and reduce operating costs.

[0018] 3. This dynamic coupling picking method for customized furniture oriented to complete set delivery collects real-time data on inventory status, work-in-process progress, and component entry time and location. It then performs unified timeline processing and anomaly removal to form a picking perception set and state vector, achieving two-way closed-loop feedback between inventory, production, and picking, thereby improving the collaborative capabilities of production planning and warehouse management.

[0019] 4. This is a dynamic coupling picking method for customized furniture aimed at complete set delivery. It calculates the probability index (GPI) for complete set delivery and compares it with a preset threshold to dynamically assess the probability of order completion. When the GPI falls below the threshold, the system automatically triggers an alert, adjusts order priorities, and reserves picking resources for critical components, thereby improving the complete set delivery rate and ensuring delivery reliability. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the steps of a dynamic coupling picking and matching method for customized furniture oriented towards complete set delivery according to the present invention. Detailed Implementation

[0021] 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.

[0022] Example 1

[0023] Please see Figure 1 This invention provides a dynamically coupled picking method for customized furniture oriented towards complete set delivery, comprising the following steps:

[0024] Step 1: Collect customer order data and production progress information, parse the order parts list, transform the strong coupling relationship between parts into a graph model, extract the dependency path of key parts, generate a kit dependency chain, and establish a standard kit dataset.

[0025] Step 2: By collecting real-time data on warehouse inventory status, work-in-process processing progress, and component entry time and location; after unified timeline processing and anomaly removal, the actual available quantity of components, remaining time of work-in-process, and warehouse coordinates are extracted based on the kitting dependency chain, and a picking perception set is constructed to form a picking status vector.

[0026] Step 3: Based on the picking awareness set, use database query and keyword matching algorithms to compare the kitting dependency chain with the inventory status, obtain the number of key components that have arrived and the total number of components required for the order, and calculate the average remaining processing time of work-in-process and the spatial distance between key components and the current picking position through the average value algorithm. Further calculate the kitting coupling coefficient CCI and compare it with the kitting coupling threshold Cth to determine whether the picking execution of the current order meets the kitting delivery requirements. If not, give an appropriate strategy.

[0027] Step 4: Based on the in-stock coordinates of components and the kitting dependency chain, use graph traversal and Dijkstra's algorithm to perform path planning, calculate the total walking path length, analyze the number of pick-up point jumps through sequence statistics, and combine knowledge graph reasoning and dependency comparison to verify whether the key components have met the kitting requirements. Calculate and obtain the dynamic path reconstruction index DPI and compare it with the dynamic path reconstruction threshold Dth to determine whether the picking path is in an acceptable state. If not, give an appropriate strategy.

[0028] Step 5: Through inventory matching, time series forecasting, and historical backtracking analysis, obtain the real-time inventory satisfaction rate, the probability of on-time warehousing of work-in-process, and the historical order completeness rate, respectively. Calculate the completeness probability index GPI and compare it with the completeness probability threshold Gth to determine whether the order completeness probability is qualified. If it is not qualified, give appropriate strategies.

[0029] In this embodiment, by constructing a complete set dependency chain, collecting real-time inventory and work-in-process status, and dynamically calculating picking paths and complete set completion probabilities, dynamic monitoring and intelligent optimization of the entire order picking process can be achieved. This can effectively improve the accuracy and efficiency of complete set delivery for customized furniture orders, reduce the risk of missing parts, and ensure production and warehousing coordination, significantly enhancing delivery reliability and overall operation management level.

[0030] Example 2

[0031] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, step one includes:

[0032] S11. ERP system interface calls and OCR intelligent recognition technology are used to collect customer order data and production scheduling information; Natural Language Processing (NLP) algorithms and structured parsing methods are used to transform unstructured component descriptions in orders into a calculable list of standard components.

[0033] S12. Based on semantic parsing and rule matching methods, identify the functional dependencies between furniture components, including connections, combinations, and size matching; introduce knowledge graph reasoning algorithms to extract strongly coupled dependency pairs and filter weakly related or redundant information.

