Green factory declaration management system

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a green factory declaration management system which comprises a pushing module used for pushing declaration material filling guidance and error warning to a declarator, a verification module used for automatically verifying a declaration material, and a management module used for managing the declaration material. The review module is used for carrying out manual verification and expert review on the declaration material passing the automatic verification; the statistical module is used for counting a single interaction value after the automatic verification in a period and respectively determining link dynamic backlog early warning indexes of a form review link and an expert review link; the evaluation module is used for judging whether the green declaration process meets the standard or not based on the single interaction value and the link dynamic backlog early warning index; and the evaluation module is used for adjusting the verification intensity, the heat weight, the dynamic task fragmentation granularity and the approval attention focus coefficient. According to the invention, real-time monitoring and intelligent adjustment of the declaration process of the green factory declaration are effectively realized, and the declaration efficiency of the green factory is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a green factory declaration and management system. Background Technology

[0002] Green factories are an important vehicle for the green development of industry promoted by the state. They refer to factories that achieve intensive land use, harmless raw materials, clean production, waste resource utilization, and low-carbon energy. Obtaining the "Green Factory" designation is a national honor, representing a benchmark level of green development for enterprises. The traditional application process involves filling out, submitting, and modifying forms, which is inefficient and prone to errors. The Green Application Management System aims to digitize and facilitate the entire application process. For applicant companies, the Green Application Management System typically includes functions such as filling guidelines, uploading and managing materials in various formats, data verification, and progress tracking. For managers, its functions typically include online expert review, data analysis, and verification. The Green Application Management System can improve the application efficiency for applicant companies and the approval efficiency for managers, and it is of great significance in promoting the creation of green factories.

[0003] Chinese Patent Publication No. CN113888142A discloses a method and system for intelligent project management of applicant enterprise information. The method includes: obtaining information on a first applicant enterprise to obtain a first enterprise category and a first enterprise level; performing enterprise maturity analysis on the applicant enterprise to generate a first enterprise tag and constructing a first project management unit, wherein the applicant enterprise project management platform includes the first project management unit; obtaining a first search rule based on the applicant enterprise project management platform; using the first search rule to find intersection projects in the applicant enterprise project management platform to obtain a first intersection application project and generate a first joint application project model, wherein the first joint application project model has a star network topology; and implementing project tracking management based on the first joint application project model.

[0004] Therefore, the above solution addresses the technical problem of low efficiency caused by the reliance on manual operation in project application management in existing technologies. However, the solution cannot achieve real-time monitoring and intelligent adjustment of the green factory application process, thus failing to guarantee the efficiency of green factory applications. Summary of the Invention

[0005] To address this issue, the present invention provides a green factory application management system to overcome the problem in the prior art that the application process for green factories cannot be monitored and intelligently adjusted in real time, resulting in low application efficiency.

[0006] To achieve the above objectives, the present invention provides a green factory application management system, comprising: The push module is used to push application material filling guidelines and error warnings to applicants based on popularity weight; A verification module, which is connected to the push module, is used to automatically verify the submitted application materials according to the set verification strength. The review module, which is connected to the verification module, is used to manually verify and expert review the application materials that have passed automatic verification based on dynamic task segmentation granularity and approval attention focus coefficient. The statistics module, which is connected to the review module, is used to calculate the value of a single interaction after automatic verification within the statistical period, and to determine the dynamic backlog warning index for the formal review stage and the expert review stage, respectively. An evaluation module, which is connected to the statistics module, is used to determine whether the green declaration process meets the standards based on the single interaction value. The evaluation module is also used to determine whether the green application process meets the standards based on the single interaction value judgment result and the dynamic backlog warning index of the process, or to generate corresponding processing instructions. The evaluation module is also used to periodically determine the green application process based on the dynamic backlog early warning index of the process, or to generate corresponding processing instructions. An adjustment module, which is connected to the push module, the verification module, the review module, and the evaluation module respectively, is used to adjust the verification intensity based on the interaction value difference, adjust the heat weight based on the error confidence level, adjust the dynamic task segmentation granularity based on the difference in the dynamic backlog warning index, increase the approval attention focus coefficient based on the increase in the dynamic task segmentation granularity, issue a manual intervention notification, or issue an approval resource shortage notification.

