A material data system collaborative management linkage method, equipment, medium and product

Abstract

This application discloses a collaborative management linkage method, device, medium, and product for a material data system, which relates to the field of material data management, including: a customer sends a data request to at least one supplier; the supplier collects and fills in product data information in the material data system based on the data request; the material data system reviews the product data information; the customer approves the product data information based on the review result; if the approval result is rejection, the step of returning to the supplier to collect and fill in the product data information; if the approval result is approval, the supplier data approval rate is calculated based on the approval result and converted into a supplier data submission quality score; the supplier rating result is updated based on the supplier data submission quality score, the supplier regulatory response capability score, and the supplier product technical content score. This application can improve data management efficiency and supplier management efficiency, and realize the three-link of data, suppliers, and customers.

Description

Technical Field

[0001] The present application relates to the field of material data management, and in particular to a collaborative management linkage method, equipment, medium and product for a material data system. Background Art

[0002] In existing systems, data management and review are unilaterally approved by the customer. High data quality improves data transmission efficiency within the supply chain. However, poor data quality often requires multiple rounds of review, rejection, and resubmission, significantly slowing overall data collection efficiency. Furthermore, suppliers' data reporting capabilities and regulatory compliance capabilities directly impact data reporting quality. Therefore, supplier management is also a component of data management. Currently, there is no industry mechanism that seamlessly integrates data analysis, management, and supplier management. Summary of the Invention

[0003] The purpose of this application is to provide a collaborative management linkage method, equipment, medium and product for a material data system, which can improve data management efficiency and supplier management efficiency, and realize the triple linkage of data, suppliers and customers.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In the first aspect, the present application provides a collaborative management linkage method for a material data system, which is applied to a material data system so that customers and suppliers can be linked through the material data system; the collaborative management linkage method for a material data system includes: a customer sends a data request to at least one supplier; when the number of suppliers is one, the customer sends a data request to the supplier individually; when the number of suppliers is multiple, the customer sends data requests to the suppliers in batches; the supplier collects and fills in product data information in the material data system according to the data request; the material data system audits the product data information and obtains an audit result; the audit result is whether the verification standard is met or not met; the customer audits the product data information according to the audit result Approve and obtain the approval result; the approval result is approval or rejection; if the approval result is rejection, return to the step of the supplier collecting and filling in the product data information in the material data system according to the data request; if the approval result is approval, calculate the supplier data approval rate based on the approval result, and convert the supplier data approval rate into the supplier data submission quality score; the material data system obtains the supplier's regulatory response capability score and the supplier's product technical content score at set time intervals, and updates the supplier rating result based on the supplier's data submission quality score, the supplier's regulatory response capability score and the supplier's product technical content score; the supplier's regulatory response capability score is determined by the supplier's data submission situation; the supplier's product technical content score is determined by the supplier's technical certification materials.

[0006] Optionally, the collaborative management linkage method of the material data system also includes: obtaining hazardous substance control regulations and custom verification rules, and entering them into the material data system to establish modeled verification standards; the hazardous substance control regulations include restricted substances, restricted applications and corresponding limits; the custom verification rules include whether optional items are filled in, component quality deviation definitions, supplier code format definitions, supply number format definitions, supply name format definitions and whether warnings are allowed to be sent.

[0007] Optionally, the material data system audits the product data information and obtains an audit result, including: the material data system audits the product data information according to the modeled verification standard to obtain an audit result; the product data information includes product structure information, product identification information, material category information involved in the product, and material composition and content information contained in the material.

[0008] Optionally, the material data system collaborative management linkage method further includes: the customer sets a supplier contact in the material data system; the supplier contact is set by creating a new contact or assigning from an existing user.

[0009] Optionally, the collaborative management linkage method of the material data system also includes: the customer views and exports supplier information in the material data system; the supplier information is the information of the supplier who has sent product data information to the customer and is approved, or the information of the supplier who is authorized to open the system for all users to check; the supplier information is used for batch sending of data requests.

[0010] Optionally, the collaborative management linkage method of the material data system also includes: the material data system pushes system update content, regulatory update content and customer requirement content to suppliers; the system update content, the regulatory update content and the customer requirement content are used to guide suppliers to collect and fill in product data information in the material data system; the system update content includes an update content summary and an update installation package; the regulatory update content includes newly released hazardous substance control regulations; the customer requirement content includes custom verification rules that customer-specified suppliers must meet.