[0034] S13. Transform the identified component dependencies into a directed graph model, where nodes represent components and edges represent dependencies; use the Neo4j graph database modeling method to manage dependency information and provide storage and retrieval of complex relationships;

[0035] S14. Use graph traversal algorithms and Dijkstra's shortest path and critical path analysis algorithms to extract critical dependency paths in the kit; generate kit dependency chains based on the extraction results and mark the key components that affect delivery.

[0036] S15. Normalize and version control the alignment dependency chain, and remove outliers using data cleaning and consistency verification algorithms; establish a standard alignment dataset through database indexing and feature encoding techniques.

[0037] In this embodiment, by efficiently converting unstructured component information in customer orders into a standardized component list and constructing a kitting dependency chain based on functional dependencies, key component identification and dependency management are achieved, providing an accurate and calculable data foundation for subsequent picking planning and kitting delivery, thereby improving the accuracy and efficiency of order processing.

[0038] Example 3

[0039] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically, step two includes:

[0040] S21. By installing RFID readers and smart shelf monitoring terminals, the inventory status of each component in the warehouse is monitored in real time, and the quantity of components, inbound and outbound events and inventory turnover data are collected to establish an inventory status data group.

[0041] S22. By connecting the Production Execution System (MES) and the CNC equipment control module, the processing progress information of work-in-process is collected in real time, the current processing stage and expected completion time of each component are obtained, and a work-in-process processing progress data group is established.

[0042] S23. By deploying automated inbound sensors and barcode and QR code recognition devices, the timestamps of newly inbound parts are recorded. Combined with the warehouse location management system, the coordinates of the parts in the warehouse are collected to establish an inbound data group.

[0043] S24. Perform unified timeline processing on the collected inventory status data group, work-in-process processing progress data group, and warehousing data group, remove redundant information and abnormal data, extract the actual available quantity of parts, the remaining time of work-in-process, and the warehouse location coordinates based on the kitting dependency chain, construct the picking perception set, and form the picking status vector.

[0044] In this embodiment, by collecting and fusing data on warehouse inventory status, work-in-process processing progress, and component entry location in real time, a precise picking perception set and state vector are constructed to achieve dynamic perception of the number of available components and the remaining processing time of work-in-process, providing reliable real-time data support for subsequent picking optimization and complete delivery.

[0045] Example 4

[0046] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically, step three includes:

[0047] S31. Based on the picking awareness set, using database query and keyword matching algorithms, quickly compare the dependency chain with the inventory status and match the list of key components according to the kitting dependency chain; obtain the quantity QN of key components that have arrived; based on customer order data, sum up the quantities of all components in the order to obtain the total number of components required for the order ZN; based on the remaining time of work-in-process, use an averaging algorithm to obtain the average remaining processing time of work-in-process.

[0048] In this embodiment, the number of key components that have arrived is quickly identified based on the picking perception set, and the picking execution status of the order is accurately assessed by combining the total component requirements of the order and the average remaining processing time of work-in-process, providing a reliable basis for judging the completeness of delivery capability and optimizing the picking strategy.

[0049] Example 5

[0050] This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 1 Specifically, step three also includes:

[0051] S32. Based on the warehouse location coordinates, extract the warehouse coordinates Pkey(x1, y1, z1) of the key component and the coordinates Pcur(x2, y2, z2) of the current picking point. Calculate the spatial distance d(Pkey, Pcur) between the key component and the current picking point using the Euclidean distance formula, as follows:

[0052]

[0053] In this embodiment, by accurately calculating the spatial distance between key components and the current picking point, a quantitative basis is provided for picking path optimization and priority picking of key components, thereby improving picking efficiency and the scientific nature of warehousing operations.

[0054] Example 6

[0055] This embodiment is an explanation based on Embodiment 5. Please refer to it. Figure 1 Specifically, step three also includes:

[0056] S33. Based on the obtained quantity of key components already in place (QN), total number of components required for the order (ZN), and average remaining processing time of work-in-process. The spatial distance d(Pkey, Pcur) between the key component and the current picking position is dimensionless, and the kitting coupling coefficient CCI is calculated using the following formula:

[0057]

[0058] In the formula, w1, w2, and w3 represent weighting coefficients;

[0059] w1 = 0.45: This represents the impact of completeness of kit fitting on the kit fitting coupling coefficient (CCI). It has a high weight and is a key indicator that directly reflects the degree of matching between the proportion of key components in place and the completion status of the order kit fitting.

