Collaboration method for DevOps and detailed design online process in distributed platform

Abstract

The invention discloses a collaboration method for DevOps and detailed design on-line process in a distributed platform, which is characterized in that for high availability, load balancing and service priority requirements of automobile financial service channel scheduling, design process standardization definition and DevOps assembly line automation capability are fused; through standardized data configuration and dynamic weight calculation, underlying linkage of the process and the DevOps assembly line is realized. The method has the advantages that seamless linkage of design and development operation and maintenance links is realized through process decoupling and interface standardization adaptation.

Description

Technical Field

[0001] This invention relates to the field of automotive finance, and particularly to a collaborative method for DevOps and detailed design of online processes in a distributed automotive finance platform. Background Technology

[0002] As the digital transformation of the automotive finance sector deepens, the demands for accelerated business iteration, distributed architecture implementation, and strengthened compliance control are becoming increasingly urgent. Against this backdrop, DevOps, as the core system ensuring integrated development and operations, and its collaborative efficiency with detailed online process design have become crucial in determining platform delivery capabilities, making the upgrading and optimization of its collaborative mechanism a focus of industry attention. Traditional collaborative models are no longer adequate for the complex needs of distributed platforms: the disconnect between design and the DevOps toolchain necessitates customized development for integrating new design solutions, resulting in high integration costs and a high risk of errors; static process integration models cannot match the dynamic pace of parallel development of multiple microservices, easily leading to version discrepancies; and the lack of a full-link traceability mechanism makes the impact of design changes uncontrollable, failing to meet the compliance audit requirements of the financial sector. These problems not only hinder the improvement of platform iteration efficiency but also increase system failure risks and operational costs.

[0003] The disadvantages of existing technologies include:

[0004] (1) Static connection failure: Fixed interface specifications and process connection thresholds cannot adapt to dynamic scenarios such as parallel development of multiple microservices and urgent changes in business requirements, which can easily lead to process blockage or mismatch of development resources. (2) Customized development bottleneck: Adding new design tools and adjusting microservice collaboration strategies require modifying service interface code and redeploying pipelines, resulting in long development cycles and difficulty in meeting the high-frequency business iteration needs of the automotive finance distributed platform. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a collaborative method for DevOps and detailed design online processes in a distributed platform for automotive finance. This method achieves seamless linkage between design, development and operation and maintenance processes through process decoupling and standardized interface adaptation.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A collaborative method for DevOps and detailed design of online processes in a distributed platform is proposed. This method addresses the high availability, load balancing, and service priority requirements of automotive finance business channel scheduling by integrating standardized design process definitions with DevOps pipeline automation capabilities. It achieves underlying linkage between processes and DevOps pipelines through standardized data configuration and dynamic weight calculation.

[0008] The underlying linkage includes data dimension standardization and process node standardization. Data dimension standardization is used to define the design specifications for the three core data dimensions of supplier configuration: the daily transmission volume needs to be associated with the historical load threshold of the channel, and the quantitative standard of the peak / valley range needs to be clearly defined; the weight ratio needs to be linked to the priority of auto finance business, and the weight value tiered range needs to be set; the activation status needs to distinguish between three status indicators: temporary shutdown, permanent deactivation, and normal activation, and the approval process and effective time of status change need to be clearly defined.

[0009] Standardization of process nodes: Detailed design process nodes for channel scheduling rules: requirement decomposition → data dimension definition → weight algorithm design → compliance review → version solidification. Each node sets admission / exit conditions and calculates its proportion in the total weight based on the preset weight ratio field to form a dynamic selection probability.

[0010] The method also includes bidirectional linkage configuration of processes and pipelines. With the supplier dynamic screening mechanism and blacklist processing rules as the core, it constructs a bidirectional strong correlation system between detailed design online processes and DevOps pipelines. Through rule synchronization, trigger control, and permission mapping, it achieves collaborative closed loop, ensuring optimal resource allocation and precise risk prevention and control.

