Method, device, medium and program product for handling sluggish material

Abstract

This application relates to the field of computer technology, and particularly to a method, apparatus, medium, and program product for handling stagnant materials. The method includes: acquiring material inventory data, which includes the storage duration of various materials in a material warehouse; identifying stagnant materials based on the inventory data, where stagnant materials are those whose storage duration has reached a preset threshold; determining the causes of stagnant materials through multiple parallel threads to obtain attribution analysis results; each thread executing one cause analysis process; determining a target processing strategy for the stagnant materials from multiple candidate processing strategies, where the target processing strategy indicates how to handle the stagnant materials; and generating a processing report based on the attribution analysis results and the target processing strategy, whereby the processing report displays the causes of stagnant materials and the processing methods. This method can improve the efficiency, objectivity, and quality of decision-making in handling stagnant materials.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and in particular relates to a method, apparatus, medium and program product for processing stagnant materials. Background Technology

[0002] In manufacturing supply chain management, obsolete materials refer to long-term stockpiled inventory that has stagnated. These materials not only occupy storage space and tie up a company's working capital, but may also depreciate completely due to technological or market changes. Therefore, timely identification and disposal of obsolete materials are crucial for companies to control costs and optimize assets.

[0003] Currently, enterprises mainly rely on ERP (Enterprise Resource Planning) systems to automatically generate obsolete material lists based on fixed inventory age thresholds (such as 180 days). However, subsequent analysis and decision-making processes still heavily depend on manual processes and are extremely cumbersome. Business personnel need to export the list from the ERP system and then log into multiple heterogeneous systems such as PLM (Product Lifecycle Management), CRM (Customer Relationship Management), and APS (Advanced Planning and Scheduling) to query fragmented information such as engineering changes, order status, and production plans. They then manually link and clean this data in spreadsheets, inferring the causes of obsolescence based on experience. Finally, cross-departmental meetings are organized to determine the disposal plan through subjective discussions.

[0004] The aforementioned manual processing methods are not only time-consuming, labor-intensive, and inefficient, but also lack standardization and digital traceability, resulting in inconsistent decision-making quality and the inability to accumulate and reuse knowledge, thus failing to meet enterprises' needs for real-time, intelligent, and data-driven refined management of the supply chain. Summary of the Invention

[0005] This application provides a method, apparatus, medium, and program product for handling stagnant materials, which can achieve full-process automation from automatic identification, intelligent attribution analysis, quantitative strategy matching to dynamic report generation, thereby significantly improving the efficiency, objectivity, and decision-making quality of stagnant material handling.

[0006] In a first aspect, embodiments of this application provide a method for processing stagnant materials, the method comprising:

[0007] Obtain material inventory data, which includes the storage duration of various materials in the material warehouse; Obsolete materials are identified based on material inventory data. Obsolete materials refer to materials that have been stored for a preset time threshold. The causes of obsolescence of obsolete materials are determined by multiple threads in parallel, and the attribution analysis results are obtained; each thread is used to perform the analysis process of one cause. The target processing strategy for stagnant materials is determined from a variety of candidate processing strategies. The target processing strategy is used to indicate how to process the stagnant materials. A processing report is generated based on the attribution analysis results and the target processing strategy. The processing report is used to show the causes of the stagnation of stagnant materials and the processing methods.

[0008] The first aspect offers the following benefits: Through a standardized automated process, it systematically solves the problems of inefficiency, reliance on experience, lack of traceability, and difficulty in knowledge accumulation inherent in traditional manual handling of obsolete materials. Specifically, this method acquires and automatically filters material inventory data, overcoming the inefficiency of manual inventory checks and verifications, and achieving objective and rapid identification of obsolete materials. At its core, this method determines the causes of obsolescence through multiple parallel threads. This design fundamentally changes the traditional serial, manual analysis model, significantly shortening the time for in-depth attribution analysis of single materials using concurrent computing, and solidifying expert experience into executable system rules, achieving automation and standardization of analysis. Furthermore, this method intelligently determines the target processing strategy from multiple candidate strategies, transforming subjective meeting decisions into objective calculations based on data and quantitative scoring, improving the scientific rigor and consistency of decision-making. Finally, it automatically generates structured processing reports, which not only intuitively display the causes and solutions but also make the entire analysis and decision-making process traceable through digital recording, providing a foundation for knowledge accumulation and process optimization. The entire solution forms an automated closed loop from identification, analysis, decision-making to output, significantly improving the precision and intelligence of supply chain inventory management.

[0009] In one implementation, the multiple threads include the i-th thread, which corresponds to the i-th cause, where i is a positive integer; The causes of obsolescence of obsolete materials are determined by using multiple parallel threads, and the attribution analysis results are obtained, including: Obtain the material identifier of stagnant materials; When analyzing stagnant materials through the i-th thread, material information associated with the i-th cause is obtained based on the material identifier of the stagnant materials. The material information associated with the i-th cause is analyzed according to the preset rules to obtain the i-th sub-analysis result. The i-th sub-analysis result is used to indicate whether the i-th cause belongs to the cause of stagnation of stagnant materials. The attribution analysis results are obtained based on the sub-analysis results corresponding to multiple threads.

[0010] This implementation method achieves independent and concurrent exploration of multi-dimensional data by assigning a cause to each thread and accurately obtaining related information using material identifiers. Finally, the method of summarizing sub-results ensures efficient decomposition of the analysis task and comprehensive integration of results, significantly improving the efficiency and systematic nature of complex attribution analysis.

[0011] In one implementation, the material information of the i-th cause association is analyzed according to preset rules to obtain the i-th sub-analysis result, including: If the material information associated with the i-th cause meets the preset rules, a cause label and prevention strategy corresponding to the i-th cause are generated, and the i-th sub-analysis result is obtained. The prevention strategy is used to indicate the handling method to prevent the i-th cause from causing the material to become stagnant. When the i-th sub-analysis result includes a cause label, it indicates that the i-th cause belongs to the stagnant material.

[0012] This implementation method clearly defines the output content of effective sub-analysis results. It not only generates "cause labels" to identify problems, but also provides actionable "prevention strategies," thereby transforming the experience of a single incident into reusable preventative knowledge. This achieves a closed loop from passive handling to proactive defense, promoting continuous knowledge accumulation and business optimization.

[0013] In one implementation, a target processing strategy for stagnant materials is determined from a variety of candidate processing strategies, including: Obtain auxiliary data for stagnant materials; this auxiliary data is required when executing various candidate processing strategies. Each candidate processing strategy is matched with the auxiliary data according to its priority, and a matching score is obtained for each candidate processing strategy. The target processing strategy for stagnant materials is determined from a variety of candidate processing strategies based on the matching score.

[0014] In this implementation, by acquiring auxiliary data and calculating matching scores according to priority, subjective strategy selection is transformed into objective evaluation based on data and models, which significantly improves the scientific nature, consistency and interpretability of the decision-making process.

[0015] In one implementation, each candidate processing strategy is matched with auxiliary data according to its priority to obtain a matching score for each candidate processing strategy, including: If the matching score of the j-th candidate processing strategy does not reach the preset score threshold, the (j+1)-th candidate processing strategy is matched with the auxiliary data to obtain the matching score of the (j+1)-th candidate processing strategy, where j is a positive integer; or, if the matching score of the j-th candidate processing strategy reaches the preset score threshold, the matching of other candidate processing strategies with lower priority than the j-th candidate processing strategy with the auxiliary data is cancelled.

[0016] This implementation describes an efficient "downgrade matching" mechanism. By combining priority order with a score threshold, the system can quickly identify the first feasible high-priority solution while ensuring decision quality (meeting basic standards), thus achieving a good balance between decision speed and effectiveness.

[0017] In one implementation, a target processing strategy for stagnant materials is determined from a variety of candidate processing strategies based on a matching score, including: The candidate processing strategy that first reaches the preset score threshold is determined as the target processing strategy for stagnant materials.

[0018] This implementation establishes a clear rule for adopting a strategy once the first threshold is reached. This effectively avoids strategy conflicts and ensures that the output meets feasibility requirements while maximally aligning with business management priorities.

[0019] In one implementation, a target processing strategy for stagnant materials is determined from a variety of candidate processing strategies based on a matching score, including: The candidate processing strategy with the highest matching score is determined as the target processing strategy for stagnant materials.

[0020] This implementation proposes a "globally optimal" decision-making logic. By comparing the matching scores of all feasible strategies and selecting the highest one, this method can more precisely weigh various factors and seek the best overall solution for important materials, thus improving the precision of decision-making.

