Supply chain hierarchical management and control method and device based on multi-source data

Abstract

This disclosure presents a method and apparatus for hierarchical supply chain management based on multi-source data. One specific implementation of the method includes: determining a product code to obtain corresponding end-to-end management information; encrypting the original user parameters corresponding to a supply chain business processing request to obtain an encrypted dataset; sending requests in parallel to at least one data source to obtain multi-source data information; encapsulating the multi-source data information and the end-to-end management information; distributing standardized data objects to corresponding target engines to determine corresponding initial fusion sub-information; mapping and optimizing the initial fusion sub-information; and sending the optimized fusion sub-information to the supply chain business system to drive the business system to perform hierarchical management operations on the supply chain. This implementation enables intelligent supply chain management, thus reducing management time delays and improving supply chain operational efficiency.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of computer technology, and more specifically to supply chain hierarchical management methods, apparatus, electronic devices, and computer-readable media based on multi-source data. Background Technology

[0002] Currently, in the field of supply chain management (e.g., supply chain finance and risk control scenarios), enterprises urgently need to implement rapid and accurate tiered control over suppliers, customers, or transactions. Existing technologies typically rely on sequentially acquiring information from multiple internal and external data sources (e.g., business registration, credit reporting, and transaction data) and performing comprehensive calculations.

[0003] However, when using the above methods for supply chain management, the following technical problems often arise: Data acquisition often uses a serial approach, which is inefficient and wastes a lot of time and computing resources. When connecting to new data sources or adjusting the computing model, it is necessary to modify the core system code, resulting in insufficient flexibility, high development and maintenance costs, and low overall operational efficiency of the supply chain.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose supply chain hierarchical management methods, apparatuses, electronic devices, and computer-readable media based on multi-source data to address one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a supply chain hierarchical management and control method based on multi-source data, comprising: responding to receiving a supply chain business processing request, determining a product code based on product feature information in the supply chain business processing request to obtain corresponding end-to-end management and control information; encrypting the user's original parameters corresponding to the supply chain business processing request to obtain an encrypted dataset; sending requests in parallel to at least one data source according to the data source list in the end-to-end management and control information and the encrypted dataset to obtain multi-source data information; encapsulating the multi-source data information and the end-to-end management and control information based on predefined standardization rules to obtain a standardized data object; distributing the standardized data object to a corresponding target engine according to the computing engine information in the end-to-end management and control information to determine the corresponding initial fusion score information; mapping and optimizing the initial fusion score information based on the optional processing logic in the end-to-end management and control information to obtain optimized fusion score information; and sending the optimized fusion score information to the supply chain business system to drive the business system to perform hierarchical management and control operations on the supply chain.

[0008] Secondly, some embodiments of this disclosure provide a supply chain hierarchical management and control device based on multi-source data, comprising: a determining unit configured to, in response to receiving a supply chain business processing request, determine a product code based on product feature information in the supply chain business processing request to obtain corresponding end-to-end management and control information; an encryption unit configured to encrypt the user's original parameters corresponding to the supply chain business processing request to obtain an encrypted dataset; a sending request unit configured to, in parallel, send requests to at least one data source according to the data source list in the end-to-end management and control information and the encrypted dataset to obtain multi-source data information; and an encapsulation unit configured to... Based on predefined standardization rules, the aforementioned multi-source data information and the aforementioned end-to-end control information are encapsulated to obtain standardized data objects. The distribution unit is configured to distribute the aforementioned standardized data objects to the corresponding target engines according to the computing engine information in the aforementioned end-to-end control information, so as to determine the corresponding initial fusion score information. The optimization unit is configured to map and optimize the aforementioned initial fusion score information based on the optional processing logic in the aforementioned end-to-end control information, so as to obtain optimized fusion score information. The hierarchical control unit is configured to send the aforementioned optimized fusion score information to the aforementioned supply chain business system to drive the aforementioned business system to perform hierarchical control operations on the supply chain.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, such that when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any implementation of the first aspect.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0011] The above-described embodiments of this disclosure have the following beneficial effects: The supply chain hierarchical management and control method based on multi-source data in some embodiments of this disclosure achieves intelligent supply chain management and control, thereby reducing management and control time delays and improving supply chain operational efficiency. Specifically, the reason for low management and control efficiency is that: the serial processing of data acquisition leads to response delays; and the rigid data processing logic is difficult to adapt to new requirements. Based on this, the supply chain hierarchical management and control method based on multi-source data in some embodiments of this disclosure firstly, in response to receiving a supply chain business processing request, determines the product code based on the product feature information in the supply chain business processing request to obtain the corresponding end-to-end management and control information. By locating the product code and end-to-end management and control information through product feature information, a precise association between the business request and management and control rules is established, ensuring that the management and control logic matches the product characteristics. Then, the user's original parameters corresponding to the supply chain business processing request are encrypted to obtain an encrypted dataset. A compliant dataset is generated by encrypting the user's original parameters to prevent the leakage of sensitive information, meet data transmission security requirements, and provide a secure carrier for multi-source data requests. Then, according to the data source list in the end-to-end management and control information and the encrypted dataset, requests are sent in parallel to at least one data source to obtain multi-source data information. Parallel requests for multi-source data reduce data acquisition time and improve efficiency. Combining encrypted datasets and a list of data sources ensures targeted requests and controllable data sources. Secondly, based on predefined standardization rules, the aforementioned multi-source data and end-to-end control information are encapsulated to obtain standardized data objects. Standardized encapsulation of multi-source data and end-to-end control information eliminates data format differences, achieving structured integration of data and control rules, and providing a unified input format for engine calculations. Thirdly, according to the calculation engine information in the end-to-end control information, the standardized data objects are distributed to the corresponding target engines to determine the initial fusion score information. Distributing standardized data objects according to calculation engine information generates initial fusion score information, achieving precise data-engine adaptation and ensuring that the fusion score calculation logic conforms to preset rules, laying the foundation for subsequent optimization. Next, based on the optional processing logic in the end-to-end control information, the initial fusion score information is mapped and optimized to obtain optimized fusion score information. Optimizing the initial fusion score information through optional processing logic improves the adaptability of the score to business scenarios, enhances the accuracy of risk assessment, and provides a reliable basis for hierarchical control. Finally, the optimized and integrated information is sent to the business systems of the aforementioned supply chain to drive these systems to perform tiered control operations. By using the optimized and integrated information to drive tiered control in the business systems, dynamic linkage between risk levels and control measures is achieved, improving the efficiency of supply chain risk response and the targeted nature of control. In summary, intelligent supply chain control is realized, thus reducing control time delays and improving the operational efficiency of the supply chain. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the supply chain hierarchical management method based on multi-source data according to this disclosure; Figure 2 This is a structural schematic diagram of some embodiments of the supply chain hierarchical management device based on multi-source data according to the present disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] refer to Figure 1The diagram illustrates a process 100 of some embodiments of a multi-source data-based supply chain hierarchical management method according to this disclosure. This multi-source data-based supply chain hierarchical management method includes the following steps: Step 101: In response to receiving a supply chain business processing request, determine the product code based on the product feature information in the supply chain business processing request to obtain the corresponding end-to-end control information.

