A multi-store order coordination management method and system based on order aggregation processing

Abstract

The application relates to the technical field of order management, in particular to a multi-store order collaborative management method and system based on order aggregation processing, which comprises the following steps: acquiring order records, extracting key time to construct an order node sequence, calculating the interval between adjacent nodes in the sequence and combining the sequence, combining inventory locking results to generate an order trigger vector, comparing the interval sequence of the vector with a preset threshold set, generating a processing judgment label, judging a flow path in combination with a process record and generating a delay processing mark, calling the mark to filter a delivery execution path to generate a collaborative management scheme. In the application, time sequence correlation is constructed by extracting key time points of multi-source order flow, trigger characteristics are generated by quantitatively analyzing the interval between sequence nodes in combination with state conversion, processing labels are accurately judged by executing interval threshold rule matching, the delivery path is locked by comprehensively considering global process records and delay marks, data source time sequence fragmentation and update delay are eliminated, inventory conflicts caused by static synchronization are avoided, and deep order collaboration distribution is realized.

Description

Technical Field

[0001] This invention relates to the field of order management technology, and in particular to a method and system for collaborative management of multi-store orders based on order aggregation processing. Background Technology

[0002] Order management technology primarily involves managing core aspects of order data generation, reception, splitting, merging, allocation, execution, and status updates during the commodity transaction process. It encompasses multi-platform order access, standardized order information processing, order and inventory data matching, shipping instruction generation, and order lifecycle tracking. It is widely used in e-commerce systems, supply chain management systems, and enterprise resource planning systems. By uniformly organizing the flow of order data across different business stages, it achieves centralized processing and business collaboration for orders from multiple sources. Traditional multi-store order collaborative management methods involve obtaining order information from multiple e-commerce platforms or stores by establishing interfaces for each platform, converting order data of different formats according to preset field rules, storing it in a unified database, splitting or merging orders based on product identifiers, allocating orders to different warehouses based on warehouse inventory records, generating shipping lists according to preset shipping rules, recording changes in order status (pending, allocated, shipped, etc.), and synchronizing order processing progress between different stores through shared data tables.

[0003] The existing system uses separate interfaces to acquire, convert, and store order data from multiple platforms into a database for product splitting and merging. In the warehouse allocation process, it is executed based on individual warehouse records, recorded through status fields, and synchronized between stores through a shared data table. Under concurrent transactions, the data flow between independent platforms lacks deep correlation control at the time sequence level. When crossing platforms, the shared data table has the risk of update delays and status conflicts. The data relies on fixed field rules for mechanical correspondence, resulting in serious time differences and execution deviations in the distribution of delivery instructions and inventory changes. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a multi-store order collaborative management method based on order aggregation processing, comprising the following steps: S1: Obtain order records through multi-platform order interfaces, extract order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, calculate and establish time sequence relationships according to time order, associate time nodes, and generate order node sequence; S2: Calculate and sort the intervals between adjacent time nodes in the order node sequence, combine the interval order, collect the inventory lock result field and perform a mark conversion, establish the correspondence between the inventory lock result field and the mark, and generate an order trigger vector; S3: Based on the order trigger vector comparison processing, a preset node interval threshold set is used to compare the order trigger vector interval order with the node interval threshold set item by item, construct comparison rules and generate order processing judgment labels; S4: Call the order processing determination label and combine it with the order process record in the order database to determine the order processing path, establish the correspondence between the order processing path and the order process record, and generate the order processing path identifier and the delayed processing mark; S5: Call the order processing path identifier and combine it with the delayed processing mark to filter the order delivery execution path, establish the correspondence between the order delivery execution path and the order processing path identifier, and generate a store order collaborative management solution.

[0005] As a further aspect of the present invention, the order node sequence includes timestamp records of order nodes, time span values ​​of stages, and a sequential list of process nodes; the order trigger vector includes a feature data matrix of node intervals, a mapping symbol of inventory lock status, and a determination identifier of trigger conditions; the order processing determination label includes a timeout warning level label, a normal flow confirmation label, and an abnormal bottleneck classification label; the order processing path identifier includes a main process execution route map, a backup branch jump pointer, and a node rollback control code; the delay processing marker includes a timeout warning level for order delays, a reason code for delayed delivery, and a determination certificate for timeout compensation; and the store order collaborative management scheme includes an optimized warehouse allocation strategy, an automatically matched logistics carrier directory, and a dynamically adjusted delivery priority queue.

[0006] As a further aspect of the present invention, the node interval threshold set refers to a pre-set set of benchmark data used to measure the allowable standard and maximum time difference between adjacent time nodes in the order processing process.