[0007] Furthermore, the evaluation module is used to determine whether the green application process meets the standard based on the single interaction value, and to determine whether the green application process meets the standard based on the dynamic backlog warning index of the process according to the determination result, or to adjust the verification intensity based on the interaction value difference.

[0008] Furthermore, the evaluation module is also used to determine whether the green application process meets the standards based on the dynamic backlog warning index of the process, and to adjust the dynamic task granularity based on the difference in the dynamic backlog warning index of the process if the green application process does not meet the standards.

[0009] Furthermore, the adjustment module is used to increase the verification strength based on the interaction value difference, and the increase in verification strength is proportional to the interaction value difference.

[0010] Furthermore, the evaluation module is also used to determine whether the green application process meets the standard based on the single interaction value after the verification intensity is increased, and to determine whether the green application process meets the standard based on the dynamic backlog warning index of the process according to the determination result, or to adjust the heat weight based on the error confidence.

[0011] Furthermore, the adjustment module is also used to increase the popularity weight based on the error confidence level, and the increase in popularity weight is proportional to the error probability value; wherein, the popularity weight is increased according to each error probability value, and the error confidence level is a list composed of each error probability value.

[0012] Furthermore, the evaluation module is also used to determine whether the green application process meets the standard based on the single interaction value after the heat weight is increased, and to determine whether the green application process meets the standard based on the dynamic backlog warning index of the link according to the determination result, or to issue a manual intervention notification.

[0013] Furthermore, the adjustment module is also used to increase the dynamic task granularity based on the difference in the dynamic backlog warning index of the link, and the increase in the dynamic task granularity is proportional to the difference in the dynamic backlog warning index of the link.

[0014] Furthermore, the adjustment module is also used to increase the approval attention focus coefficient based on the increase in the dynamic task fragment granularity, and the increase in the approval attention focus coefficient is proportional to the increase in the dynamic task fragment granularity.

[0015] Furthermore, the evaluation module is also used to determine whether the green application process meets the standards based on the dynamic backlog warning index of the process after the approval attention focus coefficient has increased, and to issue a notification of approval resource shortage if the green application process does not meet the standards.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: by setting up an evaluation module and an adjustment module, the present invention can determine whether the green application process meets the standards based on the single interaction value and the dynamic backlog warning index of the process, thus timely and accurately determining whether the green application process meets the standards and generating corresponding processing instructions based on the determination results, effectively realizing real-time monitoring of the green factory application process. The adjustment module is used to adjust the corresponding parameters or issue corresponding notifications. While effectively realizing intelligent adjustment of the green factory application process, the present invention also effectively improves the application efficiency of green factories.

[0017] Furthermore, the evaluation module set up in this invention is used to determine whether the green application process meets the standards based on the single interaction value. It accurately determines whether the green application process meets the standards based on the dynamic backlog warning index of the process or whether the verification intensity needs to be adjusted based on the interaction value difference, so as to avoid misjudgment. While further realizing real-time monitoring of the application process of green factory application, it further improves the application efficiency of green factory.

[0018] Furthermore, the evaluation module of this invention is also used to determine whether the green application process meets the standards based on the dynamic backlog early warning index of the process. This further ensures the accuracy of the determination and timely determines whether the dynamic task segmentation granularity needs to be adjusted based on the difference in the dynamic backlog early warning index of the process. This further realizes real-time monitoring of the application process for green factories and improves the application efficiency of green factories.

[0019] Furthermore, the adjustment module provided in this invention is also used to increase the verification strength based on the interaction value difference, which effectively avoids the situation where the green application process is judged to be non-compliant due to the verification strength not meeting the standard. While further realizing the intelligent adjustment of the application process for green factory applications, it further improves the application efficiency of green factories.