[0021] In one implementation, a processing report is generated based on the attribution analysis results and the target processing strategy, including: The attribution analysis results and target processing strategies are input into the large language model, and a processing report is output. The processing report includes multiple interactive controls, which are used to trigger the display of the data associated with the processing report.

[0022] This implementation utilizes a large language model to automatically generate interactive reports. This not only quickly transforms analysis results into easily understandable professional narratives, greatly saving manpower, but its embedded interactive controls also support data expansion, enhancing the report's intuitiveness, verifiability, and usability.

[0023] In one implementation, stagnant material includes at least one type of material; The causes of obsolescence of obsolete materials are determined by using multiple parallel threads, and the attribution analysis results are obtained, including: For each material in at least one material, the cause of stagnation for that material is determined by multiple threads in parallel, and the analysis results for that material are obtained. Attribution analysis results were obtained based on the analysis results corresponding to at least one material. The target handling strategy for stagnant materials was determined from a variety of candidate handling strategies, including: The target processing strategy is obtained by determining the processing strategy for each of at least one material from a variety of candidate processing strategies.

[0024] In this implementation, by concurrently executing the diagnosis and decision-making processes for multiple independent materials, the system can extend the processing efficiency from a single point to the global level, meeting the actual business needs for rapid and batch analysis and planning of massive amounts of stagnant materials, and significantly improving the overall processing capacity.

[0025] Secondly, embodiments of this application provide an apparatus for processing stagnant materials, comprising: The data preparation module is used to obtain material inventory data, which includes the storage time of various materials in the material warehouse. The data preparation module is also used to identify stagnant materials based on material inventory data. Stagnant materials refer to materials that have been stored for a preset duration threshold. The processing module is used to determine the causes of obsolescence of obsolete materials through multiple parallel threads and obtain the attribution analysis results; each thread is used to execute the analysis process of one cause. The processing module is also used to determine the target processing strategy for stagnant materials from a variety of candidate processing strategies. The target processing strategy is used to indicate how to process the stagnant materials. The processing module is also used to generate processing reports based on the attribution analysis results and target processing strategies. The processing reports are used to show the causes of stagnation of stagnant materials and the processing methods.

[0026] Thirdly, this application also provides an electronic device. The electronic device includes a memory, one or more processors, and a computer program stored in the memory and executable on the processor. The electronic device executes the computer program to implement any of the implementations of the first aspect described above.

[0027] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method of any of the implementations of the first aspect described above.

[0028] Fifthly, this application also provides a computer program product that, when run on an electronic device, causes the electronic device to execute any of the implementation methods of the first aspect described above.

[0029] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the architecture of a system for handling stagnant materials according to an embodiment of this application; Figure 2 This is a flowchart of a method for handling stagnant materials according to an embodiment of this application; Figure 3 This is a structural block diagram of a device for processing stagnant materials according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] In manufacturing supply chain management, obsolete materials specifically refer to inventory that has been stockpiled for a long time and whose turnover is stagnant. These materials not only continuously occupy storage space and tie up a significant amount of a company's working capital, but may also depreciate completely due to rapid technological iteration or sudden changes in market demand. Therefore, timely identification and proper handling of these materials have become crucial for companies to control costs and optimize assets.

[0033] Currently, the manufacturing industry generally adopts a basic operating model for managing obsolete materials, which involves initial screening by the system followed by in-depth manual analysis. This model involves companies setting a static inventory age threshold (usually 180 days) in their ERP systems to automatically identify and generate a list of obsolete materials. However, this list only serves as a starting point for subsequent work. The truly critical and complex analytical decision-making work, including attribution analysis (investigating why materials become obsolete) and strategy formulation (determining how to effectively dispose of these materials), is entirely done manually offline.

[0034] This work mode, which relies heavily on manual operation, has the following drawbacks: (1) In terms of efficiency, the manual data collection and integration work across multiple heterogeneous systems makes the analysis of a single material time-consuming. At the same time, the traditional relational database architecture has performance bottlenecks when dealing with complex cross-system related queries, making it difficult to support the business's timeliness requirements for near real-time analysis. In terms of intelligence, the existing system can only perform the preliminary "marking" function based on simple rules, and it does not have the ability to automatically diagnose and reason about the causes of stagnation. Moreover, the experience and logic formed by each manual analysis only remain in personal work documents or meeting minutes, and cannot be structurally deposited into the system to form corporate knowledge assets that can be continuously learned and reused. (2) In terms of decision quality, the disposal decision-making process relies heavily on personal experience and subjective judgment, and lacks a quantitative evaluation mechanism based on unified data standards. When a certain material is applicable to multiple disposal schemes at the same time, it is difficult to ensure the objectivity of the decision and the optimal economic benefits. (3) In terms of process evolution, the lack of standardized digital records in the entire analysis and decision-making chain makes it impossible to trace and audit the internal logic of "why such a judgment was made" and "why this decision was chosen". This seriously hinders the possibility of enterprises to conduct process review and strategy optimization based on historical data, making it difficult to achieve systematic improvement in management capabilities.

[0035] In summary, the above solutions have inherent limitations in terms of data integration efficiency, intelligence of analysis, objectivity of decision-making, and ability to continuously optimize processes, and are no longer able to meet the modern needs of supply chain management, which is developing towards refinement, real-time processing, and data-driven approaches.

[0036] Based on this, this application provides a method for handling obsolete materials, which automates the entire process from identification, analysis, decision-making to report generation. First, it acquires material inventory data and automatically identifies obsolete materials based on whether their storage time exceeds a preset threshold. Then, it initiates a multi-dimensional parallel attribution analysis architecture, using multiple parallel threads to simultaneously perform in-depth correlation analysis on the obsolete materials from different dimensions. Each thread executes a preset cause judgment logic and generates sub-analysis results, which are then aggregated to obtain structured attribution analysis results. Next, it enters the intelligent strategy matching stage, selecting from multiple candidate processing strategies according to a preset priority order, combined with pre-loaded auxiliary data, to perform weighted scoring and matching to determine the optimal target processing strategy. Finally, based on the attribution analysis results and the target processing strategy, the system automatically generates a processing report including interactive charts and supporting data drill-down. This method systematically solves the core problems of low efficiency in manual processing, inaccurate attribution analysis, subjective decision-making, and untraceable processes in existing technologies, realizing a shift from manual to automated processing.

[0037] Figure 1This is a schematic diagram of the architecture of a system for handling stagnant materials provided in an embodiment of this application. The system 100 includes four parts: a background investigation unit 110, an inventory analysis unit 120, a strategy decision-making unit 130, and a dynamic report generation unit 140.

[0038] Background investigation unit 110 is used to obtain information on the enterprise's industry type and organizational structure, and automatically configure the criteria for determining stagnant materials accordingly.

[0039] For example, the background investigation unit 110 first determines the industry type of the enterprise (such as electronics manufacturing, machinery processing, chemical industry, etc.), and automatically configures the adjustable parameters in the sluggish material judgment criteria according to the industry characteristics (for example, setting the inventory age threshold for electronics manufacturing to 180 days and the inventory age threshold for machinery processing to 270 days).

[0040] Simultaneously, the background investigation unit 110 acquires enterprise organizational structure information, including basic data such as factories, warehouses, and cost centers, providing business context for subsequent analysis. The results of the background investigation are stored in the configuration database as input parameters for the inventory analysis phase.

[0041] The data and information obtained by the background investigation unit 110 are all automatically obtained from the company's relevant resource system with the company's authorization.

[0042] The inventory analysis unit 120 is used to obtain material inventory data from the business system to identify obsolete materials, and to determine the causes of obsolescence of obsolete materials through multiple parallel threads, thereby obtaining attribution analysis results. Specifically, this unit schedules multiple parallel attribution analysis threads, each thread being used to execute an analysis process based on a preset cause, such as automatically determining different dimensions like minimum order quantity settings, engineering changes, and forecast deviations.

[0043] For example, six parallel threads are used to analyze the causes of stagnation of stagnant materials, including: (1) MOQ (Minimum Order Quantity) procurement rationality analysis thread (thread 1), used to analyze the causes of stagnation caused by unreasonable minimum order quantity settings; (2) ECN (Engineering Change Notice) change compliance analysis thread (thread 2), used to analyze the causes of stagnation caused by abnormal engineering change management process (timing conflict); (3) Forecast-driven source analysis thread (thread 3), used to analyze the causes of stagnation caused by inaccurate demand forecast (passive document driven); (4) Sales order fulfillment anomaly analysis thread (thread 4), used to analyze the causes of stagnation caused by changes in customer demand or abnormal order execution; (5) Material code normalization analysis thread (thread 5), used to analyze the causes of stagnation caused by multiple codes for one item due to inconsistent material codes; (6) Production work order progress analysis thread (thread 6), used to analyze the causes of stagnation caused by delays in production planning or execution. After performing the analysis, each thread outputs a judgment result. If the judgment result is positive, a corresponding attribution label is output, indicating that the cause analyzed by that thread is one of the reasons for the material stagnation; a stagnant material may be caused by at least one cause. In addition, each thread also outputs corresponding prevention strategy suggestions along with the judgment result. These suggestions aim to prevent similar materials from becoming stagnant again in the future from the source of business operations.

