Concrete area productivity collaborative scheduling and resource dynamic matching method

Abstract

The invention relates to a concrete area productivity collaborative scheduling and resource dynamic matching method, which belongs to the technical field of data processing, and comprises the following steps: S1, setting a matching rule by a platform according to settled data; s2, selecting whether there is a contract according to a user, if there is no contract, determining that the demand is a new demand, and entering the matching process of S3; if the contract exists, not entering the matching process; s3, the platform carries out automatic matching on the user demands according to the matching rule, after matching succeeds, the two transaction parties carry out confirmation according to the matching result, and a subsequent platform transaction process is carried out; s4, when the automatic matching fails or the matching results of the two transaction parties are not consistent, the platform performs manual matching; and S5, the platform regularly adjusts the factors for setting the matching rules according to the settled data, so that the adaptability of the matching rule model is improved. According to the invention, the information isolated island of the concrete industry is broken, and the two demanding parties can quickly complete the transaction by establishing the digital market infrastructure.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for coordinated scheduling of concrete production capacity and dynamic matching of resources. Background Technology

[0002] The current supply and demand matching mechanism in the concrete industry suffers from systemic technical defects, severely restricting the efficiency improvement of the construction industry chain. These defects manifest in three core issues:

[0003] (1) Inefficient supply and demand matching caused by information silos: In the concrete industry, construction companies need to contact 3-5 batching plants on average to compare prices, and a single inquiry process takes more than 48 hours to find a relatively suitable batching plant. At the same time, about 80% of batching plants still use Excel spreadsheets to manage production capacity data, which makes it impossible to achieve cross-enterprise data sharing.

[0004] (2) Resource mismatch caused by static matching: More than 70% of concrete procurement contracts are signed based on historical cooperation inertia, and regional capacity utilization rates differ by as much as 40 percentage points. New batching plants entering the market need additional business costs to establish trust relationships with construction companies.

[0005] (3) Transaction barriers caused by the lack of a credit system: According to relevant complaint data from the State Administration for Market Regulation on the building materials industry, concrete quality disputes account for about 40% of building materials complaints. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, this invention provides a method for coordinated scheduling of concrete production capacity and dynamic matching of resources.

[0007] Methods for coordinated scheduling of concrete production capacity and dynamic resource matching in different areas include:

[0008] Step S1: The platform sets matching rules based on the accumulated data;

[0009] Step S2: Based on the user's selection, if there is no contract, it is considered a new requirement and proceeds to the matching process in Step S3; if there is already a contract, the matching process is not initiated.

[0010] Step S3: The platform automatically matches user needs according to the matching rules. After a successful match, both parties confirm the result and proceed with the subsequent platform transaction process.

[0011] Step S4: If automatic matching fails or the two parties to the transaction do not agree on the matching result, the platform will perform manual matching.

[0012] Step S5: The platform periodically adjusts the factors for setting matching rules based on the accumulated data to improve the adaptability of the matching rule model.

[0013] Furthermore, the platform sets matching rules based on the accumulated data as follows: it sets matching factors at various levels for the current administrative region based on the delivery distance set by the supplier on the platform and the platform data; the platform data includes transaction evaluations, complaint information, quotation information, and performance information.

[0014] Furthermore, the automatic matching in step S3 specifically involves: First, prioritizing matching based on the delivery mileage range currently provided by the supplier; second, for multiple suppliers with the same delivery mileage range or those less than 5 kilometers apart, the supplier with the highest platform rating is selected by default; for suppliers with the same rating or ratings differing by no more than 1 point, the supplier with a low platform complaint rate but high transaction volume is selected by default; and for suppliers with the same complaint rate or a complaint rate difference of no more than 5 points, the supplier with the best current performance on the platform is selected by default.

[0015] Furthermore, the matching rules include the number of rules, matching dimensions, scores, and the number of matching parties.

[0016] Furthermore, the matching dimensions include ratings, complaints, performance, distance, and quotes.

[0017] Furthermore, step S3 also includes: if multiple suppliers are matched, the user selects a supplier to determine the final matching result.

[0018] Furthermore, in step S4, the platform performs manual matching by pushing all messages to the suppliers currently registered on the platform, thereby enabling more suppliers to participate in the transaction.

[0019] Furthermore, step S5 specifically involves: regularly conducting surveys on the content released by regional associations and housing and construction bureaus, as well as online and offline data, to determine whether to add or delete factors; and adjusting the factor coefficients based on the actual conditions of different regions, including raw material prices and costs, online information prices for concrete, regional traffic congestion, and platform order delivery and fulfillment data.