[0021] In some embodiments, the implementing entity (e.g., an electronic device) of the above-described supply chain hierarchical control method based on multi-source data can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed in the hardware devices listed above. It can be implemented as multiple software programs or software modules to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0022] In some embodiments, the aforementioned executing entity may, in response to receiving a supply chain business processing request, determine a product code based on the product characteristic information in the supply chain business processing request to obtain corresponding end-to-end control information. The supply chain business processing request may be a business application initiated by a supplier (e.g., a financing request initiated by a supplier). The aforementioned product characteristic information may be data describing the core attributes of the supply chain product. The aforementioned supply chain product may be a service solution in the supply chain business (e.g., a financial product for accounts receivable financing). The aforementioned product code may be a unique identifier for the supply chain product. The aforementioned end-to-end control information may be a set of product end-to-end processing rules, which may include: product code, data source, encryption standard, computing engine information, and optional processing logic.

[0023] In some optional implementations of certain embodiments, the aforementioned execution entity may, in response to receiving a supply chain business processing request, determine a product code based on the product characteristic information in the supply chain business processing request to obtain corresponding end-to-end control information, which may include the following steps: The first step, in response to a received supply chain business processing request, is to extract product characteristic information from the request. This product characteristic information includes: name, version number, and keyword combination hash value. The version number can serve as an identifier to distinguish different iterations of the product within the supply chain. The keyword combination hash value can be a unique value generated by a hash algorithm from product keywords (e.g., usage, parameters), enhancing the security of feature matching. In practice, first, a supply chain business processing request (e.g., a chip procurement financing application from an electronics manufacturer) is received. Then, the product characteristic information is extracted by parsing the supply chain business processing request.

[0024] The second step is to match the aforementioned product feature information with a pre-set product registration database to determine the corresponding product code. This pre-set product registration database can be a database that stores the correspondence between product features and codes.

[0025] The third step involves loading end-to-end management and control information from the distributed configuration center based on the aforementioned product codes. This distributed configuration center can be a system that distributes and stores end-to-end management and control information for each product, enabling dynamic distribution of this information. In practice, the product code is first used as the primary key or configuration ID. Then, a query request is sent to the distributed configuration center (e.g., a Nacos server) to retrieve the end-to-end management and control information corresponding to the product code.

[0026] Step 102: Encrypt the original user parameters corresponding to the supply chain business processing request to obtain the encrypted dataset.

[0027] In some embodiments, the executing entity may encrypt the original user parameters corresponding to the supply chain business processing request to obtain an encrypted dataset. The original user parameters may be original information describing identity or business data in the supply chain business processing request. For example, the original user parameters may include the legal representative's ID number. The encrypted data may be secure data that has been serialized and encapsulated.

[0028] In some optional implementations of certain embodiments, the execution entity may encrypt the original user parameters corresponding to the supply chain business processing request to obtain an encrypted dataset, which may include the following steps: The first step is to determine the sensitive data fields corresponding to the original user parameters based on a predefined sensitive word library and pattern matching rules. The predefined sensitive word library can be a predefined sensitive information identification rule library. For example, the sensitive word library can be a dictionary including, but not limited to, keywords such as "ID card," "bank card," and "mobile phone number." The pattern matching rules can be regular expression-based pattern recognition rules. The sensitive data fields can be data items that need to be encrypted. In practice, first, the various fields of the original user parameters are traversed, and then matched using the sensitive word library and pattern matching rules to obtain the sensitive data fields.

[0029] The second step is to generate a globally unique request session code corresponding to the aforementioned supply chain business processing request. This globally unique request session code can be a unique identifier generated specifically for the supply chain business processing request. In practice, first, the basic information of the supply chain business processing request (e.g., request time, company ID) is obtained. Then, a unique identifier can be generated using the UUID algorithm to serve as the globally unique request session code, for example, "SH-SC-20251025-0001".

[0030] The third step is to generate a dynamic encryption key based on the aforementioned globally unique request session code. This dynamic encryption key can be a temporary key generated based on the globally unique request session code, used to encrypt sensitive data. In practice, firstly, the globally unique request session code (“SH-SC-20251025-0001”) is combined with a timestamp (202510251430) to generate a dynamic encryption key using a key derivation algorithm (e.g., PBKDF2).

[0031] The fourth step involves performing differentiated encryption on the aforementioned sensitive data fields using the dynamic encryption key, resulting in a hierarchical ciphertext dataset. This hierarchical ciphertext dataset can be a collection of ciphertext obtained by differentially encrypting fields with different sensitivity levels. In practice, first, the sensitivity level of the field is determined (e.g., "ID number" is highly sensitive, "revenue" is moderately sensitive). Then, the highly sensitive field is encrypted using SM4, and the moderately sensitive field is encrypted using AES, using the dynamic key to obtain the hierarchical ciphertext dataset.