[0044] The strategy decision-making unit 130 is used to determine the target treatment strategy for stagnant materials from multiple candidate treatment strategies based on the attribution analysis results. Before matching, this unit preloads auxiliary data and performs weighted scoring and matching of strategies according to a preset multi-level priority order (such as alternative use, internal allocation, etc.) to determine the optimal strategy in a quantitative manner.

[0045] For example, the strategy decision unit 130 pre-sets seven candidate processing strategies arranged in a preset priority order, namely: alternative use, internal allocation, sales plan formulation, return to supplier, purchase freeze, reorganization and use, and scrapping.

[0046] The business meaning and core matching logic of each candidate processing strategy are as follows.

[0047] 1. Alternative Use: The purpose of this strategy is to directly consume stagnant inventory by using functionally compatible alternatives. Its matching function checks two core conditions: the existence of a maintained alternative, and the current inventory of that alternative is below the safety or demand level.

[0048] 2. Internal Allocation: The purpose of this strategy is to achieve cross-organizational optimization of resources within the enterprise. Its matching function checks whether other factories or departments within the enterprise have a near-term demand for the same material, and whether the estimated allocation and transportation costs are lower than a certain configurable percentage (e.g., 10%) of the material's value.

[0049] 3. Develop a sales plan: The purpose of this strategy is to absorb stagnant inventory through external markets. Its matching function assesses whether there is potential market demand for the material (e.g., historical sales records or market analysis) and whether the predicted complete sales cycle is shorter than a preset threshold (e.g., 90 days).

[0050] 4. Return to Supplier: The purpose of this strategy is to return goods to the supplier in accordance with the contract terms to recover losses. Its matching function verification: The original purchase contract explicitly contains a clause allowing returns, and the current date has not exceeded the return validity period stipulated in that clause.

[0051] 5. Purchase Freeze: The purpose of this strategy is to control inventory and prevent further growth. Its matching function determines whether there are any outstanding purchase orders or active purchase requests for the material, and also assesses whether its future net demand is zero based on the production plan.

[0052] 6. Reprocessing for New Use: The purpose of this strategy is to give materials new uses through reprocessing. Its matching function analysis considers whether the material has the technical feasibility to be reprocessed into other usable forms through physical or simple processes, and whether the estimated total cost of reprocessing is lower than the cost of purchasing new materials.

[0053] 7. Scrap Disposal: This strategy serves as a final disposal measure and is automatically triggered when all other strategies are inapplicable. Its function logic is as follows: when the matching conditions of the aforementioned six strategies are not met, the scrapping process is automatically initiated for the material.

[0054] When matching strategies for each stagnant material, the strategy decision-making unit 130 checks whether the material meets the applicable conditions of each strategy in the order of priority mentioned above, and calculates a quantitative comprehensive score for the strategies that meet the conditions. If the comprehensive score of a strategy reaches a preset threshold, it is determined as the optimal target processing strategy, and the matching of subsequent low-priority strategies is terminated, thereby ensuring that each material ultimately receives a clear and optimal disposal suggestion and avoiding strategy conflicts.

[0055] The dynamic report generation unit 140 is used to generate and output a processing report containing interactive controls based on the attribution analysis results and the target processing strategy.

[0056] For example, the dynamic report generation unit 140 employs a multi-model collaborative architecture to generate dynamic reports. First, it integrates basic information to form a structured static report. Then, it initiates a dynamic webpage generation process, calling a large language model to construct an HTML (HyperText Markup Language) report framework containing navigation menus and dynamic content placeholders. Next, based on the placeholders in the framework, it concurrently calls the LLM (Local Language Modeling) to generate specific HTML content for each section, such as data analysis, attribution interpretation, and strategy explanation. After generation, the content undergoes integrity and format verification to ensure accuracy and compliance with specifications. Finally, the verified content is combined with visual charts and saved as a complete HTML file, thereby generating an interactive, dynamic processing report that supports data drill-down operations via interactive controls.

[0057] In summary, the obsolete material handling system provided in this application achieves full-process automation from standard configuration, intelligent attribution analysis, quantitative strategy matching to dynamic report generation through the collaborative operation of the background investigation unit, inventory analysis unit, strategy decision-making unit, and dynamic report generation unit. This system replaces the fragmented processes of traditional methods relying on human experience and manual cross-system operations with a systematic and parallel computing architecture, enabling rapid and accurate identification of the causes of obsolescence and determination of the optimal disposal strategy. Its advantages lie in significantly improving processing efficiency and the objectivity of analytical decisions, achieving quantitative evaluation and knowledge accumulation of the disposal process, and ultimately presenting the analysis results intuitively through interactive reports, providing effective support for enterprises to conduct data-driven refined supply chain management.

[0058] Figure 2 This is a flowchart of a method for processing stagnant materials according to an exemplary embodiment of this application. The method can be performed by, for example... Figure 1 The processing system shown can be executed by a server or terminal device on which the system is deployed. This embodiment illustrates the method by example, where the terminal device provides an interactive interface as the system entry point. Users can input company information and authorize data access through this interface. Subsequently, the system automatically obtains various business data required for subsequent analysis of stagnant materials through a preset company data interaction interface. For example... Figure 2 As shown, the method specifically includes the following steps.

[0059] S201, Obtain material inventory data.

[0060] Material inventory data includes the storage time of various materials in the material warehouse; The system acquires the enterprise data required for analyzing stagnant materials. The enterprise data mainly includes material inventory data and enterprise background data.

[0061] The material inventory data includes basic information on various materials and their storage duration in the warehouse. The company background data includes the company's industry type and organizational structure information.

[0062] In some embodiments, when an enterprise authorizes and invokes the system, the system immediately initiates the analysis process. The system first extracts the aforementioned material inventory data from the enterprise's business system (such as ERP).

[0063] Simultaneously, the system utilizes acquired enterprise background data to conduct background investigations to adapt to enterprise characteristics. Specifically, the system dynamically configures differentiated criteria for judging stagnant materials based on industry type (such as electronics manufacturing, machining, or chemicals), that is, sets corresponding "preset duration thresholds" for different industries (for example, the preset duration threshold for electronics manufacturing is set to 180 days, and the preset duration threshold for machining is set to 270 days). The organizational structure information (such as factories, warehouses, and cost centers) acquired in this process provides the necessary business context for subsequent in-depth analysis.

[0064] In step S201, the system acquires material inventory data. This data includes the storage duration of various materials in the material warehouse. Specifically, the system acquires the enterprise data required for analyzing stagnant materials. This enterprise data mainly includes material inventory data and enterprise background data. The material inventory data contains basic information about various materials and their storage duration in the warehouse; the enterprise background data includes the industry type of the enterprise and its organizational structure information.

[0065] In some embodiments, when an enterprise authorizes and invokes the system, the system immediately initiates the analysis process. The system first extracts the aforementioned material inventory data from the enterprise's business system (such as ERP). Simultaneously, the system utilizes the acquired enterprise background data to perform a background investigation to adapt to the enterprise's characteristics. Specifically, the system dynamically configures differentiated criteria for determining obsolete materials based on industry type (such as electronics manufacturing, machining, or chemicals), i.e., setting corresponding "preset duration thresholds" for different industries (for example, a preset duration threshold of 180 days for electronics manufacturing and 270 days for machining). The organizational structure information (such as factories, warehouses, and cost centers) acquired during this process provides the necessary business context for subsequent in-depth analysis.

[0066] For example, for an electronics manufacturing company, the material inventory data acquired by the system may cover detailed information on all of its materials. For instance, the company may have 100 types of materials, including material A. Let's take material A as an example to illustrate the types of information stored for each material in the material inventory data.

[0067] For example, the inventory data for material A includes its unique material code, current inventory quantity, storage duration in a specific warehouse (e.g., 150 days), last receipt date, and inventory value. These detailed data records form the basis for subsequent judgments and analyses.

[0068] S202, Identify obsolete materials based on material inventory data.

