Chip sales order full life cycle visual tracking method

Abstract

This invention discloses a method for visually tracking the entire lifecycle of chip sales orders, relating to the field of order management technology based on knowledge graphs. The method includes: acquiring order events and packaging identifiers of sales orders, identifying multiple sub-batches, and extracting event interval sequences for each sub-batch; querying the humidity clock semantics of each interval using a knowledge graph based on the order event information corresponding to the event interval sequences, determining the allowable exposure time and duration of each interval in conjunction with the packaging identifier, and generating a clock calculation sequence; calculating the remaining humidity clock quantity for each sub-batch, and constructing a set of remaining humidity clocks for each order sub-batch; extracting the conservative deliverable status, clock fork quantity, and dominant sub-batch for each lifecycle node, constructing an order node status group, and generating visual tracking results for the order. This invention can accurately reflect the state differentiation during the unpacking and batching of humidity-sensitive chips, improving the authenticity and targeted management of order fulfillment tracking.

Description

Technical Field

[0001] This invention relates to the field of knowledge graph-based order management technology, and in particular to a method for visually tracking the entire lifecycle of chip sales orders. Background Technology

[0002] As chip sales gradually evolve towards multi-batch fulfillment, refined warehousing, and end-to-end visualized management, humidity-sensitive chips typically undergo multiple business stages during order execution, including order creation, warehouse locking, unpacking and picking, resealing, secure storage, re-shipment, logistics handover, and receipt confirmation. In practice, chips within the same original moisture-proof bag are often split into multiple sub-batches due to customer ordering in batches, inventory allocation, or differences in delivery schedules. Different sub-batches experience different exposure, storage, and outbound processes in the subsequent fulfillment path. For humidity-sensitive chips, order management involves not only quantity, batch, and logistics progress but also changes in the remaining exposure window across different event intervals. If the true status of each sub-batch cannot be continuously identified throughout the entire order lifecycle, discrepancies between the displayed order status and the actual deliverable condition can easily arise, impacting the accurate fulfillment and end-to-end tracking management of chip sales orders.

[0003] Existing technologies, when addressing the aforementioned issues, typically use order numbers, inventory batches, or original packaging units as the primary tracking granularity. They focus more on general business statuses such as order creation, inventory locking, outbound shipment, logistics, and receipt confirmation. They lack detailed representation of the sub-batch differences formed in the scenario of unpacking and batch delivery of humidity-sensitive chips. It is difficult to continuously calculate the remaining status of each sub-batch based on the actual exposure history under different event intervals, and it is also difficult to reflect the status differentiation between different sub-batches at order lifecycle nodes. Even if some sub-batches under the same sales order have experienced a shrinking or even exhausted remaining exposure window, the overall order progress may still be displayed in an aggregated manner, causing high-risk sub-batches to be masked. This fails to accurately reflect the most unfavorable delivery status of the order at the current node, thereby reducing the authenticity and management effectiveness of the chip sales order visualization tracking results. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, such as the inability to accurately identify the true deliverable status of each sub-batch under the same sales order during the unpacking and batching of humidity-sensitive chips, and the tendency to conceal the fulfillment risk of the most unfavorable sub-batch in the visual tracking of the entire order lifecycle. Therefore, this invention proposes a visual tracking method for the entire lifecycle of chip sales orders.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution: A method for visually tracking the entire lifecycle of chip sales orders, including: S1. Obtain the order events and packaging identifiers of the sales orders. Based on the packaging identifiers, identify chips that originate from the same original moisture-proof bag and have the same part number and traceability batch number as multiple sub-batches, and extract the event interval sequence of each sub-batch. S2. Based on the order event information corresponding to the event interval sequence, query the humidity clock semantics of each interval through the knowledge graph, combine the packaging label to determine the allowable exposure time and duration of each interval, and generate the clock calculation sequence for each sub-batch. S3. Calculate the remaining humidity clock amount for each sub-batch recursively based on the clock calculation sequence, and summarize to generate a set of remaining humidity clocks for each order sub-batch; S4. Based on the remaining humidity clock set of the order sub-batch, extract the conservative deliverable status, clock fork amount and dominant sub-batch of each lifecycle node, and construct the order node status group. S5. Determine the path fork status based on the clock fork amount in the order node status group, and use the path where the dominant sub-batch is located as the main path to generate order visualization tracking results.

[0006] Preferably, the order events include order creation events, warehouse locking events, unpacking and picking events, resealing events, safe storage events, re-outbound events, logistics handover events, and receipt events; the packaging markings include original moisture-proof bag markings, part numbers, traceability batch numbers, and moisture sensitivity level markings.

[0007] Preferably, the event interval sequence of each sub-batch is extracted, including: Chips with the same original moisture-proof bag markings and the same corresponding part number and traceability batch number are identified as belonging to the same sub-batch; Extract the consecutive order events experienced by each sub-batch in chronological order to form an event interval sequence.

[0008] Preferably, the entities in the knowledge graph include sales orders, original moisture-proof bags, sub-batch and order events, and the relationships in the knowledge graph include unpacking, resealing and safe storage.

[0009] Preferably, the semantics of humidity clocks in each interval are queried through a knowledge graph, including: Based on the order events, original moisture-proof bags, sub-batch and safe storage status corresponding to each interval in the event interval sequence, perform relationship queries in the knowledge graph; The humidity clock semantics of the corresponding interval are determined based on the relation query results. The humidity clock semantics include one of consumption, pause, and reset.

[0010] Preferably, the remaining humidity clock value for each sub-batch is calculated recursively based on the clock calculation sequence, including: Initialize the cumulative consumption of the humidity clock for any sub-batch to zero; The cumulative consumption of the humidity clock is updated recursively according to the sequence of event intervals: when the humidity clock semantics are consumption, the current value is the ratio of the cumulative consumption of the humidity clock in the previous interval to the duration of the current interval and the allowed exposure time; when the humidity clock semantics are paused, the cumulative consumption of the humidity clock in the previous interval is kept as the current value; when the humidity clock semantics are reset, the cumulative consumption of the humidity clock in the current interval is set to zero. After completing the recursion of all intervals, the remaining humidity clock quantity of the sub-batch is obtained by subtracting the final cumulative humidity clock consumption from one.

