Multi-source e-commerce order-oriented clothing production whole-process progress visualization system

Abstract

The present application belongs to the technical field of manufacturing execution system, order data governance and production process visualization, especially a garment production whole-process progress visualization system for multi-source e-commerce orders, comprising an order access unit, an order standardization unit, a process capacity unit, a production execution collection unit, a progress fusion unit, a risk deduction unit and a visual interaction unit, the order access unit interfaces two e-commerce channels to unify the time base for shop orders and live broadcast order supplement orders, the order standardization unit splits multi-source e-commerce orders into order atoms composed of style, color, size, quantity, delivery date, process package and change constraints, and generates a cross-channel unique tracking code for each order atom, solving the technical problems of inconsistent multi-channel order caliber and different source orders unable to be unified tracked, realizing whole-link atomic-level mapping and cross-channel unique tracing from order access to production batch.

Description

Technical Field

[0001] This invention relates to the cross-technical fields of manufacturing execution systems, order data governance, and production process visualization, and in particular to a visualization system for the entire process of apparel production progress for multi-source e-commerce orders. Background Technology

[0002] When orders from multiple e-commerce platforms enter garment factories, production management first faces the problem of inconsistent order definitions. Regular store orders, livestream order replenishment orders, pre-sale orders, and customized orders have different field definitions. The same garment may be split into different links for sale. Information from buyers modifying shipping addresses, changing sizes, or urging delivery remains on the customer service side and cannot be synchronized to the production end. The production schedule seen by the production workshop is a collection of production batches, while the promised milestones seen by e-commerce customer service are order-level delivery dates. The clock bases of these two are inconsistent, resulting in the workshop only being able to reply "this batch is in the sewing stage" when customer service inquires about production progress, unable to pinpoint the completion status of specific order units. Currently, conventional end-to-end order visualization solutions can summarize logistics and order status on a single interface, but their granularity is relatively coarse, only suitable for handling standardized logistics nodes and general order status tracking. The breakpoints in the garment production process are more detailed: cut pieces of the same style and color but different sizes may be split into different production bundles; rework nodes do not cancel out previous completion nodes; outsourced printing or embroidery processes may temporarily leave the factory workshop; and at the same time, the buyer cannot directly see internal data such as workstations and work groups. If the logic of a general order dashboard is directly used, the progress display will be interrupted as soon as the cut pieces are split or rework is involved.

[0003] Existing technologies, such as garment MES solutions, structure orders, generate BOMs and process routes, and rely on barcode scanning for work reporting and real-time Kanban to handle processes from cutting to packaging, solving the problem of workshop production scheduling. However, the many-to-many mapping relationship between order atoms and batch bundle identifiers cannot be effectively expressed. Low-confidence marker work reporting information is directly used to refresh promised delivery dates, and after contract revision, the old and new promised nodes are overwritten, lacking a reliable closed-loop link. In addition, some SCM solutions focus on supply chain collaboration across multiple factory nodes, but fail to establish a link between workshop-level cycle time data and e-commerce promise text, resulting in e-commerce only displaying vague states such as "produced" or "shipped," lacking the ability to perceive and respond to early signs of delay risks. These problems all lead to serious disconnects in progress coordination and data consistency among different roles. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a visualization system for the entire process of garment production progress in response to multi-source e-commerce orders, overcoming the deficiencies of existing technologies and effectively solving the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A visual system for the entire process of apparel production progress, designed for multi-source e-commerce orders, includes an order access unit, an order standardization unit, a process capability unit, a production execution data collection unit, a progress fusion unit, a risk simulation unit, and a visual interaction unit. The order access unit connects to two e-commerce channels and performs pull operations on store orders, live streaming order replenishment orders, pre-sale orders and customized orders with a unified time benchmark. The order standardization unit breaks down orders from different sources into order atoms composed of style, color, size, quantity, delivery date, process package information, and change constraint information, and generates a unique cross-channel tracking code for each order atom; The process capability unit breaks down the garment production process into the fabric and accessories matching node, pre-production sample sealing node, cutting node, subcontracting node, sewing node, finishing node, inspection node, rework node, warehousing node, and shipping node, and configures the required equipment parameters, team skill parameters, standard working hour parameters, work-in-process upper limit parameters, and quality threshold parameters for each node. The production execution acquisition unit collects node events through the workstation reporting module, the cut piece bundling identification module, and the quality inspection record module. The node events carry operator identification, equipment identification, batch bundling number identification, quantity value, defect type information, and confidence marker information. The progress fusion unit uses order atoms as the smallest display granularity, integrates customer promised delivery date information, channel sales takt information and actual workshop node events into the same progress skeleton, and corrects the expected completion time based on the confidence flag of the node event; The risk simulation unit identifies material shortage events, style change events, order insertion events, rework events, and outsourcing delay events, calculates the delay propagation path along key nodes, and outputs the identification of expedited work nodes, the quantity of orders that can be split, and the time window value for rescheduling as processing suggestions. The visual interaction unit provides progress views with different permissions to the management end, workshop end, and e-commerce end. The e-commerce end only displays the anonymized commitment nodes, risk levels, and change confirmation interfaces. The workshop end displays the batch-level work-in-process positions and process takt times. The management end displays the correspondence between cross-channel order atoms and capacity load.