[0032] The fifth step involves generating a security metadata package based on the aforementioned hierarchical encrypted dataset and the algorithm information corresponding to the dynamic encryption key. This security metadata package includes: encryption algorithm information, key version information, and timestamp information. The algorithm information can be details of the encryption algorithm used by the dynamic encryption key. For example, the algorithm information may include: algorithm type and parameters. The security metadata package can be a data package recording encryption-related information. In practice, first, the algorithm information ("SM4, 128 bits") and key version of the dynamic key are extracted. Then, the current timestamp is obtained. Finally, these are integrated to generate the security metadata package.

[0033] The sixth step is to encrypt the dynamic encryption key to obtain the encrypted data key. This encrypted data key can be a secondary encryption of the dynamic encryption key. In practice, an asymmetric encryption algorithm and a public key can be used to encrypt the dynamic encryption key to obtain the encrypted data key.

[0034] Step 7: Serialize and encapsulate the hierarchical ciphertext dataset, the encrypted data key, the globally unique request session encoding, and the security metadata to obtain the encrypted dataset. In practice, the hierarchical ciphertext dataset, the encrypted data key, the session encoding, and the security metadata can be serialized using JSON format to encapsulate the encrypted dataset.

[0035] Step 103: Based on the data source list and encrypted dataset in the end-to-end control information, send requests to at least one data source in parallel to obtain multi-source data information.

[0036] In some embodiments, the aforementioned executing entity may, based on the data source list in the aforementioned end-to-end control information and the aforementioned encrypted dataset, send requests in parallel to at least one data source to obtain multi-source data information. The aforementioned data source list may be a list of data sources to be queried and their configuration information. For example, the aforementioned configuration information may include a "credit query interface." The aforementioned data source may be a service or database that provides specific data. The aforementioned request may be a query instruction sent to the data source. The aforementioned multi-source data information may be integrated data information obtained from multiple data sources.

[0037] In addressing the technical challenges mentioned above, the application scenarios—those requiring real-time fusion and analysis of multi-source heterogeneous data (e.g., online loan approval)—often present the following problems: low data acquisition efficiency, poor system fault tolerance, and uncontrollable data quality, resulting in significant waste of time and computing resources. Considering the specific requirements of this application scenario—high efficiency, high reliability, and data consistency—we have decided to adopt the following solution: In some optional implementations of certain embodiments, the aforementioned execution entity may, based on the data source list in the aforementioned end-to-end control information and the aforementioned encrypted dataset, send requests in parallel to at least one data source to obtain multi-source data information, which may include the following steps: The first step is to extract the logical dependency and priority information from the aforementioned data source list. The logical dependency information can be the relationship between data sources; for example, source B requires the output of source A as input. The priority information can be the acquisition order weights assigned to data sources based on business importance. For example, the priority information could be: internal transaction data (high priority) > credit data (medium) > public opinion data (low). In practice, firstly, based on the configuration information in the data source list, the logical dependencies between tasks are identified, and finally, the priority weights of each task are determined.

[0038] The second step is to construct a weighted directed acyclic graph (DAG) based on the aforementioned logical dependency and priority information. This DAG can be represented by a graph structure that shows task dependencies and priorities, where nodes are query tasks and edges represent dependencies with corresponding weights. In practice, each data source in the data source list is defined as a task node. Then, directed edges are drawn based on the logical dependency information (e.g., A->B, B can only be obtained through A), and each edge is assigned a weight based on its priority (e.g., a weight of 5 for high-priority edges). Finally, the weighted DAG is constructed.

[0039] The third step involves generating dynamic task scheduling policy information based on the aforementioned weighted directed acyclic graph (DAG) and real-time system resources. The real-time system resources can refer to the current available computing resources, including CPU utilization, memory capacity, network bandwidth, and the number of database connections. The dynamic task scheduling policy information can be task execution policy information generated based on the weighted DAG and real-time system resources, including execution order and resource allocation information. In practice, firstly, based on the weighted DAG and real-time system resources (e.g., 30% CPU, 8GB memory), the node priorities and resource matching degrees in the weighted DAG are determined. Then, dynamic scheduling policy information is generated, such as the policy information for "prioritizing credit investigation tasks with 4GB of memory."

[0040] Fourth, based on the aforementioned dynamic task scheduling strategy information, the task nodes in the weighted directed acyclic graph are topologically sorted and resource-allocated, and fault handling scheme information is injected to construct a fault-tolerant executable task queue. The aforementioned fault information handling scheme can be a task queue containing fault handling schemes to ensure fault-tolerant task execution. The aforementioned fault-tolerant executable task queue can be a task sequence with fault recovery capabilities.

[0041] As an example, a topological sort is performed on the weighted directed acyclic graph to obtain a linear task execution sequence. Then, specific threads and connection resources are allocated to each task based on dynamic task scheduling policy information. Next, a predefined fault handling configuration table (e.g., a mapping between task types and policies) is used. Then, the corresponding fault handling scheme is matched based on the type of the current task node (e.g., API call / database query) to inject the appropriate fault handling scheme and generate an executable task queue.

[0042] The fifth step involves executing the tasks in the fault-tolerant executable task queue in parallel to obtain execution status information and the returned raw response data. The execution status information can be status data during task execution, such as {Task ID: “T001”, Status: “Success”, Time Elapsed: “1.2s”}. The raw response data can be unprocessed raw data returned by the data source. In practice, firstly, a thread pool can be used to execute the tasks in the fault-tolerant executable task queue in parallel. Then, the execution process of each task is recorded, and the return result (success data or exception) is captured to generate execution status information (success / failure / timeout) and the returned raw response data.

