A supply chain collaborative management system for an electric control system

Abstract

The present application relates to the technical field of supply chain management, in particular to a supply chain collaborative management system for electric control system, comprising: a knowledge graph is established and maintained in advance for a to-be-designed project, and then corresponding constraint information is output according to the knowledge graph; receiving the design data returned by the subcontractor, combining the constraint information to check the design common fault record, the design data and the optimization suggestion provided by the subcontractor to calculate the subcontractor score. In the prior art, the digital drawing review system lacks further closed-loop control process. The knowledge graph is established and maintained in advance for the to-be-designed project before starting the contracting, so that the corresponding constraint information is added in the design tool and the subcontracting contract annex, the design process is directly controlled, the design common fault record is fed back to the subcontractor through checking, the error is avoided to enter the construction stage, and the subcontractor score is measured by combining the checking result with multiple indexes to facilitate the recommendation next time, so that a good screening effect is realized.

Description

Technical Field

[0001] This invention relates to the field of supply chain management technology, and more specifically to a supply chain collaborative management system for electronic control systems. Background Technology

[0002] In the Engineering, Procurement, and Construction (EPC) model, the electrical control system is the central nervous system of the project, and its design quality directly affects the project's safety, schedule, and cost. As the standard setter, the EPC design institute needs to oversee the electrical control designs of numerous subcontractors. Typical oversight processes include specifying the standards and inspection items to be followed in subcontracting contracts and design specifications, followed by item-by-item verification of the design data returned by subcontractors to determine compliance with relevant standards.

[0003] For example, patent application CN202010390403.7 discloses a multi-channel dynamic management system for complex construction projects, including a data processing backend and multiple types of user clients. The data processing backend is a computer server with the dynamic management system installed, communicating with multiple types of user clients via a network. The data processing backend statistically analyzes, coordinates, and plans all human, financial, skill, warehousing, material, progress, speed, and time management aspects involved in the current complex construction project, serving as the basis for optimizing project progress management and schedule scheduling. This significantly improves the accuracy of personnel utilization; the timeliness and sufficiency of project fund management; and the rationality, accuracy, and timeliness of project material allocation. Through performance management, it cultivates and identifies priority construction teams, saving significant amounts of human and material resources and upgrading the level of project implementation and management.

[0004] For example, patent application CN201811535368.2 discloses a comprehensive construction method based on BIM technology, comprising three parts: determining the basis for detailed design, the BIM-based design phase, and drawing-based construction. Determining the basis for detailed design includes clearance requirement reports, design blueprints, equipment parameters, technical requirements, and relevant national and industry standards. Drawings include structural opening drawings, masonry wall opening drawings, integrated pipeline drawings, single-discipline floor plans, sectional drawings, detailed drawings of supports and hangers, detailed drawings of machine rooms, clearance drawings, and interior decoration positioning drawings. The detailed drawings of supports and hangers include drawings of integrated supports and hangers and large supports and hangers, containing detailed specifications of the support and hanger construction and their load-bearing information. The BIM-based integrated pipeline technology can integrate architectural, structural, and MEP (mechanical, electrical, and plumbing) models, allowing problems to be identified before construction begins and resolved through detailed design and optimization.

[0005] However, in actual implementation, the inventors found that such technical solutions usually only realize the online circulation and annotation of drawings, and cannot reverse the standard iteration driven by the conflict data found in the drawing review, nor can they form a closed loop between standard compliance and supply chain hierarchical management. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, a supply chain collaborative management system for electronic control systems is provided.

[0007] The specific technical solution is as follows: A supply chain collaborative management system for electronic control systems includes: a constraint output module, which pre-establishes and maintains a knowledge graph for the project to be designed, and then outputs corresponding constraint information based on the knowledge graph; the constraint information is added to the output specifications of the subcontractor's design tool and the subcontracting contract appendix; a data verification module, which receives design data returned by the subcontractor and verifies the design data in conjunction with the constraint information to obtain a design defect record; a scoring module, which calculates a subcontractor score based on the design defect record, the design data, and the optimization suggestions provided by the subcontractor; and a recommendation module, which generates and outputs recommended subcontractors based on the subcontractor score.

[0008] On the other hand, the constraint output module includes: a structured processing module, which performs structured processing on the input text to construct structured text; a graph maintenance module, which establishes the knowledge graph based on the structured text; and a constraint adding module, which matches the knowledge graph with the project to be designed and extracts the constraint information for output.