[0006] Preferably, the channel synchronization period of the order access unit is set to 5 to 60 seconds, and the order status is written to the system in the form of an event after a change. When the same buyer generates the same product order atom in 2 to 5 channels, the order standardization unit prompts the order atom to be merged based on the delivery address information, size set information and promised delivery date information. The merging ratio is controlled within the range of 10% to 60% of the total number of the same product order atom. If the merging fails, the original tracking code is retained and the reason for the conflict is recorded.

[0007] Preferably, the confidence flag of the node event is determined by the acquisition method information, time drift value, and batch continuity information. The confidence flag for node events collected by automatic equipment with a time drift of no more than 3 seconds is recorded as 0.9 to 1.0; the confidence flag for node events reported by scanning codes with a time drift of 3 to 30 seconds is recorded as 0.6 to 0.9; and the confidence flag for node events that are manually supplemented or where the batch number chain is broken is recorded as 0.1 to 0.6. The progress fusion unit does not perform the expected completion time refresh operation for node events with confidence flags below 0.6, but only marks them as pending review.

[0008] Preferably, the risk simulation unit classifies the delay risk into low risk, medium risk, and high risk; The estimated completion time is marked as low risk if it is 0 to 24 hours later than the promised delivery time, medium risk if it is 24 to 72 hours later, and high risk if it is 72 hours later or if the availability rate of key materials and accessories is less than 90%. For medium- to high-risk orders, the system outputs 1 to 3 processing suggestions. The processing suggestions include the identifier of the node that can be rushed, the quantity that can be split into smaller orders, the identifier of the node that can be outsourced, and the time window that can be modified.

[0009] Preferably, the production execution acquisition unit supports single-piece flow mode, bundled flow mode, and mixed flow mode; A many-to-many mapping relationship is established between the cut piece bundling identifier and the order atom tracking code. When the same bundle of cut pieces contains 2 to 8 order atoms, a matching verification code is generated based on the size information, color information and process package information. When a rework occurs after sewing is completed, the rework node is written back to the corresponding order atom in the form of parallel branches. The completed node events are not canceled, and the rework duration data and defect type data are appended to the parallel branches.

[0010] Preferably, the visual interaction unit clips and displays data based on role permissions; The e-commerce view hides workstation information, team information, and unit price fields, and displays the committed node identifier, estimated shipping window value, risk level identifier, and buyer-visible text. The workshop end view displays batch number identifiers, process cycle values, work-in-process upper limit values, and a shortage list; The management view overlays channel sales peak data, order atomic progress data, and factory capacity load data onto the same time axis, with the time axis scaling granularity including 15 minutes, 1 hour, 1 day, and 1 week.