[0011] Optionally, the supplier rating results are updated based on the supplier data submission quality score, the supplier regulatory response capability score and the supplier product technical content score, including: taking a weighted sum of the supplier data submission quality score, the supplier regulatory response capability score and the supplier product technical content score to obtain a total score; determining the score range of the total score according to a score and rating correspondence table, obtaining the supplier rating results and updating them to the material data system.

[0012] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the collaborative management linkage method of the material data system.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the collaborative management linkage method of the material data system.

[0014] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the material data system collaborative management linkage method.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects: This application provides a collaborative management linkage method, equipment, medium and product for a material data system, which is applied to a material data system to enable customers and suppliers to be linked through the material data system. On the one hand, this application builds in audit rules in the material data system in advance, that is, establishes modeled verification standards, and conducts an audit before the supplier sends the product data information to the customer, thereby avoiding multiple rounds of review, rejection and resubmission when the data quality is poor, thereby improving data management efficiency; on the other hand, this application improves supplier management efficiency by rating suppliers based on three dimensions: supplier data submission quality, regulatory response capabilities and product technical content; in addition, this application coordinates data management with supplier requirements by making the supplier rating results affect the audit of suppliers accordingly, thereby realizing the triple linkage of data, suppliers and customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Flowchart of the collaborative management linkage method of the material data system provided in this application.

[0018] Figure 2 Flowchart of the data review method provided for this application.

[0019] Figure 3 A flowchart of the linkage between the supplier collaborative management method and the data audit method provided for this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] This application improves data review efficiency by evaluating the data provider's capabilities in specific dimensions and further provides supplier evaluations. Furthermore, to enhance overall data and supplier management efficiency, this application also synergizes data management with supplier requirements, achieving a three-pronged approach among data, suppliers, and customers.

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0023] In an exemplary embodiment, the present application provides a collaborative management linkage method for a material data system, which is applied to a material data system to enable customers and suppliers to collaborate through the material data system, such as Figure 1 As shown, the method includes the following steps S1 to S6.

[0024] Step S1: The client sends a data request to at least one supplier. If there is only one supplier, the client sends a data request to the supplier individually; if there are multiple suppliers, the client sends data requests to the suppliers in batches.

[0025] Step S2: The supplier collects and fills in product data information in the material data system according to the data request.

[0026] Step S3: The material data system reviews the product data information and obtains a review result, which is whether the verification standard is met or not met.

[0027] Step S4: The customer approves the product data information based on the audit results and obtains an approval result, which is approval or rejection.

[0028] Step S5: If the approval result is rejection, the process returns to the step where the supplier collects product data information according to the data request and fills in the product data information in the material data system, i.e., step S2.

[0029] Step S6: If the approval result is approval, the supplier data approval rate is calculated based on the approval result, and the supplier data approval rate is converted into a supplier data submission quality score.

[0030] Step S7: The material data system obtains the supplier's regulatory compliance score and product technical content score at set intervals and updates the supplier rating results based on the supplier's data submission quality score, regulatory compliance score, and product technical content score. The supplier's regulatory compliance score is determined by the supplier's data submission. The supplier's product technical content score is determined by the supplier's technical certification materials. The set interval is preferably one calendar month.

[0031] Furthermore, the collaborative management linkage method for the material data system also includes obtaining hazardous substance control regulations and custom verification rules, entering them into the material data system, and establishing modeled verification standards. The hazardous substance control regulations include restricted substances, restricted applications, and corresponding limits. The custom verification rules include whether optional items are filled in, component quality deviation definitions, supplier code format definitions, supply number format definitions, supply name format definitions, and whether warnings are allowed to be sent.

[0032] Preferably, in step S3, the material data system reviews the product data information and obtains the review result, which includes: the material data system reviews the product data information according to the model verification standard to obtain the review result. The product data information includes product structure information, product identification information, material category information involved in the product, and material composition and content information contained in the material.

[0033] Furthermore, the material data system collaborative management linkage method further includes: the client setting a supplier contact in the material data system. The supplier contact is set by creating a new contact or assigning from an existing user.

[0034] Furthermore, the collaborative management linkage method for the material data system further includes: the client viewing and exporting supplier information in the material data system. The supplier information is information about approved suppliers who have sent product data to the client, or information about suppliers who have been authorized to access the system for all users. The supplier information is used for batch sending of data requests.