[0009] On the other hand, the knowledge graph maintenance module includes: an entity extraction module, which identifies entity units from the structured text; a triplet creation module, which searches for triplet information for the entity units; and a knowledge graph addition module, which adds entity nodes to the knowledge graph based on the triplet information and maintains the relationships between the entity nodes.

[0010] On the other hand, the constraint addition module includes: a project extraction module, which matches project keywords with the project to be designed; an item extraction module, which extracts standard clauses from the knowledge graph based on the project keywords; and a constraint generation module, which generates the constraint information according to the standard clauses and writes it.

[0011] On the other hand, the data verification module includes: a multi-source data integration module, which captures professional domain data from the corresponding external interface based on the design data, and then integrates the design data and the professional domain data into multi-source data; a rule matching module, which extracts inspection rules from a pre-maintained rule base based on the constraint information; and a conflict detection module, which inputs the inspection rules and the multi-source data into a conflict detection engine to obtain the design defect record.

[0012] On the other hand, the scoring module includes: an indicator processing module, which evaluates the standard dependency based on the design data and the constraint information, generates a conflict contribution based on the design defect record, generates an optimization contribution based on the optimization suggestions, and generates a delivery performance based on the delivery time of the design data; and a weighting module, which generates the subcontractor score according to the standard dependency, the conflict contribution, the optimization contribution, and the delivery performance.

[0013] On the other hand, the recommendation module includes: a grading module, which generates a corresponding subcontractor rating based on the subcontractor score; and a recommendation generation module, which generates a recommended subcontractor based on the subcontractor rating.

[0014] On the other hand, it also includes an auxiliary optimization module, which includes: a design margin optimization module, which calculates a design margin coefficient based on the design data and feeds back a margin coefficient correction instruction; a compatibility analysis module, which performs compatibility analysis on the design data to obtain a compatibility adjustment result; a standard unit extraction module, which extracts standard design units from the design data; and an update module, which updates the knowledge graph based on the margin coefficient correction instruction, the compatibility adjustment result, and the standard design units.

[0015] On the other hand, the knowledge graph is implemented based on a hybrid architecture combining the PostgreSQL relational database and the Neo4j graph database.

[0016] A storage medium includes computer instructions that, when executed by a computer device, establish the aforementioned supply chain collaborative management system.

[0017] The above technical solution has the following advantages or beneficial effects: Existing digital drawing review systems typically only allow online circulation and annotation of drawings without further closed-loop control. This solution establishes and maintains a knowledge graph for the design project before contracting begins, adding corresponding constraint information to design tools and subcontract attachments. This directly controls the design process, and the returned design data is checked to obtain common design defects, which are then fed back to the subcontractor to prevent errors from entering the construction phase. Furthermore, by combining the check results with multiple indicators to evaluate the subcontractor's performance for future recommendations, a better screening effect is achieved. Attached Figure Description

[0018] Embodiments of the invention will be described more fully with reference to the accompanying drawings. However, the drawings are for illustration and explanation only and do not constitute a limitation on the scope of the invention.

[0019] Figure 1 This is an overall schematic diagram of an embodiment of the present invention; Figure 2 This is a schematic diagram of the constraint output module in an embodiment of the present invention; Figure 3 This is a schematic diagram of the spectrum maintenance module in an embodiment of the present invention; Figure 4 This is a schematic diagram of the constraint addition module in an embodiment of the present invention; Figure 5 This is a schematic diagram of the data verification module in an embodiment of the present invention; Figure 6 This is a schematic diagram of the scoring module in an embodiment of the present invention; Figure 7 This is a schematic diagram of a recommended module in an embodiment of the present invention; Figure 8 This is a schematic diagram of the auxiliary optimization module in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0023] This invention includes: a supply chain collaborative management system for electronic control systems, such as... Figure 1 As shown, it includes: Constraint Output Module 1, which pre-establishes and maintains a knowledge graph for the project to be designed, and then outputs corresponding constraint information based on the knowledge graph; the constraint information is added to the output specifications of the subcontractor's design tool and the subcontracting contract appendix; Data Verification Module 2, which receives the design data returned by the subcontractor, verifies the design data in conjunction with the constraint information to obtain a record of common design defects; Scoring Module 3, which calculates the subcontractor's score based on the record of common design defects, the design data, and the optimization suggestions provided by the subcontractor; and Recommendation Module 4, which generates and outputs recommended subcontractors based on the subcontractor's score.