[0043] Step 6: Based on the execution status information described above, append status markers and metadata to the original response data to generate multi-source original data. The status markers can be status labels attached to the data, such as success, degradation, or missing. The metadata can be data describing the data itself, such as data source, acquisition time, and processing time. The multi-source original data can be a collection of original data obtained from various data sources, with status markers and metadata appended. In practice, firstly, based on the execution status information corresponding to the original response dataset (e.g., "failure -> degradation"), status markers are added to the data, along with metadata indicating the source and processing time, to serve as multi-source original data.

[0044] The seventh step involves aligning and verifying the consistency of the aforementioned multi-source raw data with business rules to generate multi-source data information. This business rule consistency can involve verifying whether the data conforms to business logic. For example, business consistency could include ensuring that the age is not negative and the date is valid. In practice, firstly, all data are aligned to a unified logical time point. Then, the validity of the data is verified according to business rules (e.g., "registered capital cannot be negative"). Finally, invalid data is removed or repaired to generate multi-source data information.

[0045] The above-described steps, as an inventive point of this disclosure, solve the technical problems mentioned in the background art: "low data acquisition efficiency, poor system fault tolerance, uncontrollable data quality, and waste of a large amount of time and computing resources." The reasons for these technical problems are as follows: traditional serial data acquisition methods are inefficient and lack effective fault tolerance mechanisms for data source anomalies, causing single-point failures to affect the overall process. This invention, by constructing a weighted directed acyclic graph for intelligent task scheduling and injecting a fault tolerance mechanism, achieves efficient parallel data acquisition and automatic fault recovery, saving the cost of decision-making errors and data collection time costs caused by data loss.

[0046] In addressing the aforementioned technical challenges by adopting technical solutions, considering the application scenario—where the supply chain needs to acquire multi-dimensional data with dependencies between these data (e.g., credit information queries depend on judicial information)—the following technical issues often arise: execution deadlocks, single points of failure, and low resource utilization efficiency due to complex task dependencies and unstable external services when acquiring multi-source data in parallel. Given the following requirements for this application scenario: high timeliness, strong dependencies, high reliability, and efficient resource utilization, we have decided to adopt the following solution: Optionally, in some embodiments, the execution entity may, based on the aforementioned dynamic task scheduling strategy information, perform topological sorting and resource allocation on the task nodes in the aforementioned weighted directed acyclic graph, and inject fault handling scheme information to construct a fault-tolerant executable task queue, which may include the following steps: The first step is to sort the task nodes in the aforementioned weighted directed acyclic graph (DAG) to obtain a topologically ordered task sequence. This topologically ordered task sequence is a linear sequence of tasks in the DAG arranged according to their dependencies. For example, the sequence could be: [Task A -> Task B -> Task C]. In practice, first, identify the task nodes in the weighted DAG with an in-degree of 0 (no prerequisite dependencies) and add them to the execution sequence. Then, remove these task nodes from the graph and update the in-degree of the remaining nodes. Repeat this process until there are no nodes left in the graph to obtain the topologically ordered task sequence.

[0047] The second step involves allocating computing resources to each topologically ordered task in the aforementioned topologically ordered task sequence to generate a resource-bound task set. This resource-bound task set can be a collection of tasks for which specific computing resources (e.g., threads, connections) have been allocated to each task. In practice, first, the resource type required by each task in the topologically ordered task sequence (e.g., CPU-intensive, I / O-intensive) and the estimated resource consumption are determined. Then, currently available computing resources (e.g., idle threads, idle database connections) are retrieved from the resource manager. Finally, the available resources are allocated to each task, forming the resource-bound task set.

[0048] The third step is to generate a task risk profile set corresponding to the resource-bound task set mentioned above. This task risk profile set can be a set of features describing the potential risks of the tasks. In practice, firstly, based on the characteristics of each resource-bound task (e.g., accessing external APIs) and historical execution logs (e.g., average execution time, failure rate), a predefined risk pattern library is matched (e.g., "External API -> High Risk -> Timeout"). Finally, a risk profile is generated for each task, forming a task risk profile set. For example, a task risk profile set might include: Task B: {Main Risk: "External API Timeout", Probability: "High", Impact: "Process Blocking"}.

[0049] The fourth step involves injecting the fault handling scheme information corresponding to the aforementioned task risk profile set into each topologically ordered task to generate a fault-tolerant enhancement task set. This fault-tolerant enhancement task set can be a collection of tasks for which each task is accompanied by a specific risk fault handling scheme.

[0050] As an example, firstly, a predefined fault handling scheme (e.g., retrying twice if timeout) is obtained for each task based on the task risk profile. Then, the corresponding processing logic (code or configuration) is injected into each task instance as a "fault tolerance plugin". Finally, a fault tolerance enhancement task set is generated. For example, task B is enhanced into a composite task of "execution -> timeout monitoring -> automatic retry -> degradation processing".

[0051] The fifth step involves generating a time-sequential execution sequence based on the aforementioned fault-tolerant enhancement task set. This time-sequential execution sequence can be a sequence of tasks whose execution order is planned along a time dimension. In practice, firstly, based on the fault-tolerant enhancement task set and the dependencies between tasks, and considering the estimated execution time of each task and the potential delays caused by fault handling, a theoretical start and end timestamp is assigned to each task. Then, an execution sequence with temporal relationships is generated as the time-sequential execution sequence.

[0052] The sixth step involves performing logical consistency and resource feasibility checks on the aforementioned time-sequential execution sequence to generate a fault-tolerant executable task queue. Logical consistency checks verify the reasonableness of task dependencies in the execution plan. Resource feasibility checks whether the total resources required by the plan exceed the system's current available resource limit.

