Material classification optimization system and method based on big data model
Through the material classification optimization system based on the big data model, the high error tolerance and low efficiency problems caused by manual classification are solved, the accuracy and timeliness of material classification are achieved, and the efficiency of material management is improved.
Patent Information
- Application Number
- CN202411339566.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2024-12-31
AI Technical Summary
Existing material classifications are collected, analyzed and classified through manual data, resulting in high fault tolerance, affecting efficiency and accuracy.
The material classification optimization system based on the big data model is adopted, including material data collection and sorting module, data extraction module, data model module and classification optimization module, and automated classification and optimization module are realized through data integration, feature selection, model training and optimization strategies.
It improves the accuracy and timeliness of material classification, reduces manual intervention and error rates, updates classification results in real time, reduces inventory costs, and improves material management efficiency.
Smart Images
Figure CN119228051A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of material classification optimization systems, and in particular to a material classification optimization system and method based on a big data model. Background Art
[0002] The purpose of material classification is to classify and categorize similar or related materials in order to better manage and utilize these materials. Through reasonable material classification, the management efficiency of the warehouse can be improved, and quick material retrieval and inventory management can be facilitated. At the same time, it also has important guiding significance for procurement and production planning, and can accurately predict the quantity of required materials and procurement plans.
[0003] The existing material classification requires manual data collection, analysis and classification, which results in a high error tolerance rate in the classification work, affects the efficiency of material classification, and affects the accuracy and timeliness of the materials. Summary of the invention
[0004] To this end, the present invention provides a material classification optimization system and method based on a big data model to solve the problem that material classification requires manual data collection, analysis and classification, which results in a high fault tolerance rate in the classification work, affects the efficiency of material classification work, and affects the accuracy and timeliness of the materials.
[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a material classification optimization system and method based on a big data model, comprising a material data collection and collation module, a material data extraction module is provided at the connection end of the material data collection and collation module, a material data model module is provided at the connection end of the material data extraction module, a material classification optimization module is provided at the connection end of the material data model module, a material data output module is provided at the connection end of the material classification optimization module, the material data collection and collation module is data-connected with the material data extraction module, the material data extraction module is data-connected with the material data model module, the material data model module is data-connected with the material classification optimization module, and the material classification optimization module is data-connected with the material data output module.
[0006] Preferably, the material data extraction module includes a material incoming management module, a material outgoing management module, a material transfer management module and a material inventory counting and statistics module; the material incoming management module includes an incoming receiving module, an incoming detection module and an incoming shelving module; the material outgoing management module includes an outgoing order processing module, an outgoing operation module and a shipping and delivery module; the material transfer management module includes a transfer operation module and a transfer operation module; the material inventory counting and statistics module includes an inventory counting module and an inventory data statistical analysis module.
[0007] Preferably, the material classification optimization module includes a material level classification module, a material classification establishment module, a material classification modification and adjustment module and a material review and confirmation module.
[0008] Preferably, the material data extraction module is respectively connected with the material warehousing management module, the material outbound management module, the material transfer management module and the material inventory statistics module; the material warehousing management module is respectively connected with the warehousing receiving module, the warehousing detection module and the warehousing shelving module; the material outbound management module is respectively connected with the outbound order processing module, the outbound operation module and the shipping and delivery module; the material transfer management module is respectively connected with the transfer operation module and the transfer operation module; the material inventory statistics module is respectively connected with the inventory counting module and the inventory data statistical analysis module.
[0009] Preferably, the material classification optimization module is respectively data-connected with the material level classification module, the material classification establishment module, the material classification modification and adjustment module and the material review and confirmation module.
[0010] Preferably, the material warehousing management module includes a conveying frame, a plurality of sliding rods are fixedly connected inside the conveying frame, support plates are fixedly provided on both sides of the bottom of the conveying frame, a top frame is fixedly provided on the top of the conveying frame, a camera is fixedly provided on the bottom of the top frame, a pushing mechanism is provided at the bottom of the conveying frame, a material dividing mechanism is provided on one side of the conveying frame, and the pushing mechanism includes a screw rod and a limit rod, both ends of the screw rod are connected to the connection points of the two support plates through bearings, and both ends of the limit rod are fixedly connected to the two support plates, a first motor is fixedly provided on one side of one of the support plates, and an output end of the first motor is fixedly connected to the screw rod, A threaded block is provided on the outside of the screw rod through a threaded sleeve, a pushing plate is fixedly provided on the top of the threaded block, a limiting plate is fixedly provided on the top of the pushing plate, a first tooth plate is provided on the top of the limiting plate, an electric push rod is fixedly provided on the top of the pushing plate, the output end of the electric push rod is fixedly connected to the first tooth plate, a toggle assembly is provided on the top of the pushing plate, and the toggle assembly includes two fixed plates, the two fixed plates are fixedly connected to the pushing plate, a rotating rod is connected between the two fixed plates via a bearing, a toggle plate is fixedly provided on the outside of the rotating rod, one end of the rotating rod is fixedly connected to the first gear, and the first gear is meshed with the first tooth plate.