[0024] Specifically, existing digital drawing review systems typically only allow online circulation and annotation of drawings without further closed-loop control. This approach involves establishing and maintaining a knowledge graph for the design project before outsourcing, adding corresponding constraint information to design tools and subcontract attachments, directly controlling the design process, and verifying the returned design data to obtain common design defects and provide feedback to subcontractors, preventing errors from entering the construction phase. Furthermore, by combining the verification results with multiple indicators to evaluate subcontractors for future recommendations, a better screening effect is achieved.

[0025] In actual implementation, the aforementioned system is primarily installed as a software implementation in computer equipment, such as the server of the project owner, for project management of the projects to be designed. Specifically, the constraint output module 1 pre-establishes and maintains a knowledge graph for the projects to be designed, and then outputs corresponding constraint information based on the knowledge graph. This constraint information is added to the output specifications of the subcontractor's design tools and the subcontract appendices, thereby pre-implanting the general contractor's electrical control technology standards into the subcontractor's design and contract performance environment during the project initiation phase, preventing deviations in standard implementation from the outset.

[0026] Data verification module 2 receives design data returned by subcontractors and verifies the design data in conjunction with constraint information to obtain a record of common design defects. The record of common design defects is categorized according to the violated standard clauses, serving both as a basis for evaluating the design quality of subcontractors and as a data source for the iterative optimization of the knowledge graph.

[0027] The scoring module 3 calculates subcontractor scores based on design defect records, design data, and optimization suggestions provided by subcontractors, enabling a quantitative evaluation of subcontractors' technical capabilities and collaborative contributions. The recommendation module 4 generates and outputs recommended subcontractors based on their scores, providing decision support for the next round of subcontractor selection and technical bid review, thereby jointly achieving iterative development of electrical control system standards and collaborative supply chain management.

[0028] In one embodiment, such as Figure 2 As shown, the constraint output module 1 includes: a structured processing module 11, which performs structured processing on the input text to construct structured text; a graph maintenance module 12, which establishes a knowledge graph based on the structured text; and a constraint addition module 13, which matches the knowledge graph with the project to be designed and extracts constraint information for output.

[0029] Specifically, in order to achieve a better constraint effect on the design process, in this embodiment, the input text is first processed by the structured processing module 11 to construct structured text.

[0030] The input text consists of unstructured standard documents from the general contractor, such as the "Design Specifications for Electrical Control Systems" and the "Technical Specifications for the Procurement of Electrical Equipment." The structured processing module 11 employs a hybrid approach combining a BERT-based named entity recognition model with rule matching: first, the NER model identifies elements in the text such as equipment names, circuit names, interface names, and parameter thresholds; then, preset rules verify and complete the identification results, extracting binding clauses and generating structured text.

[0031] The knowledge graph maintenance module 12 establishes a knowledge graph based on structured text. This includes creating entity nodes and maintaining the knowledge graph by further extracting entities and maintaining triple information based on the relationships between entities, on the basis of extracting and assembling structured text.

[0032] Finally, for the actual design project, the constraint addition module 13 matches the knowledge graph according to the project to be designed and extracts the constraint information for output. This constraint information includes two parts: one part is exported as textual rules through a specific text export tool and added to the appendix of the subcontract; the other part is exported as inspection rules and synchronously imported into the subcontractor's design software to assist in checking and outputting error information, thereby realizing the entire quality control process.

[0033] In one embodiment, such as Figure 3 As shown, the knowledge graph maintenance module 12 includes: an entity extraction module 121, which identifies entity units from structured text; a triplet creation module 122, which searches for triplet information for entity units; and a knowledge graph addition module 123, which adds entity nodes to the knowledge graph based on the triplet information and maintains the relationships between entity nodes.

[0034] Specifically, to achieve better map maintenance, in this embodiment, the entity extraction module 121 identifies entity units from structured text. The types of entity units include device type, loop type, and interface type. For example, "low-voltage motor loop" belongs to the loop type entity, and "isolated safety barrier interface" belongs to the interface type entity. Each entity unit contains four fields: unique identifier, name, type, and attribute set.

[0035] The triplet creation module 122 retrieves triplet information for entity units. Triples are represented using a resource description framework, and the types of relationship edges include mandatory adoption, recommended adoption, and prohibited adoption. Each relationship edge contains five fields: start node identifier, end node identifier, relationship type, weight, and effective version. For example, the "intrinsically safe loop" entity, associated with the "light blue wire" attribute and the "isolated safety barrier" entity through the "mandatory adoption" relationship, indicates that the intrinsically safe loop must use the light blue wire and be configured with an isolated safety barrier.