[0011] Preferably, based on the remaining humidity clock set of the order sub-batch, the conservative deliverable status, clock fork amount, and dominant sub-batch of each lifecycle node are extracted, including: Extract the sub-batch sets corresponding to the sales order at each lifecycle node of the sales order; Query the set of remaining humidity clocks for each order sub-batch, and extract the minimum value of the remaining humidity clocks in the sub-batch set as the conservative deliverable status. Calculate the difference between the maximum and minimum values ​​of the remaining humidity clock values ​​in the sub-batch set, and use this as the clock bifurcation value; The sub-batch that produces the minimum residual humidity clock value is identified as the dominant sub-batch.

[0012] Preferably, the path fork status is determined based on the clock fork amount in the order node status group, and the path where the dominant sub-batch is located is taken as the main path, including: Construct a lifecycle timeline for sales orders in chronological order; For any lifecycle node, when the corresponding clock fork amount is equal to zero, the lifecycle node is rendered and displayed as a single-path state on the lifecycle timeline; When the corresponding clock fork value is greater than zero, the lifecycle node is rendered as a fork path on the lifecycle timeline, and the path where the dominant sub-batch is located is highlighted as the main path.

[0013] Preferably, generating order visualization tracking results includes: Extract the order node status group corresponding to the lifecycle node of the current business; The conservative deliverable status, clock fork amount, and main path of the dominant sub-batch recorded in the order node status group are displayed as the visual tracking results of the current sales order.

[0014] To address the aforementioned problems, this invention also provides a chip sales order lifecycle visualization tracking system, comprising: The trajectory segmentation module is used to obtain the order events and packaging identifiers of sales orders. Based on the packaging identifiers, chips that originate from the same original moisture-proof bag and have the same part number and traceability batch number are identified as multiple sub-batches, and the event interval sequence of each sub-batch is extracted. The semantic parsing module is used to query the humidity clock semantics of each interval through a knowledge graph based on the order event information corresponding to the event interval sequence, and determine the allowable exposure time and duration of each interval by combining the packaging identifier, and generate the clock calculation sequence for each sub-batch. The clock reconstruction module is used to recursively calculate the remaining humidity clock amount of each sub-batch based on the clock calculation sequence, and summarize and generate a set of remaining humidity clocks for each order sub-batch. The state modeling module is used to extract the conservative deliverable status, clock fork amount and dominant sub-batch of each lifecycle node based on the remaining humidity clock set of the order sub-batch, and to construct the order node state group. The visualization rendering module is used to determine the path fork status based on the clock fork amount in the order node status group, and to generate order visualization tracking results with the path where the dominant sub-batch is located as the main path.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention synchronously acquires order events and packaging identifiers throughout the entire process of sales orders, including order creation, warehouse locking, unpacking and picking, resealing, secure storage, re-shipment, logistics handover, and signing. It divides chips originating from the same original moisture-proof bag and sharing the same part number and traceability batch number into multiple sub-batches. Furthermore, it uses a knowledge graph to determine the humidity clock semantics of each event interval, generating a clock calculation sequence for each sub-batch and recursively calculating the remaining humidity clock quantity. This transforms the exposure history changes of humidity-sensitive chips during unpacking and batch shipment into continuously calculable and dynamically trackable quantitative results, thereby achieving precise identification of the true deliverable status of sub-batches and improving the authenticity and data consistency of the status throughout the entire lifecycle tracking process of chip sales orders.

[0016] 2. This invention constructs a set of remaining humidity clocks for each order sub-batch, further extracts the conservative deliverable status, clock fork amount, and dominant sub-batch of each lifecycle node, and constructs an order node status group accordingly. During the order visualization tracking stage, the path fork status is determined based on the clock fork amount, and the path where the dominant sub-batch is located is displayed as the main path. This can accurately reflect the status differentiation of different sub-batch under the same sales order due to differences in exposure history, highlight the core path that determines the order fulfillment risk, and avoid the overall order progress display from obscuring local high-risk sub-batch. This improves the ability of the chip sales order visualization tracking results to express the most unfavorable physical delivery status and the targeting of fulfillment management. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a method for visually tracking the entire lifecycle of chip sales orders, provided in an embodiment of the present invention. Figure 2 This is a functional block diagram of a chip sales order full lifecycle visualization tracking system provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Example: This example provides a method for visually tracking the entire lifecycle of chip sales orders. See [link / reference]. Figure 1 Specifically, it includes the following steps: S1. Obtain the order events and packaging identifiers of the sales orders. Based on the packaging identifiers, identify chips that originate from the same original moisture-proof bag and have the same part number and traceability batch number as multiple sub-batches, and extract the event interval sequence of each sub-batch. S2. Based on the order event information corresponding to the event interval sequence, query the humidity clock semantics of each interval through the constructed knowledge graph, and determine the allowable exposure time and duration of each interval in combination with the packaging identifier, and generate the clock calculation sequence for each sub-batch. S3. Calculate the remaining humidity clock amount for each sub-batch recursively based on the clock calculation sequence, and summarize to generate a set of remaining humidity clocks for each order sub-batch; S4. Based on the remaining humidity clock set of the order sub-batch, extract the conservative deliverable status, clock fork amount and dominant sub-batch of each lifecycle node, and construct the order node status group. S5. Determine the path fork status based on the clock fork amount in the order node status group, and use the path where the dominant sub-batch is located as the main path to generate order visualization tracking results.

