Social productivity scheduling method and system based on real-time inventory matching priority

Abstract

The invention relates to the technical field of computers, and discloses a socialized productivity scheduling method and system based on real-time inventory matching priority, and the system comprises a user terminal which is used for receiving an order request and displaying a scheduling result; the intelligent scheduling server is in communication connection with the user terminal and is used for processing the order request and executing a scheduling algorithm; the database is connected with the intelligent scheduling server and is used for storing producer information, equipment states, inventory data and historical order data; wherein the intelligent scheduling server comprises a real-time state acquisition module which is used for acquiring the equipment state of the distributed production node in real time through an API (Application Program Interface) or active reporting of a producer. According to the invention, by integrating social idle distributed manufacturing resources, a multi-level collaborative production network covering local street graphic and text stores to source factories is constructed, and accurate and efficient matching of production orders and optimal producers is realized by using real-time state monitoring and a multi-dimensional weight scoring algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer technology, and in particular to a socialized production capacity scheduling method and system based on real-time inventory matching priority. BACKGROUND

[0002] In the current advertising graphics, trophy manufacturing and other industries, there are generally problems of scattered production resources and low equipment utilization. A large number of engraving machines, UV printers and other equipment are distributed in different scale enterprises and street stores, forming an idle or semi-idle production capacity state.

[0003] Traditional order allocation mainly relies on manual communication and price comparison, which not only responds slowly, but also is difficult to realize resource collaboration across regions and subjects. Although there are some production management software, their functions are usually limited to a single factory, and cannot effectively integrate socialized idle equipment and inventory resources. At the same time, due to the lack of accurate tracking and intelligent matching mechanism for real-time inventory status, the enterprise's inventory risk is high, the inventory turnover efficiency is low, and the overall efficiency of the industry is restricted. SUMMARY

[0004] The present application provides a socialized production capacity scheduling method and system based on real-time inventory matching priority to solve the existing technical problems, which solves the problem that traditional order allocation is difficult to realize resource collaboration across regions and subjects.

[0005] To solve the above technical problems, according to one aspect of the present application, more specifically, a socialized production capacity scheduling system based on real-time inventory matching priority, comprising: A user terminal for receiving order requests and displaying scheduling results; An intelligent scheduling server in communication connection with the user terminal, for processing order requests and executing scheduling algorithms; A database connected with the intelligent scheduling server, for storing producer information, device status, inventory data and historical order data; The intelligent scheduling server comprises: A real-time state acquisition module for acquiring the device status of distributed production nodes in real time through API interface or producer active reporting, the device status including order receiving, zero renting, resting, inventory locking state, completion and delivery; A priority calculation module for dynamically calculating the priority score of each candidate producer based on multi-dimensional weight factors, the weight factors including credit weight, efficiency weight, geographical weight and inventory weight; An order matching module for automatically assigning orders to the optimal producer according to the priority score; The distributed production nodes include a local core factory node, a local distributed edge node, a regional collaborative node, and a source supply node. The inventory data includes shared factory inventory, idle advertising graphic entity store inventory, nearby city factory inventory, and original place inventory. The priority calculation module uses a weighted scoring algorithm to calculate a priority score S, and the function formula is: ; In the above formula, a, b, c, and d are adjustable parameters, and the value range of a, b, c, and d is 0 to 1, and a+b+c+d=1; Wc represents the credit weight; We represents the efficiency weight; Wg represents the geographical weight; Ws represents the inventory weight; Furthermore, the priority calculation module is configured to dynamically calculate the priority score of each candidate producer based on the multi-dimensional weight factor, wherein the inventory weight is configured as: When the producer has raw material inventory matching the order, the value is much higher than other weight factors, so as to realize the priority scheduling of inventory matching.

[0006] Furthermore, the credit weight is calculated based on the historical order completion rate, quality score, and customer complaint rate of the producer; the efficiency weight is calculated based on the historical order average production time and on-time rate of the producer; the geographical weight is calculated based on the geographical distance between the producer and the order demand place; and the inventory weight is calculated based on whether the producer has raw material inventory matching the order, and if there is matching inventory, the inventory weight obtains a preset highest score.