[0011] The method also includes automated collaborative execution and problem closure steps, design process output template mapping rules and parameter specifications, which are synchronized to the DevOps rule engine through a standardized interface; the middleware accesses customer management, risk control and other system parameters in real time, the rule engine locates the optimal template according to the three-dimensional matching logic, automatically fills in variables and calls the filtered supplier to send, and key data is bidirectionally written back to the design platform and DevOps pipeline.

[0012] The method also includes a full-link monitoring and continuous optimization module. The monitoring is layered according to "business characteristics - request execution - response feedback": capturing business type tags to build feature vector monitoring and tracking matching accuracy; monitoring the validity of authentication parameters, the execution status of request templates and format compliance; parsing response data to count the sending success rate, and combining it with business indicators such as reach rate to form a two-dimensional view, which is presented through a multi-role visualization dashboard.

[0013] The method also includes closed-loop problem management, which monitors problems and errors through DevOps monitoring and intelligent log identification. When a problem or error is detected, an alert is sent to the corresponding role through a hierarchical alerting method.

[0014] The method also includes using data, rules, and execution to trace and locate the cause of the problem in three dimensions, and automatically dispatching orders to the corresponding roles based on the cause of the problem.

[0015] The end-to-end monitoring and tracing uses mobile phone numbers and timestamps as core identifiers, and links them with business, technical parameters and execution log chains to track DevOps and detailed design processes.

[0016] The advantages of this invention are as follows: This method achieves seamless linkage between design, development, and operation / maintenance stages through process decoupling and standardized interface adaptation. Detailed design changes can be synchronized to the DevOps pipeline in real time, and design review results automatically trigger subsequent development tasks; accessing new design tools or DevOps components only requires configuration, without custom development; simultaneously, a full-process traceability chain is built to adapt to the compliance requirements of auto finance. This invention improves the collaborative efficiency of design and DevOps, reduces manual coordination costs, and ensures the consistency and stability of multi-microservice development under a distributed architecture. Attached Figure Description

[0017] The following is a brief explanation of the contents of each of the accompanying drawings and the markings in the drawings:

[0018] Figure 1 This is a schematic diagram of the application call structure provided in an embodiment of the present invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and the description of the preferred embodiments.

[0020] This solution constructs a distributed, intelligent online workflow collaboration system for DevOps and detailed design. This system uses Domain-Driven Design to delineate bounded contexts for system functions, abstracting the "detailed design" phase into an independent business subdomain and defining clear, semantically explicit interface contracts. Based on this, a plug-in architecture is adopted, encapsulating the design deliverables from different design tools (such as database modeling tools, UI prototyping tools, and API design platforms) into standardized plug-in modules. Each plug-in implements a unified adapter interface, allowing it to be dynamically loaded and invoked by the DevOps pipeline.

[0021] Its core principle lies in decoupling design assets from pipeline execution logic. When developers submit code or trigger a build, the DevOps platform automatically identifies the associated design plugins, retrieves the corresponding design specifications, and embeds validation, generation, or deployment steps into the pipeline (such as automatically generating DAO code, validating API compatibility, and synchronizing database changes). This mechanism realizes the concept of "design as code," making design assets first-class citizens of the pipeline.

[0022] In the system described in this article, the "System Detailed Design Management Module" is the key component for achieving this effect. This module is responsible for storing structured detailed design documents, providing a standardized plugin registration and discovery mechanism, and exposing a unified API for CI / CD tools to call. Through this module, design results can be seamlessly injected into DevOps stages such as code generation, static analysis, and automated testing, truly achieving a "plug-and-play" collaborative effect, significantly reducing toolchain integration costs, and improving R&D efficiency and delivery quality.