[0035] Furthermore, the collaborative management linkage method for the material data system also includes: the material data system pushes system updates, regulatory updates, and customer requirements to suppliers. These system updates, regulatory updates, and customer requirements are used to guide suppliers in collecting and submitting product data information in the material data system. System updates include an update summary and an update installation package. Regulatory updates include newly released hazardous substance control regulations. Customer requirements include custom validation rules specified by the customer that suppliers must meet.

[0036] Preferably, in step S7, the supplier rating result is updated based on the supplier data submission quality score, the supplier regulatory response capability score and the supplier product technical content score, including: taking a weighted sum of the supplier data submission quality score, the supplier regulatory response capability score and the supplier product technical content score to obtain a total score; determining the score range of the total score according to the score and rating correspondence table, obtaining the supplier rating result and updating it to the material data system.

[0037] The material data system collaborative management linkage method provided in this application can achieve efficient data review on the one hand, and realize the linkage between supplier collaborative management and data management on the other hand. The following two examples respectively provide a detailed introduction to the data review method and supplier collaborative management method provided in this application.

[0038] Example 1, this example provides a method for efficient data review, such as Figure 2 As shown, the specific plan is as follows.

[0039] Step 1 Data Collection: Suppliers send product data information to target customers through the material data system. The information must include product structure information, material category information involved in the product, and the material composition and specific content of the material.

[0040] Step 2: Establish model-based validation criteria: Collect hazardous substance control regulations, including EU regulations such as ELV (End-of-Life Vehicle), REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), POPs (Persistent Organic Pollutants), and BPR (Biocidal Products Regulation). These regulations specifically cover restricted substances, restricted applications, and corresponding limits. These specific control requirements and management rules are then entered into the system backend. In addition to regulatory requirements, customers can also enter their own custom data management requirements and rules. All requirements are summarized and used as the model-based validation criteria for this application. Custom validation rules include: optional field selection, component quality deviation definitions, supplier code / supply number / supply name format definitions, warning selection, mandatory field settings, whether validation items are displayed as warnings or errors, data entry range limits, field content formatting, and regulatory project risk assessment. These rules serve as the basis for backend data validation.

[0041] Step 3: Data Verification: Three data verification methods are set up as the verification mechanism for this application's verification model. The first is that before suppliers submit data, they can perform pre-verification according to the modeled verification criteria in Step 2. Specifically, this involves selecting one or more data items in the system, further selecting the regulatory verification rules pre-set in Step 2, and clicking a button to perform verification. During the verification process, the system automatically determines whether the selected data meets the requirements based on pre-set regulatory information such as substances, limits, and applications. If not, detailed information will be displayed in the verification results. The second is that after the data is submitted to the customer, the customer performs verification according to the modeled verification criteria in Step 2. The specific example is the same as above, and the customer can still verify the selected supplier data according to the regulatory verification rules pre-set in Step 2. The third is to provide a data verification interaction mechanism, allowing customers to specify certain verification criteria in Step 2 and specify suppliers. If the data submitted by a designated supplier does not meet the customer's specified verification criteria, an interception mechanism is automatically triggered, and a prompt is displayed, requiring the supplier to modify and resubmit as required. (Suppliers or verification rules outside the specified range are not subject to restrictions.) For example, suppose Customer A selects some custom validation rules based on their data management requirements and specifies that the data from Suppliers B and C must meet these rules. When Suppliers B and C send data to Customer A, the system automatically prioritizes validation according to Customer A's specified custom validation rules. Data that meets the requirements will be sent normally, while data that doesn't will be reported as an error. Suppliers will need to make the necessary changes as prompted and resend the data.

[0042] Step 4: Data Approval: Data that meets customer requirements can be approved after customer approval. Data that does not meet customer requirements can be rejected by the customer, and the customer can enter a reason for rejection. The process will return to step 3, and the supplier will need to re-verify and send the data to the customer.

[0043] Step 5: Establish a closed-loop model standard improvement mechanism. By recording the verification run status in Step 3 and analyzing and summarizing the verification log, common verification interception scenarios, including the material classification and composition of substances intercepted by the verification standards, are summarized. This data set is then accumulated, and an algorithm is used to summarize which material classifications or substances are most likely to be intercepted. This database is then generated as a risk database to supplement the modeled verification standards in Step 2, guiding and improving the subsequent verification in Step 3. For example, by analyzing the material content ratio and material classification of all data in the database, it can be concluded based on the ratio that when a material is classified as low-alloy steel, the iron content is generally not less than 65%, and when it is classified as high-alloy steel, the iron content is generally not less than 30%. This generates verification rules to determine whether the material classification is correct. For example, based on big data analysis, if a material is classified as a polymer such as plastic or rubber, and the upper-layer part name of the material contains keywords such as tape and rubber sleeve, the material is highly likely to contain phthalates. If the user data does not contain such substances, the data authenticity or integrity is considered questionable, and a risk assessment of the substance content is generated.