[0011] A method for visualizing the entire garment production process for multi-source e-commerce orders includes the following steps: S1. Pull store orders, live stream order replenishment orders, pre-sale orders, and customized orders from two e-commerce channels, unify the time base, and perform deduplication; S2. Break down the order into order atoms consisting of style, color, size, quantity, delivery date, process package information, and change constraint information, and generate a unique cross-channel tracking code; S3. Read the garment production process route and bind the fabric and accessories matching node, pre-production sample sealing node, cutting node, subcontracting node, sewing node, finishing node, inspection node, rework node, warehousing node and shipping node to the order atom; S4. Collect work station reporting records, cut piece bundling identification information and quality inspection records to form node events carrying operator identification, equipment identification, batch bundling number identification, quantity value, defect type information and confidence mark information; S5. Using the order atom as the smallest display granularity, write the customer's promised delivery date information, channel sales takt information and actual workshop node events into the same progress skeleton; S6. Correct the estimated completion time based on the confidence flag of the node event, and mark the node event with a confidence flag lower than 0.6 as pending review and do not perform the estimated completion time refresh operation; S7. Identify material shortage events, style change events, order insertion events, rework events, and outsourcing delay events; calculate the delay propagation path along key nodes; and output handling suggestions. S8. Generate different progress views based on the permissions of the management end, workshop end, and e-commerce end, and write back the contract modification confirmation information, order splitting confirmation information, and rush work confirmation information to the corresponding order atoms.

[0012] Preferably, in step S5, the progress skeleton includes a planned beat data line, an actual beat data line, and a committed beat data line; When the deviation between the actual takt time and the planned takt time is greater than 15% for two consecutive nodes, a bottleneck location operation is triggered. When the deviation between the promised takt time and the actual takt time is greater than 24 hours, the contract modification entry on the e-commerce terminal is triggered. The planned takt time is the planned completion time of each node calculated based on standard working hours. The actual takt time is the actual completion time determined by the node event timestamp. The promised takt time is the node completion time promised to the customer. When the deviation between the actual takt time and the planned takt time is less than 15% and the confidence flag of the node event is not less than 0.6, only the management view and the workshop view are updated, and the buyer's side text is not refreshed.

[0013] Preferably, in step S7, the processing suggestions are sorted according to the weights of the factors affecting the completion time, the weights affecting the number of order atoms, and the weights of the execution cost; The maximum quantity of orders that can be split is the uncut quantity under the same process package, and the maximum time window for rescheduling is 1 to 5 days before and after the promised delivery date. In response to the contract modification confirmation information from the e-commerce platform, the original commitment node is frozen, the new commitment node takes effect, and all node events before and after the freeze are retained.

[0014] Preferably, in steps S5 to S7, the planned beat data, the actual beat data, and the promised beat data are stored in separate data fields. The planned cycle time data is calculated from the standard time parameters of the process capacity unit, the actual cycle time data is calculated from the timestamps of node events, and the promised cycle time data is determined from the delivery date information and rescheduled confirmation information of the order access unit. When performing the calculation of the delayed propagation path, the risk simulation unit simultaneously reads the planned beat data, the actual beat data, and the promised beat data.

[0015] The beneficial effects of this invention are as follows: The present invention provides a visual system for the entire process of garment production progress for multi-source e-commerce orders. By standardizing order units, the system breaks down multi-source e-commerce orders into order atoms consisting of style, color, size, quantity, delivery date, process package, and change constraints. It generates a unique cross-channel tracking code for each order atom, which solves the technical problems of inconsistent order definitions across multiple channels and the inability to track orders from different sources in a unified manner. It realizes full-link atomic-level mapping and unique cross-channel traceability from order access to production batch. The present invention provides a visual system for the entire production process of apparel manufacturing for multi-source e-commerce orders. This system uses a production execution acquisition unit to attach confidence markers to node events, determined by the acquisition method, time drift, and batch / bundling continuity. The progress fusion unit then suspends low-confidence events based on these confidence markers, preventing low-confidence work reports from directly updating promised times. Simultaneously, it utilizes parallel branches to record rework duration and defect type without canceling previously completed nodes. This solves the problems of low-quality data interfering with progress prediction and rework nodes obscuring previous progress. It achieves hierarchical management and independent presentation of abnormal work reports and rework events, effectively improving the reliability and stability of the system's progress fusion. This invention provides a visual system for the entire production process of apparel for multi-source e-commerce orders. It identifies events such as material shortages, style changes, order insertions, rework, and outsourcing delays through a risk simulation unit. Based on the deviation between the promised, planned, and actual takt times in the three-line progress framework, it calculates the delay propagation path and outputs processing suggestions including expedited work nodes, order splitting quantities, outsourcing nodes, and rescheduled time windows. This solves the technical challenge of relying on manual judgment of delay impacts under frequent multi-channel order changes, achieving quantitative propagation and positioning of delay risks, as well as automated sorting and write-back closed-loop processing of adjustment suggestions such as rescheduling and expedited work. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the garment production progress visualization system for multi-source e-commerce orders proposed in this invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Reference Figure 1 Example 1: Order Atomic Tracking and Three-Line Progress Skeleton Generation in a Standard Single-Item Bundling Flow Scenario This embodiment sets up a standard test scenario, including two e-commerce channels and a self-owned garment factory. The production object is a single style of crewneck sweatshirt, using a bundled production model. The channel synchronization cycle is set to 30 seconds, and all order status changes are written to the system via event triggering. The order access unit retrieves store orders, live-stream replenishment orders, pre-sale orders, and customized orders from e-commerce channels A and B through an API interface, aligning them with timestamps according to a unified time base. The order standardization unit breaks down orders from different sources into style, color, size, quantity, delivery date, process package information, and change constraint information, generating a unique cross-channel tracking code. The process package information is fixed as "crewneck sweatshirt basic package".

