Order intelligent batch assignment method based on pre-approval rules and element matching

Abstract

This invention belongs to the field of order allocation and intelligent scheduling technology, and discloses an intelligent batch order allocation method based on pre-approval rules and element matching. The method includes an initialization phase, a pre-approval compliance verification phase, a multi-dimensional element intelligent matching phase, a batch allocation dynamic optimization phase, and a closed-loop feedback iteration phase. This invention prioritizes compliance verification, filtering out non-compliant orders at the source and effectively avoiding legal risks, economic losses, and brand impact caused by irregular allocation. Abnormal orders also include specific reasons for their abnormality, facilitating rapid manual review and further improving the accuracy of compliance control. Standardized order processing enables unified batch control, replacing the traditional manual order-by-order allocation model and significantly reducing manpower. The hybrid optimization algorithm balances the speed of initial scheme generation with global optimality, efficiently handling the batch allocation needs of massive orders, adapting to the development needs of expanding business scale, and facilitating practical application and operation.

Description

Technical Field

[0001] This invention relates to the field of order allocation and intelligent scheduling technology, and in particular to an intelligent batch allocation method for orders based on pre-approval rules and element matching. Background Technology

[0002] In scenarios such as e-commerce promotions, peak logistics seasons, and large-scale business orders, order volumes often experience explosive growth. How to efficiently and accurately allocate these massive orders to suitable resources directly impacts a company's fulfillment efficiency, resource utilization, and customer satisfaction. Currently, order allocation methods in the industry mainly fall into two categories: one is the traditional manual allocation model, which relies on staff to screen orders and match resources based on experience. This method is not only extremely inefficient and unable to handle large-scale order scenarios, but it is also prone to resource mismatch and compliance risks due to human error. The other is a simple rule-based allocation model, which achieves semi-automatic allocation through preset fixed rules.

[0003] In practical applications, existing equipment often employs a post-compliance verification approach, typically allocating orders first and then reviewing them. This can easily lead to non-compliant orders being assigned to resources, requiring rework and recovery upon discovery of violations. This not only increases labor costs but can also potentially cause legal disputes or damage to brand reputation. Furthermore, the matching dimensions are limited and weighted, focusing on only a few fixed factors and failing to consider multiple dimensions such as order priority, delivery timeliness, and resource load. This results in a high resource mismatch rate, where either high-quality resources are occupied by low-priority orders, or resources become overloaded, compromising fulfillment quality. There is a lack of dynamic adaptation capabilities; resource status updates are not timely, and once allocation rules are set, they are difficult to adjust flexibly according to business changes. When business scenarios change, rules need to be manually re-defined, leading to high adaptation costs. Finally, batch processing capabilities are insufficient. Existing algorithms are mostly designed for single or small-batch orders, and in large-scale order scenarios, issues such as allocation delays and scheme conflicts easily arise. It is difficult to balance allocation efficiency and optimal solution, hindering practical application and operation. Summary of the Invention

[0004] One objective of this invention is to provide a method for intelligent batch allocation of orders based on prior approval rules and element matching.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: an intelligent batch allocation method for orders based on pre-approval rules and element matching, comprising the following steps:

[0006] S1 Initialization Phase: Constructing a standardized order pool and building a pre-approval rule base; Constructing a standardized order pool involves entering basic information, standardizing and cleaning, deduplicating, and structuring the collected orders to be allocated, outputting a standardized order pool with a unique identifier; Building a pre-approval rule base involves entering input information such as enterprise compliance requirements and business constraint rules, completing rule classification, parameterized configuration, and rule conflict verification and resolution, outputting a dynamically updatable pre-approval rule base;

[0007] S2 Pre-approval Compliance Verification Phase: Based on the standardized order pool and pre-approval rule base output by S1, pre-approval compliance screening is performed on batch orders. Each order is matched with hard compliance rules to complete qualification review, permission verification and risk threshold detection. Orders that fail the verification are included in the abnormal order pool. Orders that pass the verification are marked with compliance attributes and enter the next phase. A subset of compliant orders and a subset of abnormal orders with abnormal reasons are output.