[0020] In an embodiment of the present invention, the order event and packaging identifier of the sales order are obtained. Based on the packaging identifier, chips originating from the same original moisture-proof bag and having the same part number and traceability batch number are identified as multiple sub-batches. The event interval sequence of each sub-batch is extracted, including: Real-time data integration and synchronization are achieved through the deployed order management system, warehouse management system, and production traceability system to obtain order events and corresponding packaging identifiers throughout the entire lifecycle of the target sales order. The order management system collects and stores, in chronological order, the following events corresponding to the sales order: order creation event, warehouse locking event, unpacking and picking event, resealing event, secure storage event, re-outbound event, logistics handover event, and receipt event. The order creation event refers to the business event where the chip sales company receives a customer's purchase request in the order management system and generates a corresponding sales order record, marking the beginning of the order fulfillment process for the target chip. The warehouse locking event refers to the business event where the warehouse management system reserves and locks chip inventory resources that meet the order conditions from existing inventory based on the fulfillment requirements of the sales order, establishing the correspondence between the sales order and the specific inventory object. The unpacking and picking event refers to the business event where the locked original moisture-proof bag or corresponding packaging unit is opened, and the target chip quantity is extracted according to the sales order requirements; this is the starting point of the moisture-sensitive chip exposure process. The resealing event refers to the process of resealing the remaining chips that have not been exposed for further use after unpacking and picking. The business events for packaging and sealing represent the process of a sub-batch ending its current exposure state and entering a sealed state. The safe storage event refers to the business event where a sub-batch of chips, after being resealed, is placed in an environment that meets the storage requirements for humidity-sensitive chips for continuous preservation, representing the continuous storage process of the chips under non-exposure conditions. The re-outbound event refers to the business event where a sub-batch of chips that has been resealed and stored is retrieved from inventory again during subsequent order fulfillment and enters a new shipping or processing flow, indicating that the sub-batch has re-entered the fulfillment execution state from the storage state. The logistics handover event refers to the business event where a sub-batch of chips, after completing in-warehouse operations, is formally transferred to the logistics carrier or transportation link, marking that the chips have left the enterprise's internal warehousing control and entered the external delivery process. The receipt event refers to the business event where the customer or the customer's designated recipient completes the receipt confirmation of the target chip delivery, serving as the termination node of the sales order fulfillment loop, representing that the chips corresponding to the order have been delivered. Each order event synchronously records the precise timestamp of the event trigger, the operator number of the event execution, the associated warehouse location code, and the quantity of materials handled by the event.

[0021] The warehouse management system synchronously collects and stores packaging identifiers associated with each order event. These identifiers include the original moisture-proof bag identifier, part number, traceability batch number, and humidity sensitivity level identifier. The original moisture-proof bag identifier is a globally unique identification code assigned to the corresponding moisture-proof packaging when the chip leaves the factory. The part number is the standardized material code for the corresponding chip model. The traceability batch number is the unique traceability code for the corresponding chip production batch. The humidity sensitivity level identifier is the humidity sensitivity level classification code for the corresponding chip. Upon receiving a trigger signal for an unpacking and picking event, using the original moisture-proof bag identifier as the core index, chips with the same original moisture-proof bag identifier and consistent part number and traceability batch number are divided into sub-batches according to their split order flow and operation path. Chips flowing to the same order fulfillment path and experiencing consistent consecutive order events are identified as the same sub-batch. Chips split from the same original moisture-proof bag and flowing to different order paths are designated as independent sub-batches. Each sub-batch is synchronously associated with its corresponding original moisture-proof bag identifier, part number, traceability batch number, and humidity sensitivity level identifier. A unique sub-batch tracking code is assigned to each sub-batch, and a permanent mapping relationship is established between the sub-batch tracking code and the original moisture-proof bag identifier. For each sub-batch, using the sub-batch tracking code as an index, all continuous order events experienced by that sub-batch from the first unpacking of the original moisture-proof bag are extracted in chronological order of timestamps. The continuous operation process between two adjacent state switching nodes is defined as an independent event interval. The trigger condition for a state switching node is the system receiving a completion feedback signal for the corresponding order event. Each time a completion feedback signal for an order event is received, a state node switch is triggered, synchronously ending the previous event interval and starting a new event interval. This ensures that each event interval contains only one continuous and stable business state, with no cross-state interval merging. All event intervals are sorted in chronological order to form the complete event interval sequence corresponding to that sub-batch. Each event interval synchronously records the start timestamp, end timestamp, order event type corresponding to the interval, operation information associated with the interval, and sub-batch tracking code corresponding to the interval. The event interval sequence corresponding to each sub-batch is bound and stored with the packaging identifier of the sub-batch, serving as the basic data source for subsequent humidity clock semantic determination and remaining humidity clock quantity calculation.

[0022] In an embodiment of the present invention, based on the order event information corresponding to the event interval sequence, the humidity clock semantics of each interval are queried through a constructed knowledge graph, and the allowable exposure time and duration of each interval are determined in conjunction with the packaging identifier, generating a clock calculation sequence for each sub-batch, including: A knowledge graph of order humidity clocks is constructed. Entities in the knowledge graph include sales orders, original moisture-proof bags, sub-batches, and order events. Relationships in the knowledge graph include unpacking, resealing, and safe storage. The sales order entity is configured with order number, order creation time, and customer code attributes. The original moisture-proof bag entity is configured with original moisture-proof bag identification, part number, traceability batch number, humidity sensitivity level identification, and factory sealing time attributes. The sub-batch entity is configured with sub-batch tracking code, associated original moisture-proof bag identification, part number, traceability batch number, and chip quantity attributes. The order event entity is configured with event type, event trigger timestamp, event execution subject, and event-associated storage location information attributes. The unpacking relationship represents the association between sub-batch entities and unpacking-type order event entities, indicating that the sub-batch has undergone an unpacking operation. The resealing relationship represents the association between sub-batch entities and resealing-type order event entities, indicating that the sub-batch has completed a resealing operation compliant with industry storage standards. The safe storage relationship represents the association between sub-batch entities and safe storage-type order event entities, indicating that the sub-batch has been in a low-humidity safe storage environment compliant with humidity-sensitive chip storage requirements throughout the entire process. It should be noted that, to ensure the repeatability of the safe storage status determination, a dual verification method using event records and environmental records is employed to confirm whether any event interval meets the safe storage status. Specifically, firstly, it is verified whether a resealing completion event exists within the event interval, and whether the sub-batch tracking code and original moisture-proof bag identification corresponding to the resealing completion event are consistent with the current event interval. Based on this, the environmental monitoring records and storage equipment operation records of the corresponding storage location are further verified to confirm that, within the start and end time range of the event interval, the current sub-batch is consistently within the safe storage location or safe storage equipment bound to it, and that there are no repackaging events, removal from the safe storage location events, re-sorting events, or interruption events that would cause the packaging status to be re-exposed within this time range. Only when all three conditions—resealing completion event, continuous safe storage records, and no exposure interruption events—are simultaneously met is the event interval marked as being in a safe storage state, and the corresponding humidity clock semantics determined to be paused. If any condition is not met, the event interval is not determined to be paused, but rather proceeds to the subsequent consumption determination process. This determination method ensures that the pause semantics directly correspond to the objective fact that the sub-batch remains in a non-exposed state after compliance resealing, avoiding judgment bias caused by semantic inference based solely on a single business event name.