[0023] This solution establishes a scenario-based collaboration strategy by combining microservice decomposition, enabling dynamic integration of requirements design, development, testing, and deployment. This strategy decomposes the monolithic system into microservices based on dimensions such as business capabilities, data boundaries, or domain models, resulting in high cohesion and low coupling among the services. Building upon this, a "scenario-based collaboration" mechanism is constructed: mapping requirements design, development, testing, and deployment to specific business scenarios, and leveraging a unified DevOps platform to automate processes and ensure seamless information flow. Its core principle lies in utilizing service contracts and event-driven architecture to ensure consistency across teams in interfaces, data, and behaviors; simultaneously, it leverages CI / CD pipelines and environment management modules to dynamically trigger tasks at each stage, achieving rapid end-to-end feedback and delivery. The "integrated collaborative delivery platform" module, discussed later, is a key component supporting this effect. It integrates requirements management, code repositories, automated testing, and deployment orchestration, connecting the entire chain from requirements to deployment and ensuring efficient collaboration and continuous delivery of microservices across multiple teams.

[0024] This system embeds a unified log collection agent into each microservice of the automotive finance distributed platform, centrally storing business operation, system event, and user behavior logs in a standardized format at the log center. Combined with a compliance rule engine, it automatically tags, audits, and generates traceable chains for sensitive operations, meeting regulatory requirements for data integrity, immutability, and operational traceability. Its core principle is based on end-to-end tracing and structured log governance. The "Intelligent Compliance Log Platform" module, discussed later, is key to realizing this capability, supporting efficient iteration, quality monitoring, and integrated compliance auditing.

[0025] Both this invention and existing solutions focus on process collaboration and adaptation, achieving breakthroughs through process decoupling and intelligent linkage algorithms: dynamically adjusting the range of connection parameters between design and DevOps, filtering and synchronizing design change samples in real time based on business scenarios, and dynamically configuring collaboration priorities for different microservice modules to adapt to the parallel development needs of multiple scenarios on distributed platforms, rather than simply optimizing process serialization; achieving a leap from "manual connection-driven" to "intelligent design and operation integration-driven", effectively solving the problems of insufficient adaptability and lagging collaboration in distributed scenarios of existing solutions, and providing a more efficient and reliable process collaboration solution for the automotive finance sector.

[0026] This plan includes:

[0027] (1) Construct an intelligent collaborative decision-making model that integrates real-time development progress data with historical collaborative experience base:

[0028] Visualized strategy configuration: Business and technical personnel can customize collaboration rules through a graphical interface, such as setting "automatically trigger a full-scale test task + reserve operation and maintenance resources when the core risk control module design changes". The configuration takes effect immediately without code modification.

[0029] Adopting a dual-track mechanism of standardized collaborative interfaces + dynamic configuration center: Unified interface layer: Based on domain-driven design, define RESTful standard collaborative interfaces, covering core functions such as design version synchronization, review result push, and task triggering. New design tools or DevOps components only need to implement the interfaces to be connected, reducing development workload by 85%.

[0030] Configuration-driven deployment: Collaboration parameters are managed through YAML configuration files. Modifications to the configuration are automatically hot-reloaded via the configuration center, eliminating the need to restart service nodes and enabling minute-level adjustments to collaboration strategies and tool integration. This solution effectively addresses the issues of poor adaptability to static integration and long custom development cycles, supporting the rapid response of automotive finance distributed platforms to changes in business requirements.

[0031] like Figure 1 As shown, this solution includes the following components:

[0032] (I) Standardized Processes and Assembly Line Basic Construction Modules

[0033] Standardized processes and pipeline infrastructure modules achieve underlying collaboration through a "dual-standard linkage mechanism": First, standardized design processes predefine metadata such as service deployment topology, dependencies, resource specifications, and compliance checks during the detailed design phase, and solidify them into a "process configuration package" using a structured template. Second, standardized DevOps pipelines abstract the build, test, and deployment phases into orchestratable atomic task units and bind them to unified execution strategies and access control rules. These two aspects are linked through a dynamic weighted scheduling engine: this engine analyzes the business priority, SLA level, and channel load metrics in the process configuration package in real time, and, combined with the runtime cluster status, dynamically adjusts the pipeline task scheduling order and resource allocation using weighted round-robin or priority queue algorithms. For example, a high-priority loan approval service automatically increases its deployment pipeline weight during peak traffic, prioritizing the acquisition of build resources and triggering canary releases. All linkage logic is centrally managed to ensure strong consistency between configuration changes and pipeline behavior. This mechanism not only achieves closed-loop control of process definition and execution, but also drives automated decision-making through structured data, meeting the stringent requirements of auto finance for high availability, flexible scheduling, and compliance auditing.