[0019] The process capability unit breaks down the production process into ten nodes: kitting, sample sealing, cutting, packaging, sewing, finishing, inspection, rework, warehousing, and shipping. The upper limit for work-in-process at the sewing node is set at 3 bundles. The quality threshold for the inspection node is set at a first-pass yield rate that must not be lower than the baseline value for the shift, which is maintained and updated weekly by the factory's quality department. After cutting, the production execution data acquisition unit establishes a many-to-many mapping relationship between the cut piece bundle identifier and the order atomic tracking code. The threshold for the number of order atoms contained in the same bundle of cut pieces is 2 to 8. The formula for generating the cut piece bundle identifier is:

[0020] Where n takes values ​​from 2 to 8. The function uses a CRC checksum generation algorithm. Additionally, to ensure that production objects in multi-source mixed batches are not confused, the system generates a corresponding checksum:

[0021] in, This is the process package identifier. The schedule fusion unit uses the order atom as the smallest display granularity, writing the promised delivery date, sales takt time, and actual workshop takt time into the same schedule skeleton. The formula for calculating the takt time deviation in this schedule skeleton is:

[0022] When the system continuously detects two or more nodes appearing Upon such an event, the bottleneck location mechanism is immediately triggered. This mechanism aggregates the actual completion timestamps of each process and outputs a process load heatmap. Simultaneously, for deviations between the promised and actual cycle times, the system uses the following formula to issue an early warning:

[0023] like If the contract is cancelled within 1 hour, the contract modification confirmation portal on the e-commerce platform will automatically open. For In hourly scenarios, the order atom is marked as a low-risk node, and the delay trend is only displayed in the management and workshop views, without triggering changes to the buyer's copy. For benchmark testing, the system sets 1000 order atoms as a batch test unit to verify the success rate of order atom tracking code generation. The formula for calculating the tracking code generation success rate is:

[0024] in, The number of order atoms required to successfully generate a tracking code. This represents the total number of atoms in the order. Simultaneously, it records the number of broken chains mapped between batch bundles and order atoms; the chain break rate is calculated using the following formula:

[0025] in, Because it is impossible to trace back to the batch quantity of the order atoms, This represents the total number of bundles. These parameters are used to ensure that the business system can still ensure that each piece bundle identifier is associated with a specific order atom even when there is high concurrency in order input.

