A multi-tenant supply chain collaboration system and method

Abstract

The application discloses a kind of multi-tenant supply chain collaboration system and method, it is related to supply chain collaboration and multi-tenant intelligent scheduling technical field, including, acquisition collaborative request data, tenant configuration data and regional node monitoring data;Generation multidimensional context feature set and calculate comprehensive collaborative risk constraint value;According to risk constraint value generation tenant individualized collaborative strategy and regional intelligent routing result, determine target regional node, target supplier and target logistics, and execute collaborative scheduling;Collaborative execution state is monitored, and according to the execution deviation triggers abnormal back and updates collaborative feedback data.The application can improve the resource scheduling efficiency under the globalization multi-tenant supply chain collaborative scene, cross-regional compliance capability and collaborative execution stability.

Description

Technical Field

[0001] This invention relates to the field of supply chain collaboration and multi-tenant intelligent scheduling technology, and in particular to a multi-tenant supply chain collaboration system and method. Background Technology

[0002] With the development of globalized supply chain systems and multi-tenant SaaS platforms, more and more enterprises are completing business processes such as procurement, warehousing, logistics, and order fulfillment through cross-regional, multi-node, and multi-role collaboration. In cross-border e-commerce, international logistics, and global warehousing scenarios, different tenants typically have different service levels, data security requirements, and regional compliance requirements. The supply chain collaboration process not only needs to consider delivery efficiency and resource scheduling capabilities, but also needs to meet the legal constraints of data localization, cross-border data transmission restrictions, and industry regulations. Most existing supply chain collaboration systems use fixed rules or static routing for task scheduling, usually selecting nodes based only on network distance, server load, or simple priority, lacking the comprehensive analysis capabilities to consider tenant's personalized needs, product regulatory attributes, data field sensitivity, and regional legal constraints. Meanwhile, existing solutions generally lack dynamic perception of supplier performance stability, logistics and transportation stability, and real-time status of regional nodes, making it difficult to achieve collaborative risk control and intelligent routing optimization in complex cross-regional collaborative scenarios. Therefore, how to build a system and method that can combine multi-dimensional contextual features, tenant personalized configuration, and regional intelligent routing to conduct dynamic risk analysis and collaborative scheduling optimization of the global multi-tenant supply chain collaboration process has become an urgent technical problem to be solved. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a multi-tenant supply chain collaboration system and method, which solves the problems in the prior art where the supply chain collaboration process lacks multi-dimensional contextual comprehensive analysis capabilities, cannot take into account cross-regional compliance constraints and collaboration efficiency, and is difficult to dynamically generate collaboration strategies according to the personalized needs of tenants.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a multi-tenant supply chain collaboration system, which includes, The data acquisition module collects collaborative requests, tenant configurations, candidate execution entities, and regional node monitoring data. The context feature generation module calculates tenant service priority factor, delivery urgency factor, commodity supervision sensitivity factor, data field sensitivity factor, legal domain constraint factor, and execution entity stability factor based on the collected data, and calculates regional node status factor to form a multi-dimensional context feature set. The collaborative risk analysis module calculates the compliance constraint strength, performance pressure value, and comprehensive collaborative risk constraint value of the current collaborative request based on a multi-dimensional context feature set, and generates corresponding personalized collaborative strategies for tenants accordingly. The collaborative scheduling module, based on the tenant's personalized collaborative strategy, determines the direct efficiency route, the hierarchical balanced route, or the resident compliance route, and further selects the target area node, the target supplier, and the target logistics provider and executes collaborative scheduling. The collaborative detection and feedback module continuously monitors the execution status of the selected target area nodes, target suppliers, and target logistics providers during the collaborative scheduling process, as well as the data processing status of the collaborative fields. It judges the normal, warning, or abnormal status based on the execution deviation and updates the collaborative feedback data and historical records.

[0006] As a preferred embodiment of the multi-tenant supply chain collaboration system of the present invention, the data for collecting collaboration requests, tenant configurations, candidate execution entities, and regional node monitoring data refers to the basic identification data for collecting collaboration requests. Read tenant configuration data and direct record data of candidate supply chain execution entities; Collect direct monitoring data of candidate region nodes.

[0007] As a preferred embodiment of the multi-tenant supply chain collaboration system described in this invention, the calculation of tenant service priority factor, delivery urgency factor, and commodity supervision sensitivity factor based on the collected data refers to calculating these factors according to the tenant service level. Calculate tenant service priority factor ; Based on order creation time and expected delivery time Calculate the expected delivery time of an order Then calculate the delivery urgency factor. ; Product Categories The product regulatory attribute is broken down into several regulatory attributes, and a specific value is assigned to each regulatory attribute to generate a product regulatory attribute vector. And calculate commodity regulatory sensitivity factors. .