[0034] The specific execution process is as follows:

[0035] 1. Standardize the design data dimensions:

[0036] Weighting: In multi-vendor channel scheduling scenarios, this refers to the scheduling priority coefficient assigned to each vendor channel, determining its probability of being selected during request distribution. This weighting is implemented by binding it to the priority of auto finance business: the system pre-defines a mapping table between business types and priority levels (high / medium / low); then, the levels are mapped to weighted tiers. During configuration, operations or product personnel select the business type, and the system automatically fills in the corresponding weighted tier value. The purpose of setting tiered tiers is to quantify the differences in business value, enabling the scheduling engine to calculate a reasonable dynamic selection probability R based on the weights, thereby prioritizing channel resources for high-value businesses under high concurrency.

[0037] Enabled Status: This refers to the operational status of the supplier channel, indicating whether it is currently eligible for scheduling. It is categorized into three types:

[0038] Normal activation: Can receive data traffic;

[0039] Temporary shutdown: If the service is temporarily suspended due to maintenance or traffic limiting, a work order must be submitted and approved by the first-level operation and maintenance department. The configuration center will take effect within 5 minutes.

[0040] Permanent deactivation: If compliance or cooperation is terminated, requiring approval from both risk control and compliance authorities, the device will be immediately removed from the scheduling pool.

[0041] Status changes are handled through a unified configuration management platform, with the approval process embedded in the BPM engine. Change results are synchronized to the scheduling service in real time, ensuring that invalid channels are not selected and guaranteeing system stability and compliance.

[0042] 2. The role of standardization in design process nodes

[0043] Node standardization ensures that there are clear input, processing logic and output standards for each stage from requirements to deployment, enabling development, testing, operation and maintenance, compliance and other roles to collaborate under unified rules, avoiding information gaps, and supporting reliable triggering and quality access control of DevOps automated pipelines.

[0044] The detailed design process for channel scheduling rules consists of five nodes: requirement breakdown → data dimension definition → weight algorithm design → compliance review → version finalization.

[0045] 3. The relationship between admission / exit conditions and probability R, and their applications.

[0046] Each node has admission / exit criteria to ensure process quality and data consistency. In the "Weight Algorithm Design" node, the system calculates the proportion of each channel in the total weight based on the weight allocation field, resulting in a dynamic selection probability R. This R value is directly written into the scheduling policy configuration, and the intelligent routing engine randomly selects channels according to probability at runtime to achieve automated distribution that balances load balancing and business priority—this is the core application of R, not just calculation.

[0047]

[0048] R represents the result, T represents the current time, N represents the number of servers, and O represents the polling offset.

[0049] (ii) Two-way linkage configuration module for processes and production lines

[0050] This module achieves a collaborative closed loop between detailed design and the DevOps pipeline through three mechanisms: First, rule synchronization automatically synchronizes the supplier selection rules defined in the design phase to the configuration center in a structured configuration format. The DevOps pipeline loads these rules in real time during deployment, ensuring consistency between the runtime and design states. Second, dynamic trigger control: when the monitoring system detects consecutive failures or timeouts in a supplier's response, it immediately triggers two actions—automatically setting the supplier's status to "temporarily closed" and removing it from the scheduling pool, while simultaneously generating an exception handling work order on the design platform, forcing the business party to confirm within a specified period whether to permanently blacklist the supplier. Third, permission mapping and approval linkage: changes to the blacklist require approval from both risk control and compliance personnel. After approval, the IAM system verifies permissions and triggers a gray-scale verification task in the isolated environment. Only after successful verification is the system allowed to go live again. This solution achieves a closed-loop end-to-end system from risk identification, automatic isolation, manual review to security recovery, ensuring both high-availability resource scheduling and meeting the requirements of precise financial-grade risk control.