[0060] w2 = 0.35: Characterizes the impact of time urgency on CCI, with a medium weight. It reflects the constraint of average remaining processing time of work-in-process on the delivery of kits, and has important reference value for adjusting the picking execution plan.

[0061] w3 = 0.2: Characterizes the impact of spatial distance on CCI, with a minor weight, reflecting the auxiliary role of the storage distance between key components and the current picking location on picking efficiency;

[0062] By constructing a kitting coupling coefficient (CCI) that is a weighted fusion of w1, w2, and w3, the overall coordination and kitting completion capability of order picking execution can be quantified, providing a scientific basis for dynamic picking strategies and order priority adjustments.

[0063] S34. By setting a pre-defined fitting coupling threshold Cth, and comparing the fitting coupling coefficient CCI with the fitting coupling threshold Cth, the first evaluation result is obtained, including:

[0064] When the kitting coupling coefficient CCI ≥ kitting coupling threshold Cth, it means that the picking execution of the current order meets the kitting delivery requirements, and the current picking order is maintained and the execution continues.

[0065] When the kitting coupling coefficient CCI < kitting coupling threshold Cth, it indicates that the picking execution of the current order does not meet the kitting delivery requirements, triggering the first warning instruction and generating the first strategy: dynamically downgrade the picking order of the current order, postpone the execution of the current order, while increasing the priority of other orders, and prompting warehouse personnel to pay attention to the arrival time of key components on the system interface.

[0066] The method for obtaining the kitting coupling threshold Cth is as follows: By statistically analyzing historical picking data, inventory status, work-in-process processing progress, and arrival time of key components for a large number of customized furniture orders, and combining order complexity, component functional dependencies, and warehouse layout characteristics, a reasonable kitting coupling judgment value is determined. Referring to historical kitting completion rates, warehouse operation efficiency, and industry delivery standards, this threshold is used to distinguish whether the order picking execution meets the kitting delivery requirements, ensuring the rationality of the picking sequence and the timely arrival of key components.

[0067] In this embodiment, by quantifying the completeness of order picking and adjusting the picking order or priority in a timely manner based on the comparison results of the completeness coupling coefficient CCI and the threshold Cth, dynamic management of the availability of key components is achieved, thereby improving the reliability of complete order delivery and overall picking efficiency.

[0068] Example 7

[0069] This embodiment is an explanation based on Embodiment 6. Please refer to it. Figure 1 Specifically, step four includes:

[0070] S41. Based on the in-stock coordinates of components and the kitting dependency chain, a graph traversal algorithm and Dijkstra's algorithm for shortest path and critical path analysis are used to plan and accumulate the picking order in the component coordinates and dependency chain, and obtain the total walking path length Lpath required to execute the current kitting order; based on the inventory status data set and warehouse location coordinate data, a sequence statistics and state transition analysis method is used to parse the sequential picking points in the picking state vector, count the number of point jumps, and obtain the number of point switching times Sswitch in the picking process; based on functional dependencies and key component information, a knowledge graph reasoning and dependency comparison method is used to match and verify the inventory status data set and the work-in-process processing progress data set, and obtain the binary factor Kstatus indicating whether the key components have met the kitting requirements.

[0071] In this embodiment, by dynamically planning the optimal picking route based on the component storage location and dependencies, and simultaneously counting the number of location switching and verifying the completeness status of key components, the picking route is precisely optimized and the reliability of key component picking is improved, thereby reducing manual picking costs and improving warehousing operation efficiency.

[0072] Example 8

[0073] This embodiment is an explanation based on Embodiment 7. Please refer to it. Figure 1 Specifically, step four also includes:

[0074] S42. After obtaining the total walking path length Lpath required to execute the current complete order, the number of point changes Sswitch during the picking process, and the binary factor Kstatus, the dynamic path reconstruction index DPI is calculated after dimensionless processing, as follows:

[0075]

[0076] In the formula, a1, a2, and a3 represent weighting coefficients;

[0077] a1 = 0.4: This represents the impact of the total walking path length Lpath on the dynamic path reconstruction index DPI. It has a high weight and is a key indicator that directly reflects the balance between path planning efficiency and picking operation continuity.