[0023] For each generated event interval sequence corresponding to a sub-batch, each independent event interval is used as the smallest query unit to extract the corresponding order event, the associated original moisture-proof bag identifier, the sub-batch tracking code, and the safe storage status throughout the interval. The extracted information is then precisely matched with the entity attributes in the knowledge graph to locate the corresponding sub-batch entity, original moisture-proof bag entity, and order event entity. Based on the located entities, a full-path relationship traversal query is performed in the knowledge graph to obtain all valid associations between the sub-batch entity and each associated order event entity within the interval. Based on semantic judgment rules, the relation query results are matched to determine the humidity clock semantics of the corresponding interval. The humidity clock semantics include one of consumption, pause, and reset. Furthermore, only one humidity clock semantic is output for each event interval. To avoid semantic uncertainty caused by multiple relations hitting the same interval at the same time, a semantic judgment priority is pre-set, and the judgment is performed in the order of reset over pause, and pause over consumption. Specifically, when a valid relation that supports reset semantics and a valid relation that supports pause semantics or consumption semantics are found in the same event interval at the same time, the reset semantics are output first. When the judgment conditions of the reset semantics are not met in the same event interval but the conditions of the pause semantics and consumption semantics are met at the same time, the pause semantics are output first. The consumption semantics are output only when the current event interval does not meet the reset semantics and the pause semantics. For event intervals where no valid relation is found or the relation information is incomplete, the event interval is judged as a semantic consumption interval by default and marked as an interval to be reviewed, so as to ensure that the humidity clock calculation results always have single value and continuity. By setting a unique semantic output mechanism and a fixed priority order, it can be ensured that the knowledge graph query results can directly enter the subsequent clock calculation sequence, avoiding the recursion interruption caused by multiple relation concurrency or relation missingness. When the valid relationship found is that a sub-batch entity is associated with an order event entity of the unpacking type through an unpacking relationship, and no valid resealing relationship or safe storage relationship is found throughout the interval, the humidity clock semantics for that interval are determined to be consumed. When the valid relationship found is that a sub-batch entity is associated with an order event entity of the resealing type through an unsealing relationship, and the interval is continuously associated with an order event entity of the safe storage type through a safe storage relationship, the humidity clock semantics for that interval are determined to be paused. When the valid relationship found is that a sub-batch entity is associated with a drying event entity and a resealing event entity that conform to the IPC / JEDEC J-STD-033 industry standard, and compliant drying, dehumidification, and vacuum resealing operations are completed within the interval, the humidity clock semantics for that interval are determined to be reset. It should be further noted that the determination of a reset is not based solely on the existence of the two event names, drying event and resealing event, but rather on a completion verification mechanism for the reset semantics.Specifically, using the sub-batch tracking code as an index, the drying operation records, drying completion feedback records, resealing operation records, and resealing completion feedback records within the current event interval are sequentially verified to confirm that the corresponding sub-batch has completed drying, drying completion confirmation, resealing, and resealing completion confirmation in sequence, and that the above records are consecutively closed in time sequence, consistent in object identifier, and have no re-exposure events within the interval. Only when all the above completion checks pass is the event interval considered to satisfy the reset semantics; if a drying event or resealing event exists, but any completion feedback record is missing, the object identifier is inconsistent, the time sequence is not closed, or a re-exposure event exists within the interval, then the reset semantics are not output, and instead, the process is transferred to the pause semantics or consumption semantics determination according to the aforementioned semantic priority rules.

[0024] It should be noted that humidity clock semantics refers to the meaning marker of the state of a sub-batch within a certain event interval regarding its humidity-sensitive exposure process. It is used to characterize whether the humidity clock of the sub-batch will continue to be consumed, remain unchanged, or return to its initial state within that event interval. In this embodiment, humidity clock semantics includes three types: consumption, pause, and reset. Consumption indicates that the sub-batch is in an actual exposure state within the interval, pause indicates that the sub-batch is in a compliant resealing and safe storage state within the interval, and reset indicates that the existing cumulative exposure impact is cleared after the sub-batch completes compliant drying, dehumidification, and resealing within the interval. The role of humidity clock semantics is to convert the order event interval into a state basis that can participate in recursive calculations, so that the calculation of the remaining humidity clock quantity has a clear business meaning and physical correspondence.

[0025] After semantic determination, the duration and allowable exposure time of each event interval are calculated. The formula for calculating the duration of the interval is as follows:

[0026] in The duration of the current event interval. This is the end timestamp of the current event interval. The starting timestamp of the current event interval is used. The core logic is that the duration is the continuous dwell time of the sub-batch in the corresponding state within this interval, directly determining the consumption or maintenance range of the humidity clock. The difference between the interval's ending timestamp and the starting timestamp is used for calculation, which accurately covers the dwell period of the sub-batch in the corresponding state, avoiding clock calculation deviations caused by missed or repeated time points. The formula for calculating the allowable exposure time of the interval is:

[0027] in This represents the allowed exposure duration corresponding to the current event interval. This refers to the moisture sensitivity rating corresponding to the original moisture-proof bag label associated with the current sub-batch. Based on the IPC / JEDEC J-STD-033 industry standard for humidity sensitivity rating labeling The corresponding standard allows for a total exposure time; among them, the allowable exposure time of the humidity-sensitive chip is uniquely determined by its own humidity sensitivity level. All sub-batch split from the same original moisture-proof bag have the same humidity sensitivity level. Using the allowable total exposure time of the corresponding humidity sensitivity level specified by the industry general standard as the calculation benchmark can ensure the compliance, consistency and industry universality of humidity clock calculation, and avoid the deviation in deliverability judgment caused by custom parameters.