[0007] As a further aspect of the present invention, the construction of comparison rules refers to the specific logical conditions for comparing the time interval of the actual order node with a preset threshold item by item, thereby determining the order processing status and generating corresponding tags.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Retrieve order records, extract order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, uniformly convert and aggregate the execution timestamps by order identifier, and generate an order time field set; S102: Based on the order time field set, perform timestamp size comparison, verify the time sequence, construct a time sorting link and complete the order correction to obtain the time sequence chain; S103: Extract the node states according to the time sequence chain and perform serialization and arrangement to establish the mapping relationship between time nodes and states, and generate the order node sequence.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Obtain the order node sequence, extract the timestamp difference between adjacent nodes, perform item-by-item subtraction to form a time interval value sequence, arrange the interval values ​​according to the node order, and generate a time interval sequence; S202: Based on the time interval sequence, perform sequential index combination operation, concatenate adjacent intervals according to node position and construct an ordered combination structure, and at the same time collect the inventory lock result field, perform status value judgment and convert it into discrete marker value to obtain the interval combination structure; S203: Perform vector concatenation processing based on the interval combination structure and discrete marker values ​​to establish the mapping relationship between interval positions and marker values, construct vector sequences, and generate order trigger vectors.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Obtain a set of preset node interval thresholds, extract the node position index corresponding to the threshold and perform numerical verification processing to form a sequential arrangement structure consistent with the node sequence, and generate a threshold sequence structure; S302: Extract the interval sequence values ​​based on the order trigger vector, perform item-by-item size comparison operations according to the threshold sequence structure, record the relationship between the interval value and the threshold value at each position and perform Boolean value conversion to obtain the comparison result matrix; S303: Perform the prescribed construction process according to the comparison result matrix, map the Boolean value combination to the corresponding judgment category identifier, establish the association between the result matrix and the judgment identifier, and generate the order processing judgment label.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Obtain order process records from the order database, extract process node sequences and node status identifiers, perform process sequence verification and node index matching processing, form a process path structure, and generate a process path set; S402: Extract the judgment category identifier based on the order processing judgment label, perform path matching operation according to the process path set, compare the consistency between the judgment category identifier and the process path node status, record the matching position and construct the path mapping relationship to obtain the path mapping structure; S403: Perform path classification processing according to the path mapping structure, perform delay determination logic calculation based on the node matching position distribution, establish the association between path classification results and delay status identifier, and generate order processing path identifier and delay processing mark.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Obtain the order processing path identifier and the delay processing marker, extract the node sequence and delay status value corresponding to the path identifier, perform path identifier classification and delay status verification processing, form a path status set, and generate a path status structure; S502: Extract the path category and delay status according to the path status structure, perform a shipping execution path matching judgment, compare the path category with the preset shipping path rules item by item, filter the paths that meet the conditions and record the corresponding relationship to obtain a shipping path set. S503: Based on the set of shipping paths, perform path association processing, establish a mapping relationship between shipping execution paths and order processing path identifiers, and combine the combination and arrangement of delayed state execution paths to generate a store order collaborative management scheme.

[0013] A multi-store order collaborative management system based on order aggregation processing includes: The order node construction module is used to implement S1: obtain order records through multi-platform order interfaces, extract order creation time, payment completion time, inventory lock completion time and warehouse execution completion time, calculate and establish time sequence relationship according to time order, associate time nodes and generate order node sequence; The trigger vector generation module is used to implement S2: calculate and sort the intervals between adjacent time nodes in the order node sequence, combine the interval order, collect the inventory lock result field and perform a mark conversion, establish the correspondence between the inventory lock result field and the mark, and generate the order trigger vector; The order determination and analysis module is used to implement S3: based on the order trigger vector, comparison processing is performed, a preset set of node interval thresholds is used, the order of order trigger vector intervals is compared with the set of node interval thresholds item by item, comparison rules are constructed and order processing determination labels are generated; The processing path identification module is used to implement S4: calling the order processing judgment label and combining it with the order process record in the order database to determine the order processing path, establish the correspondence between the order processing path and the order process record, and generate the order processing path identifier and the delayed processing mark; The shipping collaboration decision module is used to implement S5: calling the order processing path identifier and combining it with the delayed processing flag, filtering the order shipping execution path, establishing the correspondence between the order shipping execution path and the order processing path identifier, and generating a store order collaboration management solution.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, key time nodes in the order creation and distribution process across multiple platforms are extracted to construct a time-series correlation sequence. The intervals between adjacent nodes in the sequence are quantitatively compared and recombined. A multi-dimensional trigger vector is generated by deeply integrating the label transformation of the inventory lock result. Based on a preset interval threshold set, the trigger vector is matched and judged item by item to generate processing labels. The actual flow path is determined by combining the system's global process records. The delay processing label is screened and the delivery execution path is determined. A path mapping generation management scheme is established to eliminate the time-series fragmentation and state update delay in the flow process of multiple data sources. This avoids complex inventory conflicts caused by simply relying on static synchronization and achieves deep collaboration and precise allocation control of orders from multiple stores. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a method for collaborative management of multi-store orders based on order aggregation processing, comprising the following steps: S1: Obtain order records through multi-platform order interfaces, extract order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, perform sequential calculations on order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, construct the time sequence relationship, establish the time correspondence relationship, and generate an order node sequence; S2: Calculate the interval of the order node sequence, obtain the interval order between adjacent time nodes, and combine the interval order. At the same time, collect the inventory lock result field and perform a label conversion to establish the correspondence between the inventory lock result field and the label, and generate the order trigger vector. S3: Based on the order trigger vector comparison processing, a preset set of node interval thresholds is used to compare the interval order in the order trigger vector with the set of node interval thresholds item by item, construct comparison rules, and generate order processing judgment labels; S4: Based on the order processing judgment tag, make judgment and process, call the order processing judgment tag and combine it with the order process record in the order database to judge the order processing path, establish the correspondence between the order processing path and the order process record, and generate the order processing path identifier and the delayed processing mark; S5: Based on the order processing path identifier, filter and process the order shipment execution path by calling the order processing path identifier and combining it with the delayed processing flag, establish the correspondence between the order shipment execution path and the order processing path identifier, and generate a store order collaborative management solution.

[0020] The order node sequence includes timestamp records of order nodes, time span values ​​of stages, and a sequential list of process nodes. The order trigger vector includes a feature data matrix of node intervals, a mapping symbol of inventory lock status, and a judgment flag for trigger conditions. The order processing judgment tags include timeout warning level tags, normal flow confirmation tags, and abnormal bottleneck classification tags. The order processing path identifier includes the main process execution route map, backup branch jump pointers, and node rollback control codes. The delay processing markers include the order delay time warning level, the reason code for delayed delivery, and the judgment certificate for timeout compensation. The store order collaborative management solution includes optimized warehouse allocation strategies, automatically matched logistics carrier directories, and dynamically adjusted shipping priority queues.