[0078] a2 = 0.25: This represents the impact of the number of picking point switching (Sswitch) on DPI, and has a medium weight. It reflects the constraint effect of the frequency of point switching in the picking path on operational efficiency.

[0079] a3 = 0.35: This represents the impact of Kstatus on DPI, indicating whether key components are in place. It has the second most important weight and reflects the auxiliary role of the key component availability status on path executability.

[0080] By constructing a dynamic path reconfiguration index (DPI) that is a weighted fusion of a1, a2, and a3, the feasibility and adjustment needs of picking paths can be quantified, providing a basis for decision-making on dynamic reconfiguration of picking paths.

[0081] S43. By setting a dynamic path reconstruction threshold Dth, and comparing the dynamic path reconstruction index DPI with the dynamic path reconstruction threshold Dth, the second evaluation result is obtained, including:

[0082] When the Dynamic Path Reconstruction Index (DPI) is greater than or equal to the Dynamic Path Reconstruction Thresue (Dth), it indicates that the picking path is in an acceptable state. The current path planning scheme should be maintained and continuous monitoring should be carried out.

[0083] When the Dynamic Path Reconstruction Index (DPI) is less than the Dynamic Path Reconstruction Thresue (Dth), it indicates that the picking path is not in an acceptable state, triggering a second warning instruction and generating a second strategy: dynamically reconstructing the path, skipping parts that have not yet been put into storage, prioritizing the picking of peripheral parts, and prompting warehouse personnel to pay attention to the storage progress of key parts on the path planning interface.

[0084] The dynamic path reconstruction threshold Dth is obtained by statistically analyzing the total picking path length, number of location changes, and key component satisfaction status under different warehouse layouts, picking paths, and key component distributions. Combined with the operating habits of warehouse workers and picking efficiency requirements, a reasonable dynamic path reconstruction judgment value is determined. Referring to warehouse operation time standards, path optimization models, and historical picking efficiency data, this threshold is used to determine whether the current picking path is in an acceptable state, ensuring the continuity and efficiency of picking operations.

[0085] In this embodiment, the system dynamically calculates the reconstruction index of the picking route based on the total walking path, the number of point switching times, and the availability status of key components, and judges the acceptability of the route in real time. When the route is not optimized, the system automatically triggers the route reconstruction strategy to improve picking efficiency, reduce waiting time, and ensure that key components are picked first, thereby improving the flexibility and reliability of warehousing operations.

[0086] Example 9

[0087] This embodiment is an explanation based on Embodiment 8. Please refer to it. Figure 1 Specifically, step five includes:

[0088] S51. Based on the inventory status data set and the inbound data set, an inventory matching algorithm and probabilistic statistical methods are used to compare the real-time inventory quantity of the parts required for the order with the list of key parts in the dependent chain one by one to obtain the real-time inventory satisfaction Pstock; based on the work-in-process processing progress data set, combined with the expected completion time and the historical average processing deviation, time series forecasting and probabilistic regression methods are used to predict the probability of work-in-process being inbound on time to obtain the probability of work-in-process being inbound on time Pprod; based on customer order data and standard kitting dataset, statistical analysis and historical backtracking comparison methods are used to statistically model the kitting completion status of delivered orders to obtain the historical order kitting completion rate Phis.

[0089] In this embodiment, by acquiring in real time the inventory satisfaction rate of the required components for an order, the probability of on-time warehousing of work-in-process, and the historical completeness rate, a multi-dimensional order completion prediction is formed, which helps to identify potential inventory or production risks in advance, thereby improving the accuracy and reliability of complete order delivery.

[0090] Example 10

[0091] This embodiment is an explanation based on Embodiment 9. Please refer to it. Figure 1 Specifically, step five also includes:

[0092] S52. After dimensionless processing of the obtained real-time inventory satisfaction rate Pstock, work-in-process on-time warehousing probability Pprod, and historical order completeness rate Phis, the completeness completion probability index GPI is calculated using the following formula:

[0093] GPI=s1*Pstock+s2*Pprod+s3*Phis;

[0094] In the formula, s1, s2 and s3 represent weighting coefficients;

[0095] s1 = 0.5: This represents the impact of real-time inventory satisfaction (Pstock) on the order fulfillment probability index (GPI). It has a high weight and is a core indicator that directly reflects the contribution of inventory availability to order fulfillment.