[0007] Furthermore, the real-time state acquisition module periodically polls or event-triggered updates the equipment state of the production node, to ensure the real-time and accuracy of the state data.

[0008] Furthermore, the system further includes an inventory management module, which is configured to establish a unique identifier for each batch of materials and record its procurement source, when the producer applies for return, the system checks the material identifier and state, and updates the shared factory inventory data, and releases the inventory occupation of the producer.

[0009] Furthermore, the adjustable parameters a, b, c, and d are dynamically adjusted by a machine learning algorithm to optimize the scheduling accuracy and efficiency.

[0010] Furthermore, the order matching module records the matching data after dispatching the order to update the historical order information of the producer, and feeds back to the priority calculation module to optimize the weight factor.

[0011] Furthermore, the equipment state and inventory data of the distributed production node are synchronized in real time through Internet of Things devices or manual input by the producer.

[0012] The social production scheduling method based on real-time inventory matching priority comprises the following steps: S1, receiving an order request through a user terminal, wherein the order request comprises product type, quantity, delivery time and demand location; S2, the intelligent scheduling server parses the order request to determine the required equipment type and raw materials; S3, the real-time state acquisition module acquires the equipment state and inventory data of the distributed production nodes from the database, and screens the candidate producers with the state being orderable; S4, the priority calculation module calculates the priority score of each candidate producer based on multi-dimensional weight factors, wherein the weight factors include credit weight, efficiency weight, geographic weight and inventory weight; And the priority calculation module is configured to dynamically calculate the priority score of each candidate producer based on multi-dimensional weight factors, wherein the inventory weight is configured as: When the producer has raw material inventory matching the order, the value is much higher than other weight factors; S5, the order matching module assigns the order to the producer with the highest score according to the priority score; S6, after the order is completed, the system updates the historical order data and inventory data of the producer.

[0013] Further, in step S4, the calculation of the inventory weight is prioritized over other weights, and if the candidate producer has raw material inventory matching the order, the inventory weight is set to the maximum value, otherwise it is set to zero.

[0014] Further, it further comprises analyzing the order completion data by a machine learning algorithm, and dynamically adjusting the adjustable parameters a, b, c and d to optimize subsequent scheduling decisions.

[0015] The social production scheduling method and system based on real-time inventory matching priority provided by the application have the following effects compared with the prior art: 1. The application integrates social idle distributed manufacturing resources, builds a multi-level collaborative production network covering local street graphic stores to source factories, and realizes accurate and efficient matching of production orders and optimal producers by using real-time state monitoring and multi-dimensional weight scoring algorithm. This greatly improves the global search efficiency and matching accuracy of social distributed manufacturing resources, so that idle or low utilization equipment and inventory can be fully utilized, and the overall industrial resource allocation is optimized.

[0016] 2、The core algorithm of the application takes real-time inventory matching as a key weight factor and gives it a very high priority, so that producers who have the required raw material inventory can be prioritized. This mechanism significantly reduces the production delay caused by waiting for raw materials, effectively shortens the overall delivery cycle of orders, and reduces the inventory holding cost and material turnover risk of each link in the supply chain, bringing direct economic benefits to all parties involved in collaboration.

[0017] 3、The application acquires and updates the equipment status of the production nodes in real time, and establishes a material tracking and state management mechanism that supports zero inventory return. The application builds a highly reliable and flexible supply chain collaboration environment. The system ensures that orders will only be dispatched to currently available producers, and resolves the concerns of idle capacity in society when participating in collaboration by clearly defining the responsibilities of data management, thereby enhancing the stability of the entire production network and the willingness of participants to cooperate.

[0018] 4、The parameters in the weighted scoring algorithm used by the application have the ability to be dynamically optimized through machine learning. The system can continuously learn from completed orders, automatically adjust the parameter configuration of each weight factor, and continuously improve the scheduling strategy as actual operation data accumulates. This enables the system not only to adapt to static business rules, but also to have dynamic adaptive ability to respond to market fluctuations and changes in the production network, thereby achieving continuously optimized scheduling performance. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flowchart of the technical solution in the application; Figure 2 The flowchart of the specific implementation in the application. DETAILED DESCRIPTION

[0020] To make the technical solution of the application clearer, the following will further explain the application in combination with the drawings and specific embodiments.