[0008] As a preferred embodiment of the multi-tenant supply chain collaboration system described in this invention, wherein: the calculated data field sensitivity factor, domain constraint factor, and execution entity stability factor refer to the classification of each data field type The data is broken down into several sensitive field attributes, and the sensitive field values ​​are calculated by combining these attributes to generate a sensitive field attribute vector. ; Further calculation of the first Sensitive values ​​for each field type Then calculate the data field sensitivity factor based on the sensitivity values ​​of all fields. ; The first Data field types Combined with target area Tenant compliance label set Perform domain constraint attribute identification and generate domain constraint attribute vector. ; Calculate the first Single-field domain constraint value of each field type The maximum domain constraint value among all fields will be used as the domain constraint factor for the current request. ; Calculate supplier on-time delivery rate based on historical order records. ; Calculated based on historical transport records Candidate logistics providers On-time delivery rate ; Based on the on-time performance rates of suppliers and candidate logistics providers, calculate the stability factor of candidate execution entities. .

[0009] As a preferred embodiment of the multi-tenant supply chain collaboration system of the present invention, wherein: the computational region node state factor forms a multi-dimensional context feature set referring to the calculation of the first... Candidate region nodes Delay score and node load score ; Compute node compliance label matching value Node state score of a single candidate region node ; The highest node state score among the candidate region nodes is used as the candidate region node state factor for the current request. ; Output the multidimensional context feature set of the current collaborative request .

[0010] As a preferred embodiment of the multi-tenant supply chain collaboration system described in this invention, the step of calculating the compliance constraint strength, performance pressure value, and comprehensive collaboration risk constraint value of the current collaboration request based on a multi-dimensional context feature set refers to calculating the compliance constraint strength of the current collaboration request based on commodity regulatory sensitivity factors, data field sensitivity factors, and legal domain constraint factors. ; The fulfillment pressure value of the current collaboration request is calculated based on the tenant service priority factor, delivery urgency factor, and candidate execution entity stability factor. ; The comprehensive collaborative risk constraint value of the current collaborative request is calculated based on the strength of compliance constraints, the performance pressure value, and the status factors of candidate regional nodes. .

[0011] As a preferred embodiment of the multi-tenant supply chain collaboration system of the present invention, wherein: generating the corresponding tenant personalized collaboration strategy refers to generating the risk level of the current collaboration request based on the comprehensive collaboration risk constraint value; Personalized collaboration strategies for tenants are generated based on risk level, tenant strategy weight set, and tenant compliance tag set. The candidate collaboration strategies are individually modified based on the tenant strategy weight set to obtain the final tenant collaboration strategy score. .

[0012] As a preferred embodiment of the multi-tenant supply chain collaboration system of the present invention, the following steps are taken: Based on the tenant's personalized collaboration strategy, a direct efficiency route, a hierarchical balanced route, or a resident compliance route is determined; further, target area nodes, target suppliers, and target logistics providers are selected, and collaborative scheduling is executed; and regional intelligent routing execution results are generated based on the target tenant's personalized collaboration strategy. ; Based on the execution results of intelligent routing Based on the regional node state factors Candidate execution entity stability factor Data field sensitivity factors and domain constraint factor Generate the regional intelligent routing execution result of the current collaborative request; From satisfying the execution results of regional intelligent routing Select the target region node from the candidate region nodes that meet all the conditions; From the stability factor of the execution entity corresponding to the current route execution result Select the target execution entity combination from the combination of candidate suppliers and candidate logistics providers.

[0013] As a preferred embodiment of the multi-tenant supply chain collaboration system of the present invention, the continuous monitoring of the regional nodes, suppliers, logistics providers, and field processing status during the collaboration scheduling process, determining normal, warning, or abnormal status based on execution deviation, and updating collaboration feedback data and historical records refers to calculating the supplier execution deviation value based on the expected delivery time of the current collaboration request and the supplier's estimated delivery time. ; Calculate the logistics execution deviation value based on the expected delivery time of the current collaboration request and the logistics provider's estimated arrival time. ; Based on the execution status of regional nodes, supplier execution status, and logistics execution status, generate the collaborative execution result for the current collaborative request. ; Once the current collaboration request is completed, collaboration feedback data is generated, and the candidate supplier's historical order records and candidate logistics provider's historical transportation records are updated based on the collaboration feedback data. The collaboration feedback data is then written into the tenant's collaboration record.

[0014] Secondly, this invention provides a multi-tenant supply chain collaboration method, including: Collect collaborative request data, tenant configuration data, candidate execution entity data, and regional node monitoring data; Based on the collected data, the tenant service priority factor, delivery urgency factor, commodity supervision sensitivity factor, data field sensitivity factor, legal domain constraint factor, execution entity stability factor, and regional node status factor are calculated respectively to construct a multi-dimensional context feature set for the current collaborative request. Based on the multi-dimensional context feature set, calculate the compliance constraint strength, performance pressure value and comprehensive collaboration risk constraint value of the current collaboration request, and generate the corresponding tenant personalized collaboration strategy based on the comprehensive collaboration risk constraint value. Based on the tenant's personalized collaboration strategy, determine the direct efficiency route, the hierarchical balanced route, or the resident compliance route, and further select the target area node, the target supplier, and the target logistics provider to perform collaborative scheduling. The system continuously monitors the status of regional nodes, supplier execution status, logistics execution status, and field processing status during the collaborative scheduling process. It generates normal, early warning, or abnormal collaborative execution results based on execution deviations and updates collaborative feedback data and historical records.

