CN-122022700-A - Decorative engineering material supply chain cooperative system and method

CN122022700ACN 122022700 ACN122022700 ACN 122022700ACN-122022700-A

Abstract

The application provides a coordinated system and a coordinated method of a decoration engineering material supply chain, wherein the coordinated system comprises the steps of acquiring loss, transportation and use data of materials from a purchasing link to a construction link in the supply chain, grouping the characteristics of the materials by adopting a clustering algorithm to obtain material value contribution classification, acquiring a predicted value sequence, sequencing the priority of the materials in the predicted value sequence by integrating project demand adjustment data to obtain a resource allocation priority list, inputting market change response data to the construction matching evaluation link by means of the complementary purchasing instruction sequence to obtain a matching degree scoring matrix, acquiring the optimized resource allocation plan, and feeding the integrated material value tracking data back to the acquiring link to form a closed-loop resource optimization mechanism.

Inventors

YANG QUANBAO
YANG FAN
LIU FANGFANG
WANG KANG
ZHANG BO

Assignees

武汉宝康鸿程装饰工程有限公司

Dates

Publication Date: 20260512
Application Date: 20260122

Claims (8)

1. A decorative engineering material supply chain collaboration system and method, the method comprising: the material value contribution classification is obtained by collecting loss, transportation and use data of materials from a purchasing link to a construction link in a supply chain and grouping the material characteristics by adopting a clustering algorithm; Extracting a change trend from market price fluctuation data and project progress data according to the material value contribution classification, and if the change trend exceeds a preset threshold value, performing value fluctuation prediction on the materials in the material value contribution classification by using a machine learning prediction model to obtain a predicted value sequence; Acquiring the predicted value sequence, and sequencing the priority of materials in the predicted value sequence by integrating project demand adjustment data to obtain a resource allocation priority list; Extracting inventory adjustment parameters from the resource allocation priority list, combining purchase strategy optimization data with the resource allocation priority list by adopting an information fusion method, and generating a supplementary purchase instruction sequence if the inventory level is lower than a threshold value corresponding to the predicted value sequence; Inputting market change response data to a construction matching evaluation link through the supplementary purchasing instruction sequence to obtain a matching degree scoring matrix; According to the matching degree scoring matrix, acquiring a unified measurement index from the whole flow data of a supply chain, carrying out weighted calculation on the matching degree scoring matrix, and if the calculated score is lower than a preset threshold value, adjusting a resource allocation scheme to obtain an optimized resource allocation plan; and acquiring the optimized resource allocation plan, and integrating material value tracking data and feeding back to the acquisition link to form a closed-loop resource optimization mechanism.
2. The system and method for cooperating with a supply chain of decorative engineering materials according to claim 1, wherein the grouping the material characteristics by a clustering algorithm to obtain a material value contribution classification by collecting loss, transportation and usage data of materials from a purchasing link to a construction link in the supply chain comprises: Acquiring loss data, transportation data and use data generated by materials in a purchasing link, a transportation link and a construction link through deploying sensors and a recording system in each link of a supply chain, so as to obtain an original multidimensional data set; filling missing values and removing abnormal values according to the original multi-dimensional data set to obtain a cleaned data set; Carrying out dimension unification on loss data, transportation data and usage data in the cleaned data set by adopting standardized processing to obtain a standardized data set; Grouping material loss characteristics in the standardized data set through a clustering algorithm to obtain a plurality of material characteristic clusters; Calculating the value loss rate of the materials in the clusters according to the average value of the loss data, the transportation data and the usage data in each material characteristic cluster to obtain the value loss index of each cluster; If the value loss index of a certain cluster is higher than a preset threshold, judging that the cluster material belongs to a high-value contribution loss class, otherwise, judging that the cluster material is a low-value contribution loss class, and obtaining a material value contribution classification result; And associating corresponding supply chain links according to the material value contribution classification result, determining purchasing links and transportation links or construction links which occur in the high-value contribution loss type material set, and obtaining a key loss link list.
3. The system and method according to claim 1, wherein the extracting a change trend from market price fluctuation data and project progress data according to the material value contribution classification, and applying a machine learning prediction model to materials in the material value contribution classification to predict value fluctuation if the change trend exceeds a preset threshold value, to obtain a predicted value sequence comprises: step one, obtaining fluctuation data and progress data related to the material value from a market price database and project progress records, and obtaining a standardized fluctuation data set and progress data set through data cleaning and time sequence alignment processing; step two, extracting a change trend by adopting a time sequence analysis method aiming at the standardized fluctuation data set and progress data set to generate a corresponding trend feature sequence; If the change trend in the trend feature sequence exceeds a preset threshold, carrying out abnormal marking on the fluctuation data related to the material value to obtain an abnormal marking data set; according to the abnormal marking data set, predicting the value fluctuation by using a pre-established support vector machine model to generate a preliminary predicted value sequence; Step five, smoothing the preliminary predicted value sequence to eliminate short-term noise interference and obtain an optimized predicted value sequence; Step six, grouping mapping is carried out on the optimized predicted value sequence and the contribution classification data, so as to generate a value fluctuation prediction result under classification; And seventhly, constructing a sequence generation report according to the value fluctuation prediction result under classification, and determining the final predicted value sequence output.
4. The system and method for collaborative supply chain for decorative engineering materials according to claim 1, wherein the obtaining the predicted value sequence, prioritizing materials in the predicted value sequence by integrating project demand adjustment data, obtains a resource allocation priority list, comprises: Acquiring data of a predicted value sequence from a system, and primarily classifying each element in the sequence in a batch processing mode to obtain a classified value data set; According to the classified value data sets, carrying out matching processing on the project data aiming at the data of each class, and if the correlation between the matched project data and the predicted value is higher than a preset threshold value, marking the matched project data as high priority, so as to obtain a marked priority data set; acquiring a marked priority data set, and adopting a logistic regression model to perform further value evaluation on the high priority data to determine the potential value ordering of each material; For the value sequencing result, the priority of the materials is adjusted through sequencing logic built in the system, and an adjusted priority sequence is obtained; according to the adjusted priority sequence, combining constraint conditions of resource planning, if the priority sequence position of one material is higher than that of other materials, preferentially distributing resources to obtain a preliminary resource distribution scheme; And carrying out data integration aiming at the generation rule of the allocation list through a preliminary resource allocation scheme, and determining a final resource allocation priority list.