[0021] As shown in Figure 1 , 2 , this embodiment takes a specific trophy production order as an example to illustrate the complete workflow of the system.

[0022] Step S1, system initialization and data preparation The intelligent scheduling server, database and user terminal have been deployed and connected. In the database, a distributed production node covering multiple levels has been registered, including: Local city general factory, such as "Jinan Shared Factory A", has multiple laser engraving machines and UV printers, and a large stock of common specifications of crystal, metal and other raw materials (shared factory inventory).

[0023] Local street map stores, such as "Lixia District Map Store B" and "Shizhong District Map Store C" distributed in various streets of Jinan, each have one or more engraving equipment and may have a small amount of commonly used materials (idle map store inventory).

[0024] Nearby city factories, such as "Qingdao Collaboration Factory D" located in Qingdao, whose equipment and inventory serve as an effective supplement to Jinan's local resources (nearby city inventory).

[0025] Source factories, such as "Foshan Source Factory E" located in Guangdong, have the strongest production capacity and the most complete raw material reserves (origin inventory).

[0026] The equipment status of each production node (such as "processing orders" and "resting") is actively reported to the intelligent scheduling server every 5 minutes through the software API interface on its terminal equipment, achieving quasi-real-time updates of the status. At the same time, the inventory data of each node is automatically updated to the central database through scanning equipment when materials are in and out of the warehouse.

[0027] Step S2, order receiving and analysis A service provider located in Jinan publishes a new production order through its user terminal (such as a mobile phone APP or computer web page). The order request contains the following information: the product is "100 crystal trophies", the delivery period is 2 days, and the demand location is Lixia District, Jinan.

[0028] After receiving the order request, the intelligent scheduling server immediately analyzes it. By querying the product database, the server determines that "laser engraving machines" and "20mm thick crystal material of a specific specification" are needed to produce the trophies.

[0029] Step S3, real-time state screening and candidate producer set generation The real-time state acquisition module is triggered, which filters all candidate producers from the database based on the "laser engraving machine" equipment type required by the order and the "Jinan" demand location. The filtering conditions include: 1. Geographical location in Jinan or nearby nodes that allow scheduling to Jinan.

[0030] 2. The equipment list includes "laser engraving machines".

[0031] 3. The current device state is "processing orders".

[0032] Assuming that after this step, the system filters out three candidate producers: Shared Factory A, Lixia District Map Store B, and Qingdao Collaboration Factory D. Foshan Source Factory E is temporarily excluded from the initial screening due to its distance, and Shizhong District Map Store C is excluded because its equipment state is "resting".

[0033] Step S4, priority dynamic calculation based on weighted scoring algorithm The priority calculation module starts to calculate the priority score S for the above three candidate producers. The score is calculated by a function formula. The function formula is: ; In the above formula, a, b, c, d are all adjustable parameters, and the value range of a, b, c, d is 0 to 1, and a+b+c+d=1; Wc represents the credit weight; We represents the efficiency weight; Wg represents the geographical weight; Ws represents the inventory weight. Assuming that the current adjustable parameter setting of the system is: a=0.3, b=0.2, c=0.2, d=0.3 (reflecting the emphasis on credit and inventory).

[0034] Among them, the calculation process of each weight factor is as follows: Credit weight (Wc): The module queries the historical order data of each producer. Shared factory A has a completion rate of 99%, a quality score of 4.9 / 5.0, and no complaints, and Wc=95 is calculated. Graphic store B has a completion rate of 90%, a quality score of 4.5, and 1 minor complaint, and Wc=80 is calculated. Factory D has a completion rate of 95% and a quality score of 4.8, and Wc=90 is calculated.

[0035] Efficiency weight (We): Based on the average production time and on-time rate in history. Factory A has an average time of 4 hours and an on-time rate of 100%, and We=90 is calculated. Graphic store B has an average time of 6 hours and an on-time rate of 85%, and We=75 is calculated. Factory D has an average time of 5 hours and an on-time rate of 98%, and We=88 is calculated.