[0026] Example 2: Order Atomic Merging and Rework Parallel Branch Control in Multi-Channel Mixed Flow and Outsourcing Scenarios This embodiment sets up a more complex production scenario, including four e-commerce channels (e-commerce channel A, e-commerce channel B, e-commerce channel C, and e-commerce channel D), with both in-house factories and external printing collaboration factories. The production mode is mixed-flow production with frequent order insertions, including live-stream order replenishment and pre-sale orders. The channel synchronization cycle is set to 15 seconds, and live-stream order replenishment is marked as an order insertion sensitive type. The order standardization unit automatically merges identical orders from the same buyer across 2 to 5 channels, with a merging ratio capped at 40%. If this threshold is exceeded, the system does not perform automatic merging but triggers customer service review. Outsourced printing nodes are written into the process capability unit as independent processes. Specific node events are generated for both outsourced shipments and outsourced shipments returning to the factory. The batch bundle number returning to the factory must match the original batch bundle number or its matching verification code. The success rate of the batch bundle number matching returning to the factory is defined as follows:

[0027] in, The number of outsourced batch bundles returned to the factory that successfully match the original batch bundle number or matching check code. This represents the total number of outsourced batches returned to the factory. If a matching process fails, the system immediately marks the batch as abnormal and suspends its progress. Rework nodes are high-frequency anomalies in garment production. If issues such as loose threads, size deviations, or color differences in the print are found after sewing, the system does not mark the preceding sewing node as incomplete; instead, it writes back to the rework node as a parallel branch. The parallel branch data structure is represented as follows:

[0028] in, This is a confirmed completed sewing node event. The time required for repairs The system enumerates defective product types (including but not limited to loose threads, size deviations, print color differences, and poor stitching). With this parallel branch structure, the working hours, output, and shift performance data of preceding sewing nodes are not canceled out, and the management view will display the timeline and defective product type of the rework branch. This embodiment also limits the number of orders that can be split in mixed-flow mode. When an order is inserted, the system needs to identify the uncut quantity within the same process package. The formula for calculating the upper limit of the number of orders that can be split is:

[0029] in, This represents the set of order atoms under this process package that have not yet entered the cutting process. This represents the number of atoms in the i-th order. New order atoms generated after order splitting will have their tracking codes reassigned. A mapping relationship is established between the original and new tracking codes to maintain the historical tracking link. After rework and outsourcing order insertion are completed, the system will automatically update the closed-loop rate of high-risk order atoms.

[0030] in, To handle the high-risk order atomic number that has been confirmed and implemented and returned to the normal production path, The total number of atoms marked as medium- to high-risk orders must be determined in the actual test to ensure that the closed-loop rate reaches a predetermined threshold in order to verify the reliability of the risk simulation unit in a multi-source mixed-flow scenario.

[0031] Example 3: Low-Confidence Event Filtering and Suspension Mechanism Based on Confidence Flags This embodiment focuses on describing the calculation and processing logic of node event confidence flags. Node events collected by the production execution acquisition unit carry confidence flags, the generation of which is determined by a combination of factors including the acquisition method, time drift, and batch continuity.

[0032] The data collection methods are divided into three categories: The first category is automatic equipment collection, where these events are automatically generated by intelligent cutting machines, intelligent hanging systems, or automatic inspection equipment, with a time drift of no more than 3 seconds and a confidence interval of [0.9, 1.0]. The second category is barcode scanning for reporting, where operators use handheld PDAs to scan the fabric bundle markings or process cards to report work, with a time drift of 3 to 30 seconds and a confidence interval of [0.6, 0.9]. The third category is manual entry or batch number breakage, mainly occurring when equipment is offline, network is interrupted, or during emergency repairs, with a confidence interval of [0.1, 0.6]. The processing threshold for low-confidence events is defined as follows:

[0033] If the confidence flag is below the threshold, the progress fusion unit does not perform the update operation for the expected completion time, but only marks the node event as pending review. The information for the pending review status includes the event timestamp, current deviation, and event source identifier. After review, the team leader or merchandiser modifies the confidence flag based on the supplemented or returned actual data. The corrected confidence flag is located in the range [0.6, 0.9], while retaining the original confidence flag and modification traces. The time taken for this review process can be expressed as:

[0034] in, This is the timestamp indicating that the team leader has approved the document. This is the initial timestamp written by the system for the event. This embodiment sets up an abnormal scenario: after outsourced printing work returns to the factory, the operator, due to a network outage with the barcode scanner, has to record 30 batch data entries in a concentrated manner. The batch bundle numbers are not entered in order, causing a break in the batch bundle continuity. The system determines the confidence flag for this batch node event to be 0.3 and suspends it, without directly refreshing the promised cycle time. After the factory network is restored, the operator uses the barcode scanner to rescan the associated batch bundle numbers. The system verifies the consistency between the batch bundle and the order atoms using a matching checksum mapping algorithm. Upon successful verification, a new node event with a confidence flag of 0.8 is generated and overwrites the suspended event. After the event is overwritten, the progress fusion unit performs the expected completion time refresh operation based on the new confidence flag. The identification and filtering of low-confidence events effectively prevents data contamination caused by network fluctuations and human error, ensuring the authenticity of the production progress display.