[0051] The design process outputs standardized linkage rules to the production line: clearly defining supplier weighting rules; the specific weighted random algorithm formula is as follows:

[0052] =

[0053] in, This represents the probability that element (i) is selected. This represents the weight of element (i). This represents the sum of the weights of all elements. In the formula, "element(i)" refers to the instance of a supplier channel participating in the scheduling decision. Each channel is assigned a weight W(i) during the design phase, reflecting its business priority, historical success rate, or resource capacity. P(i) = W(i) / wtotal calculates the dynamic probability of that channel being selected in the current scheduling round, used to drive the intelligent routing engine to implement weighted random selection, rather than simple round-robin or fixed priority.

[0054] This probability is not isolated but is a core component of standardized linkage rules. The detailed design process outputs a structured rule package containing the W(i) value for each channel at the "Weight Algorithm Design" node. After compliance review, this package is automatically injected into the DevOps pipeline's configuration template. During pipeline deployment, this rule is loaded into the runtime scheduler. When a business request arrives, the scheduler calculates the selection probability of each channel in real time based on P(i) and filters it according to its activation status and blacklist identifier, ultimately distributing traffic according to probability distribution. This approach achieves both priority scheduling for high-priority services (optimal resource allocation) and automatic avoidance by resetting the weight to zero when a channel is abnormal (precise risk control), forming a closed-loop linkage mechanism of design → deployment → execution → feedback.

[0055] The design version refers to the scheduling rule package that is finalized after the detailed design online process goes through "requirements decomposition → data dimension definition → weight algorithm design → compliance review". It includes structured configurations such as weight ratio, activation status, and adaptation conditions. This version is uniquely identified and stored in the configuration repository as a prerequisite for pipeline startup: before triggering, the pipeline checks whether a finalized and compliantly approved design version exists. If not, execution is blocked to ensure "no deployment without design".

[0056] The rules automatically retrieved are the scheduling metadata from this design version. After retrieval, they are injected into the scheduling policy context in the pipeline for use by subsequent filtering nodes.

[0057] The algorithm execution module is a lightweight decision engine embedded in the pipeline. It receives the real-time business type label of the current request, combines it with the retrieved rules, filters out the set of suppliers that support the type, and then calculates the selection probability of each candidate according to P(i)=W(i) / wtotal to complete the weighted random selection.

[0058] The compatibility verification interface is provided by the design platform. It receives the candidate supplier ID and business tag, and returns whether the candidate meets the constraints such as compliance policy, geographical restrictions, and certificate validity period of the business. Those that fail the verification are removed from the candidate set.

[0059] Rule change trigger update principle: The design platform listens for rule version change events and notifies the DevOps platform via Webhook; the pipeline automatically pulls the new rule, performs shadow traffic replay or policy simulation in an isolated environment, compares the differences between the old and new results, generates an impact report, and only takes effect after approval.

[0060] Access control is achieved through the IAM system, which binds the "Compliance Specialist" role to the pipeline audit view and the "Designer" role to the execution log tracking panel, enabling collaborative management under the separation of responsibilities.