[0021] Please see Figure 2 The specific steps of S1 are as follows: S101: Retrieve order records, extract order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, uniformly convert and aggregate the execution timestamps by order identifier, and generate an order time field set; By establishing a direct communication link with the underlying distributed relational storage data table, a structured query language is executed to traverse and read the data, sequentially extracting the core fields contained in the original transaction record table. Specifically, the extracted data objects include the order unique identifier text, the relative time taken during order creation, payment completion, inventory locking, and warehouse execution completion. For the extracted relative time text data, a character offset-based parsing and carry-over operation is initiated. The text is then segmented according to the order of hours, minutes, seconds, and milliseconds, extracting the separated time values ​​and substituting them into the base origin conversion logic for multiplication. Finally, the data is summed and converted into an absolute millisecond-level timestamp calculated from the specified time origin. In a specific data quantification calculation example, taking the transaction record with the unique order identifier 987654321 as an example, the text content of the order creation stage of this record is read as 10 hours 00 minutes 00 seconds 000 milliseconds. At the same time, the starting base value of the absolute timestamp of this batch of orders on the same day is obtained from the global configuration table as 17000000000000. At this time, the hour parameter value of 10 is extracted from the time-consuming text, and it is multiplied with the specified hour-to-millisecond carry-over coefficient of 3600000 to calculate the millisecond value corresponding to the current hour span as 36000000. Then, the minute parameter value of 0 and the second parameter value of 0 are extracted, and after conversion, their corresponding millisecond values ​​are both equal to 0. Finally, the millisecond parameter value of 0 is extracted. After obtaining all the above decomposition parameters, the absolute timestamp starting base value of the day, 1700000000000, is added to the calculated millisecond value, 36000000, to calculate the absolute millisecond timestamp corresponding to the order creation stage as 1700036000000. Using the same parameter decomposition and carry-over accumulation logic, the relative time consumption text content of the next stage, payment completion stage, 10 hours 05 minutes 00 seconds 000 milliseconds, is extracted. The hour parameter 10 is extracted and multiplied by 3600000, which equals 36000000. The minute parameter 5 is extracted and multiplied by the minute-to-millisecond carry-over coefficient 60000, which equals 300000. The base value 1700000000000 is added to 36000000 and then to 300000, to calculate the timestamp corresponding to the payment completion stage as 1700036300000. The text content indicating the relative time taken for the inventory lock completion phase is 10 hours, 15 minutes, 00 seconds, and 000 milliseconds. Multiplying the minute parameter 15 by 60,000 equals 900,000. Adding the base value of 36,000,000 and then 900,000, the calculated inventory lock completion timestamp is 17,000,369,000,000. The text content indicating the relative time taken for the warehouse sub-execution completion phase is 10 hours, 20 minutes, and 00 seconds, and multiplying the minute parameter 20 by 60,000 equals 1,200,000, the calculated warehouse sub-execution completion timestamp is 17,000,372,000,000.After completing the calculation and derivation of all absolute timestamp values, a storage space of a specified width is allocated in a contiguous memory stack area. The order unique identifier 987654321 is used as the global index primary key of this storage space. The four discrete timestamp parameters generated above are then written into this space consecutively according to the absolute order of business triggering. Direct association binding of key-value pairs is performed, and the output is a set of order time fields exhibiting matrix arrangement characteristics. See Table 1 for the specific data aggregation layout and numerical attributes.

[0022] Table 1. Aggregated Records of Order Time Fields

[0023] As shown in Table 1, the relative time consumption text of the original stage is extracted and high-precision conversion is performed to generate timestamp sequence data containing absolutely continuous time scales. The above data is directly used as the basic data input source for all subsequent time flow anomaly judgment and sequence span comparison calculation.

[0024] S102: Based on the order time field set, perform timestamp size comparison, verify the time order, construct the time sorting link and complete the order correction to obtain the time order chain; Read the order time field collection object stored in the memory area, allocate two independent integer addressing cursors to build a two-pointer synchronous traversal structure, extract the timestamp values ​​of adjacent positions and perform relational algebra operations to verify that they are greater than or equal to the timestamp values. During the traversal, the preceding addressing cursor is fixed at sequence index position 0, and the following addressing cursor is fixed at sequence index position 1. The payment completion timestamp 1700036300000 is extracted from the following index position 1, and the order creation timestamp 1700036000000 is extracted from the preceding index position 0. A size judgment operation is performed to determine whether 1700036300000 is greater than or equal to 1700036000000. If the operation condition is true, a logical truth value result is obtained. Simultaneously, the index displacement of the two addressing cursors is increased, the following cursor is moved to index position 2, and the preceding cursor is moved to index position 1. The inventory lock completion timestamp 1700036900000 at position 2 is extracted and compared with the timestamp value at position 1 for a greater than or equal to condition. If the condition is true, a logical truth value result is obtained. Assuming that during the verification process at this node, due to congestion and retransmission in the underlying network channel, the timestamp data packets for the completion of the warehouse execution are out of order and their values ​​are decayed, the abnormal timestamp value extracted by the post cursor at index position 3 is 1700036800000, and the timestamp at extraction position 2 is 1700036900000. A relational algebra comparison is performed to determine that 1700036800000 is less than 1700036900000, and a logical false value is obtained, which immediately triggers the time node inversion processing procedure. According to the program instructions, the underlying message queue consumption log is accessed to retrieve the absolute delay time difference generated by the data packet transmission during the warehouse allocation stage of this order. The delay difference parameter recorded in the log is 400000. Then, the incorrectly extracted decay timestamp 1700036800000 is summed with this delay difference parameter 400000 to calculate and derive the warehouse allocation completion timestamp after error compensation correction as 1700037200000. After layer-by-layer verification and additive correction compensation operations, the adjacent data nodes are traversed and checked again from index position 0 to index position 3 to confirm that the absolute value of the timestamp at any later position in this sequence is strictly greater than the absolute value of the timestamp at its immediately preceding position. Under the premise that this is confirmed to be correct, a set of linked storage memory blocks are dynamically allocated, a singly linked list data structure is instantiated, the order creation timestamp is stored in the data field of the first node of the linked list and set as the first node, and the remaining three timestamp nodes are linked sequentially through their respective pointer fields using memory address assignment operations. Finally, the time-ordered chain with strict data increment constraints and reference association is assembled and output.