[0008] S3 Multi-dimensional Element Intelligent Matching Stage: On the one hand, the allocable resources are dynamically modeled, and a dynamic resource pool model with real-time status labels is output through real-time status monitoring, load threshold calculation and service capacity quantitative evaluation; on the other hand, based on the dynamic resource pool model and the compliant order subset output by S2, matching element dimensions such as order priority and delivery time are extracted, dynamic weights are assigned, and a preliminary matching scheme set is generated through a multi-objective optimization matching algorithm.

[0009] S4 Batch Allocation Dynamic Optimization Stage: Conflict detection is performed on the preliminary matching scheme set output by S3, and conflicts of the types such as resource overload and order priority are identified and corresponding resolution strategies are executed. The optimal batch allocation scheme without conflicts is obtained through the hybrid optimization algorithm iteration. Allocation instructions are then issued and receipt information is collected. Order allocation success notification and resource acceptance status feedback are output.

[0010] S5 Closed-Loop Feedback Iteration Phase: Collect data from the entire order fulfillment process, construct an evaluation system that includes efficiency, cost, and compliance indicators, compare the expected and actual values ​​of the allocation plan and calculate the deviation rate, and output a fulfillment performance evaluation report; based on the evaluation report and deviation analysis results, optimize the parameters of the pre-approval rule base, element matching weights, and allocation algorithm parameters, and output the updated rule base and algorithm model for the next round of order allocation.

[0011] Preferably, the rule classification in S1 is specifically divided into two categories: hard compliance rules and flexible priority rules. The parameterized configuration is to convert various rules into adjustable parameter forms for storage and management.

[0012] Preferably, orders in the abnormal order pool in S2 are handled by manual review or direct rejection, and orders that pass the review can be reclassified into the compliant order subset.

[0013] Preferably, the dynamic weight in S3 is dynamically adjusted based on historical performance data and real-time business objectives, and the multi-objective optimization matching algorithm is constructed with the goal of balancing allocation efficiency and fairness.

[0014] Preferably, the hybrid optimization algorithm in S4 is a combination of a greedy algorithm and a genetic algorithm. First, the initial allocation scheme is quickly generated by the greedy algorithm, and then the global optimization is performed by the genetic algorithm.

[0015] Preferably, the conflict resolution strategies in S4 include three types: resource expansion scheduling, order priority rearrangement, and batch allocation. The corresponding strategy is selected and executed according to the conflict type.

[0016] Preferably, the fulfillment process data in S5 includes delivery timeliness achievement rate, resource utilization rate and customer satisfaction, and the deviation analysis results are used to accurately locate the parameters that need to be optimized in the rule base and algorithm model.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0018] (1) This invention moves compliance verification forward, eliminating non-compliant orders from the source, effectively avoiding legal risks, economic losses and brand impact caused by illegal allocation; abnormal orders will also be accompanied by specific reasons for the abnormality, which is convenient for manual review and processing, further improving the accuracy of compliance control. Through standardized order processing, batch unified control is achieved, replacing the traditional manual allocation mode, which greatly reduces manpower input; the hybrid optimization algorithm takes into account the initial solution generation speed and global optimality, and can efficiently handle the batch allocation needs of massive orders, adapting to the development needs of enterprise business expansion.