[0061] (III) Automated Collaborative Execution and Problem Closure Module

[0062] This module focuses on the SMS sending scenario in auto finance, constructing a closed-loop collaborative system encompassing "execution – monitoring – repair – review." In the automated collaborative execution phase, detailed process design outputs structured SMS template rules and variable parameter specifications, which are synchronized to the DevOps rule engine via standardized APIs. During runtime, the middleware pulls customer tags, event types, and compliance levels from customer management and risk control systems in real time. Based on this three-dimensional matching logic, the rule engine accurately locates the unique "optimal template" from the template library—a template that simultaneously satisfies business semantics, regulatory compliance, and user experience. Subsequently, the system invokes a dynamic supplier selection mechanism to select available SMS suppliers in real time based on channel weight, activation status, blacklist, and current business priority before sending. It then selects a specific channel with probability weighting, completes variable population, and triggers sending. Key execution data is bidirectionally written back to the design platform and the DevOps pipeline, supporting traceability and analysis.

[0063] In terms of closed-loop problem management, the system identifies two types of anomalies through intelligent log analysis: technical issues and business issues. Root cause analysis is performed based on a three-dimensional correlation graph of "data-rules-execution": if multiple failures are due to high-risk customers not matching templates containing legal clauses, it is determined to be a rule configuration defect; if a supplier experiences continuous timeouts, it is attributed to a channel failure. The system automatically creates structured work orders and assigns them to the corresponding roles based on the issue type. After a fix, automated testing is triggered to verify the correctness of template rendering, and a trial release is conducted to 1% of users via Kubernetes canary deployment. Once confirmed to be error-free, it is fully deployed. Monthly reviews are conducted to aggregate problem patterns and fix effects, providing feedback to optimize template design specifications, parameter standards, and supplier access strategies, achieving continuous iteration and capability accumulation.

[0064] (iv) End-to-end monitoring, tracing, and continuous optimization module

[0065] This module uses a business type-driven parameter dynamic parsing model as its core, anchoring SMS content, system source, and mobile phone number parameter clusters to build an end-to-end security system.

[0066] To track template matching accuracy, the system constructs a three-layer mechanism at the monitoring layer: "Business Features - Request Execution - Response Feedback." First, at the business features layer, tags such as customer risk level, overdue stage, and region are extracted in real-time from the risk control and customer systems and concatenated into a structured feature vector. Second, at the request execution layer, the actual SMS template ID and matching criteria selected by the rule engine are recorded. Finally, at the response feedback layer, "whether the match is correct" is marked based on user behavior or manual sampling results. The system periodically feeds the feature vector and template selection results into a lightweight verification model, compares the deviation between the actual matching results and the preset rule strategy, and calculates the accuracy metric. If the accuracy falls below a threshold, an alarm is automatically triggered and a problem sample set is generated for backtracking rule configuration defects, supporting subsequent repair and optimization.

[0067] This solution constructs a full-link traceability system using mobile phone number + timestamp as the unique core identifier. When an SMS sending request is triggered, the system automatically aggregates three types of data: business parameters, technical parameters, and execution logs, forming a structured event record. This record is immediately written to a blockchain-based evidence storage layer upon generation, utilizing its hash-on-chain and timestamp solidification mechanism to ensure data immutability and auditability. Users can retrieve the complete execution chain in a visual dashboard using multi-dimensional conditions. When a compliance specialist initiates a special traceability, the system quickly reconstructs the entire process from rule design to actual delivery based on blockchain evidence storage, outputting an audit report with a digital signature, meeting the financial regulatory requirements for operational traceability and accountability. Simultaneously, this traceability data feeds back into the monitoring module to calculate the actual reach rate and delivery success rate, forming a closed loop of "execution – monitoring – traceability – optimization".

[0068] This module achieves continuous optimization through data-driven closed-loop: First, based on historical matching results and user feedback, an evaluation index system is constructed, and the model feature weights are dynamically adjusted using online learning or A / B testing mechanisms—for example, if the "overdue days" feature contributes little to matching high-risk templates, its weight in the feature vector is reduced to improve overall matching accuracy. Second, it links design and DevOps: when monitoring detects frequent mismatches in a certain type of template, the system automatically generates rule optimization suggestions and pushes them to the design platform for designer confirmation; after confirmation, the DevOps pipeline automatically updates the rule engine configuration and refactors the relevant code. Simultaneously, the weights and statuses of suppliers are dynamically adjusted based on supplier monitoring data: channels with a delivery rate of <90% for 7 consecutive days are automatically downgraded, triggering a temporary shutdown process. All optimization solutions are tested on a small scale using Kubernetes (K8s) for effectiveness verification, and fully deployed after achieving the target, with experience being distilled into rule templates or development specifications. Abnormal events automatically trigger a closed-loop problem process, and repair and optimization results are synchronized in real time to the entire design, deployment, and monitoring chain, achieving collaborative evolution.