[0035] Example 4: Risk Simulation and Contract Modification Closed-Loop Processing This embodiment focuses on the risk simulation unit and the generation logic of corresponding handling suggestions. The risk simulation unit classifies the delay risk into three levels: low risk, medium risk, and high risk. The determination is based on the delay time between the expected completion time and the promised delivery date, as well as the availability rate of raw materials and accessories. Let the expected completion time be... The promised delivery time is The definition of the delay duration is:

[0036] The completeness rate of fabric and accessories is defined as:

[0037] in, This refers to the quantity of key raw materials and accessories that are currently fully stocked in the warehouse. This represents the total amount of critical surface materials required for this order's atoms. If... The system marks it as low risk within an hour; if Hourly, marked as medium risk; if Hours, or The system marks them as high risk. For those marked as medium to high risk (i.e.... For each order (within one hour), the risk simulation unit immediately outputs 1 to 3 processing suggestions. The ranking weight of the suggestions is defined as follows:

[0038] in, To suggest the amount of improvement to be made at the completion time, For the number of atoms in the affected orders, To implement the recommended labor cost adjustment factor, These are adjustable weighting parameters for the factory based on the production line. The suggested processing options include expedited production node identifiers, splittable order quantity values, outsourcing node identifiers, and contract rescheduling time window values. The upper limit for the splittable order quantity uses the undressed quantity within the same process package as a boundary condition, i.e.:

[0039] in, This refers to the total number of order atoms within the same process package that have not yet entered the cutting process. The reschedulable time window is limited to 1 to 5 days before and after the committed delivery date, i.e.:

[0040] and and The deviation is no less than 24 hours. After the buyer confirms the contract modification on the e-commerce platform, the original commitment node is frozen, the new commitment node takes effect, and all node events before and after the freeze are retained in the system, forming a complete timeline record before and after the contract modification. The formula for recording the difference in takt time before and after the contract modification is:

[0041] in, This represents the number of node events in the frozen state. This represents the number of node events after the new commitment takes effect. This takt time difference record is used to quantify the effect of contract revisions in retrospective analysis. This embodiment also emphasizes that the processing suggestions output by the risk simulation unit are limited to sorting and do not have automatic execution permissions. Specific execution (including expedited work, order splitting, outsourcing, and contract revisions) must be confirmed by management or workshop operators. After confirmation, all views are updated synchronously. Throughout this embodiment, the planned takt time data, actual takt time data, and committed takt time data in the three-line progress skeleton are always stored in independent data fields to avoid mutual contamination between planned adjustments and actual deviations. Planned takt time data originates from the standard working hour parameters and standard process scheduling strategies of the process capacity unit; actual takt time data originates from the node event timestamps and confidence flag corrections reported by the production execution acquisition unit; and committed takt time data originates from the delivery date information and contract revision confirmation information of the order access unit. This three-line coexistence data structure allows the risk simulation unit to differentiate and determine whether the delay is due to insufficient capacity, material shortages, or overly aggressive committed delivery date settings when calculating the delay propagation path, thereby outputting accurate and effective processing suggestions. The management view, workshop view, and e-commerce view all read the corresponding permission fields from this independent database, ensuring the isolation and consistency of data for each role and realizing closed-loop management with full-process progress visualization.

[0042] Starting from the node where the delay event occurs, the process traverses backward along the process nodes according to their sequential constraints. The delay time of the source node is reduced by the compressible working time (rushing margin) of each downstream node, and then the delay is passed down level by level until the shipping node is reached. The expected delay time and propagation path of each node are output.