[0069] Among them, stagnant materials refer to materials that have been stored for a preset time threshold. The preset time threshold is automatically determined by the background investigation unit in the system based on the characteristics of the enterprise, especially its industry type.

[0070] In some embodiments, a preset duration threshold can be uniformly set for all materials of each type of enterprise (such as electronics manufacturing and machining). Alternatively, to further achieve refined management, the system can also make secondary settings based on the characteristics of the materials themselves (such as material type, shelf life, and value grade) on the basis of this uniform threshold, thereby setting differentiated preset duration thresholds for different types of materials.

[0071] For example, an electronics manufacturing company has materials A, B, and C. The system has a preset storage time threshold of 140 days configured according to its industry characteristics. If the current storage times of materials A, B, and C are 100 days, 120 days, and 150 days, respectively, the system determines by comparison that the storage time of material C (150 days) has exceeded the preset storage time threshold (140 days), and therefore material C is identified as obsolete material; while the storage times of materials A and B have not exceeded the preset storage time threshold, so they are not identified as obsolete materials.

[0072] S203 uses multiple parallel threads to determine the causes of stagnation in obsolete materials and obtains the attribution analysis results.

[0073] Each thread is used to perform an analysis process for one cause. Among the multiple threads, there is the i-th thread, which corresponds to the i-th cause, where i is a positive integer.

[0074] This step is implemented through a multi-dimensional asynchronous parallel attribution analysis architecture. The system first extracts the basic data of the materials to be analyzed (including material inventory data and contextual information of the materials in the production process) from various business systems of the enterprise. Then, it initializes the concurrent task scheduler and distributes the material data to N independent attribution analysis threads (N is a configurable parameter, where N is a positive integer). Each thread then executes deep correlation analysis tasks of different dimensions in parallel.

[0075] Optionally, the material identifier of the obsolete material can be obtained. This identifier serves as a core index for accurately locating the data in various business systems. For example, the material identifier could be a unique material code for the obsolete material.

[0076] When each thread analyzes the causes of stagnant materials, the system quickly locates the corresponding material node based on the material identifier using database indexing technology. It then deeply mines all information related to the material and the cause corresponding to the thread in the data association network between heterogeneous systems such as ERP, PLM, and CRM, thereby obtaining comprehensive and relevant material information.

[0077] Optionally, when analyzing stagnant materials through the i-th thread, material information associated with the i-th cause can be obtained based on the material identifier of the stagnant material.

[0078] The material information associated with the i-th cause is analyzed according to the preset rules to obtain the i-th sub-analysis result. The i-th sub-analysis result is used to indicate whether the i-th cause belongs to the cause of stagnation of stagnant materials. The system pre-defines explicit business logic judgment rules, i.e., preset rules, for each attribution analysis thread. These rules define the specific data conditions and logical relationships that must be met for a corresponding cause to be valid.

[0079] Specifically, if the material information associated with the i-th cause meets the preset rules, a cause label and prevention strategy corresponding to the i-th cause are generated, and the i-th sub-analysis result is obtained.

[0080] The prevention strategy is used to indicate the handling method to prevent the material from becoming stagnant due to the i-th cause; wherein, when the i-th sub-analysis result includes a cause label, it indicates that the i-th cause belongs to the cause of stagnant material.

[0081] Cause tags are standardized keywords used to identify specific types of obsolescence causes. Prevention strategies are operational recommendations based on attribution analysis results, designed to intervene at the source of business operations to prevent similar materials from becoming obsolete again due to the same reasons.

[0082] The attribution analysis results are obtained based on the sub-analysis results corresponding to multiple threads.

[0083] The above describes the process of analyzing the causes of each thread, taking the i-th thread as an example. The analysis process for each thread in multiple threads is based on the same principle, but the data used for analysis differs between threads.

[0084] During this process, the system uses a concurrency control mechanism to ensure that after all parallel threads have completed execution, a result aggregator summarizes and integrates the sub-analysis results output by each thread. The final attribution analysis result is a structured dataset that clearly lists all identified causes of stagnation, corresponding evidence information, and relevant preventative recommendations.

[0085] For example, N is 6, and the analysis of the causes of sluggishness is carried out by executing six independent threads concurrently.

[0086] The six independent threads include: (1) MOQ procurement rationality analysis thread (thread 1), which is used to analyze the causes of stagnation caused by unreasonable minimum order quantity settings; (2) ECN change compliance analysis thread (thread 2), which is used to analyze the causes of stagnation caused by abnormal engineering change management process (timing conflict); (3) forecast drive source analysis thread (thread 3), which is used to analyze the causes of stagnation caused by inaccurate demand forecast (passive document drive); (4) sales order fulfillment anomaly analysis thread (thread 4), which is used to analyze the causes of stagnation caused by changes in customer demand or abnormal order execution; (5) material code normalization analysis thread (thread 5), which is used to analyze the causes of stagnation caused by multiple codes for one item due to inconsistent material codes; and (6) production work order progress analysis thread (thread 6), which is used to analyze the causes of stagnation caused by delays in production planning or execution.

[0087] The following describes the process of attribution analysis performed by each thread.

[0088] 1. MOQ Procurement Rationality Analysis Thread: The purpose is to identify passive backlog of procurement batches caused by unreasonable minimum order quantity parameter settings in the material master data, thereby accurately determining the resulting stagnant materials.

[0089] The system uses the material identifier (such as a unique material code) of stagnant materials as the index key to execute preset rule logic.

[0090] The specific analysis process is as follows: First, the system locates and retrieves the minimum order quantity parameter configured in the material master data through the relational path of the graph database; simultaneously, it obtains the current obsolete inventory quantity of the obsolete material from the material inventory data. Then, the system executes the core numerical logic judgment, namely, determining whether the current obsolete inventory quantity is less than or equal to the configured minimum order quantity. If this condition is met, the obsolescence of this material is determined to conform to the business rule of "unreasonable minimum order quantity setting".

[0091] It is worth noting that the logic behind this criterion is as follows: if the current stagnant inventory of a material does not exceed its minimum order quantity (MOQ), it means that this inventory is highly likely to originate from a "one-off" or "unplanned" over-purchase made to meet the supplier's batch requirements. Ideally, inventory formed from regular purchases to meet production needs will be consumed through continuous production and will not remain stagnant for a long time. When the remaining quantity after inventory consumption is less than the MOQ, because it does not reach the batch threshold for initiating another purchase, this "residual" inventory loses the opportunity to be consumed by subsequent demand, thus remaining stagnant for a long time. Therefore, "stagnant inventory quantity ≤ minimum order quantity" is a key characteristic rule for identifying this type of abnormally circulating inventory caused by purchase batch threshold constraints. Setting this criterion can accurately distinguish between stagnant inventory caused by purchasing strategy (MOQ setting) problems and potentially larger quantities of stagnant inventory caused by other reasons such as disappearing demand or design changes, thereby achieving more refined and accurate attribution analysis and providing a direct logical basis for subsequent targeted preventative strategies such as "adjusting MOQ" or "optimizing purchase batches."

[0092] When the determination is valid, the thread outputs its sub-analysis results, which will include a standardized cause label, such as "MOQ is unreasonable," to clearly identify the cause. Simultaneously, the results will generate targeted preventative strategy recommendations, such as "Recommend reviewing and adjusting material MOQ parameters" or "Optimize purchasing batch strategy," aiming to prevent similar materials from becoming obsolete in the future due to the same reasons, starting from the source of procurement.

[0093] When the determination is invalid, meaning the current stagnant inventory quantity exceeds the configured minimum order quantity, it indicates that the stagnant status of the material is not primarily caused by the MOQ setting. In this case, the sub-analysis results output by this thread will not include the "MOQ unreasonable" causation label, nor will any related preventative strategies be generated. The result will only record that this analysis has been executed and indicate that no corresponding cause was detected, thereby ensuring the completeness and accuracy of the attribution analysis results and avoiding misleading information.

[0094] This thread, through quantitative data comparison, can effectively distinguish between inventory generated by normal safety stock or business fluctuations and inventory stagnation caused by improper purchase batch constraints, thus improving the accuracy of attribution analysis.

[0095] 2. ECN Change Compliance Analysis Thread: The purpose is to detect timing conflicts in the engineering change management process, identify procurement and inventory management violations caused by improper change execution, and thus accurately determine the resulting stagnant materials.