[0019] (2) This invention can accurately adapt to the personalized needs of different orders by combining multi-dimensional element matching with dynamic weight adjustment, and avoid resource mismatch; the dynamic resource pool reflects the resource status in real time, which can effectively reduce resource overload or idleness, and improve the overall resource utilization and order fulfillment quality; the closed-loop feedback iteration mechanism can automatically optimize rules and algorithm parameters according to fulfillment data, without the need for frequent manual configuration adjustments, and can adapt to scenarios such as updates to enterprise compliance requirements, expansion of business scope, and changes in market demand, thereby improving the long-term applicability of the method and reducing system iteration and maintenance costs; the process of each stage is clearly layered, and the input, processing, and output elements are clear and controllable. Each operation step has specific technical support, without the need for complex special equipment or high-end technology reserves, and enterprises can quickly deploy the application. At the same time, the later maintenance difficulty is low and easy to learn. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall process structure of the present invention. Detailed Implementation

[0021] The present invention will now be further described in conjunction with specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0022] In the description of this invention, it should be noted that directional terms such as "center," "lateral," "longitudinal," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise" indicate the orientation and positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. They should not be construed as limiting the specific protection scope of this invention.

[0023] It should be noted that the terms "first" and "second" in the specification and claims of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0024] One preferred embodiment of the present invention, such as Figure 1 As shown, the intelligent batch allocation method for orders based on pre-approval rules and element matching includes the following steps:

[0025] S1 Initialization Phase: Constructing a standardized order pool and building a pre-approval rule base; Constructing a standardized order pool involves entering basic information, standardizing and cleaning, deduplicating, and structuring the collected orders to be allocated, outputting a standardized order pool with a unique identifier; Building a pre-approval rule base involves entering input information such as enterprise compliance requirements and business constraint rules, completing rule classification, parameterized configuration, and rule conflict verification and resolution, outputting a dynamically updatable pre-approval rule base;

[0026] S2 Pre-approval Compliance Verification Phase: Based on the standardized order pool and pre-approval rule base output by S1, pre-approval compliance screening is performed on batch orders. Each order is matched with hard compliance rules to complete qualification review, permission verification and risk threshold detection. Orders that fail the verification are included in the abnormal order pool. Orders that pass the verification are marked with compliance attributes and enter the next phase. A subset of compliant orders and a subset of abnormal orders with abnormal reasons are output.

[0027] S3 Multi-dimensional Element Intelligent Matching Stage: On the one hand, the allocable resources are dynamically modeled, and a dynamic resource pool model with real-time status labels is output through real-time status monitoring, load threshold calculation and service capacity quantitative evaluation; on the other hand, based on the dynamic resource pool model and the compliant order subset output by S2, matching element dimensions such as order priority and delivery time are extracted, dynamic weights are assigned, and a preliminary matching scheme set is generated through a multi-objective optimization matching algorithm.

[0028] S4 Batch Allocation Dynamic Optimization Stage: Conflict detection is performed on the preliminary matching scheme set output by S3, and conflicts of the types such as resource overload and order priority are identified and corresponding resolution strategies are executed. The optimal batch allocation scheme without conflicts is obtained through the hybrid optimization algorithm iteration. Allocation instructions are then issued and receipt information is collected. Order allocation success notification and resource acceptance status feedback are output.

[0029] S5 Closed-Loop Feedback Iteration Phase: Collect data from the entire order fulfillment process, construct an evaluation system that includes efficiency, cost, and compliance indicators, compare the expected and actual values ​​of the allocation plan and calculate the deviation rate, and output a fulfillment performance evaluation report; based on the evaluation report and deviation analysis results, optimize the parameters of the pre-approval rule base, element matching weights, and allocation algorithm parameters, and output the updated rule base and algorithm model for the next round of order allocation.

[0030] In S1, rules are specifically categorized into two types: hard compliance rules and flexible priority rules. Parameterized configuration converts various rules into adjustable parameter forms for storage and management.

[0031] Orders in the abnormal order pool in S2 are handled through manual review or direct rejection. Orders that pass the review can be reclassified into the compliant order subset.

[0032] In S3, dynamic weights are dynamically adjusted based on historical performance data and real-time business objectives. The multi-objective optimization matching algorithm is constructed with the goal of balancing allocation efficiency and fairness.

[0033] The hybrid optimization algorithm in S4 is a combination of greedy algorithm and genetic algorithm. First, the greedy algorithm is used to quickly generate the initial allocation scheme, and then the genetic algorithm is used for global optimization.