[0036] The knowledge graph adding module 123 adds entity nodes to the knowledge graph based on triple information and maintains the relationships between entity nodes. The knowledge graph has a version identifier, and a new version number is generated with each update, realizing standard version management and traceability.

[0037] In one embodiment, such as Figure 4 As shown, the constraint addition module 13 includes: a project extraction module 131, which matches project keywords with the project to be designed; an item extraction module 132, which extracts standard clauses from the knowledge graph based on project keywords; and a constraint generation module 133, which generates constraint information based on the standard clauses and writes it into the database.

[0038] Specifically, to achieve better constraint information output, in this embodiment, the project extraction module 131 automatically matches project keywords to the projects to be designed. When a project is started, the user selects information such as the load type, professional scope, and device characteristics involved in the project, and the project extraction module 131 parses these into project keywords.

[0039] The item extraction module 132 extracts standard clauses from the knowledge graph based on project keywords, that is, it calls the standardized loop templates corresponding to the project keywords and their associated binding clauses from the knowledge graph. The standardized loop templates have embedded unified power supply logic, control principles, signal interfaces and KKS coding rules.

[0040] The constraint generation module 133 generates and writes constraint information based on standard clauses. There are two writing methods: First, the constraint information is written into the output specifications of the subcontractor's design tool via a software interface. When creating a new project in the subcontractor's design software, the version number of the referenced knowledge graph must be bound, and the encoding, layers, and symbols of the output drawings must meet the requirements of the constraint information. Simultaneously, the technical appendix of the subcontract is automatically generated based on the selected knowledge graph, and mandatory constraint clauses and electronic verification codes are attached to the technical appendix to ensure consistency between contract performance and technical standards.

[0041] In one embodiment, such as Figure 5 As shown, the data verification module 2 includes: a multi-source data integration module 21, which captures professional domain data from the corresponding external interface based on the design data, and then integrates the design data and professional domain data into multi-source data; a rule matching module 22, which extracts inspection rules from a pre-maintained rule base based on constraint information; and a conflict detection module 23, which inputs the inspection rules and multi-source data into the conflict detection engine to obtain design defect records.

[0042] Specifically, to achieve better control over the design data from subcontractors, this embodiment first sets several intermediate inspection nodes in the overall design process, corresponding to different design stages, such as system design review and construction drawing review. While acquiring design data returned by subcontractors, such as electronic design documents like electrical system diagrams, network topology diagrams, cable lists, and wiring diagrams, the multi-source data integration module 21 retrieves professional domain data from corresponding external interfaces based on the design data. This includes interface files such as process flow diagrams, equipment interface lists, and civil engineering opening diagrams provided by the general contractor. Subsequently, the design data and professional domain data are integrated into multi-source data. The external interface can be a RESTful interface that interfaces with PLM systems, ERP systems, and project management systems.

[0043] Subsequently, the system pre-maintains a multi-professional interface rule base, which is divided into three levels: basic rule layer, professional rule layer, and system rule layer. The rules adopt a production rule representation. The rule matching module 22 extracts inspection rules from the rule base based on constraint information, that is, it determines the applicable rule subset according to the constraint information on which this review is based, so that the inspection rules are consistent with the standard version actually implemented in the project. Specific rules are similar to: matching the rated power of the motor with the circuit breaker setting value; the interlocking shutdown signal must be a normally closed contact for power failure safety; comparing the net width of the cable trench with the maximum cable bending radius; and checking the consistency of clock synchronization protocols between different systems, etc.

[0044] Then, the conflict checking module 23 inputs the checking rules and multi-source data into the conflict detection engine to obtain a record of common design defects.

[0045] Common types of examinations include: Drawing compliance verification: Verify whether the codes, elements and attributes in the subcontractor's design data are consistent with the constraint information; if they are inconsistent, generate a detailed error report and refuse to submit for review. The subcontractor must modify all violations and pass the verification again before it can be submitted.

[0046] Network topology conflict detection: Logically overlay the network topologies of various subcontractors to automatically check for IP address conflicts, duplicate VLAN IDs, and conflicting switch port security policies.