[0096] The system uses the material identifier of the stagnant material as the index key to execute the preset rule logic. The specific analysis process is as follows: First, the system traces two data chains in parallel: [1] traces to the original purchase business document (such as purchase order) that generated the stagnant inventory. Such documents (such as purchase order, purchase application) are legal business documents that issue purchase instructions to suppliers and ultimately form inventory, representing when and why the material was purchased; [2] traces to the engineering change order record related to the material. Such documents (i.e. ECN, Engineering Change Notice) are formal documents that authorize and record changes in product design, materials or processes. Their "effective time" marks the starting point when the old material is stopped from use in terms of technology or regulations. The system will filter out the valid engineering change orders that identify the material as "replacement deletion", "invalid" or "deletion" and obtain its effective time. "Replacement / Deletion" refers to the material being replaced by a new material in the design, requiring the original material to be removed from the product's bill of materials and discontinued. "Expiration" means the material is declared obsolete due to technological updates, regulatory restrictions, or quality issues, and is no longer permitted for use in any production. "Deletion" means the material is directly removed from the bill of materials and is no longer a component of the product. These three types of engineering change orders explicitly mark the stagnant material to be analyzed as needing replacement, expired, or to be deleted in the engineering change order record, and the change order itself is already in effect.

[0097] Correspondingly, "valid" means that the engineering change order has been approved and officially implemented. Therefore, the system filters change orders with these markers to accurately identify those change orders that have officially taken effect and explicitly require the cessation of the use of the material. This is the core premise for determining whether subsequent procurement activities constitute a violation (i.e., procurement is still carried out after the material has been officially required to be discontinued).

[0098] Subsequently, the system executes its core timing logic to determine whether the creation date of the procurement document is later than the effective date of the associated engineering change order, and whether the procurement type belongs to a specific set. If this condition is met, it is determined that there is a business violation of "declaring materials invalid before continuing procurement," which conforms to the business rule of "unreasonable engineering change management."

[0099] It is worth noting that the logic behind this criterion is as follows: the effectiveness of an engineering change order signifies that materials have been replaced or phased out in terms of design or process, and theoretically, related procurement and production activities should cease immediately. If new procurement activities continue after the change takes effect, it indicates a serious disconnect between the change process and procurement execution, meaning the purchased materials are destined to become stagnant from the moment they enter the warehouse because they cannot be used in production. Therefore, "procurement date > change effective date" is a key characteristic rule for identifying this type of stagnation caused by the failure of cross-departmental process collaboration. Setting this criterion can accurately pinpoint process breakpoints, clarify management responsibilities, and provide a direct basis for subsequent targeted preventative strategies such as "strengthening ECN process closed-loop control."

[0100] When the determination is valid, the thread outputs its sub-analysis results, which will include a standardized cause label, such as "ECN non-compliance," to clearly identify the cause. Simultaneously, the results will generate targeted prevention strategy recommendations, such as "It is recommended to strengthen the linkage verification mechanism between engineering changes and procurement execution."

[0101] If the determination is invalid, it indicates that there is no timing conflict, and the stagnant state of the material is not primarily caused by violations of the engineering change process. In this case, the sub-analysis results output by this thread will not include the cause label "ECN non-compliance".

[0102] This thread, through precise time-series comparison, can effectively identify stagnation caused by process collaboration failures, thereby enhancing the depth of attribution analysis in the management dimension.

[0103] 3. Forecast-driven source analysis thread: The purpose is to identify stagnant materials that were purchased based solely on forecasts or plans due to a lack of real customer demand, thereby locating data breakpoints in the forecasting and planning process.

[0104] The system uses the material identifier of the stagnant material as the index key to execute preset rule logic. The specific analysis process is as follows: First, the system traces back to the original source documents of the stagnant material inventory. Then, the system intelligently identifies and classifies the document type, determining whether it belongs to the "non-source documents" set (such as manual plans, manufacturing orders, sales forecasts) and not to the "source documents" set (such as sales orders, customer contracts). If this condition is met, the system determines that the cause of the material's stagnation conforms to the business rule of "inaccurate forecasting".

[0105] It is worth noting that the logic behind this criterion is that supply chain inventory should theoretically be driven by genuine customer demand (i.e., "source documents"). If materials are entirely driven by internal forecasts or plans (i.e., "non-source documents"), their inventory consumption lacks rigid constraints, and once forecast deviations or plan changes occur, stagnation is easily formed. Therefore, "source documents belonging to the non-source document set" is a key characteristic rule for identifying this type of stagnation caused by unrealistic demand forecasts and plan-driven processes. Setting this criterion can effectively distinguish between "pull" and "push" inventory, accurately pinpoint the source of inventory problems as the market forecasting mechanism rather than subsequent execution stages, and provide a direct basis for proposing targeted preventative strategies such as "optimizing demand forecasting models" or "implementing sales and operations synergy plans."

[0106] When the determination is correct, the thread outputs its sub-analysis results, which will include a standardized cause label, such as "inaccurate prediction," to clearly identify the cause. Simultaneously, the results will generate targeted prevention strategy recommendations.

[0107] If the determination is invalid, it indicates that the material is supported by real orders, and its stagnation is not primarily caused by forecast deviation. In this case, the sub-analysis results output by this thread will not include the cause label "inaccurate forecast".

[0108] This thread can effectively expose the gap between the forecasting system and the actual demand by identifying the document-driven source.

[0109] 4. Sales Order Fulfillment Anomaly Analysis Thread: The purpose is to detect material stagnation caused by a combination of scenarios such as changes in customer-side requirements, abnormal order execution, or product structure failure.

[0110] The system uses the material identifier of stagnant materials as the index key and executes preset rule logic. The specific analysis process is as follows: The system first links to the sales orders involved in the material, and then performs parallel checks from four dimensions: checking whether the order has been closed but has not reached the minimum delivery limit; checking whether the order has been open for a long time and the material has become stagnant; checking whether there are any incomplete sales change orders; and checking whether the product structure associated with the order has become invalid. If any of the above detection conditions are met, a logical "OR" operation is performed to determine that the cause of the material's stagnation meets the business rule of "customer demand change".

[0111] It's worth noting that the logic behind this judgment condition is that sales orders are the direct basis for material production. Early order closure, prolonged suspension, order changes leading to interruption, or product obsolescence all indicate that the initial production driver has disappeared or become distorted, rendering the materials prepared for them useless. This multi-dimensional, composite judgment rule can cover various breakpoints that may cause stagnation throughout the entire chain from order receipt to product completion, avoiding omissions of complex scenarios by single rules. It accurately identifies the direct impact of market fluctuations on inventory and provides a basis for subsequent targeted preventative strategies such as "strengthening order change management" and "optimizing product lifecycle management."

[0112] When the determination is valid, the thread outputs its sub-analysis results, which will include a standardized cause label, such as "customer requirement change," and may further subdivide into specific root causes. Simultaneously, targeted preventative strategy recommendations will be generated from this result.

[0113] When all dimensions of the analysis fail, it indicates that no clear anomalies were found at the sales order fulfillment level. In this case, the sub-analysis results output by this thread will not include the cause label "customer demand change". This thread comprehensively screened the impact of client-side factors on inventory obsolescence through multi-dimensional exploration.

[0114] 5. Material Coding Normalization Analysis Thread: The purpose is to identify hidden stagnant inventory caused by inconsistent material coding within the enterprise and the phenomenon of "one item, multiple codes", that is, the same physical material is repeatedly purchased and stored because of different codes.

[0115] The system uses the material identifier of the stagnant material as the index key to execute preset rule logic. The specific analysis process is as follows: First, the system uses a "three-way recall" mechanism to search for other material codes that may point to the same entity as the current stagnant material, based on three dimensions: manufacturer part number, material name, and existing conversion relationships. Then, the system performs "supply chain origin verification" on the recalled candidate materials, checking whether they share a common procurement source and similar usage scenarios. If the verification passes, it is determined that there is a "one item, multiple codes" problem, which conforms to the business rule of non-standardized material coding.

[0116] It is worth noting that the logic behind this judgment condition is that "one item, multiple codes" can cause the same physical item to be treated as multiple materials within the system, compromising inventory visibility, leading to duplicate purchases and scattered stockpiling, which is difficult to detect through conventional obsolescence analysis. Therefore, "multiple codes pointing to the same physical entity" is a key characteristic rule for identifying this type of implicit, global obsolescence risk caused by fundamental data management issues. Setting this dual judgment condition, which includes recall and verification, can effectively uncover cross-departmental material data inconsistencies while ensuring the accuracy of the analysis, and provides a direct basis for proposing the fundamental preventative strategy of "promoting the standardization of material master data."

[0117] When the determination is valid, the thread outputs its sub-analysis results, which will include a standardized cause label, such as "one item, multiple codes," to clearly identify the cause. Simultaneously, the result will generate targeted prevention strategy suggestions, such as "suggest merging material codes and clearing inventory."