[0043] 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 modifications 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 visual system for the entire garment production process, oriented towards multi-source e-commerce orders, characterized in that: It includes an order access unit, an order standardization unit, a process capability unit, a production execution data collection unit, a schedule integration unit, a risk simulation unit, and a visual interaction unit; The order access unit connects to two e-commerce channels and performs pull operations on store orders, live stream order replenishment orders, pre-sale orders and customized orders with a unified time benchmark. The order standardization unit breaks down orders from different sources into order atoms composed of style, color, size, quantity, delivery date, process package information, and change constraint information, and generates a unique cross-channel tracking code for each order atom; The process capability unit breaks down the garment production process into the fabric and accessories matching node, pre-production sample sealing node, cutting node, subcontracting node, sewing node, finishing node, inspection node, rework node, warehousing node, and shipping node, and configures the required equipment parameters, team skill parameters, standard working hour parameters, work-in-process upper limit parameters, and quality threshold parameters for each node. The production execution acquisition unit collects node events through the workstation reporting module, the cut piece bundling identification module, and the quality inspection record module. The node events carry operator identification, equipment identification, batch bundling number identification, quantity value, defect type information, and confidence marker information. The progress fusion unit uses order atoms as the smallest display granularity, integrates customer promised delivery date information, channel sales takt information and actual workshop node events into the same progress skeleton, and corrects the expected completion time based on the confidence flag of the node event; The risk simulation unit identifies material shortage events, style change events, order insertion events, rework events, and outsourcing delay events, calculates the delay propagation path along key nodes, and outputs the identification of expedited work nodes, the quantity of orders that can be split, and the time window value for rescheduling as processing suggestions. The visual interaction unit provides progress views with different permissions to the management end, workshop end, and e-commerce end. The e-commerce end only displays the anonymized commitment nodes, risk levels, and change confirmation interfaces. The workshop end displays the batch-level work-in-process positions and process takt times. The management end displays the correspondence between cross-channel order atoms and capacity load.

2. The visual system for the entire process of garment production progress oriented towards multi-source e-commerce orders as described in claim 1, characterized in that, The channel synchronization cycle of the order access unit is set to 5 to 60 seconds, and the order status is written to the system as an event after a change. When the same buyer generates the same product order atom in 2 to 5 channels, the order standardization unit will merge the order atom based on the delivery address information, size set information and promised delivery date information. The number of merged order atoms is controlled to be between 10% and 60% of the total number of the same product order atoms. If merging fails, the original tracking code is retained and the reason for the conflict is recorded.

3. The visual system for the entire process of garment production progress oriented towards multi-source e-commerce orders as described in claim 1, characterized in that, The confidence flag for the node event is determined by the acquisition method information, time drift value, and batch continuity information. The confidence flag for node events collected by automatic equipment with a time drift of no more than 3 seconds is recorded as 0.9 to 1.0; the confidence flag for node events reported by scanning codes with a time drift of 3 to 30 seconds is recorded as 0.6 to 0.9; and the confidence flag for node events that are manually supplemented or where the batch number chain is broken is recorded as 0.1 to 0.

6. The progress fusion unit does not perform the expected completion time refresh operation for node events with confidence flags below 0.6, but only marks them as pending review.

4. The garment production process visualization system for multi-source e-commerce orders as described in claim 1, characterized in that, The risk simulation unit classifies the risk of delay into low risk, medium risk, and high risk; The estimated completion time is marked as low risk if it is 0 to 24 hours later than the promised delivery time, medium risk if it is 24 to 72 hours later, and high risk if it is 72 hours later or if the availability rate of key materials and accessories is less than 90%. For medium- to high-risk orders, the system outputs 1 to 3 processing suggestions. The processing suggestions include the identifier of the node that can be rushed, the quantity that can be split into smaller orders, the identifier of the node that can be outsourced, and the time window that can be modified.

5. The visual system for the entire process of garment production progress oriented towards multi-source e-commerce orders as described in claim 1, characterized in that, The production execution acquisition unit supports single-piece flow mode, bundled flow mode and mixed flow mode; A many-to-many mapping relationship is established between the cut piece bundling identifier and the order atom tracking code. When the same bundle of cut pieces contains 2 to 8 order atoms, a matching verification code is generated based on the size information, color information and process package information. When a rework occurs after sewing is completed, the rework node is written back to the corresponding order atom in the form of parallel branches. The completed node events are not canceled, and the rework duration data and defect type data are appended to the parallel branches.