[0025] S103: Extract node states from the time sequence chain and perform serialization and arrangement, establish a mapping relationship between time nodes and states, and generate an order node sequence; Based on the head pointer address of the linked list, the system accesses the time-series chain output from the preceding steps. Following the reference pointers within each node, it performs a penetrating access. Upon entering the memory address space of each individual node, it extracts the built-in business feature description text used to record the original triggering operation. It then accesses the global business state hash mapping dictionary pre-loaded in the constant pool, performing a character-by-character matching operation to forcibly convert the complex feature description text into a fixed-length integer quantized status identifier. In the specific execution flow, it dereferences the data field of the first node in the linked list, obtaining the description text as the business start instruction. This is then retrieval and comparison in the hash mapping dictionary, quantizing and outputting the integer status identifier 101. Following the pointer, it accesses the second node in the linked list, extracting the description text as fund settlement and transfer, comparing and outputting the integer status identifier 102. It continues by accessing the third node, extracting the warehouse quota deduction, and outputting the integer status identifier 103. Finally, it accesses the fourth node, extracting the logistics distribution route, and outputting the integer status identifier 104. After completing the quantization mapping operation from all descriptive text to integer identifiers, a typical character object instance supporting high-frequency retrieval is constructed. The millisecond-level timestamp parameter carried by each node in the time sequence chain is set as an unalterable retrieval key name, and the previously extracted and converted integer status identifier parameter is set as the corresponding storage key value. Dynamic nested assembly is performed based on the native physical access order of the linked list. Substituting the data obtained from the previous operation, a structured key-value pair is formed with the key name 1700036000000 associated with value 101, 1700036300000 associated with value 102, 1700036900000 associated with value 103, and 1700037200000 associated with value 104. After unified serialization and encapsulation, this key-value pair is written bit-by-bit into a continuous cache list structure, outputting an order node sequence that retains the complete business flow topology.

[0026] Please see Figure 3 The specific steps of S2 are as follows: S201: Obtain the order node sequence, extract the timestamp difference between adjacent nodes, perform item-by-item subtraction to form a time interval value sequence, arrange the interval values ​​according to the node order, and generate a time interval sequence; Retrieve the order node sequence data temporarily stored in the cache list structure, and re-enable the linear traversal probing operation based on position index. Utilize relative coordinate deviation to lock the timestamp values ​​contained in two closely connected nodes within the sequence. Set the timestamp parameter in the later sequential node as the minuend, and the timestamp parameter in the immediately preceding sequential node as the subtrahend, strictly performing the mathematical subtraction operation between the two. Substitute existing data to deduce the detailed calculation trajectory: Extract the payment completion timestamp 1700036300000 of the second node and use it as the minuend; extract the order creation timestamp 1700036000000 of the first node and use it as the subtrahend; calculate 1700036300000 minus 1700036000000, obtaining the first time interval value existing between the first and last actions as 300000; the cursor advances synchronously, extracting the inventory lock timestamp 1700036900000 of the third node as the minuend, the second... Using the timestamp 1700036300000 of the node as the subtrahend, we calculate 1700036900000 minus 1700036300000, resulting in a second time interval value of 600000. Then, we execute the last cursor advance, extracting the sub-timestamp 1700037200000 of the fourth node as the minuend, and using the timestamp 1700036900000 of the third node as the subtrahend, we calculate 1700037200000 minus 1700036900000, resulting in a third time interval value of 300000. After three consecutive independent and non-overlapping subtraction operations, a new contiguous one-dimensional array memory region is allocated. Based on the physical order of the subtraction operations, the calculated pure numbers 300000, 600000, and 300000 are sequentially pushed into the coordinate address spaces corresponding to the 0th, 1st, and 2nd positions within the one-dimensional array, forming a time interval sequence that directly reflects the time consumption parameters of adjacent spans of each service.

[0027] S202: Based on the time interval sequence, perform sequential index combination operation, concatenate adjacent intervals according to node position and construct an ordered combination structure, and at the same time collect the inventory lock result field, perform status value judgment and convert it into discrete tag value to obtain the interval combination structure; For time interval sequence data, the built-in access function component with counting cursor is invoked. While accessing each pure numeric object of a time interval layer by layer, the relative integer array position coordinate index of the numeric object is extracted simultaneously. Using the underlying basic string concatenation method, the extracted integer coordinate index, a specific character underscore constant, and the time interval value containing time-consuming characteristics are concatenated to construct a composite text structure with strong positional attribute binding. Based on existing parameters, entity deduction is performed: accessing and retrieving coordinate index 0 with a corresponding interval value of 300000, performing text concatenation yields the character 0_300000; retrieving coordinate index 1 with a corresponding interval value of 600000, performing concatenation yields the character 1_600000; retrieving coordinate index 2 with a corresponding interval value of 300000, performing concatenation yields the character 2_300000; these three newly constructed character sets are then laid out in their original coordinate order to form an ordered composite structure. Simultaneously, a query command is initiated to the underlying order relationship table to retrieve the inventory details of the current target order. This retrieves specific Boolean feedback log content indicating whether inventory deduction was successful or not, and performs conditional matching verification logic on this content. The verification logic presupposes that when the received feedback indicates a confirmation of inventory deduction completion, an integer constant of 1 is thrown outwards as a discrete marker value; when a condition of insufficient inventory rejection or resource lock timeout is received, an integer constant of 0 is thrown outwards as a discrete marker value. Assuming that the feedback collected during this information capture process is a confirmation of inventory deduction completion, based on the pre-defined branch judgment, the discrete marker value for this order is directly determined to be 1. Finally, by calling the binning method of the aggregation container class, the three composite texts carrying location information and the generated integer discrete marker value of 1 are encapsulated and stacked into the same collection object's wrapping area, outputting an interval combination structure that covers time-series data and stage success / failure conclusion features.