[0020] Furthermore, the evaluation module set up in this invention is also used to determine whether the green application process meets the standard based on the single interaction value after the verification intensity is increased. It can effectively judge the effect of increasing the verification intensity, accurately determine whether the single interaction value still does not meet the standard after the verification intensity is increased, and promptly determine whether the heat weight needs to be adjusted based on the error confidence level. While further realizing real-time monitoring of the green factory application process, it further improves the application efficiency of green factories.

[0021] Furthermore, the adjustment module provided in this invention is also used to increase the heat weight based on the error confidence level, which effectively avoids the situation where the green application process is judged to be non-compliant due to the heat weight not meeting the standard. While further realizing the intelligent adjustment of the application process for green factory applications, it further improves the application efficiency of green factories.

[0022] Furthermore, the evaluation module set up in this invention is also used to determine whether the green application process meets the standards based on the single interaction value after the heat weight is increased. The effective determination needs to be based on the dynamic backlog warning index of the process to determine whether the green application process meets the standards or to issue a manual intervention notification, so as to avoid misjudgment. While further realizing real-time monitoring of the application process of green factory application, it further improves the application efficiency of green factories.

[0023] Furthermore, the adjustment module of this invention is also used to increase the dynamic task granularity based on the difference in the dynamic backlog warning index of each link, which effectively avoids the situation where the green application process is judged to be non-compliant due to the dynamic task granularity not meeting the standard. While further realizing the intelligent adjustment of the application process for green factory applications, it further improves the application efficiency of green factories.

[0024] Furthermore, the adjustment module in this invention is also used to increase the approval attention focus coefficient based on the increase in dynamic task fragment granularity, ensuring the matching accuracy between the approval attention focus coefficient and the dynamic task fragment granularity. This further realizes intelligent adjustment of the green factory application process and improves the application efficiency of green factories.

[0025] Furthermore, the evaluation module of this invention is also used to determine whether the green application process meets the standards based on the dynamic backlog warning index after the approval attention focus coefficient has increased. It effectively determines whether an approval resource shortage notice needs to be issued, ensuring the accuracy of the determination. While further realizing real-time monitoring of the application process for green factories, it further improves the application efficiency of green factories. Attached Figure Description

[0026] Figure 1 This is a structural block diagram of the green factory application management system according to an embodiment of the present invention; Figure 2 This is a flowchart of the green factory application management system according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the process for determining whether a green application meets the standards and the reasons for non-compliance, as described in this embodiment of the invention. Figure 4 This is a flowchart illustrating the reasons why the green application process fails to meet the standards, as described in this embodiment of the invention. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0030] Please see Figure 1 The diagram shown is a structural block diagram of the green factory application management system according to an embodiment of the present invention. The green factory application management system according to this embodiment includes a push module, a verification module, a review module, a statistics module, an evaluation module, and an adjustment module; wherein, The push module is used to push application material filling guidelines and error warnings to applicants based on popularity weight; The verification module is connected to the push module and is used to automatically verify the submitted application materials according to the set verification strength. The review module is connected to the verification module and is used to manually verify and expert review the application materials that have passed automatic verification based on dynamic task segmentation granularity and approval attention focus coefficient. The statistics module is connected to the review module and is used to calculate the single interaction value after automatic verification within the statistical period, and to determine the dynamic backlog warning index of the formal review stage and the expert review stage respectively. The evaluation module is connected to the statistics module and is used to determine whether the green declaration process meets the standards based on the single interaction value. The evaluation module is also used to determine whether the green application process meets the standards based on the single interaction value judgment result and the dynamic backlog warning index of the process, or to generate corresponding processing instructions. The evaluation module is also used to periodically determine the green application process based on the dynamic backlog early warning index of the process, or to generate corresponding processing instructions. The adjustment module is connected to the push module, the verification module, the review module and the evaluation module respectively, and is used to adjust the verification intensity based on the interaction value difference, adjust the heat weight based on the error confidence, adjust the dynamic task segmentation granularity based on the difference of the dynamic backlog warning index, increase the approval attention focus coefficient based on the increase of the dynamic task segmentation granularity, issue a manual intervention notification, or issue an approval resource shortage notification. Specifically, manual verification refers to the formal review process.