[0047] After conducting relevant checks, for each conflict detected, the system automatically links it to the original text of the violated standard clauses in the knowledge graph and stores the conflict in the "Common Design Problems and Errors" set, categorized by type, specialty, and severity level. Each design problem record includes fields such as problem identifier, problem type, severity level, violation of standard clause identifier, associated drawing number, discovery node, responsible subcontractor, rectification status, and number of occurrences, providing data support for standard optimization.

[0048] As needed, the review engineer will review the automatic detection results of the conflict detection engine, supplement them with manual analysis, and use the review feedback to optimize the weight parameters of the rule base, so as to continuously improve the accuracy of conflict detection.

[0049] In one embodiment, such as Figure 6 As shown, the scoring module 3 includes: an indicator processing module 31, which evaluates the standard dependency based on the design data and constraint information, generates a conflict contribution based on the design defect record, generates an optimization contribution based on the optimization suggestions, and generates a delivery performance based on the delivery time of the design data; and a weighting module 32, which generates a subcontractor score based on the standard dependency, conflict contribution, optimization contribution, and delivery performance.

[0050] Specifically, to achieve a quantitative assessment of the supplier's capabilities demonstrated during the design process, this embodiment first calculates four types of indicators through the indicator processing module 31, including: Standard Dependency Fc: Evaluated based on design data and constraint information, it reflects the subcontractor's compliance with standards and is calculated by weighting the first-pass rate of design review and the compliance rate with coding specifications.

[0051] Conflict Contribution Fi: Generated based on design defect records, determined by the ratio of cross-disciplinary interface conflicts caused by this subcontractor to the total number of conflicts.

[0052] Optimization Contribution Fo: Generated based on optimization suggestions provided by subcontractors, quantified by the number of design optimization suggestions that have been adopted and solidified into enterprise standards.

[0053] Delivery performance Fd: Generated based on the delivery time of design data, and calculated by combining the drawing delivery delay rate and the on-site service response score.

[0054] Weighted module 32 generates subcontractor scores based on standard dependency, conflict contribution, optimization contribution, and delivery performance. The calculation formula is as follows: S=w1·Fc+w2·(1-Fi)+w3·Fo+w4·Fd The weight coefficients w1 to w4 satisfy w1 + w2 + w3 + w4 = 1, and the system supports adaptive configuration of the weight coefficients according to different management strategies such as quality management, cost control, or schedule priority. In this embodiment, the default values ​​are w1 = 0.4, w2 = 0.3, w3 = 0.2, and w4 = 0.1.

[0055] In one embodiment, such as Figure 7 As shown, the recommendation module 4 includes: a rating module 41, which generates corresponding subcontractor ratings based on subcontractor scores; and a recommendation generation module 42, which generates recommended subcontractors based on the subcontractor ratings.

[0056] Specifically, to achieve better supplier classification results, in this embodiment, the subcontractor rating is first generated based on the subcontractor score through the grading module 41. Multiple grading thresholds are pre-set for the subcontractor scores, and these thresholds are determined based on the distribution of historical scoring data to ensure that "Certified Ecosystem Partners" account for approximately 10%–15%, and "Under Observation" accounts for approximately 5%–10%. Based on this, subcontractors are dynamically divided into three levels: Certified Ecosystem Partners; Qualified Subcontractors; and Under Observation.

[0057] In the subsequent review process, the recommendation generation module 42 generates recommended subcontractors based on subcontractor ratings. For certified ecosystem partners, the system automatically generates a technical review exemption mark, granting them "technical bid exemption" qualification for subsequent projects, allowing them to directly enter the commercial quotation or negotiation stage, and output them as priority recommended subcontractors; their submitted design optimization suggestions enter the fast review channel, and once adopted and incorporated into the standard, the system automatically calculates incentives and triggers the settlement process; at the same time, the system records the dedicated assets invested by the subcontractor in this system, and when the proportion of dedicated assets exceeds 30%, a recommendation to sign a long-term framework agreement is generated to consolidate supply chain stability.

[0058] For subcontractors under observation, the system restricts them from undertaking complex subsystems and does not include them in the recommended subcontractor list; if there is no improvement after tracking 3 consecutive projects, they will be removed from the qualified list.

[0059] In one embodiment, such as Figure 8As shown, it also includes an auxiliary optimization module 5, which includes: a design margin optimization module 51, which calculates the design margin coefficient based on the design data and feeds back the margin coefficient correction instruction; a compatibility analysis module 52, which performs compatibility analysis on the design data to obtain compatibility adjustment results; a standard unit extraction module 53, which extracts standard design units from the design data; and an update module 54, which updates the knowledge graph based on the margin coefficient correction instruction, the compatibility adjustment results, and the standard design units.