[0028] S203: Perform vector concatenation processing based on the interval combination structure and discrete marker values, establish the mapping relationship between interval positions and marker values, construct vector sequences, and generate order trigger vectors; The obtained set of interval combination structures is decomposed by unpacking. A specialized data cleaning regular expression rule library is used to remove the integer coordinate index content previously appended to the composite text, as well as the character underscore constant used for concatenation, completely restoring the core pure time interval values ​​after removing irrelevant information. For the extracted group of time interval values ​​and the synchronously encapsulated integer discrete marker value 1, a data precision unification transformation process is initiated, performing a calculation to forcibly upcast the integer parameters to single-precision floating-point numbers. Specifically, the conversion process is as follows: the value 300000 is extracted and converted to a floating-point parameter 300000.0; the value 600000 is extracted and converted to a floating-point parameter 600000.0; the value 300000 at the end is extracted and converted to a floating-point parameter 300000.0; simultaneously, the single integer discrete marker value is processed, and the number 1 is forcibly converted to a floating-point parameter 1.0. After all data objects involved in the subsequent evaluation were converted to floating-point specifications, according to the pre-declared feature matrix arrangement principle, the three time interval floating-point parameters generated by the conversion were defined as the pre-input feature bits of the matrix sequence, and the state discrete label floating-point parameter was defined as the end-point feature bit of the matrix sequence. End-to-end seamless data splicing and loading was performed in the L1 cache associated with computer registers, facilitating the binding of interval span data and label result data. This resulted in a one-dimensional floating-point number set object with a fixed length of 4 and a single dimension. This object stored four data items: 300000.0, 600000.0, 300000.0, and 1.0, which was named the order trigger vector carrying historical anomaly investigation clues.

[0029] Please see Figure 4 The specific steps of S3 are as follows: S301: Obtain a set of preset node interval thresholds, extract the node position index corresponding to the threshold and perform numerical verification processing to form a sequential arrangement structure consistent with the node sequence, and generate a threshold sequence structure; The system submits structured data retrieval request messages to the external public parameter configuration distribution nodes to extract a set of preset node interval thresholds calculated through long-term offline batch processing. The underlying derivation logic for each preset value in this set is as follows: It extracts 500,000 historical orders that are in a normal, completed state, starting from the baseline time point. It separates the actual time span value of each order within this interval according to node intervals. It sums all 500,000 span values ​​and divides the sum by the total number 500,000 to obtain the average span value parameter for this interval. Next, it calculates the square of the difference between each actual span value and the average span value. It sums all the squares and divides the sum by 500,000, then performs a square root operation to obtain the standard deviation parameter. Finally, it adds twice the standard deviation parameter to the obtained average span value parameter, and uses this sum as the threshold baseline for this interval. Taking the first interval as an example, after performing the above mean and standard deviation calculations on 500,000 historical data points, the average time from order creation to payment is calculated to be 200,000, and the standard deviation is 50,000. Executing the mixed calculation rule of adding 200,000 to 50,000 multiplied by 2, the baseline threshold for the first interval is calculated to be 300,000. Continuing with the same offline calculation framework, the baseline threshold for the payment to lock-up interval is calculated to be 500,000, and the baseline threshold for the lock-up to sub-warehouse interval is calculated to be 300,000. After extracting these three independently generated baseline thresholds, the index indicators fixed in the configuration node data messages are used to assign them to their respective positions: 300,000 is associated with the 0th index, 500,000 with the 1st index, and the last 300,000 with the 2nd index. To avoid configuration pollution, parameter boundary current limiting verification is then performed to determine whether the three floating-point numbers mentioned above all fall within the limited range of 0 to 86,400,000. After all verifications pass, the numbers are loaded into a continuous one-dimensional linear list carrier in ascending order of the 0th, 1st, and 2nd bits, thereby constructing a threshold sequence structure with a total length completely equal to the aforementioned interval sequence and each bit having a baseline upper limit constraint. The internal data is represented as 300,000.0, 500,000.0, and 300,000.0.

[0030] S302: Extract the interval sequence values ​​based on the order trigger vector, perform item-by-item size comparison operations according to the threshold sequence structure, record the relationship between the interval value and the threshold value at each position and perform Boolean value conversion to obtain the comparison result matrix; The order trigger vector and threshold sequence structure are placed in the working area register. A synchronous moving read cursor is allocated to ensure that the cursor can simultaneously extract the actual consumption floating-point parameter and the reference baseline floating-point parameter at the same data coordinate position to perform a greater-than comparison. The moving read cursor is positioned at coordinate position 0, and the actual consumption parameter 300000.0 is retrieved from the order trigger vector, and the corresponding baseline upper limit parameter 300000.0 is retrieved from the threshold sequence structure. A comparison is performed to determine whether 300000.0 is greater than 300000.0. If a false conclusion is reached, a Boolean value conversion process is triggered, directly replacing the output with the pure number 0. The cursor is then moved to coordinate position 1, and the actual consumption parameter 600000.0 and the baseline upper limit parameter are extracted. The parameter 500000.0 is compared and judged. If 600000.0 is greater than 500000.0, the logical conclusion is true and converted to the pure number 1. The cursor then moves to coordinate position 2 and extracts the actual time parameter 300000.0 and compares it with the baseline upper limit parameter 300000.0. Since no strictly greater than was detected, 300000.0 is not greater than 300000.0 and the logical conclusion is false, converted to the pure number 0. After completing the cursor inspection and comparison verification of all nodes, based on the original cursor reading of the order of coordinates 0, 1, 2, the converted discrete pure numbers are filled into a pre-declared one-dimensional Boolean array to accurately mark whether each micro-link in the single order flow violates the time constraint rules. Finally, a comparison result matrix covering 0, 1, 0 data is output.

[0031] S303: Perform the prescribed construction process based on the comparison result matrix, map the Boolean value combination to the corresponding judgment category identifier, establish the association relationship between the result matrix and the judgment identifier, and generate the order processing judgment label; For the comparison result matrix containing the numbers 0, 1, and 0, the built-in object flattening and text concatenation operations are triggered. Data separators are removed from the array structure, and the three pure numbers are concatenated to generate a complete and seamless union string "010". Then, a tree-structured hash table residing in the process's long-term memory region is read. This table has been pre-loaded with and defined absolute pointer relationships between all boolean strings and the final delayed judgment category long text constants, such as all-zero text pointing to a perfect flow constant. The union string "010" generated in the previous step is substituted into this hash table to initiate a precise search operation. The feature combination of a middle bit being 1 and the rest being 0 precisely points to a single execution stage timeout exception constant pre-stored in the dictionary. After confirming the constant match, an independent address block is allocated in the execution context of this processing thread. Through strong binding of internal references, the original generated comparison result matrix structure containing the numbers 0, 1, and 0 is physically and permanently associated with the single execution stage timeout exception constant output by the query. This operation creates and outputs an order processing judgment label that coherently includes anomaly inference credentials and macro-level business qualitative classification conclusions.