[0053] The above-described operational steps, as an inventive point of this disclosure, solve the technical problem mentioned in the background art: "execution deadlock, single point of failure, and low resource utilization efficiency caused by complex task dependencies and unstable external services when acquiring multi-source data in parallel." The causes of these technical problems are as follows: lack of accurate prediction and prevention mechanisms for task execution risks; mismatch between resource allocation and task characteristics leading to resource idleness or contention; disconnect between fault handling mechanisms and specific task risk characteristics; and lack of time-series planning and pre-verification of execution plans. This invention establishes a four-layer protection system of "risk prediction -> accurate resource binding -> proactive fault-tolerant injection -> time-series verification," achieving optimal resource allocation and adaptive fault recovery during task execution, saving idle resource waiting time, and shortening recovery time after task failure.

[0054] Step 104: Based on predefined standardization rules, encapsulate multi-source data information and end-to-end control information to obtain standardized data objects.

[0055] In some embodiments, the aforementioned executing entity can encapsulate the aforementioned multi-source data information and the aforementioned end-to-end control information based on predefined standardization rules to obtain standardized data objects. The aforementioned predefined standardization rules can be a pre-set set of data format conversion and mapping rules. The aforementioned standardized data objects can be structured data resulting from the encapsulation of multi-source data information and end-to-end control information according to a unified standard.

[0056] As an example, firstly, based on predefined standardization rules (e.g., date format, field mapping rules), multi-source data information and end-to-end control information are encapsulated into structured standardized data objects (e.g., JSON objects) in a unified data format according to the rules.

[0057] Step 105: Based on the computing engine information in the end-to-end management and control information, distribute the standardized data objects to the corresponding target engines to determine the corresponding initial fusion score information.

[0058] In some embodiments, the aforementioned execution entity can distribute the standardized data objects to corresponding target engines based on the computation engine information in the aforementioned end-to-end control information to determine the corresponding initial fusion score information. The computation engine information can be the engine type and specific logic identifier corresponding to the end-to-end control information. For example, the computation engine type can be one of the following: a customer-provided JAR engine (a customer-provided Java package that encapsulates specific computation logic), a PMML script engine (a machine learning model script encapsulated using the PMML (Predictive Model Markup Language) standard), and a built-in SDK algorithm engine (pointing to built-in algorithm logic integrated in an SDK (Software Development Kit)). The target engine can be the engine entity that specifically executes the computation logic. The initial fusion score information includes: the initial fusion score (the original result returned by the target engine after calculating the standardized data object) and corresponding traceability information. The initial fusion score can be a data package including the original score and the calculation process. The traceability information can be key process data used to trace the source of the score and for auditing. For example, the above traceability information could be {engine type: "JAR", computation logic: "vendor-score-v2", input data hash: "x1y2z3", timestamp: "20251001"}. In some optional implementations of certain embodiments, the execution entity may distribute the standardized data object to the corresponding target engine based on the computing engine information in the end-to-end control information to determine the corresponding initial fusion score information, which may include the following steps: The first step is to generate engine routing instructions based on the engine type in the aforementioned computing engine information. These engine routing instructions can be commands that guide data distribution to a specific engine. For example, the engine routing instruction could be {engine type: "PMML", file path: " / models / risk_v2.pmml", timeout: "3000ms"}. In practice, first, the computing engine information corresponding to the product number in the end-to-end control information is determined. Then, the execution parameters are determined based on the engine type in the computing engine information. Finally, the engine routing instructions are generated.

[0059] The second step is to initialize the sandbox execution environment corresponding to the engine routing instructions mentioned above. This sandbox execution environment can be an isolated, secure computing environment. For example, it could use a separate JVM to load the client JAR file, limit memory to 1GB, and provide network isolation.

[0060] The third step involves distributing the standardized data objects to the sandbox execution environment to generate corresponding initial fusion score information. This initial fusion score information includes the initial fusion score and corresponding source information. In practice, the standardized data objects are encapsulated according to the format required by the target engine (e.g., converted to a specific Java object type or Map structure expected by the JAR package entry method). Then, the encapsulated data is distributed to the initialized target engine in the sandbox execution environment for computation through a predefined interface (e.g., method call). Finally, the engine's execution result is used as the initial fusion score, and the source information is recorded.

[0061] Step 106: Based on the optional processing logic in the end-to-end control information, the initial fused sub-information is mapped and optimized to obtain the optimized fused sub-information.

[0062] In some embodiments, the aforementioned executing entity can map and optimize the initial fusion score information based on the optional processing logic in the aforementioned end-to-end control information to obtain optimized fusion score information. The optional processing logic can be a configurable set of score optimization rules. For example, the optional processing logic could be "If the supplier is a high-tech enterprise, then add 5 points to the final fusion score." The optimized fusion score information can be a data packet including the optimized fusion score and the optimization process.

[0063] In some optional implementations of certain embodiments, the aforementioned execution entity may map and optimize the initial fused sub-information based on the optional processing logic in the aforementioned end-to-end control information to obtain optimized fused sub-information, which may include the following steps: The first step is to determine the executable rule chain corresponding to the above optional processing logic. This executable rule chain can be an ordered sequence of instructions that organizes the processing logic and can be interpreted and executed by the rule engine. For example, the executable rule chain could be: [Rule A: Detect high -> Rule B: Add 5 to score -> Rule C: Cap score at 100].

[0064] The second step involves generating rule execution context information based on the initial fusion score and business context parameters. These business context parameters can be external business variables required for the optimization process and do not directly originate from the initial fusion score. For example, {Current industry policy: "Support", Funding situation: "Easy"}. The rule execution context information can be the complete data environment required for rule execution. For example, {Original score: 72, Company age: 5, Industry type: "High-tech industry"}. In practice, the initial fusion score, business context parameters, and business context parameters can be integrated into the rule execution context information.

[0065] The third step involves generating an intermediate optimization result set based on the aforementioned executable rule chain and rule execution context information. This intermediate optimization result set can be a collection of intermediate results generated during the execution of the rule chain. For example, the intermediate optimization result set could be: {Rank Mapping Result: "B", Compensation Score: 8, Final Rank: "B+"}. In practice, firstly, each rule in the executable rule chain is executed sequentially to record the intermediate results of each rule's output. Then, all intermediate results are aggregated into the intermediate optimization result set.