[0060] Specifically, in order to achieve better iterative results for the knowledge graph, in this embodiment, each module generates corresponding optimization instructions.

[0061] The design margin optimization module 51 calculates the design margin coefficient based on the design data and feeds back the margin coefficient correction command. Specifically, it obtains operational data such as the actual load rate of the transformer, the actual backup time of the UPS, and the measured value of the cable current carrying capacity from the project commissioning and operation records, and calculates the design margin coefficient β = design value / actual maximum operating value. When the β value of three or more consecutive normal projects exceeds the range of 0.6 to 1.0, a margin revision prompt (i.e., margin coefficient correction command) is issued. After expert confirmation, it is submitted to the update module 54 to ensure that the design margin takes into account both safety and economy.

[0062] The compatibility analysis module 52 performs compatibility analysis on the design data to obtain compatibility adjustment results. Specifically, it statistically analyzes the first-time communication success rate (sr) and the number of debugging days (d) of the fieldbus protocols used in different projects during field integration, and calculates the compatibility score using the weighted formula C = 0.6·sr + 0.3·(1-d) + 0.1·u, where u is the protocol usage rate; based on the compatibility score, it generates adjustment results for the recommended level of the corresponding protocol.

[0063] The standard unit extraction module 53 extracts standard design units from the design data. Specifically, it uses the DBSCAN clustering algorithm to identify high-frequency, structurally consistent combinations of electrical control units from the design data. After review by a standardization engineer, these are packaged into reusable "black box" modules, i.e., standard design units. Each standard design unit includes a complete electrical schematic, control logic function blocks, a bill of materials, a terminal wiring table, and a communication point table, and employs semantic version management. After being stored in the modular design library, when designing a new project, only a few parameters such as load power need to be input, and the system can automatically complete the selection and adaptation and export a complete set of related drawings.

[0064] The update module 54 updates the knowledge graph based on margin coefficient correction instructions, compatibility adjustment results, and standard design units: It writes the confirmed design margin recommendation values ​​and protocol recommendation levels into the attributes and relation edges of the corresponding entities in the knowledge graph, stores the standard design units and their standardized interface definitions in the modular design library associated with the knowledge graph, and generates a new knowledge graph version number. The updated knowledge graph is then re-output by the constraint output module 1 for subsequent design projects, embedding subcontractor design tools and subcontracting contracts. This ensures the standard system continuously evolves with engineering practice and transforms the design wisdom of excellent subcontractors into the general contractor's enterprise-level technical assets.

[0065] In one embodiment, the knowledge graph is implemented based on a hybrid architecture combining a PostgreSQL relational database and a Neo4j graph database.

[0066] Specifically, to achieve better data storage performance, the database of the aforementioned software system adopts a hybrid architecture combining a PostgreSQL relational database and a Neo4j graph database, with the knowledge graph implemented based on this hybrid architecture. PostgreSQL stores structured data, including user tables, project tables, drawing tables, conflict record tables, subcontractor evaluation tables, etc.; Neo4j stores the topology of the knowledge graph and modular design library; the data in the two databases are linked through unique identifiers.

[0067] The system provides RESTful API interfaces for integration with other engineering management systems, including synchronizing design drawings and BOM data with PLM systems, synchronizing equipment procurement and subcontractor information with ERP systems, and synchronizing project milestones and progress data with project management systems. All interfaces use JSON data format and support HTTPS encrypted transmission and OAuth2.0 authentication.

[0068] A storage medium includes computer instructions that, when executed by a computer device, establish the aforementioned supply chain collaborative management system.

[0069] Those skilled in the art will understand that various aspects, or possible implementations of various aspects, of the present invention can be embodied as systems, methods, or computer program products. Therefore, various aspects, or possible implementations of various aspects, of the present invention can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, etc.), or embodiments combining software and hardware aspects, all collectively referred to herein as "circuit," "module," or "system." Furthermore, various aspects, or possible implementations of various aspects, of the present invention can take the form of computer program products, which are computer instructions stored in memory.

[0070] The memory can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination thereof, such as random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, and portable read-only memory (CD-ROM).