[0011] Preferably, the material dividing mechanism includes a side groove, which is opened on one side of the conveying frame, and a threaded rod is connected to the inside of the side groove through a bearing, and a second motor is fixedly provided on one side of the conveying frame, and the output end of the second motor is fixedly connected to the threaded rod, and a slider is provided on the outside of the threaded rod through a threaded sleeve, and the slider is slidably connected in the side groove, and an empty groove is opened on one side of the slider, and a second tooth plate is provided in the empty groove, and the second tooth plate slides in the empty groove, and a third motor is fixedly provided on one side of the slider, and a transmission rod is fixedly connected to the output end of the third motor, and the transmission rod is connected to the side wall of the empty groove through a bearing, and a second gear is fixedly provided on the outside of the transmission rod, and the second gear is meshed with the second tooth plate, and a material dividing plate is fixedly provided on one side of the second tooth plate.
[0012] Preferably, a plurality of conveyor belts are provided on one side of the material data collection and sorting module.
[0013] A material classification optimization method based on a big data model, the specific steps are as follows:
[0014] S1. Collect basic material attribute information and material inventory information through the material data collection and sorting module, extract, convert and load data through data integration technology, realize data integration and unified storage, clean the integrated data and pre-process the data, including data standardization and normalization, so as to eliminate the dimensional differences between different data features and improve the accuracy of subsequent analysis;
[0015] S2. Through the material data extraction module, the data is deeply analyzed to extract the attributes that can reflect the essential characteristics of the material and the classification requirements as features. Then, the feature selection technology is used to screen out the feature subset that has the greatest impact on the classification results, laying the foundation for the subsequent classification model construction;
[0016] S3, the material data model module builds a model suitable for material classification based on the extracted and selected features, uses the cleaned and preprocessed data to train the classification model, optimizes the performance of the model by continuously adjusting the parameters and structure of the model, and uses strategies such as ensemble learning to combine multiple models together to improve the accuracy and stability of classification;
[0017] S4. The material classification optimization module evaluates the classification results through confusion matrix, accuracy, recall rate, F value and other indicators. According to the evaluation results of the classification results, the corresponding optimization strategy is formulated. In combination with business scenarios and actual needs, a more targeted optimization plan is proposed. With the development of business and the accumulation of data, the classification model is continuously iterated and optimized to adapt to new needs and scenarios, and the inventory, procurement, production and other links of materials are optimized. The classification results are visualized to help users better understand the data distribution and classification.
[0018] S5. The classification results and optimization suggestions are output to users through the material data output module. Users can make decisions and adjustments based on this information to improve the efficiency and accuracy of material management.
[0019] The embodiments of the present invention have the following advantages:
[0020] 1. By collecting and analyzing massive amounts of data related to materials, including material attributes, usage frequency, inventory, procurement cycle, etc., efficient and accurate classification of materials can be achieved. This can not only improve the efficiency of material management and reduce inventory costs, but also display the classification results, so that users can intuitively understand the material classification situation, automatically complete tasks such as data collection, analysis and classification, reduce manual intervention and error rate, improve work efficiency, update and analyze material data in real time, dynamically adjust the classification results according to data changes, and ensure the accuracy and timeliness of classification;
[0021] 2. The material is pushed intermittently through the pushing mechanism, and the camera at the bottom of the top frame scans the material on the top of the slide bar. After scanning the label information of the material, the material is pushed to the conveyor belt by the toggle component, or the slider of the material dividing mechanism slides in the side groove and drives the second tooth plate and the dividing plate to move, so that the dividing plate pushes the material to move to one of the other two conveyor belts for material transportation and transfer, thereby improving the efficiency of material classification and transportation and facilitating material transfer. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.
[0023] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.
[0024] Figure 1 A schematic diagram of the overall system structure provided by the present invention;
[0025] Figure 2 A schematic diagram of the structure of the material data extraction module system provided by the present invention;
[0026] Figure 3A schematic diagram of the structure of the material classification optimization module system provided by the present invention;
[0027] Figure 4 A three-dimensional diagram of the conveyor belt provided by the present invention;
[0028] Figure 5 A three-dimensional diagram of the conveyor frame provided by the present invention;
[0029] Figure 6 A top view of the conveyor frame provided by the present invention;
[0030] Figure 7 A three-dimensional diagram of the pushing mechanism provided by the present invention;
[0031] Figure 8 A three-dimensional diagram of the push plate provided by the present invention;
[0032] Fig. 9 The present invention provides Figure 5 A magnified view of the structure of the middle part A;
[0033] Fig.10 The present invention provides Figure 6 Enlarged view of the structure of part B in the middle.