[0096] s2=0.3: Characterizes the impact of the on-time entry probability of work-in-process inventory Pprod on GPI, with a medium weight, reflecting the constraint effect of production schedule on the probability of order completion;

[0097] s3 = 0.2: This represents the impact of the historical order completion rate Phis on GPI, and has an auxiliary weight, reflecting the auxiliary reference role of historical completion status on the probability of order completion.

[0098] By constructing a kit completion probability index (GPI) that is a weighted fusion of s1, s2 and s3, the likelihood of order kit completion can be quantified, providing a scientific basis for optimizing picking and production coordination strategies.

[0099] S53. By setting a predefined completion probability threshold Gth, and comparing the completion probability index GPI with the completion probability threshold Gth, the third evaluation results are obtained, including:

[0100] When the probability index of complete kitting is greater than or equal to the probability threshold of complete kitting, it means that the probability of complete kitting of an order is qualified. The existing picking and production coordination strategy should be maintained and continuous monitoring should be carried out.

[0101] When the probability index of complete kitting is less than the probability threshold of complete kitting, it indicates that the probability of complete kitting of the order is not up to standard, triggering the third warning instruction and generating the third strategy: automatically increasing the priority of the order in the task queue, reserving picking time and storage space for key components that are about to be completed, and prompting warehouse and production personnel to pay close attention in the system interface.

[0102] The threshold Gth for completeness probability is obtained by statistically analyzing historical order completeness rates, real-time inventory satisfaction, and the probability of on-time work-in-process warehousing, combined with order urgency, the importance of key components, and production scheduling patterns, to determine a reasonable completeness probability judgment value. Referring to enterprise delivery reliability standards, production scheduling models, and customer satisfaction data, this threshold is used to distinguish whether the order completeness probability is acceptable, ensuring timely and complete order delivery and the rational allocation of production resources.

[0103] In this embodiment, by comprehensively considering real-time inventory, work-in-process warehousing progress, and historical completion status, the probability index of order completion is calculated. When the target is not met, the task priority and picking resources are automatically adjusted to achieve dynamic optimization and risk warning for order completion, thereby improving overall delivery reliability and production scheduling efficiency.

[0104] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0105] The above formulas are all derived from software simulation using a large amount of data, and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art based on the actual situation. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic coupling picking and matching method for customized furniture oriented towards complete set delivery, characterized in that, Includes the following steps: Step 1: Collect customer order data and production progress information, parse the order parts list, transform the strong coupling relationship between parts into a graph model, extract the dependency path of key parts, generate a kit dependency chain, and establish a standard kit dataset. Step 2: By collecting real-time data on warehouse inventory status, work-in-process processing progress, and component entry time and location; after unified timeline processing and anomaly removal, the actual available quantity of components, remaining time of work-in-process, and warehouse coordinates are extracted based on the kitting dependency chain, and a picking perception set is constructed to form a picking status vector. Step 3: Based on the picking awareness set, use database query and keyword matching algorithms to compare the kitting dependency chain with the inventory status, obtain the number of key components that have arrived and the total number of components required for the order, and calculate the average remaining processing time of work-in-process and the spatial distance between key components and the current picking position through the average value algorithm. Further calculate the kitting coupling coefficient CCI and compare it with the kitting coupling threshold Cth to determine whether the picking execution of the current order meets the kitting delivery requirements. If not, give an appropriate strategy. Step 4: Based on the in-stock coordinates of components and the kitting dependency chain, use graph traversal and Dijkstra's algorithm to perform path planning, calculate the total walking path length, analyze the number of pick-up point jumps through sequence statistics, and combine knowledge graph reasoning and dependency comparison to verify whether the key components have met the kitting requirements. Calculate and obtain the dynamic path reconstruction index DPI and compare it with the dynamic path reconstruction threshold Dth to determine whether the picking path is in an acceptable state. If not, give an appropriate strategy. Step 5: Through inventory matching, time series forecasting, and historical backtracking analysis, obtain the real-time inventory satisfaction rate, the probability of on-time warehousing of work-in-process, and the historical order completeness rate, respectively. Calculate the completeness probability index GPI and compare it with the completeness probability threshold Gth to determine whether the order completeness probability is qualified. If it is not qualified, give appropriate strategies.