[0036] Geographical weight (Wg): calculated according to the distance from the order demand place (Lixia District). Graphic store B is in Lixia District and is the closest, Wg=100. Factory A is located in the suburbs of Jinan, Wg=85. Factory D is in Qingdao, Wg=60.

[0037] Inventory weight (Ws): This is a key factor. The system checks the real-time inventory of the three. Only the warehouse of shared factory A has ready-made "20mm thick crystal material". According to the rules set by the present application, the producer with matching inventory will get the highest score of Ws (set to 100). Graphic store B and factory D have no such inventory, so Ws=0.

[0038] Then, the priority score calculation is: Shared factory A: S=0.3x95+0.2x90+0.2x85+0.3x100=28.5+18+17+30=93.5; Lixia District graphic store B: S=0.3x80+0.2x75+0.2x100+0.3x0=24+15+20+0=59; The priority score of Qingdao Collaborative Factory is D:S = 0.3*90 + 0.2*88 + 0.2*60 + 0.3*0 = 27 + 17.6 + 12 + 0 = 56.6.

[0039] Step S5, Order Matching and Dispatch According to the calculation results, the order matching module selects the shared factory A with the highest priority score as the optimal producer. The system automatically dispatches the order of "100 crystal trophies" to the shared factory A and sends a production notification through its user terminal.

[0040] Step S6, Data Recording and Parameter Optimization After the order is completed, the shared factory A updates the order status to "completed" through the terminal. The system records the successful results of this matching, including the on-time completion of the order, no quality complaints, and other data, as historical data of the shared factory A, which is used to update its credit weight (Wc) and efficiency weight (We) in the future.

[0041] In addition, the overall efficiency data generated by this dispatch (such as the total time from accepting the order to completion) is collected and analyzed through machine learning algorithms. The algorithm will evaluate the effect of this parameter combination (a=0.3, b=0.2, c=0.2, d=0.3) and may fine-tune these adjustable parameters a, b, c, d to continuously optimize the scheduling accuracy and efficiency of the entire system.

[0042] Step S7, Inventory Coordination Management Example During the above-mentioned order production process, it is assumed that the shared factory A uses a batch of crystal materials purchased from the "Shared Factory Cloud Warehouse". The system establishes a unique identifier (such as a two-dimensional code) for this batch of materials. Later, Lixia District Graphic Store B needs to return a batch of unused crystal materials also purchased from the cloud warehouse due to plan changes.

[0043] Graphic Store B initiates a return application through the system, and the platform selects the product application. The inventory management module of the system is triggered to verify the identifier and status of the material, confirming that it is indeed from the cloud warehouse and has not been used. After verification, the system performs two key operations: 1) Update the inventory data of the shared factory, add the quantity of this batch of materials back to the cloud warehouse inventory.

[0044] 2) Release the inventory occupation of Graphic Store B, reduce its inventory on the books, and release its management responsibility for this batch of materials.

[0045] The above process realizes the clear transfer of rights and responsibilities of physical materials in the virtual system through a set of data tracking and state management mechanism, effectively solving the technical problem of "social idle capacity not daring to stock up".

[0046] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A socialized capacity scheduling system based on real-time inventory matching priority, characterized in that: include: The user terminal is used to receive order requests and display scheduling results; The intelligent scheduling server is communicatively connected to the user terminal and is used to process order requests and execute scheduling algorithms; The database, connected to the intelligent scheduling server, is used to store producer information, equipment status, inventory data, and historical order data; The intelligent scheduling server includes: The real-time status acquisition module is used to acquire the device status of distributed production nodes in real time through API interface or active reporting by producers. The device status includes order acceptance, rent in progress, resting, inventory locked, completed, and shipment in progress. The priority calculation module is used to dynamically calculate the priority score of each candidate producer based on multi-dimensional weighting factors, including credit weight, efficiency weight, geographical weight, and inventory weight. The order matching module is used to automatically dispatch orders to the optimal producer based on the priority score; The distributed production nodes include local core factory nodes, local distributed edge nodes, regional collaborative nodes, and source supply nodes. The inventory data includes shared factory inventory, idle advertising and graphic physical store inventory, nearby city factory inventory, and country of origin inventory; The priority calculation module uses a weighted scoring algorithm to calculate the priority score S, and its function formula is as follows: ； In the above formula, a, b, c, and d are all adjustable parameters, and the values ​​of a, b, c, and d range from 0 to 1, and a+b+c+d=1; Wc represents credit weight; We represents efficiency weight; Wg represents geographical weight; Ws represents inventory weight. Furthermore, the priority calculation module is used to dynamically calculate the priority score of each candidate producer based on multi-dimensional weighting factors, wherein the inventory weight is configured as follows: When a producer has raw material inventory to match orders, its value is much higher than other weighting factors.