[0069] Obviously, the specific implementation of this invention is not limited to the above-described methods. Any non-substantial improvements made using the inventive concept and technical solution of this invention are within the protection scope of this invention.

Claims

1. A collaborative method for DevOps and detailed design online processes in a distributed platform, characterized by: To meet the high availability, load balancing, and service priority requirements of automotive finance business channel scheduling, the design process standardization definition and DevOps pipeline automation capabilities are integrated. The underlying linkage between the process and the DevOps pipeline is achieved through standardized data configuration and dynamic weight calculation.

2. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 1, characterized in that: The underlying linkage includes data dimension standardization and process node standardization. Data dimension standardization is used to define the design specifications for the three core data dimensions of supplier configuration: the daily transmission volume needs to be associated with the historical load threshold of the channel, and the quantitative standard of the peak / valley range needs to be clearly defined; the weight ratio needs to be linked to the priority of auto finance business, and the weight value tiered range needs to be set; the activation status needs to distinguish between three status indicators: temporary shutdown, permanent deactivation, and normal activation, and the approval process and effective time of status change need to be clearly defined.

3. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 1, characterized in that: Standardization of process nodes: Detailed design process nodes for channel scheduling rules: requirement decomposition → data dimension definition → weight algorithm design → compliance review → version solidification. Each node sets access / exit conditions and calculates its proportion in the total weight based on the preset weight ratio field to form a dynamic selection probability.

4. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 1, characterized in that: The method also includes bidirectional linkage configuration of processes and pipelines. With the supplier dynamic screening mechanism and blacklist processing rules as the core, it constructs a bidirectional strong correlation system between detailed design online processes and DevOps pipelines. Through rule synchronization, trigger control, and permission mapping, it achieves collaborative closed loop, ensuring optimal resource allocation and precise risk prevention and control.

5. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 1, characterized in that: The method also includes automated collaborative execution and problem closure steps, design process output template mapping rules and parameter specifications, which are synchronized to the DevOps rule engine through a standardized interface; the middleware accesses customer management, risk control and other system parameters in real time, the rule engine locates the optimal template according to the three-dimensional matching logic, automatically fills in variables and calls the filtered supplier to send, and key data is bidirectionally written back to the design platform and DevOps pipeline.

6. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 1, characterized in that: The method also includes a full-link monitoring and continuous optimization module. The monitoring is layered according to "business characteristics - request execution - response feedback": capturing business type tags to build feature vector monitoring and tracking matching accuracy; monitoring the validity of authentication parameters, the execution status of request templates and format compliance; parsing response data to count the sending success rate, and combining it with business indicators such as reach rate to form a two-dimensional view, which is presented through a multi-role visualization dashboard.

7. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 5, characterized in that: The method also includes closed-loop problem management, which monitors problems and errors through DevOps monitoring and intelligent log identification. When a problem or error is detected, an alert is sent to the corresponding role through a hierarchical alerting method.

8. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 5, characterized in that: The method also includes using data, rules, and execution to trace and locate the cause of the problem in three dimensions, and automatically dispatching orders to the corresponding roles based on the cause of the problem.

9. The collaborative method for DevOps and detailed design online processes in a distributed platform as described in claim 6, characterized in that: The end-to-end monitoring and tracing uses mobile phone numbers and timestamps as core identifiers, and links them with business, technical parameters and execution log chains to track DevOps and detailed design processes.

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