[0066] The fourth step involves determining the optimized fusion score information based on the aforementioned intermediate optimization result set. This optimized fusion score information includes: optimization logs and interpretable optimized fusion scores. The optimization logs can be operation logs recording the entire score optimization process. The interpretable optimized fusion scores can be the final scores with complete decision-making support. In practice, the optimized fusion scores are first determined based on the intermediate optimization result set, then the optimization process records are compiled, and finally, optimization logs and interpretable optimized fusion scores are generated.

[0067] Step 107: Send the optimized and integrated sub-information to the business system of the supply chain to drive the business system to perform hierarchical management and control of the supply chain.

[0068] In some embodiments, the aforementioned implementing entity may send the optimized and integrated sub-information to the business system of the aforementioned supply chain to drive the business system to perform tiered control operations on the supply chain. The aforementioned business system may be a collection of software systems that perform specific supply chain operations (e.g., accounts receivable financing approval, inventory pledge limit management, and order prepayment disbursement). The aforementioned tiered control may involve implementing differentiated control measures based on risk levels.

[0069] In addressing the technical challenges of the aforementioned background technologies, and considering the application scenario—supply chain operations (e.g., supply chain finance)—which often involve multi-layered decision-making and approval processes, the following technical problems arise: difficulties in cross-system business operation collaboration, poor reliability and inconsistency in execution, resulting in significant waste of processing time. Given the specific requirements of this application scenario—high concurrency, multi-system collaboration, operational atomicity, and process observability—we have decided to adopt the following solution: In some optional implementations of certain embodiments, the aforementioned executing entity may send the optimized and merged sub-information to the business system of the aforementioned supply chain to drive the business system to perform hierarchical management and control operations on the supply chain, which may include the following steps: The first step is to generate a set of business instruction elements based on the optimized and integrated score information. This set of elements can be a collection of key elements extracted from the optimized and integrated score information used to generate business instructions. For example, the set could be {Risk Level: "High", Score: 35, Key Risk Point: ["Financial Risk"]}. In practice, first, key elements (e.g., risk level and recommended amount) are extracted from the structured data of the optimized and integrated score. Then, a standardized set of business instruction elements is generated. For example, the element set {Risk Level: "Good", Recommended Action: "Automatic Approval"} might be extracted from {Score: 85, Level: "Good"}.

[0070] The second step involves generating a set of instruction requirements with context-aware information based on the aforementioned set of business instruction elements and real-time business context information. This context-aware information can be data generated by the business system after recognizing changes in the environment. For example, load status: "normal," funding status: "sufficient," service availability: "good." The aforementioned business context information can be the system's current business environment data at the time of executing the business instruction. This business environment data can be real-time status information of external business conditions. For example, this business environment data could be {funding balance: "sufficient," pending business volume: 150, system load: "normal"}. The aforementioned instruction requirement set can be specific operational requirements combined with the context. In practice, firstly, real-time data (e.g., system load and funding status) is used, then the set of business instruction elements is adapted, and finally, precise operational requirements are generated. For example, combining the awareness information of "sufficient funds + normal load," medium-level requirements are determined as {requiring approval, requiring quota allocation, requiring contract generation}.

[0071] The third step involves generating a set of control strategies corresponding to the aforementioned instruction requirement set using a pre-defined control strategy library. This pre-defined control strategy library can be a set of predefined business rules. For example, it could include: {Medium Risk: [“Secondary Approval,” “Standard Interest Rate”], Low Risk: [“Automatic Approval,” “Preferential Interest Rate”]}. The control strategy set can be a specific set of strategies matched from the strategy library to address the current instruction requirement. For example, it could be {Approval Strategy: “Secondary Approval,” Interest Rate Strategy: “Standard Interest Rate,” Credit Limit Strategy: “800,000”}. In practice, the rule configurations in the control strategy library are first queried, then multi-dimensional matching is performed based on the requirement characteristics to finally obtain the control strategy set.

[0072] The fourth step is to generate an executable set of control instructions based on the aforementioned control policy set and the current system status information. The current system status information can refer to the availability status of system resources and services. For example, the current system status information could be {Approval Service: Normal, Credit Limit Service: Normal, Interest Rate Service: Under Maintenance}. The executable set of control instructions can be instructions that can be directly issued to the business execution module. In practice, first, the current system status information is determined. Then, the control policy set is converted into specific operation instructions. Finally, the executability of the instructions is verified to generate an executable set of control instructions.

[0073] The fifth step involves sending the aforementioned executable control instruction set to the corresponding business execution module within the supply chain to generate distributed execution transaction information. This distributed execution transaction information includes multiple execution transactions. The aforementioned business execution module can be a software module responsible for specific business operations. For example, it could include an approval engine, a credit management system, or a contract service. The distributed execution transaction information can be coordination information across multiple business execution modules. For example, the distributed execution transaction information could be {Transaction ID: "TX001", Participating Modules: ["Approval", "Credit", "Contract"]}. In practice, first, a distributed transaction ID is created for the executable control instruction set. Then, each executable control instruction is routed to its corresponding business execution module to generate distributed execution transaction information.

[0074] The sixth step is to generate a time-sequential execution workflow based on the dependencies between the various execution transactions in the aforementioned distributed execution transaction information. These dependencies can be sequential constraints between business operations. For example, the dependencies could be that approval is required before quota allocation, and quota allocation is required before contract generation. The time-sequential execution workflow can be a sequence of operations arranged in chronological order. In practice, first, the logical dependencies between each execution transaction are determined. Then, the execution order is determined based on the dependencies, and finally, the triggering conditions between operations are set to generate the time-sequential execution workflow. For example, a time-sequential workflow could be orchestrated as [Approval Approved -> Trigger Quota Allocation -> Quota Allocation Successful -> Trigger Contract Generation].