[0031] Please see Figure 2The diagram shows the workflow of the green factory application management system according to an embodiment of the present invention. When the green factory application management system is running, the push module pushes application material filling guidelines and error warnings to applicants based on the popularity weight. The verification module automatically verifies the submitted application materials according to the set verification intensity. The review module performs manual verification and expert review on the application materials that pass automatic verification based on the dynamic task segmentation granularity and the approval attention focus coefficient. The statistics module calculates the single interaction value after automatic verification within the period and determines the dynamic backlog warning index for the formal review stage of manual verification and the expert review stage, respectively. The evaluation module determines the green factory status based on the single interaction value. The evaluation module determines whether the green application process meets the standards based on the single interaction value judgment result and the dynamic backlog warning index of the process, or generates corresponding processing instructions. The evaluation module completes the periodic judgment of the green application process or generates corresponding processing instructions based on the dynamic backlog warning index judgment result. The adjustment module adjusts the verification intensity based on the interaction value difference, adjusts the heat weight based on the error confidence, and adjusts the dynamic task segmentation granularity based on the dynamic backlog warning index difference. The adjustment module increases the approval attention focus coefficient based on the increase of the dynamic task segmentation granularity, issues a manual intervention notice, or issues an approval resource shortage notice.

[0032] Please see Figure 3 The diagram shown illustrates a flowchart of an embodiment of the present invention for determining whether a green application process meets the standards and the reasons for non-compliance. The evaluation module described in this embodiment is used to determine whether a green application process meets the standards based on the single interaction value. If the single interaction value is greater than or equal to the preset single interaction value H set in the evaluation module, the evaluation module determines whether the green application process meets the standard based on the dynamic backlog warning index of the process. In this embodiment, the preset single interaction value H = 80%. If the single interaction value is less than the preset single interaction value H, the evaluation module determines that the green declaration process does not meet the standard, and adjusts the verification intensity based on the difference in the interaction value. Specifically, the single interaction value is the ratio of the number of times the application materials submitted by each applicant are manually verified and approved in one go within the period to the total number of applications. In this embodiment, the preset single interaction value is set to 80%, which is an empirical threshold. The detection cycle is 1 time / 30 minutes; The interaction value difference is the difference between the preset single interaction value and the single interaction value; Adjusting the verification intensity can regulate the intensity of automatic checks and interventions on the applicant's application materials before submission.

