CN-122022237-A - Method, system, terminal and medium for judging dynamic residual buffering time of material delivery

CN122022237ACN 122022237 ACN122022237 ACN 122022237ACN-122022237-A

Abstract

The invention relates to the field of supply chain management, and particularly discloses a method, a system, a terminal and a medium for judging dynamic residual buffering time of material delivery, wherein the method comprises the steps of obtaining initial planning buffering time of an order; the method comprises the steps of calculating accumulated consumed buffer time based on deviation of actual progress of orders in links of a supply chain and planned progress, obtaining dynamic risk adjustment quantity based on real-time internal and external risk data through a rule engine or a pre-trained risk adjustment quantity prediction model, and calculating current dynamic residual buffer time according to the initial planned buffer time, the accumulated consumed buffer time and the dynamic risk adjustment quantity. The invention improves the authenticity and timeliness of time judgment and improves the reliability and efficiency of material delivery.

Inventors

ZHANG YUYANG
ZHANG HONGYUAN
WANG YAOHAI
WU JINGANG
XU XINYUAN
LI HAIBO
LI JIANGHUA
ZHAO HONGWEI
WU JIANG
LIU TAO

Assignees

国网山东省电力公司物资公司
山东鲁软数字科技有限公司

Dates

Publication Date: 20260512
Application Date: 20251216

Claims (10)

1. A method for judging dynamic residual buffering time of material delivery is characterized by comprising the following steps: Acquiring an initial planning buffer time of an order; Calculating accumulated consumed buffer time based on the deviation of the actual progress of the order in each link of the supply chain and the planned progress; Based on real-time internal and external risk data, obtaining dynamic risk adjustment through a rule engine or a pre-trained risk adjustment prediction model; And calculating to obtain the current dynamic residual buffer time according to the initial planned buffer time, the accumulated consumed buffer time and the dynamic risk adjustment quantity.
2. The method for determining dynamic remaining buffering time for delivering materials according to claim 1, wherein the step of obtaining an initial planned buffering time for an order comprises: Acquiring target feature data of an order, wherein the target feature data comprise material complexity, transportation distance and historical performance indexes of suppliers; Inputting the target characteristic data into a pre-trained initial buffering time prediction model to obtain initial planning buffering time for the order; the complexity of the materials is a complexity score obtained by quantifying a predefined rule according to the weight, the volume, whether dangerous materials are needed and whether constant-temperature transportation is needed of the materials, and the historical performance index of the suppliers is an integrated performance score obtained according to the historical on-time delivery rate, the average quality defect rate and response communication timeliness of the suppliers in the past preset time.
3. The method for determining the remaining buffering time of dynamic delivery of materials according to claim 1, wherein the calculating the accumulated consumed buffering time based on the deviation between the actual schedule of the order at each link of the supply chain and the planned schedule comprises: aiming at each predefined link in the supply chain flow, acquiring the planned time consumption and the actual time consumption of the link; Calculating time consumption deviation of each link, wherein the time consumption deviation is the difference value between the actual time consumption of the link and the planned time consumption; And accumulating the time-consuming deviations of all links to obtain accumulated consumed buffer time.
4. The method for determining a dynamic remaining buffering time for delivering materials according to claim 1, wherein the dynamic risk adjustment amount is obtained by a rule engine based on real-time internal and external risk data, and the method specifically comprises: constructing a risk knowledge base, wherein a plurality of risk event types are predefined, and corresponding data sources, feature extraction rules and event identification rules are configured for each risk event type; Acquiring real-time heterogeneous data from an internal and external data source through a data interface; performing feature extraction and standardization processing on the real-time heterogeneous data to generate a structured risk feature vector; matching the structured risk feature vector with an event identification rule to identify a risk event instance; For each risk event instance, inquiring a predefined delay influence mapping relation according to the type of the risk event to which the risk event instance belongs, and obtaining the expected delay time corresponding to the risk event; and adding the estimated delay time corresponding to all the identified risk event instances to obtain the dynamic risk adjustment quantity.
5. The method for determining a dynamic remaining buffering time for delivering materials according to claim 4, wherein the dynamic risk adjustment is obtained by a pre-trained risk adjustment prediction model based on real-time internal and external risk data, comprising: inputting real-time internal and external risk data into an input layer of a risk adjustment quantity prediction model to obtain standardized data to be processed, wherein the data to be processed comprises time sequence data, space geographic data, text data and structured data; embedding and encoding the input features of the data to be processed into a layer, and respectively extracting the features of the time sequence data, the space geographic data, the text data and the structured data to obtain corresponding time sequence features, space features, text features and structured features; Weighting and fusing the time sequence features, the space features, the text features and the structural features through a multi-modal feature fusion layer to generate a joint feature representation; and based on the joint characteristic representation, obtaining the dynamic risk adjustment quantity through output layer prediction.
6. The method for determining dynamic remaining buffering time for delivery of materials according to claim 1, further comprising: Identifying whether a predefined positive opportunity event exists by monitoring status data of the supply chain execution system with an external data source; for each positive opportunity event identified, deriving an expected savings in time based on predefined rules; Accumulating the expected saved time corresponding to all the identified positive opportunity events to obtain a dynamic opportunity gain amount; Wherein the positive opportunity event comprises that the production progress of the supplier advances the planning progress, and a transportation mode or route superior to the original planning is started in the logistics link; The calculation mode of the expected time saving corresponding to the event that the production progress of the supplier is advanced to the planned progress is that the final production completion time is predicted based on the real-time report data of the production management system and compared with the planned completion time, and the difference value of the final production completion time and the planned completion time is the expected time saving; the calculation mode of the expected time saving corresponding to the event of the transportation mode or the route which is superior to the original plan in the logistics link is that the difference value between the expected transportation time length of the original plan transportation route and the expected transportation time length of the newly started transportation route is calculated, and the difference value is the expected time saving.
7. The method according to claim 6, wherein the dynamic chance gain is calculated as a forward adjustment factor when calculating the current dynamic remaining buffer time.
8. A system for determining dynamic remaining buffering time for delivery of materials, comprising: The initial plan buffer time acquisition module is used for acquiring the initial plan buffer time of the order; the consumed buffer time calculation module is used for calculating accumulated consumed buffer time based on the deviation of the actual progress of the order in each link of the supply chain and the planned progress; The dynamic risk adjustment quantity generation module is used for obtaining dynamic risk adjustment quantity through a rule engine or a pre-trained risk adjustment quantity prediction model based on real-time internal and external risk data; and the dynamic residual buffer time calculation module is used for calculating the current dynamic residual buffer time according to the initial planned buffer time, the accumulated consumed buffer time and the dynamic risk adjustment quantity.
9. A terminal, comprising: the storage is used for storing a dynamic residual buffer time judging program for delivering materials; a processor for implementing the steps of the method for determining a dynamic remaining buffering time for delivery of materials according to any one of claims 1 to 7 when executing the program for determining a dynamic remaining buffering time for delivery of materials.
10. A computer readable storage medium, wherein a material delivery dynamic remaining buffering time determination program is stored on the readable storage medium, and the material delivery dynamic remaining buffering time determination program, when executed by a processor, implements the steps of the material delivery dynamic remaining buffering time determination method according to any one of claims 1 to 7.