[0075] The seventh step involves driving the aforementioned time-sequential execution workflow within the corresponding business execution modules to achieve layered control over the supply chain. In practice, firstly, the hardware control module of the supply chain business system (e.g., a server cluster scheduler) sends the first step instruction of the time-sequential execution workflow to the physical server of the corresponding business execution module (e.g., the approval module server). Then, real-time monitoring of hardware resource usage (e.g., server CPU and memory) ensures that the first step execution is free of hardware bottlenecks. Upon completion, hardware signals (e.g., inter-server communication instructions) are triggered to initiate the next step. Finally, a hardware firewall blocks unauthorized interference throughout the entire process until the entire workflow is completed, achieving layered control over the supply chain.

[0076] The above-described operational steps, as an inventive point of this disclosure, solve the technical problem mentioned in the background art: "difficulty in cross-system business operation collaboration, poor reliability of the execution process, and difficulty in ensuring consistency, resulting in a significant waste of processing time." The reasons for these technical problems are as follows: lack of a unified instruction coordination mechanism among various business systems; lack of dynamic awareness of the business context; lack of effective transaction management and workflow scheduling mechanisms in a distributed environment; and susceptibility to resource bottlenecks and illegal interference due to the lack of hardware-level monitoring and protection to ensure execution. This invention establishes an automated, precision-controlled pipeline from instruction element extraction to time-sequential execution workflow, achieving intelligent generation, dynamic adaptation, and reliable execution of business instructions. Combined with hardware control, resource monitoring, and firewall protection, it ensures execution reliability. This achieves automated collaboration and end-to-end closed-loop management of cross-system business operations, saving time and coordination costs between systems.

[0077] The above-described embodiments of this disclosure have the following beneficial effects: The supply chain hierarchical management and control method based on multi-source data in some embodiments of this disclosure achieves intelligent supply chain management and control, thereby reducing management and control time delays and improving supply chain operational efficiency. Specifically, the reason for low management and control efficiency is that: the serial processing of data acquisition leads to response delays; and the rigid data processing logic is difficult to adapt to new requirements. Based on this, the supply chain hierarchical management and control method based on multi-source data in some embodiments of this disclosure firstly, in response to receiving a supply chain business processing request, determines the product code based on the product feature information in the supply chain business processing request to obtain the corresponding end-to-end management and control information. By locating the product code and end-to-end management and control information through product feature information, a precise association between the business request and management and control rules is established, ensuring that the management and control logic matches the product characteristics. Then, the user's original parameters corresponding to the supply chain business processing request are encrypted to obtain an encrypted dataset. A compliant dataset is generated by encrypting the user's original parameters to prevent the leakage of sensitive information, meet data transmission security requirements, and provide a secure carrier for multi-source data requests. Then, according to the data source list in the end-to-end management and control information and the encrypted dataset, requests are sent in parallel to at least one data source to obtain multi-source data information. Parallel requests for multi-source data reduce data acquisition time and improve efficiency. Combining encrypted datasets and a list of data sources ensures targeted requests and controllable data sources. Secondly, based on predefined standardization rules, the aforementioned multi-source data and end-to-end control information are encapsulated to obtain standardized data objects. Standardized encapsulation of multi-source data and end-to-end control information eliminates data format differences, achieving structured integration of data and control rules, and providing a unified input format for engine calculations. Thirdly, according to the calculation engine information in the end-to-end control information, the standardized data objects are distributed to the corresponding target engines to determine the initial fusion score information. Distributing standardized data objects according to calculation engine information generates initial fusion score information, achieving precise data-engine adaptation and ensuring that the fusion score calculation logic conforms to preset rules, laying the foundation for subsequent optimization. Next, based on the optional processing logic in the end-to-end control information, the initial fusion score information is mapped and optimized to obtain optimized fusion score information. Optimizing the initial fusion score information through optional processing logic improves the adaptability of the score to business scenarios, enhances the accuracy of risk assessment, and provides a reliable basis for hierarchical control. Finally, the optimized and integrated information is sent to the business systems of the aforementioned supply chain to drive these systems to perform tiered control operations. By using the optimized and integrated information to drive tiered control in the business systems, dynamic linkage between risk levels and control measures is achieved, improving the efficiency of supply chain risk response and the targeted nature of control. In summary, intelligent supply chain control is realized, thus reducing control time delays and improving the operational efficiency of the supply chain.

[0078] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a supply chain hierarchical management device based on multi-source data. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this multi-source data-based supply chain hierarchical management device can be specifically applied to various electronic devices.

[0079] like Figure 2 As shown, a supply chain hierarchical management and control device 200 based on multi-source data includes: a determination unit 201, an encryption unit 202, a request sending unit 203, an encapsulation unit 204, a distribution unit 205, an optimization unit 206, and a hierarchical management and control unit 207. The determination unit 201 is configured to: in response to receiving a supply chain business processing request, determine a product code based on the product feature information in the supply chain business processing request to obtain corresponding end-to-end management and control information. The encryption unit 202 is configured to: encrypt the user's original parameters corresponding to the supply chain business processing request to obtain an encrypted dataset. The request sending unit 203 is configured to: send requests in parallel to at least one data source according to the data source list in the end-to-end management and control information and the encrypted dataset to obtain multi-source data information. The encapsulation unit 204 is configured to: encapsulate the multi-source data information and the end-to-end management and control information based on predefined standardization rules to obtain standardized data objects. The distribution unit 205 is configured to: distribute the standardized data objects to the corresponding target engines according to the computing engine information in the end-to-end management and control information to determine the corresponding initial fusion score information. The optimization unit 206 is configured to: map and optimize the initial fusion score information based on the optional processing logic in the aforementioned end-to-end control information to obtain the optimized fusion score. The hierarchical control unit 207 is configured to: send the optimized fusion score to the business system of the aforementioned supply chain to drive the aforementioned business system to perform hierarchical control operations on the supply chain.

[0080] It is understandable that the units recorded in the multi-source data-based supply chain hierarchical control device 200 are related to the reference... Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the image segmentation apparatus 200 and the units contained therein, and will not be repeated here.