[0044] The process of establishing the model standard improvement mechanism specifically includes the following steps.

[0045] (1) Data collection and preprocessing, including: collecting the calibration operation log in step 3; cleaning the data to remove invalid or incomplete records; and structuring the material classification, material composition and other information in the calibration operation log for analysis.

[0046] (2) Data analysis and pattern recognition, including: statistical analysis of structured data to identify common verification interception situations; analysis of the proportion of substance content under different material classifications; and identification of substance content thresholds under specific material classifications.

[0047] (3) Algorithm development, that is, developing algorithms to generate verification rules based on the analysis results.

[0048] (4) Risk database construction, i.e., storing the verification rules generated by the above algorithm in the risk database. The risk database should contain information such as material classification, substance content threshold, and specific keywords.

[0049] (5) Supplementation and improvement of model verification standards, including: using the verification rules in the risk database as a supplement to the model verification standards; regularly updating the risk database to reflect the latest verification experience and data.

[0050] (6) Feedback loop, including: applying the verification rules in the risk database to the verification process in step 3; collecting new verification operation logs, and continuously iterating and improving the algorithm.

[0051] Through the above steps (1) to (6), a closed-loop model standard improvement mechanism can be established to continuously optimize and improve the accuracy and efficiency of verification.

[0052] Example 2: This example provides a supplier collaborative management method that will be linked with data management. That is, on the basis of data review, a supplier collaborative management process is added to make data review and supplier management work together to better promote the quality of supplier data filling. Figure 3 As shown, the specific plan is as follows.

[0053] Step 1: Supplier Contact Setup: Set up a new contact in the system, which will serve as a window for subsequent customer feedback on data issues and needs. Contacts can be newly created or assigned from existing users. Contact information can be made public to downstream customers or all users within the system. If public, subsequent customers can query and export the company's contact information. This information includes the contact's company, name, phone number, email address, and position. This contact information will be used for subsequent upstream and downstream communication steps, such as request dispatch, information disclosure, and push notifications. This step maximizes the possibility of efficient communication within the supply chain while ensuring the confidentiality of user information.

[0054] Step 2: View and export supplier information: The system provides a separate menu to search for suppliers. Companies that have submitted a form to your company and have been approved are considered your suppliers. You can also search for information on suppliers outside your company's supply chain, provided they have authorized full user access within the system. Search results can be exported for use in supplier management and contact with other channels.

[0055] Step 3: Data Request Dispatch: This provides an information transmission path, where the downstream customer sends a data request to the upstream supplier. Upon receiving the data request, the upstream supplier directly creates or assigns a corresponding data form based on the data request to complete the request response. This request supports batch dispatch of multiple data requests to multiple suppliers. This step is the opposite of Step 1 in the data verification function (Example 1), and when combined with this step, forms a closed-loop data interaction mechanism. Specifically, the customer sends a data request to the upstream supplier, and the supplier responds to the downstream customer with the corresponding product data information. Upon customer approval, the data flow is closed and concluded.

[0056] Step 4: Information Disclosure and Push: This includes system update push, regulatory update push, and customer request push. System Update Push: When the system is updated or changed, an update description will be published on the official website. Customers can push the official website link, updated content text, or attachments to all or designated suppliers. Regulatory Update Push: When relevant regulations and standards related to the system are updated or changed, an update description will be published on the corresponding regulatory website, and the system website will also provide a simultaneous notification. Customers can push the official website link, updated content text, or attachments to all or designated suppliers. Customer Request Push: This example addresses personalized customer requirements, such as requirements for filling in a specific system field or whether a field is mandatory, by pushing text or attachments to all or designated suppliers. This example enables batch push of corresponding content to suppliers with one click. Push content supports web links, text messages, and attachments. After receiving the push information, suppliers can view the status of the information through system feedback, including "viewed," "unread," and "responded," allowing customers to easily understand the status and results of the information delivery. The pushed information helps guide suppliers in data collection and improve data quality. The information push in this step is interconnected with Step 3 of the data verification function and Step 3 of the supplier collaboration function (Example 2). Based on the verification results in Step 3 of the data verification function, machine learning algorithms and AI (artificial intelligence) are used to associate content and prompt users about the type of information to be pushed to that supplier. When designing the information push submodule, machine learning algorithms can be used to achieve a more intelligent and personalized push service. Specifically, this system can be implemented through the following steps.