[0033] Please continue reading. Figure 3 As shown, the evaluation module of this invention is also used to determine whether the green application process meets the standards based on the dynamic backlog early warning index of the aforementioned process: If the dynamic backlog warning index of the process is less than the preset dynamic backlog warning index F set in the evaluation module, the evaluation module determines that the periodic judgment of the green application process has been completed and judges the green application process for the next cycle. In this embodiment, the preset dynamic backlog warning index F = 2.0. If the dynamic backlog warning index of the process is greater than or equal to the preset dynamic backlog warning index F, the evaluation module determines that the green application process does not meet the standard, and adjusts the dynamic task segmentation granularity based on the difference in the dynamic backlog warning index of the process. Specifically, in this embodiment, the preset dynamic backlog early warning index is set to 2.0, where 2.0 is an empirical threshold. As can be seen from the above, the determination of the dynamic backlog early warning index for the aforementioned process includes both the formal review process and the expert review process. The dynamic backlog warning index is a real-time indicator of the matching status between the backlog of tasks and the current processing capacity. The specific determination results of the dynamic backlog warning index are as follows: Task processing capacity = Number of approvers × Inspection cycle duration ÷ Single task processing time Dynamic backlog warning index for each process = Number of pending tasks / Task processing capacity value; The scenario where the dynamic backlog warning index of the process is less than the preset dynamic backlog warning index F set in the evaluation module is when the dynamic backlog warning indices of both the formal review process and the expert review process are less than the preset dynamic backlog warning index. The application scenario where the dynamic backlog warning index of the process is greater than or equal to the preset dynamic backlog warning index F is that the dynamic backlog warning index of one of the formal review process and the expert review process is greater than or equal to the preset dynamic backlog warning index, or the dynamic backlog warning index of both processes is greater than or equal to the preset dynamic backlog warning index. The difference between the dynamic backlog early warning index of the link is the difference between the dynamic backlog early warning index of the link and the preset dynamic backlog early warning index of the link; The specific adjustments to the dynamic task fragmentation granularity based on the dynamic backlog early warning index difference include: If the dynamic backlog warning index of either the formal review stage or the expert review stage is greater than or equal to the preset dynamic backlog warning index, the dynamic task fragmentation granularity of that stage is adjusted based on the difference in the dynamic backlog warning index of that stage. If the dynamic backlog warning index of both the formal review stage and the expert review stage is greater than or equal to the preset dynamic backlog warning index, the dynamic task fragmentation granularity of a certain stage is adjusted according to the difference of the dynamic backlog warning index of a certain stage, so as to adjust the dynamic task fragmentation granularity based on the dynamic backlog warning index of the corresponding stage. Adjusting the granularity of the dynamic task partitioning can adjust the fineness of splitting a task into independent and parallel subtasks.

[0034] Please continue reading. Figure 3 As shown, the adjustment module of the present invention is used to increase the verification strength based on the interaction value difference: If the interaction value difference is greater than the second preset interaction value difference set in the evaluation module The adjustment module increases the verification strength to 2.67 times the initial verification strength, wherein the second preset interaction value difference in this embodiment... =15%; If the interaction value difference is less than or equal to the second preset interaction value difference And greater than the difference of the first preset interaction value set in the evaluation module. The adjustment module increases the verification strength to 2.01 times the initial verification strength, wherein, in this embodiment, the first preset interaction value difference... =5%; If the interaction value difference is less than or equal to the first preset interaction value difference The adjustment module increases the verification strength to 1.52 times the initial verification strength; Specifically, the interaction value difference and the verification strength mentioned in this embodiment are both derived from the actual debugging results.

[0035] Please continue reading. Figure 3 As shown, the evaluation module of this invention is also used to determine whether the green application process meets the standard based on the single interaction value after the verification intensity is increased: If the single interaction value is greater than or equal to the preset single interaction value H, the evaluation module determines whether the green application process meets the standard based on the dynamic backlog warning index of the process. If the single interaction value is less than the preset single interaction value H, the evaluation module determines that the green declaration process does not meet the standard, and adjusts the popularity weight based on the error confidence level. Specifically, the application materials that were not approved on the first attempt and were returned were analyzed to determine the types of errors that led to the return, and the error probability value corresponding to each type of error was determined. The error confidence level is a list consisting of the error probability values; The heat weight is a dynamic coefficient attached to each specific error point in the knowledge graph. Adjusting the heat weight can adjust the intensity of the resources used.

[0036] Please see Figure 4 As shown, this is a flowchart illustrating the reasons why the green application process fails to meet the standards in an embodiment of the present invention. The adjustment module described in this embodiment of the present invention is further used to increase the heat index weight based on the erroneous confidence level: If the error probability value is greater than or equal to the second preset error probability value set in the evaluation module The adjustment module increases the heat weight to 2.89 times the initial heat weight. In this embodiment, the second preset error probability value... ; If the error probability value is less than the second preset error probability value And greater than or equal to the first preset error probability value set in the evaluation module. The adjustment module increases the heat weight to 1.58 times the initial heat weight, wherein, in this embodiment, the first preset error probability value... ; If the error probability value is less than the first preset error probability value The adjustment module increases the heat weight to 1.17 times the initial heat weight; Specifically, as mentioned above, the error confidence level is a list composed of the error probability values. When adjusting the popularity weight, the corresponding popularity weight is adjusted according to the error probability value corresponding to each error type. In this embodiment, the error probability value ranges from 0.2 to 1. The values ​​of the error probability value and the popularity weight are both derived from the actual debugging results.