[0081] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (e.g., an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0082] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0083] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0084] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0085] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0086] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0087] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: respond to receiving a supply chain business processing request; determine a product code based on the product characteristic information in the supply chain business processing request to obtain corresponding end-to-end control information; encrypt the user's original parameters corresponding to the supply chain business processing request to obtain an encrypted dataset; send requests in parallel to at least one data source according to the data source list in the end-to-end control information and the encrypted dataset to obtain multi-source data information; encapsulate the multi-source data information and the end-to-end control information based on predefined standardization rules to obtain a standardized data object; distribute the standardized data object to the corresponding target engine according to the computing engine information in the end-to-end control information to determine the corresponding initial fusion score information; map and optimize the initial fusion score information based on the optional processing logic in the end-to-end control information to obtain optimized fusion score information; and send the optimized fusion score information to the supply chain's business system to drive the business system to perform hierarchical control operations on the supply chain.

[0088] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0090] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a determining unit, an encryption unit, a request sending unit, an encapsulation unit, a distribution unit, an optimization unit, and a hierarchical management unit. The names of these units do not necessarily limit the specific unit itself; for example, the determining unit may also be described as "a unit that, in response to receiving a supply chain business processing request, determines a product code based on the product characteristic information in the supply chain business processing request to obtain corresponding end-to-end management information."

[0091] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0092] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A supply chain hierarchical management method based on multi-source data, comprising: In response to receiving a supply chain business processing request, the product code is determined based on the product feature information in the supply chain business processing request in order to obtain the corresponding end-to-end control information; The original user parameters corresponding to the supply chain business processing request are encrypted to obtain an encrypted dataset. Based on the data source list in the end-to-end control information and the encrypted dataset, requests are sent in parallel to at least one data source to obtain multi-source data information; Based on predefined standardization rules, the multi-source data information and the end-to-end control information are encapsulated to obtain standardized data objects; Based on the computing engine information in the end-to-end management and control information, the standardized data object is distributed to the corresponding target engine to determine the corresponding initial fusion score information; Based on the optional processing logic in the full-link management and control information, the initial fused sub-information is mapped and optimized to obtain the optimized fused sub-information; The optimized and integrated information is sent to the business system of the supply chain to drive the business system to perform hierarchical management and control of the supply chain.

2. The method according to claim 1, wherein, The response to receiving a supply chain business processing request involves determining a product code based on the product characteristic information in the supply chain business processing request, in order to obtain corresponding end-to-end control information, including: In response to receiving a supply chain business processing request, product feature information is extracted from the supply chain business processing request, wherein the product feature information includes: name, version number, and keyword combination hash value; The product feature information is matched with a preset product registration database to determine the corresponding product code; Based on the product code, full-link management and control information is loaded from the distributed configuration center.

3. The method according to claim 1, wherein, The step of encrypting the original user parameters corresponding to the supply chain business processing request to obtain an encrypted dataset includes: Based on a predefined sensitive word library and pattern matching rules, the sensitive data fields corresponding to the original user parameters are determined; Generate a globally unique request session code corresponding to the supply chain business processing request; Based on the globally unique request session encoding, a dynamic encryption key is generated; Based on the dynamic encryption key, differential encryption processing is performed on the sensitive data fields to obtain a hierarchical ciphertext dataset; Based on the hierarchical ciphertext dataset and the algorithm information corresponding to the dynamic encryption key, a security meta-data packet is generated, wherein the security meta-data packet includes: encryption algorithm information, key version information, and timestamp information; The dynamic encryption key is encrypted to obtain the encrypted data key; The hierarchical encrypted dataset, the encrypted data key, the globally unique request session encoding, and the security metadata are serialized and encapsulated to obtain the encrypted dataset.

4. The method according to claim 1, wherein, The step of distributing the standardized data object to the corresponding target engine based on the computing engine information in the end-to-end management information to determine the corresponding initial fusion score information includes: Based on the engine type in the computing engine information, generate engine routing instructions; Initialize the sandbox execution environment corresponding to the engine routing command; The standardized data object is distributed to the sandbox execution environment to generate corresponding initial fusion score information, wherein the initial fusion score information includes: initial fusion score and corresponding traceability information.

5. The method according to claim 1, wherein, The optional processing logic based on the end-to-end control information is used to map and optimize the initial fused sub-information to obtain optimized fused sub-information, including: Determine the executable rule chain corresponding to the optional processing logic; Based on the initial fusion information and business context parameters, rule execution context information is generated; Based on the executable rule chain and the rule execution context information, an intermediate optimization result set is generated; Based on the intermediate optimization result set, the optimized fusion score information is determined, wherein the optimized fusion score information includes: optimization logs and interpretable optimized fusion scores.

6. A supply chain hierarchical management and control device based on multi-source data, comprising: The determining unit is configured to, in response to receiving a supply chain business processing request, determine a product code based on the product feature information in the supply chain business processing request, so as to obtain the corresponding end-to-end control information; The encryption unit is configured to encrypt the original user parameters corresponding to the supply chain business processing request to obtain an encrypted dataset. The request sending unit is configured to send requests in parallel to at least one data source based on the data source list in the end-to-end control information and the encrypted dataset to obtain multi-source data information. The encapsulation unit is configured to encapsulate the multi-source data information and the end-to-end control information based on predefined standardization rules to obtain a standardized data object; The distribution unit is configured to distribute the standardized data object to the corresponding target engine based on the computing engine information in the end-to-end management and control information, so as to determine the corresponding initial fusion score information; The optimization unit is configured to map and optimize the initial fused sub-information based on the optional processing logic in the full-link management and control information to obtain optimized fused sub-information. The hierarchical control unit is configured to send the optimized and integrated sub-information to the business system of the supply chain, so as to drive the business system to perform hierarchical control operations on the supply chain.

7. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-5.

8. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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