2. The method for dynamically coupled picking and matching of customized furniture oriented towards complete set delivery as described in claim 1, characterized in that, Step one includes: S11. ERP system interface calls and OCR intelligent recognition technology are used to collect customer order data and production scheduling information; Natural Language Processing (NLP) algorithms and structured parsing methods are used to transform unstructured component descriptions in orders into a calculable list of standard components. S12. Based on semantic parsing and rule matching methods, identify the functional dependencies between furniture components, including connections, combinations, and size matching; introduce knowledge graph reasoning algorithms to extract strongly coupled dependency pairs and filter weakly related or redundant information. S13. Transform the identified component dependencies into a directed graph model, where nodes represent components and edges represent dependencies; use the Neo4j graph database modeling method to manage dependency information and provide storage and retrieval of complex relationships; S14. Use graph traversal algorithm and critical path analysis algorithm to extract critical dependency paths in the kit; generate kit dependency chains based on the extraction results and mark the key components that affect delivery. S15. Normalize and version control the alignment dependency chain, and remove outliers using data cleaning and consistency verification algorithms; establish a standard alignment dataset through database indexing and feature encoding techniques.

3. The method for dynamically coupled picking and matching of customized furniture oriented towards complete set delivery as described in claim 2, characterized in that, Step two includes: S21. By installing RFID readers and smart shelf monitoring terminals, the inventory status of each component in the warehouse is monitored in real time, and the quantity of components, inbound and outbound events and inventory turnover data are collected to establish an inventory status data group. S22. By connecting the Production Execution System (MES) and the CNC equipment control module, the processing progress information of work-in-process is collected in real time, the current processing stage and expected completion time of each component are obtained, and a work-in-process processing progress data group is established. S23. By deploying automated inbound sensors and barcode and QR code recognition devices, the timestamps of newly inbound parts are recorded. Combined with the warehouse location management system, the coordinates of the parts in the warehouse are collected to establish an inbound data group. S24. Perform unified timeline processing on the collected inventory status data group, work-in-process processing progress data group, and warehousing data group, remove redundant information and abnormal data, extract the actual available quantity of parts, the remaining time of work-in-process, and the warehouse location coordinates based on the kitting dependency chain, construct the picking perception set, and form the picking status vector.

4. The method for dynamically coupled picking and matching of customized furniture oriented towards complete set delivery as described in claim 3, characterized in that, Step three includes: S31. Based on the picking awareness set, using database query and keyword matching algorithms, quickly compare the dependency chain with the inventory status and match the list of key components according to the kitting dependency chain; obtain the quantity QN of key components that have arrived; based on customer order data, sum up the quantities of all components in the order to obtain the total number of components required for the order ZN; based on the remaining time of work-in-process, use an averaging algorithm to obtain the average remaining processing time of work-in-process. .

5. The method for dynamically coupled picking and matching of customized furniture for complete set delivery according to claim 4, characterized in that, Step three also includes: S32. Based on the warehouse location coordinates, extract the warehouse coordinates Pkey(x1,y1,z1) where the key component is located and the current picking point coordinates Pcur(x2,y2,z2). Use the Euclidean distance formula to calculate the spatial distance d(Pkey,Pcur) between the key component and the current picking point.

6. The method for dynamically coupled picking and matching of customized furniture oriented towards complete set delivery as described in claim 5, characterized in that, Step three also includes: S33. Based on the obtained quantity of key components already in place (QN), total number of components required for the order (ZN), and average remaining processing time of work-in-process. The spatial distance d(Pkey, Pcur) between the key component and the current picking position is dimensionless, and the kit coupling coefficient CCI is calculated. S34. By setting a pre-defined fitting coupling threshold Cth, and comparing the fitting coupling coefficient CCI with the fitting coupling threshold Cth, the first evaluation result is obtained, including: When the kitting coupling coefficient CCI ≥ kitting coupling threshold Cth, it means that the picking execution of the current order meets the kitting delivery requirements, and the current picking order is maintained and the execution continues. When the kitting coupling coefficient CCI < kitting coupling threshold Cth, it indicates that the picking execution of the current order does not meet the kitting delivery requirements, triggering the first warning instruction and generating the first strategy: dynamically downgrade the picking order of the current order, postpone the execution of the current order, while increasing the priority of other orders, and prompting warehouse personnel to pay attention to the arrival time of key components on the system interface.