[0028] For each sub-batch, the humidity clock semantics, allowable exposure time, and duration corresponding to each interval are bound one by one according to the chronological order of the event interval sequence, forming multiple sets of data arranged in chronological order. This data set is the clock calculation sequence corresponding to the sub-batch. Each set of data in the clock calculation sequence forms a one-to-one mapping relationship with each event interval in the event interval sequence. The generated clock calculation sequence is bound and stored with the sub-batch tracking code and the original moisture-proof bag identifier of the corresponding sub-batch, serving as the data source for the subsequent recursive calculation of the remaining humidity clock quantity.

[0029] It should be noted that the clock calculation sequence refers to an ordered set of data formed by binding the humidity clock semantics, allowed exposure duration, and duration of each event interval according to the chronological order of the sub-batch event interval sequence. This set of data serves as the direct input basis for recursively calculating the remaining humidity clock quantity of the sub-batch. Each set of data in the clock calculation sequence corresponds to a specific event interval, which can fully reflect the exposure status, allowed exposure boundary, and actual duration of the sub-batch in each continuous interval from the first unpacking to the current life cycle node. Thus, the cumulative consumption of humidity clock can be updated interval by interval along the time axis, and the remaining humidity clock quantity of the sub-batch can be obtained in the end.

[0030] In an embodiment of the present invention, the remaining humidity clock amount for each sub-batch is recursively calculated based on the clock calculation sequence, and a set of remaining humidity clocks for each order sub-batch is generated, including: For any sub-batch participating in the calculation, its cumulative humidity clock consumption is initialized to zero. The core basis of this initialization logic is that the humidity-sensitive chip inside the unopened original desiccant bag has no environmental exposure consumption before the first unpacking, which ensures that the calculation starting point of all sub-batches of the same origin is consistent, avoiding incomparable clock calculation results of sub-batches of the same origin due to initial value deviations. After initialization, according to the time sequence of the event interval corresponding to the sub-batch, each event interval is traversed from front to back, and the cumulative humidity clock consumption of the sub-batch is updated recursively interval by interval. The recursive calculation process follows a unified recursive formula:

[0031] in For the current number The cumulative consumption of the humidity clock after calculation for each event interval. This represents the cumulative humidity clock consumption after the calculation of the previous adjacent event interval in the current interval. This applies when the current interval is the first interval in the event interval sequence. The value is set to the initial zero value. This represents the duration of the current k-th event interval. This represents the allowed exposure time corresponding to the current k-th event interval. The absolute exposure time of the humidity-sensitive chip is converted into a consumption ratio relative to the total allowed exposure time. Chips of different humidity sensitivity levels are unified to a standardized consumption dimension of 0 to 1. This accurately characterizes the cumulative exposure level of the chip and allows for direct comparison of consumption levels between different sub-batches from the same source, providing a unified quantitative benchmark for subsequent humidity clock branching determination. To ensure the stability of the recursive calculation process, before updating the cumulative consumption of the current k-th event interval, the unique humidity clock semantics corresponding to that interval in the clock calculation sequence, as well as the allowed exposure time and duration bound to that interval, are read, and integrity checks are performed on these three data items. When all three data items exist and their correspondence is consistent, the calculation proceeds to the consumption, pause, or reset branch according to the corresponding semantics. When any data is missing or inconsistent with the current sub-batch tracking code, the current event interval is marked as an abnormal interval, and the automatic recursive calculation of that sub-batch is stopped. An abnormal message is also output to prevent erroneous data from directly entering the cumulative consumption calculation chain. During the interval-by-interval recursion process, when the humidity clock semantics corresponding to the current event interval are "consumption," the cumulative humidity clock consumption obtained from the previous interval recursion, plus the ratio of the current interval's duration to the allowed exposure duration, is used as the cumulative humidity clock consumption after the current interval recursion is completed. For humidity-sensitive chips in an exposed state, the humidity clock consumes linearly with the exposure time; the ratio of the exposure duration to the total allowed exposure duration represents the new consumption generated within that interval, quantifying the chip's deliverability degradation within that interval. When the humidity clock semantics corresponding to the current event interval are "pause," the cumulative humidity clock consumption obtained from the previous interval recursion is directly retained as the current interval's cumulative consumption. The cumulative consumption of the humidity clock after the inter-agency recursion is completed. For humidity-sensitive chips that are compliantly resealed and stored in a safe environment, the environmental exposure process is interrupted, the humidity clock no longer generates new consumption, and the cumulative consumption remains unchanged. This can accurately restore the chip's deliverability under compliant storage conditions. When the humidity clock semantics corresponding to the current event interval are reset, the cumulative consumption of the humidity clock after the current interval recursion is completed is directly set to zero. Its industry-standard drying, dehumidification, and vacuum resealing operations can eliminate the impact of the previous cumulative exposure of the humidity-sensitive chip, restoring its humidity clock to its initial factory state. The zeroing of the cumulative consumption can accurately characterize the restoration of chip deliverability.

[0032] After completing the recursive calculation of all event intervals in the sub-batch event interval sequence, obtain the final cumulative humidity clock consumption of the sub-batch. Then, calculate the remaining humidity clock quantity of the sub-batch using the remaining humidity clock quantity calculation formula. The remaining humidity clock quantity calculation formula is as follows:

[0033] in This represents the remaining humidity level in this sub-batch. This is the final cumulative humidity clock consumption obtained after completing all interval recursion for this sub-batch. When the calculated remaining humidity clock is less than or equal to zero, the remaining humidity clock of this sub-batch is directly set to zero, and the sub-batch is simultaneously marked as an undeliverable sub-batch that exceeds the allowed exposure time. In subsequent node status calculations and visualization, the remaining humidity clock of this sub-batch always participates in the calculation with a zero value. The remaining humidity clock represents the proportion of the chip's remaining exposeable window to the total allowed exposure window, which directly corresponds to the chip's remaining deliverability. The larger the value, the longer the remaining exposeable time and the stronger the deliverability. The smaller the value, the shorter the remaining exposeable time and the weaker the deliverability. This can intuitively and accurately quantify the true deliverability status of the sub-batch. It should be noted that the remaining humidity clock value refers to the proportion of continued exposure that a sub-batch retains relative to the total permissible exposure window for its corresponding humidity sensitivity level after experiencing all currently recorded event intervals. It is used to characterize the remaining deliverable capacity of the sub-batch at the current moment. This value is derived in reverse from the final cumulative consumption of the humidity clock value of the sub-batch. The larger the value, the more unused permissible exposure window the sub-batch has, and the greater the deliverable margin for subsequent fulfillment, storage, or shipment. The smaller the value, the more exposure window the sub-batch has consumed, the less space there is for continued exposure, and the more significant the adverse impact on the order fulfillment status. When this value drops to zero, it indicates that the permissible exposure window corresponding to the sub-batch has been exhausted, and it should be treated as a sub-batch that cannot be handled in a normal humidity-sensitive delivery state.