[0015] The beneficial effects of this invention are as follows: By collecting collaborative request data, tenant configuration data, historical data of executing entities, and regional node monitoring data, this invention constructs a multi-dimensional contextual feature system. Combined with commodity regulatory sensitivity factors, data field sensitivity factors, legal domain constraint factors, executing entity stability factors, and regional node status factors, it conducts a comprehensive risk constraint analysis of the current collaborative request. Furthermore, based on risk level, tenant strategy weight, and compliance tags, it generates personalized collaborative strategies for tenants. Combined with a regional intelligent routing mechanism, it achieves dynamic collaborative scheduling of target regional nodes, target suppliers, and target logistics providers, satisfying cross-regional compliance constraints and data residency requirements while also considering delivery timeliness and performance stability. Simultaneously, by continuously monitoring the status of regional nodes, suppliers, logistics providers, and field processing, and combining anomaly rollback and collaborative feedback update mechanisms, it achieves dynamic optimization and closed-loop adjustment of the collaborative execution process, thereby improving resource scheduling efficiency, cross-regional compliance capabilities, and collaborative execution stability in global multi-tenant supply chain collaboration scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a structural diagram of a multi-tenant supply chain collaboration system in Example 1.

[0018] Figure 2 This is a flowchart of a multi-tenant supply chain collaboration method in Example 1. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a multi-tenant supply chain collaboration system and method, including the following steps: S1, Data Acquisition Module, collects collaborative requests, tenant configurations, candidate execution entities, and regional node monitoring data; Collect basic identification data for collaborative requests, including tenant identifiers. User roles Order Identifier Product Categories Order creation time Expected delivery time Target area The collection of data field types involved in the request ,in, Indicates the first Data field types, such as delivery address, contract price, purchase quantity, inventory status, customs documents, logistics tracking, etc.; Read tenant configuration data, including tenant service level. Tenant-allowed service area set Tenant compliance tag set Tenant strategy weight set ,in , Indicates the weight of delivery timeliness. Indicates compliance weight, Represents the stability weight, and satisfies Preferably: It meets the requirements of personalized collaboration and regionally compliant routing in a globalized multi-tenant supply chain scenario; Collect directly recorded data from candidate supply chain implementers, including candidate supplier sets. Candidate logistics provider pool Historical order records of each candidate supplier (Including promised delivery time and actual delivery time), and historical shipping records of each candidate logistics provider. (Including promised arrival time and actual arrival time); Collect direct monitoring data of candidate region nodes, including the candidate region node set. and the real-time network latency of each candidate region node. Service load Node availability status Data residency capability level Node Applicable Compliance Tag Set .

[0023] S2, Context Feature Generation Module, based on the collected data, calculates tenant service priority factor, delivery urgency factor, commodity supervision sensitivity factor, data field sensitivity factor, legal domain constraint factor, execution entity stability factor, and regional node status factor, forming a multi-dimensional context feature set; First, based on the tenant's service level Calculate tenant service priority factor :

[0024] in, Service levels for tenants, This indicates the highest service level preset by the platform, preferably. It can effectively differentiate tenants without causing complex configuration and maintenance issues due to overly granular levels; Based on order creation time and expected delivery time Calculate the expected delivery time of an order Then calculate the delivery urgency factor. : ;

[0025] in, This indicates the minimum delivery time allowed by the platform. This indicates the maximum delivery time allowed by the platform, preferably. sky, The system can cover emergency replenishment, routine procurement, and general cross-border transportation scenarios. Product Categories The product regulatory attribute is broken down into several regulatory attributes, and a specific value is assigned to each regulatory attribute to generate a product regulatory attribute vector. ,in, Indicates food and drug regulatory attributes. Indicates cold chain regulatory attributes. Indicates the regulatory attributes of hazardous materials. Indicates the nature of import and export controls. Indicates a general low-regulation attribute. ,in, For example, when the product category When it is a medicine, When the product category When it is frozen food, and When the product category When it is a lithium battery, chemical reagent or flammable material, When the product category When the electronic components or special equipment are subject to export controls, When the product category When the items are ordinary screws, standard packaging materials, or office supplies, ; Further calculation of commodity regulatory sensitivity factors :

[0026] in, Indicates the basic regulatory sensitivity coefficient; Indicates the weight of food and drug regulatory attributes; Indicates the weight of cold chain regulatory attributes; Indicates the weight of the regulatory attributes of hazardous materials; Indicates the weight of import and export control attributes; This indicates the deduction weight for ordinary low-regulation attributes; preferably: This enables a clear distinction between commodity regulatory sensitivity factors and ordinary commodities, conditionally regulated commodities, and heavily regulated commodities, while avoiding the problem of different regulatory attributes not being able to be expressed by simply assigning fixed values ​​based on commodity categories; Each data field type The data is broken down into several sensitive field attributes, and the sensitive field values ​​are calculated by combining these attributes to generate a sensitive field attribute vector. ,in, Indicates personal or recipient identification attributes, Indicates commercial price attributes, Indicates the nature of trade compliance documents. Indicates the operating status attribute. Indicates the attributes of a normal procedure record; , For example, when the field type When providing the delivery address, contact person's name, contact number, or customer identification information, When the field type When specifying contract price, unit price, discount information, and settlement amount, When the field type When preparing customs declaration documents, certificates of origin, license numbers, and regulatory declaration materials, When the field type When checking inventory quantity, purchase quantity, supplier delivery status, and order fulfillment status, When the field type When making remarks for general logistics tracks, node scanning times, and routine status, ; Further calculation of the first Sensitive values ​​for each field type Then calculate the data field sensitivity factor based on the sensitivity values ​​of all fields. :