[0034] The conflict resolution strategies in S4 include three types: resource expansion scheduling, order priority rearrangement, and batch allocation. The corresponding strategy is selected and executed according to the conflict type.

[0035] In S5, the full-process data of fulfillment includes delivery timeliness, resource utilization, and customer satisfaction. Deviation analysis results are used to accurately locate the parameters that need to be optimized in the rule base and algorithm model.

[0036] Working principle:

[0037] In use, the system employs a layered collaborative logic of pre-compliance verification, multi-dimensional dynamic matching, and closed-loop iterative optimization to achieve compliant, accurate, and efficient batch order allocation. The overall process revolves around a progressive framework of infrastructure development, compliance oversight, core matching, optimization implementation, and continuous upgrades. Data is interconnected at each stage, and steps are closely linked, forming a complete closed loop for order allocation.

[0038] The infrastructure construction phase involves two main stages: First, collecting all information on orders to be allocated, and then standardizing, deduplicating, and structuring the data to create a uniquely identified standardized order pool. This provides a unified and standardized data foundation for subsequent batch processing. Second, integrating information such as enterprise compliance requirements, business constraints, and resource access conditions, the rules are categorized, parameterized, and conflict resolution is achieved. This results in a dynamically updatable pre-approval rule base, ensuring the rule system is scientifically sound and feasible. The compliance screening phase uses the standardized order pool and the pre-approval rule base as input to conduct compliance screening on each batch of orders. The focus is on verifying whether the qualifications and permissions corresponding to the orders meet the standards and whether the risks exceed thresholds, eliminating non-compliant orders at the source. Orders that pass the verification are marked with compliance attributes and directly enter the subsequent matching stage, avoiding cost waste caused by rework due to violations after allocation. The core matching stage is the crucial step in ensuring allocation accuracy, employing a dual-track model of resource modeling and dynamic weighted matching. First, allocable resources are dynamically modeled in real time, and the status of resource load, service capacity, geographical coverage, etc. are monitored to generate a dynamic resource pool model with real-time status labels to ensure the timeliness of resource information. Then, multi-dimensional matching elements are extracted from compliant orders and dynamic resource pools, and the weights of each element are dynamically allocated according to historical performance data and real-time business objectives. The initial matching of orders and resources is completed through a multi-objective optimization algorithm to form a preliminary matching scheme set.

[0039] Optimization and Implementation Phase: Focusing on the feasibility of the initial plan, conflict detection is first performed on the plan set to accurately identify problems such as resource overload, order priority conflicts, and failure to meet timeliness requirements. For different conflict types, targeted strategies such as resource expansion scheduling, order priority rearrangement, and batch allocation are adopted to resolve conflicts. Then, a hybrid algorithm of greedy algorithm to quickly generate the initial plan and genetic algorithm for global optimization is used to iterate and obtain the optimal batch allocation plan without conflicts. Finally, allocation instructions are issued and resource acceptance receipts are collected to complete the order allocation implementation. Continuous Upgrade Phase: To ensure that the method adapts to business changes, data from the entire order fulfillment process is collected to build a multi-dimensional evaluation system including efficiency, cost, and compliance. The expected effects of the allocation plan are compared with the actual fulfillment results, deviations are calculated, and the causes are analyzed. Based on the analysis results, the parameters of the pre-approval rule base, element matching weights, and algorithm parameters are optimized in reverse. The optimized model is applied to the next round of order allocation to achieve self-upgrading and continuous adaptation of the method.