[0032] Please see Figure 5 The specific steps of S4 are as follows: S401: Obtain order process records from the order database, extract process node sequences and node status identifiers, perform process sequence verification and node index matching processing, form a process path structure, and generate a process path set; A batch export command is issued to the remote offline historical data storage unit to extract detailed operation log data of historical orders that have reached their final state within the previous 30 regular natural transaction cycles. Faced with massive amounts of unstructured text, a word segmentation cursor is used to slice the data into paragraphs. For each historical transaction record, the codes of each specific action node it went through in the business simulation process and the final global qualitative status description text attached at the end of the record are extracted separately. After extracting multiple node codes for a record, a process sequence comparison and verification to prevent forgery and tampering is introduced. The cursor is used to compare the size of two consecutive code parameters in the sequence to determine whether the code parameter of the currently extracted node is always greater than the code parameter of the previous node. If the current parameter is found to be not greater than or equal to the previous parameter, it is determined that there is a logical error, and the cleaning and discarding program is activated to delete the problematic record. The record sequence that remains after rigorous verification is remapped and indexed. Using a counter replacement rule, the irregular code sequence with business characteristics is overwritten into an equidistant increasing natural number coordinate sequence starting from the integer 0. After converting the natural numerical coordinate sequence, the class constructor in the memory allocator packages and combines it with the final global qualitative state description text to which it belongs into a data transfer tuple object. This verification, screening, and packaging process is repeated iteratively on the acquired 15,000 qualified historical records, resulting in a large set of process paths that can serve as a standard traceability reference.

[0033] S402: Extract the judgment category identifier based on the order processing judgment label, perform path matching operation according to the process path set, compare the consistency between the judgment category identifier and the process path node status, record the matching position and construct the path mapping relationship to obtain the path mapping structure; The system retrieves order processing judgment tag data distributed during the preceding judgment process, extracts the judgment conclusions regarding the type of exception (single execution stage timeout exception constant), and designates this as a global matching anchor parameter for large-scale search and comparison. Using this anchor parameter, a full traversal query operation is initiated on the previously constructed process path set of 15,000 records. When iteratively accessing each historical tuple object record in the process path set, its built-in global qualitative status description text is extracted, and its consistency is checked bitwise against the character encoding array at the single execution stage timeout exception execution memory address level of the anchor parameter. A simulation of an actual hit process was performed: When the comparison and traversal cursor moved to the 1200th coordinate index position in the entire history set, a bitwise check confirmed that the ASCII code arrangement of the qualitative status description text recorded at this position was completely symmetrical with the encoding arrangement of the anchor parameter. Immediately, a high-priority register interrupt recording action was triggered, capturing the coordinate value 1200 of the current cursor position and storing it in a key variable. Simultaneously, the complete auto-incrementing sequence of the node encapsulated within the 1200th item was exported. In this anomaly repair process, based on the unique primary key, the process identifier of the currently processed target order was bound to the memory address of the newly retrieved 1200th historical index sequence. This action created and confirmed the alignment between the blocked order feature sequence and historical experience-based response strategies, outputting a path mapping structure with a robust pointing dependency relationship.

[0034] S403: Perform path classification processing based on the path mapping structure, perform delay determination logic calculation based on the node matching position distribution, establish the association between path classification results and delay status identifiers, and generate order processing path identifiers and delay processing markers; The system retrieves the target matching coordinate value distribution information from the path mapping structure and the Boolean array of 0, 1, and 0 records generated by the current order. It extracts the coordinate positions of the number 1 appearing in the comparison result matrix and performs classification logic based on the depth of abnormal coordinate positions using a segmented comparison judgment framework. The regional rules set in this classification logic are: data positions with node coordinate values ​​between 0 and 1 are defined as the front-end user interaction influence zone, and data positions with coordinate values ​​greater than or equal to 2 are defined and defined as the back-end fulfillment and warehousing influence zone. Based on the actual parameters, the logic is applied: in the current comparison result matrix 0, 1, and 0, only the Boolean number 1, representing an anomaly, is detected at coordinate index position 1. Condition checks are performed according to the set regional division parameter intervals, determining that the position of the number 1 is completely encompassed within the front-end user interaction influence zone. Subsequently, an order-of-magnitude accumulation is performed to obtain the cumulative frequency of single-point delays falling within this interval, which is 1. Combining the region category and the anomaly frequency value, the execution condition combination output judgment determines that the feature matches the general hysteresis logic classification of a single node within the region. Then, it retrieves the pre-defined low-order delay state integer value 2 from the constant library to represent the mild hysteresis level. Using a specific placeholder as a fusion medium, the derived general hysteresis classification text is tightly coupled with the obtained integer value 2 at the character level. After this combination and assembly, the order processing path identifier with clear direction and risk numerical classification, along with the associated delay processing marker, is distributed and pushed to the external integrated routing and dispatching component.

[0035] Please see Figure 6 The specific steps of S5 are as follows: S501: Obtain the order processing path identifier and delay processing marker, extract the node sequence and delay status value corresponding to the path identifier, perform path identifier classification and delay status verification processing, form a path status set, and generate a path status structure; The integrated routing component receives the encrypted message of the order processing path identifier and its associated low-order delay status integer assignment flag 2. The text segmentation and restoration function is initiated, securely deconstructing the long string of identifiers from the encrypted message using specific delimiter placeholders to extract the general delay text describing the anomaly type. Simultaneously, this integer delay assignment parameter 2 is received and parsed. Next, the deconstructed general delay text is pushed to the logical routing determination component. Based on the pre-injected multi-level mapping configuration dictionary, character classification and conversion operations are performed, securely and reliably matching this anomaly type phrase to the standard same-city, same-warehouse, single-line delivery method feature description text. Additionally, a range check is performed on the returned integer delay assignment parameter 2 to determine if the number 2 is completely within the acceptable range of the lower bound parameter 1 and the upper bound tolerance parameter 5 for triggering emergency rerouting. The result is that 2 falls within the normal, controllable, and legal value space. Based on the confirmation that the transformed delivery method characteristics are correct and the risk classification parameters are compliant, the underlying byte stream object serialization technology interface is used to merge the same-city, same-warehouse single-line operation delivery method text with the integer compliant parameter 2 into a lightweight state carrier entity object that can be read and written by the process. Then, it is stacked and aggregated into the high-speed access buffer that the application resides in for storage, thereby establishing a static and persistent path state structure for high-frequency retrieval.