[0027]

[0028] in, Indicates the sensitivity coefficient of the underlying field; Indicates the weight of the personal or recipient identification attribute; Indicates the weight of commercial price attributes; Indicates the weight of trade compliance document attributes; Indicates the weight of the operating status attribute; This represents the deduction weight of the attribute of a normal process record, preferably... It can clearly distinguish between customer identification fields, commercial price fields, trade document fields, business status fields and ordinary process record fields, while avoiding the problem of sensitive attributes not being able to be expressed by simply assigning fixed values ​​according to field names; Indicates the number of data field types involved in the current request; The first Data field types Combined with target area Tenant compliance label set Perform domain constraint attribute identification and generate domain constraint attribute vector. ， in, This indicates the cross-border restriction attribute of the field. Indicates the regulatory attributes of the target area. This indicates the tenant compliance label constraint attribute. This indicates a low-constraint attribute for ordinary business operations. , For example, when the field type When dealing with data that is not suitable for free cross-regional transfer, such as delivery addresses, customer identification information, contract prices, and customs documents, When the target area When the corresponding business scenarios involve data localization, cross-border transmission review, or specific regulatory region requirements, When tenant compliance label set When it includes labels such as data localization, cross-border transfer restrictions, industry regulation, and customer data protection, When the field type When only general business process data such as inventory status, normal logistics status, and node scan time are available, and these data do not trigger compliance label constraints for destination areas or tenants, ; Further calculation of the first Single-field domain constraint value of each field type The maximum domain constraint value among all fields will be used as the domain constraint factor for the current request. :

[0029]

[0030] in, Indicates the basic domain constraint coefficients; Indicates the cross-border restriction attribute weight of the field; Indicates the weight of regulatory attributes for the target area; This indicates the weight of the tenant compliance label constraint attribute; This represents the deduction weight for low-constraint attributes in ordinary business operations, preferably... ， It enables the legal domain constraint value to simultaneously reflect three aspects: "what the field is", "where the data is going", and "what compliance requirements the tenant is subject to", rather than relying solely on a single field type or a single region. Based on candidate supplier's historical order records Historical shipping records of candidate logistics providers Calculate the stability factor of candidate execution entities First, calculate the supplier on-time delivery rate based on historical order records. :

[0031] in, Indicates supplier The number of orders in the historical order record whose actual delivery time is no later than the promised delivery time. Indicates supplier Total number of historical orders; Calculated based on historical transport records Candidate logistics providers On-time delivery rate :

[0032] in, Indicates logistics provider The number of transport missions in the historical transport record whose actual arrival time is no later than the promised arrival time. Indicates logistics provider The total number of historical transportation tasks.

[0033] Based on the on-time performance rates of suppliers and candidate logistics providers, calculate the stability factor of candidate execution entities. :

[0034] in, Represents the set of candidate suppliers. This represents the set of candidate logistics providers. Indicates supplier stability weight. This represents the stability weight of the logistics provider, and satisfies... Preferably, , This enables the formation of optimal performance stability; Based on the candidate region node set and the real-time network latency of each node Service load Node availability status Data residency capability level Node Applicable Compliance Tag Set Calculate the state factors of candidate region nodes First calculate the first... Candidate region nodes Delay score :

[0035] in, For the first Real-time network latency of candidate region nodes, and These represent the minimum and maximum delays among the candidate region nodes, respectively; Calculate the node load score again :

[0036] in, For the first The service load of each candidate region node, with a value range of [value missing]. to ; Further calculate the node compliance label matching value :

[0037] in, A set of tenant compliance tags, For the first A set of compliance labels applicable to each candidate region node; Calculate the node state score of a single candidate region node. :

[0038] in, Indicates the first The state score of each candidate region node. Indicates the node's availability status; , , , , These represent the weights for matching latency, load, availability, and compliance labels, respectively, and satisfy the following conditions: Preferably This avoids the problem in existing technologies where node selection is based solely on network distance or load balancing, while ignoring data residency and tenant compliance constraints. The highest node state score among the candidate region nodes is used as the candidate region node state factor for the current request. :

[0039] Output the multidimensional context feature set of the current collaborative request .