7. The method for dynamically coupled picking and matching of customized furniture oriented towards complete set delivery as described in claim 6, characterized in that, Step four includes: S41. Based on the in-stock coordinates of components and the kitting dependency chain, a graph traversal algorithm and Dijkstra's shortest path analysis algorithm are used to plan and accumulate the picking order of component coordinates and dependency chains, obtaining the total walking path length Lpath required to execute the current kitting order; based on the inventory status data set and warehouse location coordinate data, sequence statistics and state transition analysis methods are used to parse the sequential picking points in the picking state vector, count the number of point jumps, and obtain the number of point switching times Sswitch in the picking process; based on functional dependencies and key component information, a knowledge graph reasoning and dependency comparison method is used to match and verify the inventory status data set and the work-in-process processing progress data set, obtaining the binary factor Kstatus indicating whether the key components have met the kitting requirements.

8. A dynamic coupling picking and matching method for customized furniture oriented towards complete set delivery as described in claim 7, characterized in that, Step four also includes: S42. After dimensionless processing, the dynamic path reconstruction index DPI is calculated by obtaining the total walking path length Lpath required to execute the current complete order, the number of point switching during the picking process Sswitch, and the binary factor Kstatus. S43. By setting a dynamic path reconstruction threshold Dth, and comparing the dynamic path reconstruction index DPI with the dynamic path reconstruction threshold Dth, the second evaluation result is obtained, including: When the Dynamic Path Reconstruction Index (DPI) is greater than or equal to the Dynamic Path Reconstruction Thresue (Dth), it indicates that the picking path is in an acceptable state. The current path planning scheme should be maintained and continuous monitoring should be carried out. When the Dynamic Path Reconstruction Index (DPI) is less than the Dynamic Path Reconstruction Thresue (Dth), it indicates that the picking path is not in an acceptable state, triggering a second warning instruction and generating a second strategy: dynamically reconstructing the path, skipping parts that have not yet been put into storage, prioritizing the picking of peripheral parts, and prompting warehouse personnel to pay attention to the storage progress of key parts on the path planning interface.

9. A dynamic coupling picking and matching method for customized furniture oriented towards complete set delivery as described in claim 8, characterized in that, Step five includes: S51. Based on the inventory status data set and the inbound data set, an inventory matching algorithm and probabilistic statistical methods are used to compare the real-time inventory quantity of the parts required for the order with the list of key parts in the dependent chain one by one to obtain the real-time inventory satisfaction Pstock; based on the work-in-process processing progress data set, combined with the expected completion time and the historical average processing deviation, time series forecasting and probabilistic regression methods are used to predict the probability of work-in-process being inbound on time to obtain the probability of work-in-process being inbound on time Pprod; based on customer order data and standard kitting dataset, statistical analysis and historical backtracking comparison methods are used to statistically model the kitting completion status of delivered orders to obtain the historical order kitting completion rate Phis.

10. A dynamic coupling picking and matching method for customized furniture oriented towards complete set delivery as described in claim 9, characterized in that, Step five also includes: S52. After dimensionless processing of the real-time inventory satisfaction rate Pstock, the on-time entry probability of work-in-process Pprod, and the historical order completeness rate Phis, calculate and obtain the completeness completion probability index GPI. S53. By setting a predefined completion probability threshold Gth, and comparing the completion probability index GPI with the completion probability threshold Gth, the third evaluation results are obtained, including: When the probability index of complete kitting is greater than or equal to the probability threshold of complete kitting, it means that the probability of complete kitting of an order is qualified. The existing picking and production coordination strategy should be maintained and continuous monitoring should be carried out. When the probability index of complete kitting is less than the probability threshold of complete kitting, it indicates that the probability of complete kitting of the order is not up to standard, triggering the third warning instruction and generating the third strategy: automatically increasing the priority of the order in the task queue, reserving picking time and storage space for key components that are about to be completed, and prompting warehouse and production personnel to pay close attention in the system interface.

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