[0034] After calculating the remaining humidity clock values ​​for all sub-batches associated with the target sales order, the sub-batch tracking code for each sub-batch is bound to its corresponding remaining humidity clock value. The sub-batch tracking codes are then sorted and summarized in the index order to generate a set of remaining humidity clock values ​​for the order sub-batch that is uniquely bound to the sales order. This set covers the actual deliverable status data of all source sub-batches involved in the fulfillment of the sales order. This set is then associated with the order number of the corresponding sales order and stored as the data source for the construction of the subsequent order lifecycle node status group.

[0035] In an embodiment of the present invention, based on the remaining humidity clock set of the order sub-batch, the conservative deliverable status, clock fork amount, and dominant sub-batch of each lifecycle node are extracted to construct an order node status group, including: Define all lifecycle nodes for the entire sales order lifecycle. Each lifecycle node corresponds one-to-one with the previously collected order events. These include the order creation node, warehouse locking node, unpacking and picking node, resealing node, secure storage node, re-outbound node, logistics handover node, and receipt node. Each lifecycle node is configured with a unique node code, node triggering conditions, and node effective association rules. For a target sales order, traverse each lifecycle node in chronological order. For the currently traversed target lifecycle node, based on the effective association rules corresponding to that node, select all sub-batches with a valid fulfillment association with the sales order within the time frame of that node. A collection of these selected sub-batches constitutes the sub-batch set corresponding to the target lifecycle node. The specific rules for effective association are as follows: the order creation node corresponds to the sub-batch that has completed part number and batch number matching and belongs to the inventory to be allocated under the sales order; the inventory locking node corresponds to the sub-batch that has completed inventory locking and belongs to the fulfillment scope of the sales order; the unpacking and picking node corresponds to the sub-batch that has completed unpacking and belongs to the picking scope of the sales order; the resealing node corresponds to the sub-batch that has completed resealing and still belongs to the fulfillment scope of the sales order; the secure storage node corresponds to the sub-batch that is in compliant storage and still belongs to the fulfillment scope of the sales order; the re-outbound node corresponds to the sub-batch that has completed repacking and belongs to the second shipment scope of the sales order; the logistics handover node corresponds to the sub-batch that has completed outbound handover and belongs to the shipment scope of the sales order; and the receipt node corresponds to the sub-batch that has completed customer receipt and belongs to the fulfillment closure loop of the sales order. This ensures that the set of sub-batch corresponding to each lifecycle node accurately covers all valid sub-batch directly related to order fulfillment under that node.

[0036] After constructing the sub-batch set for the target lifecycle node, the remaining humidity clock set of the order sub-batch uniquely bound to the sales order is retrieved. Using the sub-batch tracking code as the unique matching index, the remaining humidity clock quantity corresponding to each sub-batch in the order sub-batch remaining humidity clock set is retrieved and extracted to form the remaining humidity clock quantity data group corresponding to the node. Based on this data group, the conservative deliverability status of the node is calculated. The formula for calculating the conservative deliverability status is:

[0037] in This represents the conservative deliverable state of the target lifecycle node. to Within the sub-batch set of this node The remaining humidity clock value corresponding to each sub-batch To minimize the value of the function, the deliverability of a humidity-sensitive chip sales order is determined by the worst-performing sub-batch within that order. Even if other sub-batches within the same order have sufficient remaining humidity clock data, if any sub-batch has insufficient remaining humidity clock data, the order faces the risk of delivery delays and humidity-sensitive chip failure. Using the minimum remaining humidity clock data of the sub-batch as a conservative deliverable state avoids the risk of aggregated display masking and ensures that the order node status matches the worst-case deliverable state of the physical product. The clock fork amount at that node is calculated synchronously using the following formula:

[0038] in The clock fork amount for the target lifecycle node. To find the maximum value function, to Consistent with the parameter definitions in the conservative deliverable status calculation formula, the original sub-batches of the same source bag have the same initial residual humidity clock value. The difference between the maximum and minimum values ​​of their residual humidity clock values ​​can directly quantify the degree of humidity clock bifurcation caused by different exposure histories of the same source sub-batches. A difference of zero indicates that all sub-batches have the same exposure history and no bifurcation. The larger the difference, the greater the difference in exposure history between sub-batches, the more serious the humidity clock bifurcation, and the more obvious the differentiation of deliverable status. This realizes the quantitative characterization of the humidity clock bifurcation phenomenon of sub-batches with the same bag and the same label.

[0039] It should be noted that the conservative deliverable status refers to the current deliverable status of an order at a certain lifecycle node, determined by the sub-batch with the smallest remaining humidity clock value among all sub-batches corresponding to the target sales order. It is used to characterize the actual delivery capability of the order at that node after being evaluated according to the worst-case physical condition. Since insufficient remaining exposure window for any sub-batch under the same order may lead to overall fulfillment quality risk, the state corresponding to the smallest remaining humidity clock value is used as the conservative deliverable status to avoid the average display masking local high-risk sub-batches, and to ensure that the order node status always remains consistent with the actual deliverable situation of the worst-case sub-batch. Clock bifurcation refers to the degree of dispersion difference among the remaining humidity clock values ​​of all sub-batches corresponding to the target sales order at a certain lifecycle node. It is used to characterize the degree of humidity clock differentiation caused by sub-batches from the same source undergoing different unpacking, resealing, safe storage, and re-shipment paths. In this embodiment, the clock bifurcation is obtained by the difference between the maximum and minimum values ​​of the remaining humidity clock values ​​of each sub-batch at that node. The larger the value, the more obvious the difference in the exposure history of each sub-batch under the same order, the more serious the humidity clock differentiation, and the less suitable it is to use a single path to simply display the order status. When the value is equal to zero, it means that the remaining humidity clock values ​​of each sub-batch at that node are consistent, and there is no bifurcation phenomenon.