[0118] If the determination is invalid, it means that no valid evidence of code duplication was found. In this case, the sub-analysis results output by this thread will not include the cause label of "one item, multiple codes". This thread can discover and eliminate hidden inventory waste caused by data inconsistency through intelligent matching and supply chain verification.

[0119] 6. Production Work Order Progress Analysis Thread: The purpose is to identify stagnation caused by obstructions in the production plan or execution process, resulting in materials prepared for the production being stuck in the warehouse and unable to be consumed.

[0120] The system uses the material identifier of the stagnant material as an index key to execute preset rule logic. The specific analysis process is as follows: The system first locates the production order associated with the material. Subsequently, the system performs a double delay check: checking whether the planned completion time has expired but the work order has not been completed; checking whether the planned start time has expired but the work order has not started. If either condition is met, and combined with the low consumption of the material in the work order, the system determines that the cause of the material's stagnation meets the business rule of "production delay".

[0121] It is worth noting that the logic behind this criterion is that the value of material inventory lies in supporting production. When production work orders fail to start or complete as planned, the materials prepared for them remain in the warehouse, transforming from "materials awaiting use" to "stagnant materials." Therefore, "planned time expired and work orders not reaching the corresponding status" is a key characteristic rule for identifying this type of stagnation caused by poor production execution. Setting this criterion directly links inventory problems to production and operational performance, accurately pinpoints bottlenecks in the production process, and provides a direct basis for subsequent targeted preventative strategies such as "optimizing production scheduling," "strengthening equipment maintenance," or "improving material availability."

[0122] When the determination is valid, the thread outputs its sub-analysis results, which will include a standardized cause label, such as "production delay," and may further subdivide the specific root causes. Simultaneously, targeted preventative strategy recommendations will be generated from this result.

[0123] If the determination is invalid, it indicates that the production progress is normal and the issue is not the primary cause of material stagnation. In this case, the sub-analysis results output by this thread will not include the "production delay" causation label. By monitoring the deviation between the production plan and the actual production, this thread can effectively identify inventory stagnation caused by problems in the internal manufacturing process.

[0124] During the execution of all parallel attribution analysis threads, the system uses an asynchronous synchronization mechanism for unified scheduling and control. The system monitors the execution status of each thread in real time and sets synchronization points to ensure that the result integration operation is triggered only after all attribution dimension analysis tasks represented by N independent threads have been completed, thereby guaranteeing the integrity of the analysis results.

[0125] After all threads have completed execution, the system integrates the collected sub-analysis results from each thread. These sub-results include the judgment conclusions for each dimension, possible causal labels, and prevention strategies. The system first aggregates all analysis results for the same material using the material code as a unique key. Then, for the determined causes (i.e., dimensions containing causal labels in the sub-results), the system summarizes the corresponding causal labels into a set of attribution dimensions for that material and extracts and records the amount of stagnation for that material. For each identified cause, the system dynamically calculates a confidence score (typically ranging from 0 to 100) as a quantitative indicator to assess the reliability of the judgment.

[0126] The confidence score is primarily calculated based on two factors: 1) Data completeness: assessing the completeness and timeliness of key data fields (such as purchase date and change order status) upon which this dimension's analysis relies; 2) Historical accuracy: the frequency with which the judgment rules or models used in this dimension accurately judge similar historical cases. A higher confidence score indicates a more reliable judgment result.

[0127] Finally, the system generates a structured attribution analysis report as the attribution analysis result. This report can show why the material is stagnant, the credibility of the judgment on the cause of stagnation, and the attribution analysis results provide standardized and quantitative input for subsequent intelligent strategy matching, and form the data foundation for presenting the analysis conclusions to the user and generating the final processing report.

[0128] S204, determine the target processing strategy for stagnant materials from a variety of candidate processing strategies.

[0129] The target processing strategy is used to indicate how to handle stagnant materials.

[0130] Optionally, auxiliary data for stagnant materials can be obtained. This auxiliary data is the data required when executing multiple candidate processing strategies.

[0131] Each candidate processing strategy is matched with auxiliary data according to its priority to obtain a matching score for each candidate processing strategy; the target processing strategy for stagnant materials is determined from multiple candidate processing strategies based on the matching score.

[0132] To improve matching efficiency, the system employs a concurrent caching preheating mechanism to acquire auxiliary data. Specifically, based on the bill of materials to be analyzed, the system simultaneously initiates queries to multiple business systems, preloading key information such as inventory organization, sales plans, and purchase order status into the memory cache to eliminate real-time query latency.

[0133] Subsequently, the system calls the condition judgment function corresponding to each strategy in a preset priority order, and uses cached data to make logical judgments to determine whether the strategy is initially applicable.

[0134] In an optional embodiment, the system adopts the following process to determine the target processing strategy: First, the system concurrently executes seven key auxiliary data query tasks to obtain the required auxiliary data. The query tasks include obtaining inventory organization information, sales plan information, purchase frozen order status, modification work order type, inventory details, internal allocation feasibility conditions, and return supplier information. The query results are uniformly stored in the memory cache pool to provide high-speed data access for subsequent strategy matching.

[0135] Secondly, the system iterates through seven candidate processing strategies according to a preset priority order. For example, the priority order can be: alternative use → internal allocation → sales plan formulation → return to supplier → purchase freeze → reorganization → scrapping.

[0136] Next, the system creates an independent strategy matching task for each stagnant material, and sequentially calls the condition judgment function corresponding to each strategy for logical judgment according to the above priority order.

[0137] For the alternative use strategy, the judgment function checks whether there is a defined alternative material and whether the current inventory of the alternative material is below the safety or demand level.

[0138] For internal allocation strategies, the decision function checks whether other factories or departments within the enterprise have a near-term demand for the material, and the estimated internal allocation transportation cost is less than a configurable percentage (e.g., 10%) of the material's value.

[0139] When developing a sales planning strategy, the decision function assesses whether there is market demand for the material and whether the expected sales cycle is shorter than a configurable time threshold (e.g., 90 days).

[0140] For the return-to-supplier strategy, its decision function verifies whether the original purchase contract contains a return clause that is still valid.

[0141] For the procurement freeze strategy, the judgment function determines whether there are still unfulfilled purchase orders or applications for the material, and at the same time, it determines whether its future net demand is zero based on the production plan.

[0142] For the restructuring and utilization strategy, the judgment function analyzes whether the material can be reprocessed into other usable forms, and the estimated total restructuring cost is lower than the cost of purchasing new materials.

[0143] For the disposal strategy, the judgment function is automatically triggered when none of the aforementioned strategies apply, serving as the final disposal method.

[0144] Through the above steps, the system completes the acquisition of auxiliary data and the preliminary matching of conditions for each strategy.

[0145] When a material meets the initial conditions of a certain strategy, the system will further calculate the quantitative matching score (i.e., the comprehensive score) of that strategy. This score is calculated based on a multi-dimensional weighted model. For the current material and strategy, the system will acquire or calculate multiple independent scoring factors, assign corresponding weights to these factors, and obtain the matching score based on the weighted sum of the scoring factors and their respective weights.

[0146] For example, several scoring factors include: cost-saving coefficient, implementation difficulty coefficient, and time window coefficient. These coefficients are derived by retrieving pre-set business rules, analyzing related data (such as procurement costs, organizational process complexity, and demand timeliness), and combining them with a pre-set scoring model, and are quantified from the dimensions of economy, operational feasibility, and timeliness, respectively.

[0147] Each scoring factor is assigned a weight, which can be preset or dynamically configured based on industry characteristics, company strategy, or historical decision-making results. For example, the system can index a preset weight configuration mapping table based on key dimensions such as "company type" and "material category" to obtain a weight value suitable for the current business scenario. For instance, for a cost-sensitive electronics manufacturing company, the system might use a configuration with a higher weight for cost savings; for project-based businesses with strict delivery deadlines, it might use a configuration with a higher weight for time windows. This mechanism ensures that the scoring model maintains a standardized framework while flexibly adapting to different decision priorities and business realities.

[0148] Finally, the overall score is obtained by summing the products of each scoring factor and its corresponding weight.

[0149] For example, consider the weights for cost savings (a), implementation difficulty (b), and time window (c). The overall score would then be calculated as: Cost Savings Coefficient × a + Implementation Difficulty Coefficient × b + Time Window Coefficient × c. This score is used to objectively evaluate the overall applicability of the strategy.

[0150] The sum of a, b, and c is a preset value. For example, a is 0.3, b is 0.4, and c is 0.3.