[0037] Please continue reading. Figure 4 As shown, the evaluation module in this embodiment of the invention is also used to determine whether the green application process meets the standard based on the single interaction value after the heat weight has been increased: If the single interaction value is greater than or equal to the preset single interaction value H, the evaluation module determines whether the green application process meets the standard based on the dynamic backlog warning index of the process. If the single interaction value is less than the preset single interaction value H, the evaluation module determines that the green application process does not meet the standard and issues a manual intervention notification.

[0038] Please continue reading. Figure 4 As shown, the adjustment module in this embodiment of the invention is further used to increase the dynamic task granularity based on the difference in the dynamic backlog early warning index of the link: If the difference in the dynamic backlog early warning index of the aforementioned link is greater than the second preset dynamic backlog early warning index difference set in the evaluation module... The adjustment module increases the dynamic task fragmentation granularity to 2.49 times the initial dynamic task fragmentation granularity. In this embodiment, the difference in the dynamic backlog warning index for the second preset stage... ; If the difference in the dynamic backlog early warning index of the aforementioned links is less than or equal to the difference in the second preset dynamic backlog early warning index of the aforementioned links And it is greater than the difference of the first preset dynamic backlog early warning index set in the evaluation module. The adjustment module increases the dynamic task fragmentation granularity to 1.93 times the initial dynamic task fragmentation granularity. In this embodiment, the difference in the dynamic backlog warning index for the first preset stage... ; If the difference in the dynamic backlog early warning index of the aforementioned links is less than or equal to the difference in the dynamic backlog early warning index of the first preset links... The adjustment module increases the dynamic task fragmentation granularity to 1.52 times the initial dynamic task fragmentation granularity; Specifically, the values ​​of the dynamic backlog warning index difference and the dynamic task granularity mentioned in this embodiment are both derived from actual debugging results.

[0039] Please continue reading. Figure 4 As shown, the adjustment module in this embodiment of the invention is further used to increase the approval attention focus coefficient based on the increase in the dynamic task fragmentation granularity: If the dynamic task fragmentation granularity is increased to 2.49 times the initial dynamic task fragmentation granularity, the adjustment module will increase the approval attention focus coefficient to 1.98 times the initial approval attention focus coefficient; If the dynamic task fragmentation granularity is increased to 1.93 times the initial dynamic task fragmentation granularity, the adjustment module will increase the approval attention focus coefficient to 1.64 times the initial approval attention focus coefficient; If the dynamic task fragmentation granularity is increased to 1.52 times the initial dynamic task fragmentation granularity, the adjustment module will increase the approval attention focus coefficient to 1.31 times the initial approval attention focus coefficient; Specifically, increasing the approval attention focus coefficient after increasing the dynamic task fragment granularity enables the approval interface to better adapt to the increased dynamic task fragment granularity. The value of the approval attention focus coefficient is derived from actual debugging results.

[0040] Please continue reading. Figure 4 As shown, the evaluation module in this embodiment of the invention is also used to determine whether the green application process meets the standard based on the dynamic backlog warning index after the approval attention focus coefficient has increased: If the dynamic backlog warning index of the process is less than the preset dynamic backlog warning index F, the evaluation module determines that the periodic judgment of the green application process has been completed, and judges the green application process for the next cycle. If the dynamic backlog warning index of the process is greater than or equal to the preset dynamic backlog warning index F, the evaluation module determines that the green application process does not meet the standards and issues a notification of shortage of approval resources.