[0040] The sub-batch with the minimum remaining humidity clock value is located from the sub-batch set and designated as the dominant sub-batch for the target lifecycle node. When multiple sub-batches at the same node have the same minimum remaining humidity clock value, they are sorted by the number of chips in the sub-batch from largest to smallest, and the sub-batch with the most chips and the most unfavorable chip count is designated as the dominant sub-batch. If the number of chips is the same, they are sorted by the order of their first unpacking time, and the sub-batch with the earlier first unpacking time is designated as the dominant sub-batch. The core basis of this determination logic is that the remaining humidity clock value of the sub-batch determines the conservative deliverable status of the entire order node, is the core source of order fulfillment risk at this node, and is also the core path that needs to be highlighted in subsequent visualization tracking, providing a clear risk indication for order fulfillment management. After completing all calculations and determinations of the conservative deliverable status, clock fork amount, and dominant sub-batch for the target lifecycle node, these three data items are bound one by one to the node code and node name of the node to construct the order node status group corresponding to the target lifecycle node. After constructing the order node status groups for all lifecycle nodes in the entire lifecycle of the sales order in chronological order, all order node status groups are arranged and summarized in chronological order of node, and associated with the order number of the sales order for storage, serving as the data source for generating subsequent order lifecycle visualization tracking results.

[0041] In an embodiment of the present invention, the path fork status is determined based on the clock fork amount in the order node status group, and the path where the dominant sub-batch is located is taken as the main path to generate an order visualization tracking result, including: The sales order lifecycle timeline is constructed chronologically. The timeline uses a two-dimensional coordinate system for rendering, with the horizontal axis representing time and the vertical axis representing the fulfillment progress. The horizontal axis arrangement of each lifecycle node within the coordinate system is determined by the node coordinate calculation formula:

[0042] in This represents the coordinate value of the nth lifecycle node on the horizontal axis of the timeline. This is the starting coordinate reference value for the horizontal axis of the time axis. This is the time difference between the trigger timestamp of the nth lifecycle node and the order creation timestamp. This serves as the time scale conversion factor for the time axis. Using the order creation time as the starting point of the time axis, the nodes are arranged in chronological order according to their actual trigger times. This ensures the time axis accurately reflects the actual time progress of order fulfillment, avoiding discrepancies between the visualization results and the actual fulfillment process caused by disordered node order. Based on the calculated node coordinates, the order creation node, warehouse locking node, unpacking and picking node, resealing node, secure storage node, re-outbound node, logistics handover node, and receipt node of the target sales order are arranged sequentially on the lifecycle time axis. Each lifecycle node is linked to its corresponding order node status group in real-time data binding.

[0043] For any lifecycle node on the lifecycle timeline, the clock fork value recorded in the order node status group bound to that node is retrieved for determination. When the corresponding clock fork value is zero, the lifecycle node is rendered as a single-path state on the lifecycle timeline. Specifically, a single continuous fulfillment path line connects the node to the previous adjacent lifecycle node. The path line is simultaneously marked with the corresponding conservative deliverable status value. The color of the path line is adjusted according to the conservative deliverable status value range: green for values ​​greater than or equal to 0.7, yellow for values ​​between 0.3 and 0.7, and red for values ​​less than 0.3, ensuring that the visualization results can intuitively reflect the true deliverable status of the order. When the corresponding clock fork value is greater than zero, the lifecycle node is displayed as a single-path state on the lifecycle timeline. The rendering on the timeline shows a branching path state. Specifically, starting from the previous adjacent lifecycle node, parallel path branches are generated with the same number of sub-batches as the sub-batch set corresponding to that node. Each path branch corresponds to an independent sub-batch. The remaining humidity clock value of the corresponding sub-batch is simultaneously marked on each path branch. At the same time, the path branch containing the dominant sub-batch recorded in the order node status group bound to that node is determined as the main path. The main path is highlighted with a bold line and filled with a color corresponding to the conservative deliverable status value of that node. The conservative deliverable status value and clock branching value corresponding to that node are simultaneously marked on the main path line to ensure that the visualization results can clearly distinguish the different fulfillment paths and deliverable status differences of sub-batches from the same source, highlighting the core risk paths that determine the overall deliverability of the order.

[0044] Real-time monitoring of the fulfillment progress and business operation triggering status of target sales orders. Based on the event type and trigger timestamp of the current business operation, the system locates and matches the corresponding lifecycle node. When a business operator triggers an unpacking and picking operation on the warehouse management terminal, the system simultaneously collects the trigger timestamp and event type of the operation, positioning the unpacking and picking node as the lifecycle node corresponding to the current business. Simultaneously, the system retrieves the complete order node status group bound to this lifecycle node. The system then compares the conservative deliverable status, clock fork amount, and main path of the dominant sub-batch recorded in the retrieved order node status group with the data from the completed full node rendering. The lifecycle timeline integrates data to provide a visual tracking result for current sales orders. This result is simultaneously displayed on the order fulfillment management dashboard, warehouse management terminal, sales business terminal, and customer query interface. The displayed content also includes the complete event interval sequence and remaining humidity clock quantity details for the main path corresponding to the main sub-batch, the remaining humidity clock quantity comparison data for the same source sub-batch, and the fulfillment time progress data for the entire order lifecycle. This ensures that the visual tracking result for the entire process is consistent with the most unfavorable deliverable state of the chip corresponding to the order, realizing visual tracking of the entire lifecycle of chip sales orders from order creation to receipt.

[0045] like Figure 2 The diagram shown is a functional block diagram of a chip sales order full lifecycle visualization tracking method provided by an embodiment of the present invention.

[0046] In this embodiment, the functions of each module / unit are as follows: The trajectory segmentation module is used to obtain the order events and packaging identifiers of sales orders. Based on the packaging identifiers, chips that originate from the same original moisture-proof bag and have the same part number and traceability batch number are identified as multiple sub-batches, and the event interval sequence of each sub-batch is extracted. The semantic parsing module is used to query the humidity clock semantics of each interval through a knowledge graph based on the order event information corresponding to the event interval sequence, and determine the allowable exposure time and duration of each interval by combining the packaging identifier, and generate the clock calculation sequence for each sub-batch. The clock reconstruction module is used to recursively calculate the remaining humidity clock amount of each sub-batch based on the clock calculation sequence, and summarize and generate a set of remaining humidity clocks for each order sub-batch. The state modeling module is used to extract the conservative deliverable status, clock fork amount and dominant sub-batch of each lifecycle node based on the remaining humidity clock set of the order sub-batch, and to construct the order node state group. The visualization rendering module is used to determine the path fork status based on the clock fork amount in the order node status group, and to generate order visualization tracking results with the path where the dominant sub-batch is located as the main path.