2. The socialized capacity scheduling system based on real-time inventory matching priority as described in claim 1, characterized in that: The credit weight is calculated based on the producer's historical order completion rate, quality score, and customer complaint rate; the efficiency weight is calculated based on the producer's historical average order production time and on-time rate; and the geographical weight is calculated based on the geographical distance between the producer and the order demand location. The inventory weight is calculated based on whether the producer has raw material inventory that matches the order. If there is matching inventory, the inventory weight will receive the preset maximum score.

3. The socialized capacity scheduling system based on real-time inventory matching priority as described in claim 1, characterized in that: The real-time status acquisition module periodically polls or updates the device status of production nodes through event triggering to ensure the real-time nature and accuracy of status data.

4. The socialized capacity scheduling system based on real-time inventory matching priority as described in claim 1, characterized in that: The system also includes an inventory management module, which is used to establish a unique identifier for each batch of materials and record their source of purchase. When a producer applies for a return, the system verifies the material identifier and status, updates the shared factory inventory data, and releases the producer from inventory occupation.

5. The socialized capacity scheduling system based on real-time inventory matching priority as described in claim 1, characterized in that: The adjustable parameters a, b, c, and d are dynamically adjusted through machine learning algorithms to optimize scheduling accuracy and efficiency.

6. The socialized capacity scheduling system based on real-time inventory matching priority as described in claim 1, characterized in that: After dispatching an order, the order matching module records the matching data to update the producer's historical order information and feeds it back to the priority calculation module to optimize the weighting factor.

7. The socialized capacity scheduling system based on real-time inventory matching priority as described in claim 1, characterized in that: The device status and inventory data of the distributed production nodes are synchronized in real time through IoT devices or manual input by the producers.

8. A socialized capacity scheduling method based on real-time inventory matching priority, characterized in that, The socialized capacity scheduling method based on real-time inventory matching priority, applicable to any one of claims 1-7, includes the following steps: S1. Receive an order request through a user terminal, the order request including product type, quantity, delivery time, and required location; S2. The intelligent scheduling server parses the order request and determines the required equipment type and raw materials; S3. The real-time status acquisition module retrieves the equipment status and inventory data of the distributed production nodes from the database and filters candidate producers whose status is available to accept orders. S4. The priority calculation module calculates the priority score of each candidate producer based on multi-dimensional weighting factors, including credit weight, efficiency weight, geographical weight, and inventory weight. Furthermore, the priority calculation module is used to dynamically calculate the priority score of each candidate producer based on multi-dimensional weighting factors, wherein the inventory weight is configured as follows: When a producer has raw material inventory that matches an order, its value is much higher than other weighting factors, so as to achieve priority scheduling of inventory matching; S5. The order matching module dispatches orders to the producer with the highest priority score based on priority score. S6. After an order is completed, the system updates the producer's historical order data and inventory data.

9. The socialized capacity scheduling method based on real-time inventory matching priority as described in claim 8, characterized in that: In step S4, the calculation of the inventory weight takes precedence over other weights. If the candidate producer has raw material inventory that matches the order, the inventory weight is set to the maximum value; otherwise, it is set to zero.

10. The socialized capacity scheduling method based on real-time inventory matching priority as described in claim 8, characterized in that: It also includes using machine learning algorithms to analyze order completion data and dynamically adjust adjustable parameters a, b, c, and d to optimize subsequent scheduling decisions.

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