5. The system and method for collaborative supply chain for a decorative engineering material according to claim 1, wherein extracting inventory adjustment parameters from the resource allocation priority list, combining purchase policy optimization data with the resource allocation priority list by an information fusion method, and generating a supplemental purchase instruction sequence if inventory level is lower than a threshold corresponding to the predictive value sequence, comprises: Firstly, acquiring inventory adjustment parameters from a resource allocation priority list, and separating out parameter fields directly related to inventory levels by adopting a data screening method to obtain a preliminary inventory adjustment basis; Step two, integrating purchase strategy optimization data and a resource allocation priority list by an information fusion method aiming at the preliminary inventory adjustment basis, and determining a fused purchase strategy adjustment scheme; Step three, according to the fused purchasing strategy adjustment scheme, current stock level data are obtained and compared with the predicted value sequence, and if the stock level is lower than a threshold value corresponding to the predicted value sequence, a judgment result of stock shortage is generated; Aiming at the judgment result of insufficient inventory, generating a corresponding supplementary purchasing instruction sequence by adopting a preset supplementary purchasing rule, and determining the priority ordering of purchasing instructions; step five, according to the priority order of the supplementary purchasing instruction sequence, obtaining the available resource data of the related supply chain nodes, and if the resource data of the supply chain nodes meet the requirement of the purchasing instruction sequence, generating a resource allocation plan; step six, acquiring data records after inventory updating through a resource allocation plan, judging whether the inventory level meets the threshold requirement of a predicted value sequence, and if not, generating a purchasing instruction supplement of secondary adjustment; And seventhly, acquiring a final inventory adjustment execution scheme according to the purchase instruction supplement of the secondary adjustment, and automatically distributing the final inventory adjustment execution scheme to a supply chain node through a system to finish the dynamic balance processing of the inventory level.
6. The system and method for collaborative supply chain for decorative engineering materials according to claim 1, wherein the inputting market change response data to a construction matching evaluation link via the supplemental purchase order sequence to obtain a matching degree scoring matrix comprises: Acquiring market change signals through supplementing a purchasing instruction sequence, and acquiring change response data; analyzing the market change type according to the change response data, and determining a response data feature vector; vector similarity calculation is carried out through the response data feature vector and a pre-stored construction matching template, and an initial matching degree score is obtained; If the initial matching degree score is lower than a preset threshold value, triggering purchasing instruction sequence adjustment to obtain an adjusted instruction sequence; re-extracting the change response data according to the adjusted instruction sequence, and determining the updated response data feature vector; calculating vector similarity through the updated response data feature vector and the construction matching template to obtain an optimized matching degree scoring matrix; And sequencing all construction matching options according to the optimized matching degree scoring matrix, and determining a final matching degree scoring matrix.
7. The system and method for collaborative supply chain for decorative engineering materials according to claim 1, wherein the obtaining a unified measure from the overall flow data of the supply chain according to the matching degree scoring matrix, performing weighted calculation on the matching degree scoring matrix, and if the calculated score is lower than a preset threshold, adjusting a resource allocation scheme to obtain an optimized resource allocation plan, includes: acquiring running state data of each link through supply chain data and full-flow information, and adopting data cleaning and standardization processing to obtain a structured supply chain data set; Constructing a matching degree scoring matrix according to the structured supply chain data set, calculating a matching degree score according to the resource allocation condition of each node, and determining the distribution condition of scoring matrix values; aiming at the scoring matrix value, introducing a unified weighing value as an evaluation reference, combining with an index weight ratio, obtaining a comprehensive scoring result through a weighted calculation method, and judging whether the scoring reaches an expected standard or not; if the comprehensive scoring result is lower than the scoring threshold line, triggering an adjustment mechanism of the resource allocation scheme, acquiring deviation data of current resource allocation, and determining the direction of the adjustment scheme; According to the direction of the adjustment scheme, adopting an optimization allocation method to reallocate the resource nodes to generate a new resource planning table, and obtaining an optimized configuration result; Updating the running state of the supply chain data through the optimized configuration result, acquiring real-time feedback of the whole flow information, and judging whether the resource planning table meets the service requirement; Aiming at the real-time feedback full-flow information, continuously monitoring the change of the matching degree score, analyzing the potential deviation by adopting a logistic regression model, and determining the stability of the resource allocation scheme.
8. The system and method for collaborative delivery of a decorative engineering material according to claim 1, wherein the obtaining the optimized resource allocation plan, integrating material value tracking data feedback to the collection element, forming a closed-loop resource optimization mechanism, comprises: the application effect of the data of the optimization scheme in different links is analyzed by extracting the data of the optimization scheme from the resource allocation system, and a preliminary allocation matching result is obtained; Calculating the value contribution proportion of each link according to the preliminary allocation matching result and the material value tracking data, and determining the value distribution condition of the key links; if the value distribution condition of the key link is lower than a preset threshold value, triggering a data integration flow, comparing the tracking data with the data of the acquisition link, and judging whether deviation exists; Obtaining a specific range and an influence link of the deviation through a comparison result, and obtaining an adjusted resource optimization parameter by adopting a pre-established correction model; updating a feedback path in a closed-loop mechanism according to the adjusted resource optimization parameters, and determining a priority order of value feedback; acquiring a priority order of value feedback, and adjusting an execution logic of resource allocation aiming at a link with higher priority to obtain a final optimized execution scheme; and updating a data circulation path in the mechanism circulation through a final optimization execution scheme, judging the data consistency in the circulation process, and completing the closed-loop processing of resource optimization.