Description

Method, system, terminal and medium for judging dynamic residual buffering time of material delivery Technical Field The invention relates to the field of supply chain management, in particular to a method, a system, a terminal and a medium for judging dynamic residual buffering time of material delivery. Background In the existing supply chain management system, a static time management mode is adopted for monitoring the material delivery time, namely, the system tracks the progress according to a preset fixed time node, and the delivery risk is judged by simply comparing the difference between the planning date and the actual date. However, the linear calculation is only performed based on the calendar time, the dynamic consumption process of the whole buffering time of the actual execution efficiency of each link cannot be reflected, the data of each link cannot be associated, the progress deviation cannot be uniformly quantized into the whole buffering time system, global risk assessment cannot be formed, the accuracy of buffering time judgment is affected, the passive response can be performed only after the actual occurrence of the risk, the predictability of potential risks is lacking, and the buffering time expectation cannot be adjusted in advance. Disclosure of Invention In order to solve the problems, the invention provides a method, a system, a terminal and a medium for judging the dynamic residual buffering time of material delivery, which are used for improving the authenticity and timeliness of time judgment and improving the reliability and efficiency of material delivery. In a first aspect, the present invention provides a method for determining a dynamic remaining buffering time for delivering materials, including the following steps: Acquiring an initial planning buffer time of an order; Calculating accumulated consumed buffer time based on the deviation of the actual progress of the order in each link of the supply chain and the planned progress; Based on real-time internal and external risk data, obtaining dynamic risk adjustment through a rule engine or a pre-trained risk adjustment prediction model; And calculating to obtain the current dynamic residual buffer time according to the initial planned buffer time, the accumulated consumed buffer time and the dynamic risk adjustment quantity. In a second aspect, the present invention provides a system for determining a dynamic remaining buffering time for delivering materials, including: The initial plan buffer time acquisition module is used for acquiring the initial plan buffer time of the order; the consumed buffer time calculation module is used for calculating accumulated consumed buffer time based on the deviation of the actual progress of the order in each link of the supply chain and the planned progress; The dynamic risk adjustment quantity generation module is used for obtaining dynamic risk adjustment quantity through a rule engine or a pre-trained risk adjustment quantity prediction model based on real-time internal and external risk data; and the dynamic residual buffer time calculation module is used for calculating the current dynamic residual buffer time according to the initial planned buffer time, the accumulated consumed buffer time and the dynamic risk adjustment quantity. In a third aspect, a technical solution of the present invention provides a terminal, including: the storage is used for storing a dynamic residual buffer time judging program for delivering materials; And the processor is used for realizing the steps of the dynamic residual buffer time judging method for delivering materials when executing the dynamic residual buffer time judging program for delivering materials. In a fourth aspect, the present invention provides a computer readable storage medium, where a program for determining a dynamic remaining buffering time for delivering materials is stored, where the program for determining a dynamic remaining buffering time for delivering materials, when executed by a processor, implements the steps of the method for determining a dynamic remaining buffering time for delivering materials according to any one of the above. According to the technical scheme, the method has the advantages that the limitation of a traditional fixed threshold value is broken through by combining order target characteristic data to determine initial planning buffer time, personalized adaptation of the buffer time is achieved, resource waste caused by redundant buffering is avoided, accumulated consumed buffer time is calculated based on deviation of actual progress of a supply chain and planning progress, meanwhile dynamic risk adjustment quantity is quantized by integrating real-time internal and external risk data, the influence of flow progress and potential risk is reflected in real time, the problem that traditional static calculation is disjointed with an actual scene is solved, the authenticity and timeliness of time judgm