[0151] In an optional embodiment, if the matching score of the j-th candidate processing strategy does not reach a preset score threshold, the (j+1)-th candidate processing strategy is matched with the auxiliary data to obtain the matching score of the (j+1)-th candidate processing strategy, where j is a positive integer; or, if the matching score of the j-th candidate processing strategy reaches a preset score threshold, the matching of other candidate processing strategies with lower priority than the j-th candidate processing strategy with the auxiliary data is cancelled.

[0152] The candidate processing strategy that first reaches the preset score threshold is determined as the target processing strategy for stagnant materials.

[0153] This embodiment describes a decision-making process using a "degradation processing mechanism." Under this mechanism, the system strictly evaluates each candidate processing strategy sequentially according to a preset priority order. The execution logic is as follows: starting with the highest priority strategy, the system calls its conditional judgment function, uses auxiliary data for matching, and calculates the matching score for that strategy. If the matching score of the currently evaluated strategy reaches a preset score threshold, the system considers it a feasible and effective solution, immediately identifies it as the final target processing strategy, and stops evaluating all subsequent lower priority strategies. Conversely, if the score of the current strategy does not reach the preset score threshold, the system automatically moves on to evaluate the next priority strategy and repeats this process. This mechanism ensures decision-making efficiency, and its output is the first candidate processing strategy that meets the acceptable standard in the quantitative evaluation and has the highest priority.

[0154] In an optional embodiment, the system may also employ another decision-making logic, namely, instead of relying on a strict priority order for downgrading and screening, it performs a matching process in parallel or sequentially for all candidate processing strategies, calculating their respective matching scores. Each candidate processing strategy is matched with auxiliary data sequentially according to its corresponding priority, resulting in a matching score for each strategy; the candidate processing strategy with the highest matching score is then determined as the target processing strategy for the stagnant materials.

[0155] This embodiment describes a "globally optimal" selection mechanism. In this mode, the system independently calculates the quantified matching score for all candidate treatment strategies that meet the basic applicability conditions. After scoring all strategies, the system selects the strategy with the highest score as the target treatment strategy through comparison. This mechanism aims to select the treatment recommendation with the best comprehensive evaluation score from all feasible options, thereby ensuring the global optimality of the decision result. At the same time, each material is associated with only one finally determined treatment strategy, avoiding potential multi-strategy conflicts.

[0156] In some embodiments, to improve strategy matching efficiency, a mapping relationship can be pre-established between the ranking results of multiple candidate processing strategies and the specific cause types contained in the causes of stagnation. That is, different priorities of candidate processing strategies are dynamically set according to the different cause types contained in the causes of stagnation. After obtaining the attribution analysis results, the corresponding candidate processing strategy priority data is indexed from the preset priority order mapping table according to the attribution analysis results. The candidate processing strategy priority data stores the priorities of multiple candidate processing strategies. When performing target processing strategy matching analysis, multiple candidate processing strategies are matched one by one according to the priorities in the candidate processing strategy priority data indexed by the attribution analysis results.

[0157] S205, Generate a processing report based on the attribution analysis results and the target processing strategy.

[0158] The processing report is used to show the causes of stagnation and the methods of handling stagnant materials.

[0159] Optionally, the attribution analysis results and target processing strategies can be input into a large language model to output a processing report. The processing report includes multiple interactive controls, which are used to trigger the display of data associated with the processing report.

[0160] For example, when generating a processing report, the system performs the following specific steps: First, the system structurally integrates the attribution analysis results and the target processing strategy to form the data foundation of the report. Next, the system calls a large language model to generate an HTML report framework containing a navigation bar, chapter titles, and dynamic content placeholders based on structured prompts. Then, the system identifies the placeholder positions and concurrently calls the large language model to generate detailed interpretations of each chapter, including data analysis, causal explanations, and treatment recommendations. After automatic validation, the generated content is combined with pre-defined chart components to finally output a complete HTML report file. This report allows users to click on interactive controls to view the underlying detailed data associated with the report content.

[0161] Among them, the interactive controls are closely integrated with the intelligent analysis and disposal decision-making process of this solution. Their types include, but are not limited to: (1) Cause label details expansion control: Clicking on any attribution label in the report (such as "MOQ is unreasonable") can expand to view the specific rules, comparison data values ​​(such as minimum order quantity and actual inventory quantity) and related business document links used by the system to determine the cause. (2) Disposal strategy feasibility assessment panel expansion control: For each disposal strategy recommended in the report, an expandable panel is provided to display in detail the auxiliary data called by the system when making matching judgments (such as the inventory level of substitute materials, internal transfer cost calculation, and sales cycle forecast) and weighted scoring details (such as the scores and weights of cost savings, implementation difficulty, and time window dimensions). (3) Batch disposal task progress viewing control: When the report generates disposal suggestions for multiple stagnant materials, a clickable entry is provided to associate and jump to the subsequent actual disposal task creation and execution tracking interface for that batch of materials. (4) Historical decision comparison and feedback viewing control: Provide controls next to key conclusions to allow users to quickly view historical disposal cases and effects of similar materials in the system, and submit feedback such as adoption, modification or rejection of current system suggestions to drive the optimization of feedback learning loop.

[0162] Interactive controls transform static reports into interactive, verifiable, and executable user interfaces, intuitively demonstrating the entire closed-loop process from automated analysis to decision support and execution tracking.

[0163] In some embodiments, stagnant materials include at least one material; then, for each of the at least one material, the cause of stagnation of the material is determined by multiple threads in parallel, and the analysis result of the material is obtained; the attribution analysis result is obtained based on the analysis results corresponding to the at least one material respectively; and the processing strategy for each of the at least one material is determined from multiple candidate processing strategies to obtain the target processing strategy.

[0164] For example, the system identifies materials A, B, and C as stagnant materials. The system will create separate analysis tasks for these three materials simultaneously.

[0165] 1. Parallel Attribution Analysis Task: The system simultaneously launches multiple attribution analysis threads. For example, thread group 1 is launched for material A, thread group 2 for material B, and thread group 3 for material C. Each thread group contains six analysis threads, which perform concurrent attribution determinations for their respective materials. Ultimately, the system obtains the analysis results for material A (e.g., attributed to "inaccurate forecast"), the analysis results for material B (e.g., attributed to "ECN non-compliance"), and the analysis results for material C (e.g., attributed to "production delay"). These independent results are aggregated to form a set of attribution analysis results for this batch of materials.

[0166] 2. Parallel Strategy Matching Task: Based on the attribution analysis results above, the system simultaneously executes strategy decision-making processes for materials A, B, and C. The system independently acquires auxiliary data for each material, matches candidate strategies according to priority, and calculates scores. For example, material A might be matched with "internal allocation," material B with "return to supplier," and material C with "reconfiguration and reuse." These independent handling suggestions collectively constitute the target processing strategy set for this batch analysis.

[0167] In this way, the system can simultaneously and efficiently process multiple stagnant materials, and integrate the attribution and disposal results of all materials into a single processing report for unified display and management.

[0168] In some embodiments, the sluggish material handling system of this application further includes some auxiliary functional modules, and... Figure 1 The functional units shown together constitute a more complete and intelligent solution.

[0169] To adapt the system to the specific requirements of different enterprises, it provides a dynamic configuration function for business rules. Users can directly modify key business parameters through a graphical interface, such as adjusting the inventory age for judging stagnant materials and modifying the priority of disposal strategies. In this way, the same system can be easily used in different types of factories, such as electronics factories and machinery factories, without the need for professional technicians to modify the program code.

[0170] Furthermore, the system possesses the ability to learn from practice and self-optimize. It automatically records the final execution results of each proposed action, such as whether materials were successfully consumed and how much money was actually saved. Based on this real-world business feedback, the system automatically fine-tunes its internal analysis models and scoring algorithms. This means that the longer the system is used, the higher the accuracy of its analysis and the practicality of its recommendations will become.

[0171] To ensure a smooth process and knowledge accumulation, the system also includes a process support module. Before analysis begins, it helps companies complete basic setup and data integration; after generating a report, it sends the report to relevant personnel and tracks the execution of subsequent tasks. Simultaneously, successful handling experiences are summarized into rules and stored in the system's knowledge base for easy reference when encountering similar problems in the future.

[0172] It should be noted that the method provided in this application can be implemented in various ways. For example, in addition to using common databases, the underlying data storage of the system can also employ graph databases, which are better at handling complex relationships, thus enabling faster in-depth cause tracing. When analyzing the causes of material stagnation, besides using preset rules for judgment, a pre-trained machine learning model can be used to allow the system to automatically discover patterns from historical data. When selecting treatment methods for stagnant materials, in addition to comparing and scoring them one by one, mathematical optimization algorithms can be used for comprehensive calculation to find the overall optimal treatment combination for a batch of materials. These different technical paths can be selected and combined according to actual needs.