[0036] S502: Extract the path category and delay status based on the path status structure, perform a shipping execution path matching judgment, compare the path category with the preset shipping path rules item by item, filter the paths that meet the conditions and record the corresponding relationship to obtain the shipping path set; The path status structure instance object is retrieved from the resident buffer via a direct connection, from which the previously processed text of the same-city, same-warehouse single-line operation delivery method and the risk integer parameter 2 are extracted. With these two important parameters, a carrier network candidate rule screening and comparison command is sent to the underlying logistics gateway scheduler. The scheduler accesses the delivery available path configuration constraint master table stored in a separate form, which lists the trigger permission pre-restriction threshold data for various fulfillment trunk lines. A multi-level filtering and matching operation is initiated: for the base table containing more than 100 different network rules, the business category constraint range supported by the rule declaration is first extracted to determine whether it includes the previously extracted same-city, same-warehouse single-line operation delivery method requirement, filtering out high-cost logistics network rule schemes that force cross-provincial joint commissioning; based on the available candidate set remaining after the first round of reduction, the lower limit of the tolerance delay limit set for each rule is further extracted, and it is determined whether the previously extracted risk integer parameter 2 is greater than or equal to this lower limit number. After undergoing two layers of rigorous union filtering and comparison verification calculations, two candidate trunk lines were extracted from the original complex route configuration table. These lines fully meet the characteristics of a single warehouse and are sufficient to compensate for minor delays. These are identified as a local land transport general dedicated line and a same-city food delivery connection trunk line. These two candidate identifier strings, which highly conform to the admission matching constraints, are uniformly incorporated into a dynamically constructed array set variable. The output is a set of shipping routes encompassing definitive solutions. For details of all numerical verifications and identifier selection and matching mapping conclusions, please refer to Table 2.

[0037] Table 2. Results of Shipping Route Matching Judgment

[0038] As shown in Table 2, by performing a dual-rule matching calculation and verification that is refined to the feature parameters and risk assessment integers, non-compliant or excessively redundant carrier rules are stripped away.

[0039] S503: Based on the set of delivery paths, perform path association processing, establish a mapping relationship between delivery execution paths and order processing path identifiers, and combine the execution paths in the delayed state to generate a collaborative management solution for store orders; Retrieve the list of parameters for the two shipping execution path identifiers provided in Table 2, and invoke the path optimization decision-making comprehensive evaluation calculation engine framework embedded in the logistics scheduling core. Extract the cost loss constant and time-saving value-added benefit constant pre-associated with these two alternative solutions: based on the configuration file information, retrieve the cost loss constant corresponding to the local land transport ordinary dedicated line identifier, which is 4, and its time-saving value-added constant is 2; simultaneously retrieve the cost loss constant of the same-city food delivery connecting trunk line identifier, which is 6, and its time-saving value-added constant is 5. Directly designate the risk integer parameter 2 carried over from the previous steps as the weighting calculation multiplier factor, and comprehensively implement the calculation and scoring of the impact of the attached parameters: extract the cost loss constant, multiply by the weighting multiplier factor 2, and then subtract the extracted time-saving value-added constant value. Calculate for the local land transport ordinary dedicated line: multiply 4 by 2 and then subtract 2 to obtain a comprehensive evaluation discretion score of 6. The same scoring rule applies to same-city food delivery trunk lines: multiply 6 by 2 and subtract 5 to calculate a comprehensive evaluation score of 7. Based on the two evaluation scores, a numerical sorting and filtering rule is applied, showing that parameter 6 is significantly lower than parameter 7. Following the evaluation rule that a lower score represents better overall cost-effectiveness, the local land transport ordinary dedicated line is ultimately selected as the optimal remedial action plan. Once this execution path is confirmed, the delivery execution path identifier is forcibly bound to the order processing path identifier parameter record that previously triggered the entire anomaly response flow through the underlying configuration center's data mounting mechanism. This deep binding and traffic-driving function forms and issues a comprehensive store order collaborative management plan that includes complete anomaly level definitions and guidance on the best remedial logistics selection.

[0040] A multi-store order collaborative management system based on order aggregation processing includes: The order node construction module is used to implement S1: obtain order records through multi-platform order interfaces, extract order creation time, payment completion time, inventory lock completion time and warehouse execution completion time, calculate and establish time sequence relationship according to time order, associate time nodes and generate order node sequence; The trigger vector generation module is used to implement S2: calculate and sort the intervals between adjacent time nodes in the order node sequence, combine the interval order, collect the inventory lock result fields and perform mark conversion, establish the correspondence between the inventory lock result fields and the marks, and generate the order trigger vector; The order determination and analysis module is used to implement S3: based on the order trigger vector comparison processing, a preset set of node interval thresholds is used to compare the order of order trigger vector intervals with the set of node interval thresholds, construct comparison rules and generate order processing determination labels; The processing path identification module is used to implement S4: call the order processing judgment label and combine it with the order process record in the order database to determine the order processing path, establish the correspondence between the order processing path and the order process record, and generate the order processing path identifier and the delayed processing mark; The shipping collaboration decision module is used to implement S5: call the order processing path identifier and combine it with the delayed processing mark to filter the order shipping execution path, establish the correspondence between the order shipping execution path and the order processing path identifier, and generate a store order collaboration management solution.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A multi-store order collaborative management method based on order aggregation processing, characterized in that, Includes the following steps: S1: Obtain order records through multi-platform order interfaces, extract order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, calculate and establish time sequence relationships according to time order, associate time nodes, and generate order node sequence; S2: Calculate and sort the intervals between adjacent time nodes in the order node sequence, combine the interval order, collect the inventory lock result field and perform a mark conversion, establish the correspondence between the inventory lock result field and the mark, and generate an order trigger vector; S3: Based on the order trigger vector comparison processing, a preset node interval threshold set is used to compare the order trigger vector interval order with the node interval threshold set item by item, construct comparison rules and generate order processing judgment labels; S4: Call the order processing determination label and combine it with the order process record in the order database to determine the order processing path, establish the correspondence between the order processing path and the order process record, and generate the order processing path identifier and the delayed processing mark; S5: Call the order processing path identifier and combine it with the delayed processing mark to filter the order delivery execution path, establish the correspondence between the order delivery execution path and the order processing path identifier, and generate a store order collaborative management solution.

2. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The order node sequence includes timestamp records of order nodes, time span values ​​of stages, and a sequential list of process nodes. The order trigger vector includes a feature data matrix of node intervals, a mapping symbol of inventory lock status, and a judgment flag for trigger conditions. The order processing judgment label includes a timeout warning level label, a normal flow confirmation label, and an abnormal bottleneck classification label. The order processing path identifier includes a main process execution route map, a backup branch jump pointer, and a node rollback control code. The delay processing marker includes a timeout warning level for order delays, a reason code for delayed delivery, and a judgment certificate for timeout compensation. The store order collaborative management solution includes an optimized warehouse allocation strategy, an automatically matched logistics carrier directory, and a dynamically adjusted shipping priority queue.

3. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The node interval threshold set refers to a pre-set set of benchmark data used to measure the allowable standard and maximum time difference between adjacent time nodes in the order processing process.

4. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The construction comparison rule refers to the specific logical conditions for comparing the time interval of the actual order node with the preset threshold item by item, thereby determining the order processing status and generating corresponding tags.

5. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Retrieve order records, extract order creation time, payment completion time, inventory lock completion time, and warehouse execution completion time, uniformly convert and aggregate the execution timestamps by order identifier, and generate an order time field set; S102: Based on the order time field set, perform timestamp size comparison, verify the time order, construct time sorting link and complete order correction to obtain time order chain; S103: Extract the node states according to the time sequence chain and perform serialization and arrangement to establish the mapping relationship between time nodes and states, and generate the order node sequence.

6. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Obtain the order node sequence, extract the timestamp difference between adjacent nodes, perform item-by-item subtraction to form a time interval value sequence, arrange the interval values ​​according to the node order, and generate a time interval sequence; S202: Based on the time interval sequence, perform sequential index combination operation, concatenate adjacent intervals according to node position and construct an ordered combination structure, and at the same time collect the inventory lock result field, perform status value judgment and convert it into discrete marker value to obtain the interval combination structure; S203: Perform vector concatenation processing based on the interval combination structure and discrete marker values ​​to establish the mapping relationship between interval positions and marker values, construct vector sequences, and generate order trigger vectors.

7. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Obtain a set of preset node interval thresholds, extract the node position index corresponding to the threshold and perform numerical verification processing to form a sequential arrangement structure consistent with the node sequence, and generate a threshold sequence structure; S302: Extract the interval sequence values ​​based on the order trigger vector, perform item-by-item size comparison operations according to the threshold sequence structure, record the relationship between the interval value and the threshold value at each position and perform Boolean value conversion to obtain the comparison result matrix; S303: Perform the prescribed construction process according to the comparison result matrix, map the Boolean value combination to the corresponding judgment category identifier, establish the association between the result matrix and the judgment identifier, and generate the order processing judgment label.

8. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Obtain order process records from the order database, extract process node sequences and node status identifiers, perform process sequence verification and node index matching processing, form a process path structure, and generate a process path set; S402: Extract the judgment category identifier based on the order processing judgment label, perform path matching operation according to the process path set, compare the consistency between the judgment category identifier and the process path node status, record the matching position and construct the path mapping relationship to obtain the path mapping structure; S403: Perform path classification processing according to the path mapping structure, perform delay determination logic calculation based on the node matching position distribution, establish the association between path classification results and delay status identifier, and generate order processing path identifier and delay processing mark.

9. The multi-store order collaborative management method based on order aggregation processing according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Obtain the order processing path identifier and the delay processing marker, extract the node sequence and delay status value corresponding to the path identifier, perform path identifier classification and delay status verification processing, form a path status set, and generate a path status structure; S502: Extract the path category and delay status according to the path status structure, perform a shipping execution path matching judgment, compare the path category with the preset shipping path rules item by item, filter the paths that meet the conditions and record the corresponding relationship to obtain a shipping path set. S503: Based on the set of shipping paths, perform path association processing, establish a mapping relationship between shipping execution paths and order processing path identifiers, and combine the combination and arrangement of delayed state execution paths to generate a store order collaborative management scheme.

10. A multi-store order collaborative management system based on order aggregation processing, characterized in that, The system is used to implement the multi-store order collaborative management method based on order aggregation processing as described in any one of claims 1-9, including: The order node construction module is used to implement S1: obtain order records through multi-platform order interfaces, extract order creation time, payment completion time, inventory lock completion time and warehouse execution completion time, calculate and establish time sequence relationship according to time order, associate time nodes and generate order node sequence; The trigger vector generation module is used to implement S2: calculate and sort the intervals between adjacent time nodes in the order node sequence, combine the interval order, collect the inventory lock result field and perform a mark conversion, establish the correspondence between the inventory lock result field and the mark, and generate the order trigger vector; The order determination and analysis module is used to implement S3: based on the order trigger vector, comparison processing is performed, a preset set of node interval thresholds is used, the order of order trigger vector intervals is compared with the set of node interval thresholds item by item, comparison rules are constructed and order processing determination labels are generated; The processing path identification module is used to implement S4: calling the order processing judgment label and combining it with the order process record in the order database to determine the order processing path, establish the correspondence between the order processing path and the order process record, and generate the order processing path identifier and the delayed processing mark; The shipping collaboration decision module is used to implement S5: calling the order processing path identifier and combining it with the delayed processing flag, filtering the order shipping execution path, establishing the correspondence between the order shipping execution path and the order processing path identifier, and generating a store order collaboration management solution.

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