[0047] 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 method for visually tracking the entire lifecycle of chip sales orders, characterized in that, Includes the following steps: S1. Obtain the order events and packaging identifiers of the sales orders. Based on the packaging identifiers, identify chips that originate from the same original moisture-proof bag and have the same part number and traceability batch number as multiple sub-batches, and extract the event interval sequence of each sub-batch. S2. Based on the order event information corresponding to the event interval sequence, query the humidity clock semantics of each interval through the knowledge graph, combine the packaging label to determine the allowable exposure time and duration of each interval, and generate the clock calculation sequence for each sub-batch. S3. Calculate the remaining humidity clock amount for each sub-batch recursively based on the clock calculation sequence, and summarize to generate a set of remaining humidity clocks for each order sub-batch; S4. Based on the remaining humidity clock set of the order sub-batch, extract the conservative deliverable status, clock fork amount and dominant sub-batch of each lifecycle node, and construct the order node status group. S5. Determine the path fork status based on the clock fork amount in the order node status group, and use the path where the dominant sub-batch is located as the main path to generate order visualization tracking results.

2. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, The order events include order creation events, warehouse locking events, unpacking and picking events, resealing events, safe storage events, re-outbound events, logistics handover events, and receipt events; the packaging labels include original moisture-proof bag labels, part numbers, traceability batch numbers, and moisture sensitivity level labels.

3. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, Extract the event interval sequence for each sub-batch, including: Chips with the same original moisture-proof bag markings and the same corresponding part number and traceability batch number are identified as belonging to the same sub-batch; Extract the consecutive order events experienced by each sub-batch in chronological order to form an event interval sequence.

4. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, The entities in the knowledge graph include sales orders, original moisture-proof bags, sub-batch and order events, and the relationships in the knowledge graph include unpacking, resealing and safe storage.

5. The method for visually tracking the entire lifecycle of chip sales orders according to claim 4, characterized in that, The semantics of humidity clocks for each interval are queried using a knowledge graph, including: Based on the order events, original moisture-proof bags, sub-batch and safe storage status corresponding to each interval in the event interval sequence, perform relationship queries in the knowledge graph; The humidity clock semantics of the corresponding interval are determined based on the relation query results. The humidity clock semantics include one of consumption, pause, and reset.

6. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, The remaining humidity clock value for each sub-batch is calculated recursively based on the clock calculation sequence, including: Initialize the cumulative consumption of the humidity clock for any sub-batch to zero; The cumulative consumption of the humidity clock is updated recursively according to the sequence of event intervals: when the humidity clock semantics are consumption, the current value is the ratio of the cumulative consumption of the humidity clock in the previous interval to the duration of the current interval and the allowed exposure time; when the humidity clock semantics are paused, the cumulative consumption of the humidity clock in the previous interval is kept as the current value; when the humidity clock semantics are reset, the cumulative consumption of the humidity clock in the current interval is set to zero. After completing the recursion of all intervals, the remaining humidity clock quantity of the sub-batch is obtained by subtracting the final cumulative humidity clock consumption from one.

7. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, Based on the remaining humidity clock set of the order sub-batch, extract the conservative deliverable status, clock fork amount, and dominant sub-batch for each lifecycle node, including: Extract the sub-batch sets corresponding to the sales order at each lifecycle node of the sales order; Query the set of remaining humidity clocks for each order sub-batch, and extract the minimum value of the remaining humidity clocks in the sub-batch set as the conservative deliverable status. Calculate the difference between the maximum and minimum values ​​of the remaining humidity clock values ​​in the sub-batch set, and use this as the clock bifurcation value; The sub-batch that produces the minimum residual humidity clock value is identified as the dominant sub-batch.

8. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, The path fork status is determined based on the clock fork value in the order node status group, and the path where the dominant sub-batch is located is taken as the main path, including: Construct a lifecycle timeline for sales orders in chronological order; For any lifecycle node, when the corresponding clock fork amount is equal to zero, the lifecycle node is rendered and displayed as a single-path state on the lifecycle timeline; When the corresponding clock fork value is greater than zero, the lifecycle node is rendered as a fork path on the lifecycle timeline, and the path where the dominant sub-batch is located is highlighted as the main path.

9. The method for visually tracking the entire lifecycle of chip sales orders according to claim 1, characterized in that, Generate order visualization tracking results, including: Extract the order node status group corresponding to the lifecycle node of the current business; The conservative deliverable status, clock fork amount, and main path of the dominant sub-batch recorded in the order node status group are displayed as the visual tracking results of the current sales order.

10. A chip sales order lifecycle visualization tracking system, used to execute the chip sales order lifecycle visualization tracking method according to any one of claims 1-9, characterized in that, include: The trajectory segmentation module is used to obtain the order events and packaging identifiers of sales orders. Based on the packaging identifiers, chips that originate from the same original moisture-proof bag and have the same part number and traceability batch number are identified as multiple sub-batches, and the event interval sequence of each sub-batch is extracted. The semantic parsing module is used to query the humidity clock semantics of each interval through a knowledge graph based on the order event information corresponding to the event interval sequence, and determine the allowable exposure time and duration of each interval by combining the packaging identifier, and generate the clock calculation sequence for each sub-batch. The clock reconstruction module is used to recursively calculate the remaining humidity clock amount of each sub-batch based on the clock calculation sequence, and summarize and generate a set of remaining humidity clocks for each order sub-batch. The state modeling module is used to extract the conservative deliverable status, clock fork amount and dominant sub-batch of each lifecycle node based on the remaining humidity clock set of the order sub-batch, and to construct the order node state group. The visualization rendering module is used to determine the path fork status based on the clock fork amount in the order node status group, and to generate order visualization tracking results with the path where the dominant sub-batch is located as the main path.

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