[0071] A processor in a computer reads computer instructions stored in memory, enabling the processor to execute the functional actions specified in each step or combination of steps in a flowchart; and to generate means for implementing the functional actions specified in each block or combination of blocks in a flowchart.

[0072] It should be understood that a processor in a computer can be understood as one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components used to execute the aforementioned computer instructions.

[0073] Computer instructions may be executed entirely on the user's local computer, partially on the user's local computer, as a separate software package, partially on the user's local computer and partially on a remote computer, or entirely on a remote computer or server. It should also be noted that in some alternative implementations, the functions indicated by the steps in the flowchart or the blocks in the block diagram may not occur in the order shown in the diagram. For example, depending on the functions involved, two consecutive steps or blocks may actually be executed approximately simultaneously, or these blocks may sometimes be executed in reverse order.

[0074] Of course, in practical applications, the various components of a computer system are coupled together through a bus system. The bus system is used to enable communication and connection between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0075] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A supply chain collaborative management system for electronic control systems, characterized in that, include: The constraint output module pre-establishes and maintains a knowledge graph for the project to be designed, and then outputs the corresponding constraint information based on the knowledge graph. The constraint information is added to the output specifications of the subcontractor's design tool and the subcontract appendix; The data verification module receives design data returned by the subcontractor and verifies the design data in conjunction with the constraint information to obtain a record of common design defects. The scoring module calculates the subcontractor score based on the design defect record, the design data, and the optimization suggestions provided by the subcontractor. The recommendation module generates and outputs recommended subcontractors based on the subcontractor rating.

2. The supply chain collaborative management system according to claim 1, characterized in that, The constraint output module includes: a structured processing module, which performs structured processing on the input text to construct structured text; a graph maintenance module, which establishes the knowledge graph based on the structured text; and a constraint adding module, which matches the knowledge graph with the project to be designed and extracts the constraint information for output.

3. The supply chain collaborative management system according to claim 2, characterized in that, The knowledge graph maintenance module includes: an entity extraction module, which identifies entity units from the structured text; a triplet creation module, which searches for triplet information for the entity units; and a knowledge graph addition module, which adds entity nodes to the knowledge graph based on the triplet information and maintains the relationships between the entity nodes.

4. The supply chain collaborative management system according to claim 2, characterized in that, The constraint addition module includes: a project extraction module, which matches project keywords to the project to be designed; an item extraction module, which extracts standard clauses from the knowledge graph based on the project keywords; and a constraint generation module, which generates constraint information based on the standard clauses and writes it into the database.

5. The supply chain collaborative management system according to claim 1, characterized in that, The data verification module includes: a multi-source data integration module, which captures professional domain data from the corresponding external interface based on the design data, and then integrates the design data and the professional domain data into multi-source data; a rule matching module, which extracts inspection rules from a pre-maintained rule base based on the constraint information; and a conflict detection module, which inputs the inspection rules and the multi-source data into a conflict detection engine to obtain the design defect record.

6. The supply chain collaborative management system according to claim 1, characterized in that, The scoring module includes: an indicator processing module, which evaluates the standard dependency based on the design data and the constraint information, generates a conflict contribution based on the design defect record, generates an optimization contribution based on the optimization suggestions, and generates a delivery performance based on the delivery time of the design data; and a weighting module, which generates the subcontractor score based on the standard dependency, the conflict contribution, the optimization contribution, and the delivery performance.

7. The supply chain collaborative management system according to claim 1, characterized in that, The recommendation module includes: a rating module, which generates a corresponding subcontractor rating based on the subcontractor score; and a recommendation generation module, which generates recommended subcontractors based on the subcontractor rating.

8. The supply chain collaborative management system according to claim 1, characterized in that, It also includes an auxiliary optimization module, which comprises: a design margin optimization module, which calculates a design margin coefficient based on the design data and feeds back a margin coefficient correction instruction; a compatibility analysis module, which performs compatibility analysis on the design data to obtain a compatibility adjustment result; a standard unit extraction module, which extracts standard design units from the design data; and an update module, which updates the knowledge graph based on the margin coefficient correction instruction, the compatibility adjustment result, and the standard design units.

9. The supply chain collaborative management system according to claim 1, characterized in that, The knowledge graph is implemented based on a hybrid architecture combining the PostgreSQL relational database and the Neo4j graph database.

10. A storage medium comprising computer instructions, characterized in that, When the computer device executes the computer instructions, a supply chain collaborative management system as described in any one of claims 1-9 is established.

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