[0040] S3, the Collaborative Risk Analysis Module, calculates the compliance constraint strength, performance pressure value and comprehensive collaborative risk constraint value of the current collaborative request based on the multi-dimensional context feature set, and generates corresponding personalized collaborative strategies for tenants accordingly. Based on the multi-dimensional context feature set of the current collaboration request, the collaboration risk constraint calculation is performed on the current collaboration request to generate a personalized collaboration strategy for the tenant; First, the compliance constraint strength of the current collaborative request is calculated based on commodity regulatory sensitivity factors, data field sensitivity factors, and legal domain constraint factors. :

[0041] in, , , These represent the impact weights of commodity regulation, data field sensitivity, and legal domain constraints, respectively, and satisfy the following conditions: Preferably, , , This allows for prioritizing regional and legal constraints in cross-border supply chain collaboration scenarios, while also taking into account the impact of commodity regulatory attributes and data field sensitivity attributes on the selection of collaboration paths. The fulfillment pressure value of the current collaboration request is calculated based on the tenant service priority factor, delivery urgency factor, and candidate execution entity stability factor. :

[0042] in, , , These represent the impact weights of tenant priority, delivery urgency, and fulfillment stability, respectively, and satisfy the following conditions: Preferably, , , It can form a comprehensive assessment among high-tier tenants, urgent delivery orders, and entities with unstable historical performance, avoiding the determination of collaboration priorities based solely on a single factor such as delivery time or tenant level; Furthermore, the comprehensive collaborative risk constraint value of the current collaborative request is calculated based on the strength of compliance constraints, the performance pressure value, and the status factors of candidate region nodes. :

[0043] in, , , These represent the weights of compliance constraints, performance pressures, and regional node status on overall collaborative risk, respectively, and satisfy the following conditions: Preferably, , , It can simultaneously reflect cross-regional compliance restrictions, business delivery pressure, and regional node carrying capacity in a global multi-tenant supply chain collaboration scenario; The risk level of the current collaboration request is generated based on the comprehensive collaboration risk constraint value: when At that time, the current collaboration request will be classified as a low-risk collaboration request; when At that time, the current collaboration request will be classified as a medium-risk collaboration request; when At that time, the current collaboration request will be identified as a high-risk collaboration request; in, This represents the first risk threshold between low and medium risk. This represents a second risk threshold between medium and high risk, preferably. , This enables a clear stratification of collaboration among ordinary business operations, sensitive business operations, and cross-border business operations under strong regulation; Personalized collaboration strategies for tenants are generated based on risk level, tenant strategy weight set, and tenant compliance tag set. When the current collaboration request is a low-risk collaboration request, an efficiency-first collaboration strategy is generated. For example, regional nodes with low network latency, low service load and good node availability are prioritized, and suppliers and logistics providers with high historical fulfillment stability are prioritized. When the current collaboration request is a medium-risk collaboration request, a balanced collaboration strategy is generated. For example, under the premise of satisfying the set of service areas allowed by the tenant and the set of tenant compliance labels, a weighted selection is made on delivery timeliness, compliance requirements and stability of the execution entity, and sensitive fields are classified and anonymized, and fields are minimized in transmission or processed within the region. When the current collaboration request is a high-risk collaboration request, a compliance-first collaboration strategy is generated. For example, regional nodes that meet the data residency capability level requirements, have the highest compliance tag matching degree, and are within the tenant's allowed service area set are selected first, and the cross-regional flow of sensitive fields is restricted. For candidate regional nodes that do not meet the compliance tag matching requirements or data residency capability level requirements, they are removed from the candidate route set. The candidate collaboration strategies are individually modified based on the tenant strategy weight set to obtain the final tenant collaboration strategy score. :

[0044] Among them, the final tenant collaboration strategy score The higher the better. , , These are the delivery timeliness weight, compliance weight, and stability weight from the tenant policy weight set, respectively, and satisfy the following conditions: Preferably, It can strike a balance between delivery timeliness, compliance, and performance stability, and highlight the collaborative scheduling goal of prioritizing compliance, taking into account timeliness, and ensuring performance stability in a globalized multi-tenant supply chain scenario.

[0045] S4, the collaborative scheduling module, determines direct efficiency routes, hierarchical balanced routes, or resident compliant routes based on the tenant's personalized collaborative strategy, and further selects target area nodes, target suppliers, and target logistics providers and executes collaborative scheduling. Generate regional intelligent routing execution results based on the target tenant's personalized collaboration strategy. :

[0046] Based on the execution results of intelligent routing Based on the regional node state factors Candidate execution entity stability factor Data field sensitivity factors and domain constraint factor Generate the regional intelligent routing execution result of the current collaborative request; Specifically, when the judgment result is At this time, the current collaboration request must simultaneously meet the following conditions: ， ， , The system merges order collaboration tasks, supplier collaboration tasks, and logistics collaboration tasks into a unified collaboration task package and automatically distributes it. When the judgment result is At this time, the current collaboration request must simultaneously meet the following conditions: , 、( or The system splits the current collaboration request into business execution task packages and controlled data task packages. First, it completes the controlled data processing within the target area node, and then sends the business execution content to the target supplier and the target logistics provider. When the judgment result is At this time, the current collaboration request must simultaneously meet the following conditions: , 、( or ) ， The system generates a resident processing task package, which retains data involving sensitive fields or legal domain constraint fields for processing within the region to which the target region node belongs. It should be noted that when the initial route execution result obtained by the tenant's personalized collaboration strategy mapping is inconsistent with the interval conditions of the regional node state factor, execution subject stability factor, data field sensitivity factor or legal domain constraint factor, the route result with the higher constraint level shall prevail. Furthermore, in determining the results of regional intelligent routing execution... Then, select the target region node from the candidate region nodes that meet all the conditions corresponding to the current route execution result; when multiple candidate region nodes meet the conditions, select the candidate region node with the highest node state score as the target region node; when multiple candidate region nodes have the same node state score, select the candidate region node with the lowest real-time network latency as the target region node; and select the execution entity stability factor corresponding to the current route execution result. Select the target execution entity combination from the candidate supplier and candidate logistics provider combinations that meet the conditions; when multiple candidate execution entity combinations meet the conditions, score them according to the final tenant collaboration strategy. Sort and select The candidate supplier and candidate logistics provider combination with the highest numerical value is selected as the target supplier and target logistics provider; The results of the regional intelligent routing execution include the target area node, backup area node, target supplier, target logistics provider, collaborative task package type, data processing scope, collaborative task execution order, and anomaly rollback method. The backup area node is determined as follows: among candidate area nodes that meet all conditions corresponding to the current judgment result, the candidate area node with the highest node status score (excluding the target area node) is selected as the backup area node. The anomaly rollback method includes: when the target area node is unavailable, if a backup area node exists, the system switches to the backup area node; if the backup area node does not exist, or if the backup area node does not meet the conditions corresponding to the current judgment result, automatic switching is prohibited, and the current collaborative request is marked as a request pending review.