[0040] The basic principles, main features, and advantages of this invention have been described above. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made without departing from the spirit and scope of the invention, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection claimed by this invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent batch allocation of orders based on pre-approval rules and element matching, characterized in that: Includes the following steps: S1 Initialization Phase: Constructing a standardized order pool and building a pre-approval rule base; The construction of the standardized order pool involves inputting basic information, standardizing cleaning, deduplication, and structured storage of the collected orders to be allocated, and outputting a standardized order pool with a unique identifier; The process of building a pre-approval rule base involves inputting information such as enterprise compliance requirements and business constraint rules, completing rule classification, parameter configuration, and rule conflict verification and resolution, and outputting a dynamically updatable pre-approval rule base. S2 Pre-approval Compliance Verification Phase: Based on the standardized order pool and pre-approval rule base output by S1, pre-approval compliance screening is performed on batch orders. Each order is matched with hard compliance rules to complete qualification review, permission verification and risk threshold detection. Orders that fail the verification are included in the abnormal order pool. Orders that pass the verification are marked with compliance attributes and enter the next phase. A subset of compliant orders and a subset of abnormal orders with abnormal reasons are output. S3 Multi-dimensional Element Intelligent Matching Stage: On the one hand, the allocable resources are dynamically modeled, and a dynamic resource pool model with real-time status labels is output through real-time status monitoring, load threshold calculation and service capacity quantitative evaluation; on the other hand, based on the dynamic resource pool model and the compliant order subset output by S2, matching element dimensions such as order priority and delivery time are extracted, dynamic weights are assigned, and a preliminary matching scheme set is generated through a multi-objective optimization matching algorithm. S4 Batch Allocation Dynamic Optimization Stage: Conflict detection is performed on the preliminary matching scheme set output by S3, and conflicts of the types such as resource overload and order priority are identified and corresponding resolution strategies are executed. The optimal batch allocation scheme without conflicts is obtained through the hybrid optimization algorithm iteration. Allocation instructions are then issued and receipt information is collected. Order allocation success notification and resource acceptance status feedback are output. S5 Closed-Loop Feedback Iteration Phase: Collect data from the entire order fulfillment process, construct an evaluation system that includes efficiency, cost, and compliance indicators, compare the expected and actual values ​​of the allocation plan and calculate the deviation rate, and output a fulfillment performance evaluation report; based on the evaluation report and deviation analysis results, optimize the parameters of the pre-approval rule base, element matching weights, and allocation algorithm parameters, and output the updated rule base and algorithm model for the next round of order allocation.

2. The intelligent batch allocation method for orders based on pre-approval rules and element matching as described in claim 1, characterized in that: The rule classification in S1 is specifically divided into two categories: hard compliance rules and flexible priority rules. The parameterized configuration is to convert various rules into adjustable parameter forms for storage and management.

3. The intelligent batch allocation method for orders based on pre-approval rules and element matching as described in claim 1, characterized in that: Orders in the abnormal order pool in S2 are handled through manual review or direct rejection. Orders that pass the review can be reclassified into the compliant order subset.

4. The intelligent batch allocation method for orders based on pre-approval rules and element matching as described in claim 1, characterized in that: The dynamic weights in S3 are dynamically adjusted based on historical performance data and real-time business objectives. The multi-objective optimization matching algorithm is constructed with the goal of balancing allocation efficiency and fairness.

5. The intelligent batch allocation method for orders based on pre-approval rules and element matching as described in claim 1, characterized in that: The hybrid optimization algorithm in S4 is a combination of greedy algorithm and genetic algorithm. First, the initial allocation scheme is quickly generated by the greedy algorithm, and then the global optimization is performed by the genetic algorithm.

6. The intelligent batch allocation method for orders based on pre-approval rules and element matching as described in claim 1, characterized in that: The conflict resolution strategies in S4 include three types: resource expansion scheduling, order priority rearrangement, and batch allocation. The corresponding strategy is selected and executed according to the conflict type.

7. The intelligent batch allocation method for orders based on pre-approval rules and element matching as described in claim 1, characterized in that: The S5 fulfillment process data includes delivery timeliness achievement rate, resource utilization rate and customer satisfaction. The deviation analysis results are used to accurately locate the parameters that need to be optimized in the rule base and algorithm model.

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