6. The garment production process visualization system for multi-source e-commerce orders according to claim 1, characterized in that, The visual interaction unit clips and displays data based on role permissions; The e-commerce view hides workstation information, team information, and unit price fields, and displays the committed node identifier, estimated shipping window value, risk level identifier, and buyer-visible text. The workshop end view displays batch number identifiers, process cycle values, work-in-process upper limit values, and a shortage list; The management view overlays channel sales peak data, order atomic progress data, and factory capacity load data onto the same time axis, with the time axis scaling granularity including 15 minutes, 1 hour, 1 day, and 1 week.

7. A method for visualizing the entire process progress of garment production for multi-source e-commerce orders, characterized in that, Includes the following steps: S1. Pull store orders, live stream order replenishment orders, pre-sale orders, and customized orders from two e-commerce channels, unify the time base, and perform deduplication; S2. Break down the order into order atoms consisting of style, color, size, quantity, delivery date, process package information, and change constraint information, and generate a unique cross-channel tracking code; S3. Read the garment production process route and bind the fabric and accessories matching node, pre-production sample sealing node, cutting node, subcontracting node, sewing node, finishing node, inspection node, rework node, warehousing node and shipping node to the order atom; S4. Collect work station reporting records, cut piece bundling identification information and quality inspection records to form node events carrying operator identification, equipment identification, batch bundling number identification, quantity value, defect type information and confidence mark information; S5. Using the order atom as the smallest display granularity, write the customer's promised delivery date information, channel sales takt information and actual workshop node events into the same progress skeleton; S6. Correct the estimated completion time based on the confidence flag of the node event, and mark the node event with a confidence flag lower than 0.6 as pending review and do not perform the estimated completion time refresh operation; S7. Identify material shortage events, style change events, order insertion events, rework events, and outsourcing delay events; calculate the delay propagation path along key nodes; and output handling suggestions. S8. Generate different progress views based on the permissions of the management end, workshop end, and e-commerce end, and write back the contract modification confirmation information, order splitting confirmation information, and rush work confirmation information to the corresponding order atoms.

8. The method for visualizing the entire process progress of garment production for multi-source e-commerce orders according to claim 7, characterized in that, In step S5, the progress skeleton includes planned beat data lines, actual beat data lines, and committed beat data lines; When the deviation between the actual takt time and the planned takt time is greater than 15% for two consecutive nodes, a bottleneck location operation is triggered. When the deviation between the promised takt time and the actual takt time is greater than 24 hours, the contract modification entry on the e-commerce terminal is triggered. The planned takt time is the planned completion time of each node calculated based on standard working hours. The actual takt time is the actual completion time determined by the node event timestamp. The promised takt time is the node completion time promised to the customer. When the deviation between the actual takt time and the planned takt time is less than 15% and the confidence flag of the node event is not less than 0.6, only the management view and the workshop view are updated, and the buyer's side text is not refreshed.

9. The method for visualizing the entire process progress of garment production for multi-source e-commerce orders according to claim 7, characterized in that, In step S7, the processing recommendations are sorted according to the weights of the factors affecting the completion time, the weights affecting the number of order atoms, and the weights of the execution cost; The maximum quantity of orders that can be split is the uncut quantity under the same process package, and the maximum time window for rescheduling is 1 to 5 days before and after the promised delivery date. In response to the contract modification confirmation information from the e-commerce platform, the original commitment node is frozen, the new commitment node takes effect, and all node events before and after the freeze are retained.

10. The method for visualizing the entire process progress of garment production for multi-source e-commerce orders according to claim 8, characterized in that, In steps S5 to S7, the planned beat data, the actual beat data, and the promised beat data are stored in separate data fields. The planned cycle time data is calculated from the standard time parameters of the process capacity unit, the actual cycle time data is calculated from the timestamps of node events, and the promised cycle time data is determined from the delivery date information and rescheduled confirmation information of the order access unit. When performing the calculation of the delayed propagation path, the risk simulation unit simultaneously reads the planned beat data, the actual beat data, and the promised beat data.

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