[0047] S5, the collaborative detection and feedback module, continuously monitors the regional nodes, suppliers, logistics providers and field processing status during the collaborative scheduling process, judges the normal, warning or abnormal status based on the execution deviation, and updates the collaborative feedback data and historical records. Based on the results of the regional intelligent routing, the execution process of the current collaborative request is monitored, the collaborative execution status of the target area nodes, target suppliers and target logistics providers is collected, and it is determined whether an abnormal rollback is triggered based on the execution status, and the collaborative execution feedback result of the current collaborative request is generated. Specifically, the collaborative execution status includes the execution status of regional nodes, suppliers, logistics providers, and field processing. The regional node execution status includes whether the target regional node is available, the real-time network latency of the target regional node, the service load of the target regional node, the data residency processing status of the target regional node, and the completion status of controlled data processing. The supplier execution status includes whether the target supplier has confirmed the order, confirmed inventory, completed outbound preparation, and the supplier's estimated delivery time. The logistics provider execution status includes whether the target logistics provider has confirmed transportation resources, confirmed the transportation route, returned the estimated arrival time, and the logistics node registration status. The field processing status includes whether the unified collaborative task package has been distributed, whether the controlled data task package has been anonymized or digested, and whether the residency processing task package has been processed within the region of the target regional node. Calculate the supplier execution deviation value based on the expected delivery time of the current collaboration request and the supplier's estimated delivery time. :

[0048] in, Indicates the supplier's estimated delivery time. Indicates the expected delivery time. Indicates the order creation time; when When the supplier's estimated delivery time is no later than the expected delivery time, the supplier's execution status is determined to be normal; when When the supplier's estimated delivery time exceeds the expected delivery time but the deviation is within an acceptable range, the supplier's execution status is determined as a warning; when When this occurs, it indicates that the supplier's estimated delivery time significantly exceeds the expected delivery time, and the supplier's execution status is determined to be abnormal. Furthermore, based on the expected delivery time of the current collaborative request and the logistics provider's estimated arrival time, the logistics execution deviation value is calculated. :

[0049] in, This indicates the logistics provider's estimated arrival time. Indicates the expected delivery time. Indicates the order creation time; when When the logistics provider anticipates that the arrival time will not be later than the expected delivery time, the logistics execution status is determined to be normal; when When the logistics provider anticipates an arrival time exceeding the expected delivery time but the deviation is within an acceptable range, the logistics execution status is designated as a warning; when When this occurs, it indicates that the logistics provider's estimated arrival time significantly exceeds the expected delivery time, and the logistics execution status is determined to be abnormal.

[0050] Furthermore, based on the execution status of regional nodes, supplier execution status, and logistics execution status, the collaborative execution result of the current collaborative request is generated. :

[0051] When the collaborative execution result is At that time, continue to execute collaborative tasks according to the regional intelligent routing execution results, and write the target area node, target supplier, target logistics provider, collaborative task package type and execution status into the collaborative execution record; When the collaborative execution result is At the same time, the current regional intelligent routing execution result remains unchanged, while the status monitoring frequency of the current collaborative request is increased, and the warning reason is recorded; among them, the warning reasons include the deviation of the supplier's expected delivery time, the deviation of the logistics expected arrival time, the increase of the service load of the target area node, or the failure of the controlled data processing to be completed within the preset time. When the collaborative execution result is When an exception occurs, the rollback method is invoked for handling. If the target area node is unavailable, it is determined whether there is a backup area node. If there is a backup area node and the backup area node meets all the conditions corresponding to the current region intelligent routing execution result, the unfinished collaborative task is switched to the backup area node to continue execution. Furthermore, once the current collaborative request is completed, collaborative feedback data is generated, including order identifier, tenant identifier, target area node, target supplier, target logistics provider, collaborative task package type, actual delivery time, actual arrival time, field processing method, whether an abnormal rollback occurred, and the final execution result. Update the candidate supplier's historical order records and the candidate logistics provider's historical transportation records based on collaborative feedback data; when the target supplier's actual delivery time is no later than the promised delivery time, the execution record will be included in the supplier's on-time delivery record; when the target logistics provider's actual arrival time is no later than the promised arrival time, the execution record will be included in the logistics provider's on-time transportation record.