Description

Decorative engineering material supply chain cooperative system and method Technical Field The invention relates to the technical field of information, in particular to a coordinated system and a coordinated method of a decorative engineering material supply chain. Background The supply chain management of decorative engineering materials is an important field concerning engineering project cost, progress and quality, and occupies a non-negligible position in the construction industry. As engineering projects grow in size and complexity, efficient collaboration of supply chains directly affects the competitiveness of the enterprise and the success rate of the project, the criticality of which is self-evident. However, current management methods often have difficulty in coping with variable market environments and complex project requirements, exposing some deep problems. The existing method often lacks deep mining of dynamic association between different links when facing the whole process management of a material supply chain, and particularly, in the aspects of material value evaluation and resource allocation, accurate matching and real-time adjustment are difficult to achieve. This not only results in wasted resources, but may also affect engineering progress and cost control due to information asymmetry. Particularly when multiple material types are involved, the management mode often cannot adapt to the characteristic difference of different materials, and thus, the decision is delayed and inefficient. In this field, the core technical difficulty mainly focuses on how to realize the whole-course value tracking of materials from purchasing to construction and how to dynamically optimize resource allocation according to market and project changes. The difficulty in tracking the value of materials is that the loss, transportation and use links of various materials in the supply chain are different, and a unified measurement standard is lacking to comprehensively evaluate the actual contribution of the materials to the project. For example, some materials may be lost during transportation, while others may be wasted due to mismatching of construction, which makes it difficult to accurately evaluate the true value of each batch of materials simply by empirical judgment. Then, the problem further evolves into a difficult problem of dynamic adjustment of resource allocation, because the fluctuation trend of the material value cannot be accurately mastered, and when the market price changes or the engineering progress is adjusted, the inventory and purchasing strategies are difficult to optimize in time, which often results in backlog or shortage of the material. Therefore, how to construct a collaborative mechanism capable of comprehensively evaluating the contributions of different material values in the decoration engineering material supply chain and dynamically adjusting the resource allocation according to the market and project requirements becomes a key problem to be solved. Disclosure of Invention The invention provides a coordinated system and a coordinated method of a decorative engineering material supply chain, which mainly comprise the following steps: the material value contribution classification is obtained by collecting loss, transportation and use data of materials from a purchasing link to a construction link in a supply chain and grouping the material characteristics by adopting a clustering algorithm; Extracting a change trend from market price fluctuation data and project progress data according to the material value contribution classification, and if the change trend exceeds a preset threshold value, performing value fluctuation prediction on the materials in the material value contribution classification by using a machine learning prediction model to obtain a predicted value sequence; Acquiring the predicted value sequence, and sequencing the priority of materials in the predicted value sequence by integrating project demand adjustment data to obtain a resource allocation priority list; Extracting inventory adjustment parameters from the resource allocation priority list, combining purchase strategy optimization data with the resource allocation priority list by adopting an information fusion method, and generating a supplementary purchase instruction sequence if the inventory level is lower than a threshold value corresponding to the predicted value sequence; Inputting market change response data to a construction matching evaluation link through the supplementary purchasing instruction sequence to obtain a matching degree scoring matrix; According to the matching degree scoring matrix, acquiring a unified measurement index from the whole flow data of a supply chain, carrying out weighted calculation on the matching degree scoring matrix, and if the calculated score is lower than a preset threshold value, adjusting a resource allocation scheme to obtain an optimized resource alloca