[0057] (1) Data collection and processing: First, it is necessary to collect historical interaction data from suppliers, including their data approval status, as well as behavioral data such as click-through rates and feedback time for different types of information. This data will serve as the basis for training machine learning models.

[0058] (2) Feature extraction: Extract features from the collected data that can help predict the causes of supplier data problems, such as the supplier's product categories, previous click history, reasons for rejection, etc.

[0059] (3) Model training: Use appropriate machine learning algorithms, such as random forests, gradient boosting machines, or neural networks, to train a model that can predict the usefulness of different types of information to suppliers based on their characteristics and behavioral data.

[0060] (4) Content association: Combined with step 3 of the data verification function, when a supplier encounters problems during data verification, such as non-compliance with regulations, the machine learning model will identify this pattern and associate it with the regulatory update push.

[0061] (5) Intelligent Push: Based on the model’s predictions, the system will automatically push information to suppliers that is most likely to be helpful to them. For example, if the model predicts that a supplier is particularly interested in regulatory updates, the system will prioritize pushing relevant regulatory updates and provide corresponding web links or attachments.

[0062] (6) Feedback Learning: Suppliers’ feedback on push content will be used to further train and optimize the machine learning model to improve the accuracy and relevance of push notifications.

[0063] (7) Effectiveness evaluation: Regularly use model validation techniques, such as cross-validation, to evaluate the effectiveness of the push system and adjust model parameters based on the evaluation results to ensure the quality of the pushed content and supplier satisfaction.

[0064] Step 5: Supplier Rating Management: Rating involves three dimensions, each with a corresponding score. The first is the quality of the supplier's data submission, specifically the data approval rate. This factor is directly assigned a score by the system based on the data approval rate. The second is the supplier's regulatory compliance, specifically the labeling of hazardous substances and polymers, and data compliance. This factor is automatically assigned a score based on the supplier's data submission. The score is determined based on the reason for the form rejection and whether it relates to the three key criteria in this example: "labeling of hazardous substances and polymers, and data compliance." If so, a corresponding point deduction will be applied. The third is the technical content of the supplier's products. Suppliers can upload supporting documentation, such as patents or high-tech enterprise qualifications, to the system. This factor is automatically assigned a score based on the supplier's technical documentation. The scores for these three dimensions are weighted, with higher scores indicating a higher supplier rating. The weighted calculation formula is: Total score = (w1 × S1) + (w2 × S2) + (w3 × S3).

[0065] Assume that the weights of the three dimensions are w1, w2, and w3, respectively, where w1 corresponds to the weight of the supplier data submission quality, w2 corresponds to the weight of the supplier's regulatory response capability, and w3 corresponds to the weight of the supplier's product technology content. The weights are assigned according to the importance of the three dimensions, set by the system administrator in the background, and apply to the ratings of all suppliers registered in the system. The initial weights are assigned as 40%, 40%, and 20%. S1 is the score for the quality of supplier data submission, which is calculated based on the supplier data approval rate; S2 is the score for the supplier's regulatory response capability; and S3 is the score for the supplier's product technology content. In addition, the supplier rating management rules and rating results can be pushed to designated suppliers through step 4 of this example.

[0066] Step 6: Supplier Rating Results: Set different score ranges to correspond to different supplier ratings. After scoring suppliers in Step 5, perform ratings based on the score-rating correspondence table in this step, and support sorting suppliers based on rating results. Supplier scoring can be conducted monthly. The supplier rating results will influence supplier audits, including the strictness of data audits and the number of audits. Specifically, the supplier rating results will affect the rule setting in Step 2 and the data approval in Step 3 of Example 1. For suppliers with high ratings, the audit process will be relaxed or simplified as appropriate in subsequent rule development and audit processes.

[0067] In summary, the collaborative management linkage method of the material data system provided in this application further improves the capabilities of traditional material data systems in data review and analysis, and can be combined with supplier management to guide data review and submission in the process of supplier management. At the same time, it feeds back the evaluation basis and results of suppliers based on the quality of the submitted data, thus making up for the current disconnect between data management and supplier management in the industry. This application provides a solution path for how the current data material system can further empower enterprise data management. Through this path, the efficiency of enterprise data review can be improved, the efficiency of communication with suppliers can be improved, the efficiency of delivering data requests to suppliers can be improved, and the supplier's capabilities in data management and regulatory response can be comprehensively evaluated, so that corresponding incentives and penalties can be proposed based on the evaluation results.