[0052] Finally, the collaborative feedback data is written into the tenant collaborative record for subsequent generation of multi-dimensional context features, calculation of collaborative risk constraints, generation of personalized collaborative strategies for tenants, and execution of regional intelligent routing under the same tenant or the same business scenario.

[0053] This embodiment also provides a multi-tenant supply chain collaboration method, including: Collect collaborative request data, tenant configuration data, candidate execution entity data, and regional node monitoring data; Based on the collected data, the tenant service priority factor, delivery urgency factor, commodity supervision sensitivity factor, data field sensitivity factor, legal domain constraint factor, execution entity stability factor, and regional node status factor are calculated respectively to construct a multi-dimensional context feature set for the current collaborative request. Based on the multi-dimensional context feature set, calculate the compliance constraint strength, performance pressure value and comprehensive collaboration risk constraint value of the current collaboration request, and generate the corresponding tenant personalized collaboration strategy based on the comprehensive collaboration risk constraint value. Based on the tenant's personalized collaboration strategy, determine the direct efficiency route, the hierarchical balanced route, or the resident compliance route, and further select the target area node, the target supplier, and the target logistics provider to perform collaborative scheduling. The system continuously monitors the status of regional nodes, supplier execution status, logistics execution status, and field processing status during the collaborative scheduling process. It generates normal, early warning, or abnormal collaborative execution results based on execution deviations and updates collaborative feedback data and historical records.

[0054] In summary, this invention constructs a multi-dimensional contextual feature system by collecting collaborative request data, tenant configuration data, historical data of executing entities, and regional node monitoring data. It then combines commodity regulatory sensitivity factors, data field sensitivity factors, legal domain constraint factors, executing entity stability factors, and regional node status factors to conduct a comprehensive risk constraint analysis of the current collaborative request. Furthermore, it generates personalized collaborative strategies for tenants based on risk levels, tenant strategy weights, and compliance tags. Combined with a regional intelligent routing mechanism, it achieves dynamic collaborative scheduling of target regional nodes, target suppliers, and target logistics providers, satisfying cross-regional compliance constraints and data residency requirements while also considering delivery timeliness and fulfillment stability. Simultaneously, by continuously monitoring the processing status of regional nodes, suppliers, logistics providers, and fields, and combining anomaly rollback and collaborative feedback update mechanisms, it achieves dynamic optimization and closed-loop adjustment of the collaborative execution process, thereby improving resource scheduling efficiency, cross-regional compliance capabilities, and collaborative execution stability in global multi-tenant supply chain collaboration scenarios.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-tenant supply chain collaboration system, characterized in that, include: The data acquisition module collects collaborative requests, tenant configurations, candidate execution entities, and regional node monitoring data. The context feature generation module calculates tenant service priority factor, delivery urgency factor, commodity supervision sensitivity factor, data field sensitivity factor, legal domain constraint factor, and execution entity stability factor based on the collected data, and calculates regional node status factor to form a multi-dimensional context feature set. The collaborative risk analysis module calculates the compliance constraint strength, performance pressure value, and comprehensive collaborative risk constraint value of the current collaborative request based on a multi-dimensional context feature set, and generates corresponding personalized collaborative strategies for tenants accordingly. The collaborative scheduling module, based on the tenant's personalized collaborative strategy, determines the direct efficiency route, the hierarchical balanced route, or the resident compliance route, and further selects the target area node, the target supplier, and the target logistics provider and executes collaborative scheduling. The collaborative detection and feedback module continuously monitors the execution status of the selected target area nodes, target suppliers, and target logistics providers during the collaborative scheduling process, as well as the data processing status of the collaborative fields. It judges the normal, warning, or abnormal status based on the execution deviation and updates the collaborative feedback data and historical records.

2. The multi-tenant supply chain collaboration system as described in claim 1, characterized in that: The collection of collaborative requests, tenant configurations, candidate execution entities, and regional node monitoring data specifically includes: Collect basic identification data for collaborative requests; Read tenant configuration data and direct record data of candidate supply chain execution entities; Collect direct monitoring data of candidate region nodes.

3. The multi-tenant supply chain collaboration system as described in claim 2, characterized in that: The calculation of tenant service priority factor, delivery urgency factor, and commodity supervision sensitivity factor based on the collected data refers to the calculation of these factors according to the tenant service level. Calculate tenant service priority factor ; Based on order creation time and expected delivery time Calculate the expected delivery time of an order Then calculate the delivery urgency factor. ; Product Categories The product regulatory attribute is broken down into several regulatory attributes, and a specific value is assigned to each regulatory attribute to generate a product regulatory attribute vector. And calculate commodity regulatory sensitivity factors. .