[0041] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A green factory application management system, characterized in that, include: The push module is used to push application material filling guidelines and error warnings to applicants based on popularity weight; A verification module, which is connected to the push module, is used to automatically verify the submitted application materials according to the set verification strength. The review module, which is connected to the verification module, is used to manually verify and expert review the application materials that have passed automatic verification based on dynamic task segmentation granularity and approval attention focus coefficient. The statistics module, which is connected to the review module, is used to calculate the value of a single interaction after automatic verification within the statistical period, and to determine the dynamic backlog warning index for the formal review stage and the expert review stage, respectively. An evaluation module, which is connected to the statistics module, is used to determine whether the green declaration process meets the standards based on the single interaction value. The evaluation module is also used to determine whether the green application process meets the standards based on the single interaction value judgment result and the dynamic backlog warning index of the process, or to generate corresponding processing instructions. The evaluation module is also used to periodically determine the green application process based on the dynamic backlog early warning index of the process, or to generate corresponding processing instructions. An adjustment module, which is connected to the push module, the verification module, the review module, and the evaluation module respectively, is used to adjust the verification intensity based on the interaction value difference, adjust the heat weight based on the error confidence level, adjust the dynamic task segmentation granularity based on the difference in the dynamic backlog warning index, increase the approval attention focus coefficient based on the increase in the dynamic task segmentation granularity, issue a manual intervention notification, or issue an approval resource shortage notification.

2. The green factory application management system according to claim 1, characterized in that, The evaluation module is used to determine whether the green application process meets the standards based on the single interaction value, and to determine whether the green application process meets the standards based on the dynamic backlog warning index of the process based on the determination result, or to adjust the verification intensity based on the interaction value difference.

3. The green factory application management system according to claim 2, characterized in that, The evaluation module is also used to determine whether the green application process meets the standards based on the dynamic backlog warning index of the process, and to adjust the dynamic task granularity based on the difference in the dynamic backlog warning index of the process if the green application process does not meet the standards.

4. The green factory application management system according to claim 3, characterized in that, The adjustment module is used to increase the verification strength based on the interaction value difference, and the increase in verification strength is proportional to the interaction value difference.

5. The green factory application management system according to claim 4, characterized in that, The evaluation module is also used to determine whether the green application process meets the standard based on the single interaction value after the verification intensity is increased, and to determine whether the green application process meets the standard based on the dynamic backlog warning index of the link according to the determination result, or to adjust the heat weight based on the error confidence.

6. The green factory application management system according to claim 5, characterized in that, The adjustment module is further configured to increase the popularity weight based on the error confidence level, and the increase in popularity weight is proportional to the error probability value; wherein, the popularity weight is increased according to each error probability value, and the error confidence level is a list composed of each error probability value.

7. The green factory application management system according to claim 6, characterized in that, The evaluation module is also used to determine whether the green application process meets the standard based on the single interaction value after the heat weight is increased, and to determine whether the green application process meets the standard based on the dynamic backlog warning index of the link according to the determination result, or to issue a manual intervention notice.

8. The green factory application management system according to claim 3, characterized in that, The adjustment module is also used to increase the dynamic task granularity based on the difference in the dynamic backlog early warning index of the link, and the increase in the dynamic task granularity is proportional to the difference in the dynamic backlog early warning index of the link.

9. The green factory application management system according to claim 8, characterized in that, The adjustment module is also used to increase the approval attention focus coefficient based on the increase in the dynamic task fragment granularity, and the increase in the approval attention focus coefficient is proportional to the increase in the dynamic task fragment granularity.

10. The green factory application management system according to claim 9, characterized in that, The evaluation module is also used to determine whether the green application process meets the standards based on the dynamic backlog warning index of the process after the approval attention focus coefficient has increased, and to issue a notification of approval resource shortage if the green application process does not meet the standards.

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