[0068] In an exemplary embodiment, the present application further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0069] In an exemplary embodiment, the present application further provides a computer-readable storage medium storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0070] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0071] In this application, all actions to obtain signals, information, or data are performed in compliance with the relevant data protection laws and policies of the country in which they are located and with the authorization of the corresponding device owner. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, storage, and display, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with relevant laws and regulations.

[0072] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0073] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0074] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A collaborative management linkage method for a material data system, characterized in that: The collaborative management linkage method of the material data system is applied to the material data system, so that customers and suppliers can be linked through the material data system; the collaborative management linkage method of the material data system includes: The client sends a data request to at least one supplier. If there is only one supplier, the client sends a data request to the supplier individually. If there are multiple suppliers, the client sends data requests to the suppliers in batches. Suppliers collect and fill in product data information in the material data system based on data requests; The material data system reviews product data information and obtains review results, including: The material data system reviews product data information according to the modeled verification standards and obtains the audit results; the product data information includes product structure information, product identification information, material category information involved in the product, and material composition and content information contained in the materials; before the supplier submits the data, it is pre-verified according to the modeled verification standards; after the data is submitted to the customer, the customer performs verification according to the modeled verification standards; a data verification interaction mechanism is provided, allowing the customer to specify the verification standards and designate suppliers. If the data submitted by the designated supplier does not meet the customer-specified verification standards, the interception and sending mechanism is automatically triggered, and relevant prompts are displayed, requiring the supplier to modify and resubmit as required; the audit results are either met or not met the verification standards; The customer approves the product data information based on the audit results and obtains an approval result; the approval result is approval or rejection; If the approval result is rejection, the process returns to the step where the supplier collects and reports product data information in the material data system according to the data request; If the approval result is approved, the supplier data approval rate is calculated based on the approval result, and the supplier data approval rate is converted into the supplier data submission quality score; The material data system obtains the supplier's regulatory response capability score and supplier product technical content score at set time intervals, and updates the supplier rating results based on the supplier data submission quality score, supplier regulatory response capability score, and supplier product technical content score, including: The total score is obtained by taking the weighted sum of the supplier's data submission quality score, supplier's regulatory response capability score, and supplier's product technical content score; The score range of the total score is determined according to the score and rating correspondence table, and the supplier rating result is obtained and updated to the material data system; the supplier's regulatory response capability score is determined by the supplier's data submission; the supplier's product technical content score is determined by the supplier's technical certification materials.

2. The collaborative management linkage method of the material data system according to claim 1, characterized in that: The material data system collaborative management linkage method further includes: Obtain hazardous substance control regulations and custom verification rules, enter them into the material data system, and establish modeled verification standards; the hazardous substance control regulations include restricted substances, restricted applications, and corresponding limit values; the custom verification rules include whether optional items are filled in, component quality deviation definitions, supplier code format definitions, supply number format definitions, supply name format definitions, and whether warnings are allowed to be sent.

3. The collaborative management linkage method of the material data system according to claim 1, characterized in that: The material data system collaborative management linkage method further includes: The customer sets up a supplier contact in the material data system; the supplier contact is set up by creating a new contact or assigning from an existing user.

4. The collaborative management linkage method of material data system according to claim 1, characterized in that: The material data system collaborative management linkage method further includes: Customers view and export supplier information in the material data system; the supplier information is the information of approved suppliers who have sent product data information to customers or the information of suppliers who have been authorized to open the system for all users to view; the supplier information is used for batch sending of data requests.

5. The collaborative management linkage method of the material data system according to claim 1, characterized in that: The material data system collaborative management linkage method further includes: The material data system pushes system updates, regulatory updates and customer requirements to suppliers; the system updates, regulatory updates and customer requirements are used to guide suppliers to collect and fill in product data information in the material data system; the system updates include an update summary and an update installation package; the regulatory updates include newly released hazardous substance control regulations; and the customer requirements include custom verification rules that the customer-specified supplier must meet.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the collaborative management linkage method of the material data system according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the material data system collaborative management linkage method according to any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the material data system collaborative management linkage method according to any one of claims 1 to 5 is implemented.

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