4. A multi-tenant supply chain collaboration system as described in claim 3, characterized in that: The calculated data field sensitivity factor, legal domain constraint factor, and execution entity stability factor refer to the factors that define each data field type. The data is broken down into several sensitive field attributes, and the sensitive field values ​​are calculated by combining these attributes to generate a sensitive field attribute vector. ; Further calculation of the first Sensitive values ​​for each field type Then calculate the data field sensitivity factor based on the sensitivity values ​​of all fields. ; The first Data field types Combined with target area Tenant compliance label set Perform domain constraint attribute identification and generate domain constraint attribute vector. ; Calculate the first Single-field domain constraint value of each field type The maximum domain constraint value among all fields will be used as the domain constraint factor for the current request. ; Calculate supplier on-time delivery rate based on historical order records. ; Calculated based on historical transport records Candidate logistics providers On-time delivery rate ; Based on the on-time performance rates of suppliers and candidate logistics providers, calculate the stability factor of candidate execution entities. .

5. A multi-tenant supply chain collaboration system as described in claim 4, characterized in that: The computational region node state factors form a multi-dimensional context feature set, which is used to calculate the first... Candidate region nodes Delay score and node load score ; Compute node compliance label matching value Node state score of a single candidate region node ; The highest node state score among the candidate region nodes is used as the candidate region node state factor for the current request. ; Output the multidimensional context feature set of the current collaborative request .

6. A multi-tenant supply chain collaboration system as described in claim 5, characterized in that: The calculation of the compliance constraint strength, performance pressure value, and comprehensive collaboration risk constraint value of the current collaboration request based on the multi-dimensional context feature set refers to calculating the compliance constraint strength of the current collaboration request based on commodity regulatory sensitivity factors, data field sensitivity factors, and legal domain constraint factors. ; The fulfillment pressure value of the current collaboration request is calculated based on the tenant service priority factor, delivery urgency factor, and candidate execution entity stability factor. ; The comprehensive collaborative risk constraint value of the current collaborative request is calculated based on the strength of compliance constraints, the performance pressure value, and the status factors of candidate regional nodes. .

7. A multi-tenant supply chain collaboration system as described in claim 6, characterized in that: The aforementioned generation of corresponding personalized collaboration strategies for tenants refers to generating the risk level of the current collaboration request based on the comprehensive collaboration risk constraint value. Personalized collaboration strategies for tenants are generated based on risk level, tenant strategy weight set, and tenant compliance tag set. The candidate collaboration strategies are individually modified based on the tenant strategy weight set to obtain the final tenant collaboration strategy score. .

8. A multi-tenant supply chain collaboration system as described in claim 7, characterized in that: The process involves determining direct efficiency routes, hierarchical balanced routes, or resident compliance routes based on tenant-specific collaborative strategies. Further, it selects target area nodes, target suppliers, and target logistics providers, and executes collaborative scheduling. Based on the target tenant's personalized collaborative strategy, it generates regional intelligent routing execution results. ; Based on the execution results of intelligent routing Based on the regional node state factors Candidate execution entity stability factor Data field sensitivity factors and domain constraint factor Generate the regional intelligent routing execution result of the current collaborative request; From satisfying the execution results of regional intelligent routing Select the target region node from the candidate region nodes that meet all the conditions; From the stability factor of the execution entity corresponding to the current route execution result Select the target execution entity combination from the combination of candidate suppliers and candidate logistics providers.

9. A multi-tenant supply chain collaboration system as described in claim 8, characterized in that: The process involves continuous monitoring of regional nodes, suppliers, logistics providers, and field processing status during collaborative scheduling. Based on execution deviations, it determines normal, warning, or abnormal states and updates collaborative feedback data and historical records. Specifically, it calculates the supplier execution deviation value based on the expected delivery time of the current collaborative request and the supplier's estimated delivery time. ; Calculate the logistics execution deviation value based on the expected delivery time of the current collaboration request and the logistics provider's estimated arrival time. ; Based on the execution status of regional nodes, supplier execution status, and logistics execution status, generate the collaborative execution result for the current collaborative request. ; Once the current collaboration request is completed, collaboration feedback data is generated, and the candidate supplier's historical order records and candidate logistics provider's historical transportation records are updated based on the collaboration feedback data. The collaboration feedback data is then written into the tenant's collaboration record.

10. A multi-tenant supply chain collaboration method, based on the multi-tenant supply chain collaboration system according to any one of claims 1 to 9, characterized in that, include: Collect collaborative request data, tenant configuration data, candidate execution entity data, and regional node monitoring data; Based on the collected data, the tenant service priority factor, delivery urgency factor, commodity supervision sensitivity factor, data field sensitivity factor, legal domain constraint factor, execution entity stability factor, and regional node status factor are calculated respectively to construct a multi-dimensional context feature set for the current collaborative request. Based on the multi-dimensional context feature set, calculate the compliance constraint strength, performance pressure value and comprehensive collaboration risk constraint value of the current collaboration request, and generate the corresponding tenant personalized collaboration strategy based on the comprehensive collaboration risk constraint value. Based on the tenant's personalized collaboration strategy, determine the direct efficiency route, the hierarchical balanced route, or the resident compliance route, and further select the target area node, the target supplier, and the target logistics provider to perform collaborative scheduling. The system continuously monitors the status of regional nodes, supplier execution status, logistics execution status, and field processing status during the collaborative scheduling process. It generates normal, early warning, or abnormal collaborative execution results based on execution deviations and updates collaborative feedback data and historical records.

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