[0020] The beneficial effects of this invention are: This invention breaks down information silos by integrating multi-source data, and by establishing a digital market infrastructure, enables concrete industry enterprises and demanders to quickly complete transaction needs and improve the utilization rate of market capacity. Through transaction closure and transparency, it reduces the risk of disputes between the two parties. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the figures. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In this embodiment, as Figure 1 As shown, the method for coordinated scheduling of concrete production capacity and dynamic matching of resources includes:

[0024] S1: Based on the data already accumulated on the platform (such as reviews, complaints, fulfillment records, distance, and pricing), set the matching rules for the current platform. This includes the number of matches (one or more rules), matching dimensions (reviews, complaints, fulfillment records, distance, pricing), matching rules (high scores, low scores, etc.), and matching parties (unique or multiple matches). During setup, set matching factors at various levels for the current administrative region based on the delivery distance set by the platform's suppliers and platform data (including transaction reviews, complaint information, pricing information, and fulfillment information). Matching factors will be dynamically added later based on the platform's accumulated data.

[0025] S2: Based on the user's selection of whether there is a contract, if there is no contract, it is considered new data and will need to enter the matching process later. If there is an existing contract, the original transaction process will be followed, and the matching process will not be repeated, thus avoiding the need for matching for every transaction.

[0026] S3: Platform matching uses the rules configured in step S1 for default matching. After matching, both parties need to confirm the result. If multiple confirmations are received, the buyer decides which match to confirm. If there is only one match, both parties confirm and proceed with the platform's transaction process. 3. The platform's pre-set matching priority factor is used by default. If there is no match in the current region, the platform's dynamic matching factor is used by default, with the specific rules as follows:

[0027] 3.1. Prioritize matching based on the delivery mileage range currently provided by the supplier;

[0028] 3.2 If multiple delivery ranges are the same or less than 5 kilometers apart, the supplier with the higher rating on the selected area platform will be chosen by default;

[0029] 3.3 If there are identical ratings or ratings that differ by no more than 1 point, the supplier with a low platform complaint rate but a high transaction volume will be selected by default;

[0030] 3.4 If there are suppliers with the same complaint rate or whose complaint rates differ by no more than 5 percentage points, the supplier with the best current performance on the platform will be selected by default.

[0031] S4: If the platform cannot match or the matching parties do not reach an agreement, then the platform needs to manually push a full message to all companies that have already joined the platform, in order to encourage more companies to participate.

[0032] S5: The platform regularly adjusts its model based on the data it accumulates, such as adding or removing factors to achieve a more suitable platform model. Specifically: it regularly conducts surveys on content released by regional associations and housing and construction bureaus, as well as online and offline data, to determine whether to add or delete factors; and it adjusts factor coefficients based on the actual conditions of different regions, including raw material prices and costs, online information prices for concrete, regional traffic congestion, and platform order delivery and fulfillment data.

[0033] In the description of embodiments of the present invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for coordinated scheduling of concrete production capacity and dynamic matching of resources, characterized in that, include: Step S1: The platform sets matching rules based on the accumulated data; Step S2: Based on the user's selection, if there is no contract, it is considered a new requirement and proceeds to the matching process in Step S3; if there is already a contract, the matching process is not initiated. Step S3: The platform automatically matches user needs according to the matching rules. After a successful match, both parties confirm the result and proceed with the subsequent platform transaction process. Step S4: If automatic matching fails or the two parties to the transaction do not agree on the matching result, the platform will perform manual matching. Step S5: The platform periodically adjusts the factors for setting matching rules based on the accumulated data to improve the adaptability of the matching rule model.

2. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources in accordance with claim 1, characterized in that, The platform sets matching rules based on the accumulated data, specifically by setting matching factors at various levels for the current administrative region based on the delivery distance set by the supplier and the platform data. The platform data includes transaction evaluations, complaint information, pricing information, and contract fulfillment information.

3. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources according to claim 1, characterized in that, The automatic matching in step S3 is as follows: First, priority matching is performed based on the delivery mileage range currently provided by the supplier; second, if multiple delivery mileage ranges are the same or differ by less than 5 kilometers, the supplier with the higher rating on the platform is selected by default; if the ratings are the same or differ by no more than 1 point, the supplier with a low complaint rate but a large transaction volume is selected by default; if the complaint rates are the same or differ by no more than 5 points, the supplier with the best current performance on the platform is selected by default.

4. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources in accordance with claim 1, characterized in that, The matching rules include the number of rules, matching dimensions, scores, and the number of matching parties.

5. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources in accordance with claim 3, characterized in that, The matching dimensions include reviews, complaints, performance, distance, and price quotes.

6. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources according to claim 1, characterized in that, Step S3 further includes: if multiple suppliers are matched, the user selects a supplier to determine the final matching result.

7. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources in accordance with claim 1, characterized in that, In step S4, the platform performs manual matching by pushing all messages to the suppliers already registered on the platform, enabling more suppliers to participate in the transaction.

8. The method for coordinated scheduling of concrete production capacity and dynamic matching of resources according to claim 1, characterized in that, Step S5 specifically involves: regularly conducting surveys on the content released by regional associations and housing and construction bureaus, as well as online and offline data, to determine whether to add or delete factors; and adjusting the factor coefficients based on the actual conditions of different regions, including raw material prices and costs, online information prices for concrete, regional traffic congestion, and platform order delivery and fulfillment data.

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