[0173] In summary, the method for handling obsolete materials provided in this application systematically solves the problems of low efficiency, reliance on experience, difficulty in traceability, and difficulty in knowledge accumulation inherent in traditional manual handling of obsolete materials through a standardized automated process. Specifically, this method acquires and automatically filters material inventory data, overcoming the inefficiency of manual inventory and verification, and achieving objective and rapid identification of obsolete materials. At its core, this method determines the causes of obsolescence through multiple parallel threads. This design fundamentally changes the traditional serial, manual analysis mode, significantly shortening the time for in-depth attribution analysis of a single material by utilizing concurrent computing, and solidifying expert experience into executable system rules, achieving automation and standardization of analysis. Furthermore, this method intelligently determines the target processing strategy from multiple candidate strategies, transforming subjective meeting decisions into objective calculations based on data and quantitative scoring, improving the scientific rigor and consistency of decision-making. Finally, it automatically generates a structured processing report, which not only intuitively displays the causes and solutions but also makes the entire analysis and decision-making process traceable through digital recording, providing a foundation for knowledge accumulation and process optimization. The entire solution forms an automated closed loop from identification, analysis, decision-making to output, significantly improving the precision and intelligence of supply chain inventory management.

[0174] Corresponding to the method for handling stagnant materials in the above embodiments, Figure 3 A structural block diagram of an apparatus for processing stagnant materials according to an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0175] Reference Figure 3The device 300 includes: a data preparation module 310 for acquiring material inventory data, which includes the storage duration of various materials in the material warehouse; the data preparation module 310 is also used to identify stagnant materials based on the material inventory data, where stagnant materials refer to materials whose storage duration has reached a preset duration threshold; a processing module 320 for determining the causes of stagnant materials through multiple parallel threads to obtain attribution analysis results; each thread is used to execute an analysis process for one cause; the processing module 320 is also used to determine a target processing strategy for stagnant materials from multiple candidate processing strategies, where the target processing strategy indicates how to process the stagnant materials; and the processing module 320 is also used to generate a processing report based on the attribution analysis results and the target processing strategy, where the processing report displays the causes of stagnant materials and the processing methods.

[0176] It should be noted that the information interaction and execution process between the above-mentioned devices / modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0177] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0178] To implement the above embodiments, this application also proposes an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0179] like Figure 4 As shown, the above-mentioned electronic device 400 includes: The system includes a memory 410 and at least one processor 420, and a bus 430 connecting the different components (including the memory 410 and the processor 420). The memory 410 stores a computer program, and when the processor 420 executes the program, it implements the method for processing stagnant materials according to the embodiments of this application.

[0180] Bus 430 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0181] Electronic device 400 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 400, including volatile and non-volatile media, removable and non-removable media.

[0182] Memory 410 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 440 and / or cache memory 450. Electronic device 400 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 460 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 430 via one or more data media interfaces. Memory 410 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0183] A program / utility 480 having a set (at least one) of program modules 470 may be stored in, for example, memory 410. Such program modules 470 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 470 typically perform the functions and / or methods described in the embodiments of this application.

[0184] Electronic device 400 can also communicate with one or more external devices 490 (e.g., keyboard, pointing device, display 491, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 494. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 493. As shown, network adapter 493 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0185] The processor 420 performs various functional applications and data processing by running programs stored in the memory 410.

[0186] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the method for processing stagnant materials in the embodiments of this application, and will not be repeated here.

[0187] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the above-described method embodiments.

[0188] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0189] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some regions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0190] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0191] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0192] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0193] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] In the foregoing, specific details such as particular system architectures and techniques have been set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted to avoid unnecessary detail from obscuring the description of this application.

[0195] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0196] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0197] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0198] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0199] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0200] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for handling stagnant materials, characterized in that, The method includes: Obtain material inventory data, which includes the storage duration of various materials in the material warehouse; Obsolete materials are identified based on the material inventory data. Obsolete materials refer to materials whose storage time has reached a preset time threshold. The causes of the stagnation of the stagnant materials are determined by multiple threads in parallel, and the attribution analysis results are obtained; each thread is used to perform the analysis process of one cause. A target processing strategy for the stagnant material is determined from a variety of candidate processing strategies, the target processing strategy indicating how the stagnant material is processed; A processing report is generated based on the attribution analysis results and the target processing strategy. The processing report is used to show the causes of the stagnation of the stagnant materials and the processing methods.

2. The method according to claim 1, characterized in that, The plurality of threads includes the i-th thread, which corresponds to the i-th cause, where i is a positive integer; The process of determining the causes of stagnation in the stagnant materials through multiple parallel threads and obtaining attribution analysis results includes: Obtain the material identifier of the stagnant material; When analyzing the stagnant material through the i-th thread, material information associated with the i-th cause is obtained based on the material identifier of the stagnant material; The material information associated with the i-th cause is analyzed according to preset rules to obtain the i-th sub-analysis result. The i-th sub-analysis result is used to indicate whether the i-th cause belongs to the cause of stagnation of the stagnant material. The attribution analysis results are obtained based on the sub-analysis results corresponding to the multiple threads.

3. The method according to claim 2, characterized in that, The step of analyzing the material information related to the i-th cause according to preset rules to obtain the i-th sub-analysis result includes: If the material information associated with the i-th cause conforms to the preset rules, a cause label and prevention strategy corresponding to the i-th cause are generated, and the i-th sub-analysis result is obtained; the prevention strategy is used to indicate the handling method to prevent the i-th cause from causing the material to become stagnant; wherein, when the i-th sub-analysis result includes the cause label, it indicates that the i-th cause belongs to the stagnant material's stagnant cause.

4. The method according to claim 1, characterized in that, The step of determining the target processing strategy for the stagnant material from multiple candidate processing strategies includes: Obtain auxiliary data for the stagnant materials, wherein the auxiliary data is the data required when executing the multiple candidate processing strategies; Each candidate processing strategy is matched with the auxiliary data according to its priority, and a matching score is obtained for each candidate processing strategy. The target processing strategy for the stagnant material is determined from the multiple candidate processing strategies based on the matching score.

5. The method according to claim 4, characterized in that, The step of matching each candidate processing strategy with the auxiliary data according to its priority to obtain a matching score for each candidate processing strategy includes: If the matching score of the j-th candidate processing strategy does not reach the preset score threshold, the (j+1)-th candidate processing strategy is matched with the auxiliary data to obtain the matching score of the (j+1)-th candidate processing strategy, where j is a positive integer. Alternatively, if the matching score of the j-th candidate processing strategy reaches a preset score threshold, the matching of other candidate processing strategies with lower priority than the j-th candidate processing strategy with the auxiliary data is cancelled.

6. The method according to claim 5, characterized in that, The step of determining the target processing strategy for the stagnant material from the multiple candidate processing strategies based on the matching score includes: The candidate processing strategy that first reaches the preset score threshold is determined as the target processing strategy for the stagnant material.

7. The method according to claim 4, characterized in that, The step of determining the target processing strategy for the stagnant material from the multiple candidate processing strategies based on the matching score includes: The candidate processing strategy with the highest matching score is determined as the target processing strategy for the stagnant material.

8. The method according to any one of claims 1 to 7, characterized in that, The step of generating a processing report based on the attribution analysis results and the target processing strategy includes: The attribution analysis results and the target processing strategy are input into the large language model, and the processing report is output. The processing report includes multiple interactive controls, which are used to trigger the display of data associated with the processing report.

9. The method according to any one of claims 1 to 7, characterized in that, The stagnant materials include at least one type of material; The process of determining the causes of stagnation in the stagnant materials through multiple parallel threads and obtaining attribution analysis results includes: For each of the at least one material, the cause of stagnation of the material is determined by the multiple threads in parallel, and the analysis result of the material is obtained. The attribution analysis results are obtained based on the analysis results corresponding to each of the at least one material; The step of determining the target processing strategy for the stagnant material from multiple candidate processing strategies includes: The target processing strategy is obtained by determining a processing strategy for each of the at least one material from a variety of candidate processing strategies.

10. An electronic device comprising a memory, one or more processors, and a computer program stored in the memory and executable on the one or more processors, characterized in that, When the one or more processors execute the computer program, the electronic device performs the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 9.

12. A computer program product, characterized in that, Includes a computer program that, when run on an electronic device, causes the electronic device to perform the method as described in any one of claims 1 to 9.

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