[0034] In the figure: 1. Material data collection and sorting module; 2. Material data extraction module; 3. Material data model module; 4. Material classification optimization module; 5. Material data output module; 6. Material warehousing management module; 7. Material outbound management module; 8. Material transfer management module; 9. Material inventory statistics module; 10. Inbound receiving module; 11. Inbound detection module; 12. Inbound shelving module; 13. Outbound order processing module; 14. Outbound operation module; 15. Shipping and distribution module; 16. Transfer operation module; 17. Transfer operation module; 18. Inventory counting module; 19. Inventory data statistical analysis module; 20. Material level classification module; 21. Material classification Establish module; 22. Material classification modification and adjustment module; 23. Material review and confirmation module; 24. Conveyor rack; 25. Conveyor belt; 26. Slide bar; 27. Top rack; 28. Camera; 29. Support plate; 30. First motor; 31. Screw; 32. Limit rod; 33. Threaded block; 34. Push plate; 35. First tooth plate; 36. Electric push rod; 37. Toggle plate; 38. Dividing plate; 39. Fixed plate; 40. Rotating rod; 41. First gear; 42. Limit plate; 43. Side groove; 44. Threaded rod; 45. Second motor; 46. Slider; 47. Third motor; 48. Second tooth plate; 49. Empty groove; 50. Transmission rod; 51. Second gear. DETAILED DESCRIPTION
[0035] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0036] See attached Figure 1 -Attached Fig.10 The present invention provides a material classification optimization system based on a big data model, comprising a material data collection and sorting module 1, a material data extraction module 2 is provided at a connection end of the material data collection and sorting module 1, a material data model module 3 is provided at a connection end of the material data extraction module 2, a material classification optimization module 4 is provided at a connection end of the material data model module 3, a material data output module 5 is provided at a connection end of the material classification optimization module 4, the material data collection and sorting module 1 is data-connected with the material data extraction module 2, the material data extraction module 2 is data-connected with the material data model module 3, the material data model module 3 is data-connected with the material classification optimization module 4, and the material classification optimization module 4 is data-connected with the material data output module 5;
[0037] In this implementation scheme, the material data collection and sorting module 1 collects basic attribute information of materials such as name, specification, material, etc., material usage such as usage amount, usage frequency, usage scenario, etc., and material inventory information such as inventory quantity, inventory turnover rate, etc., and extracts, converts and loads data through data integration technology to achieve data integration and unified storage. The integrated data is cleaned and preprocessed, including data standardization and normalization, to eliminate the dimensional differences between different data features and improve the accuracy of subsequent analysis. The material data extraction module 2 conducts in-depth analysis of the data and extracts attributes that can reflect the essential characteristics and classification requirements of the material as features. Then, feature selection techniques such as statistical methods and machine learning methods are used to screen out the feature subsets that have the greatest impact on the classification results, laying the foundation for the subsequent classification model construction. The material data model module 3 constructs a model suitable for material classification based on the extracted and selected features. Common classification models include decision trees, naive Bayes, support vector machines, neural networks, etc. When selecting a model, it is necessary to comprehensively consider the characteristics of the data. The classification model is trained using cleaned and preprocessed data based on factors such as the model's properties, model complexity, and classification performance. The model's performance is optimized by continuously adjusting the model's parameters and structure. Multiple models are combined using strategies such as ensemble learning to improve the accuracy and stability of classification. The material classification optimization module 4 evaluates the classification results through indicators such as confusion matrix, accuracy, recall, and F1 value. The indicators fully reflect the performance of the classification model. According to the evaluation results of the classification results, corresponding optimization strategies are formulated. In combination with business scenarios and actual needs, more targeted optimization solutions are proposed. With the development of the business and the accumulation of data, the classification model is continuously iterated and optimized to adapt to new needs and scenarios. The inventory, procurement, production, and other links of the material are optimized, such as adjusting inventory strategies and optimizing production plans. At the same time, the classification results are visualized to help users better understand the data distribution and classification. The classification results and optimization suggestions are output to users through the material data output module 5. Users can make decisions and adjustments based on this information to improve the efficiency and accuracy of material management.
[0038] Among them, in order to achieve the purpose of material data extraction, the present device adopts the following technical solutions: the material data extraction module 2 includes a material warehousing management module 6, a material outbound management module 7, a material transfer management module 8 and a material inventory statistics module 9, the material warehousing management module 6 includes an inbound receiving module 10, an inbound detection module 11 and an inbound shelving module 12, the material outbound management module 7 includes an outbound order processing module 13, an outbound operation module 14 and a shipping and distribution module 15, the material transfer management module 8 includes a transfer operation module 16 and a transfer operation module 17, the material inventory statistics module 9 includes an inventory counting module 18 and an inventory data statistical analysis module 19, the material data extraction Module 2 is respectively connected with material storage management module 6, material storage management module 7, material transfer management module 8 and material inventory statistics module 9. The material storage management module 6 is respectively connected with storage receiving module 10, storage detection module 11 and storage shelf module 12. The material storage management module 7 is respectively connected with storage order processing module 13, storage operation module 14 and delivery and distribution module 15. The material transfer management module 8 is respectively connected with storage transfer operation module 16 and transfer operation module 17. The material inventory statistics module 9 is respectively connected with inventory counting module 18 and inventory data statistical analysis module 19. The material storage management module 6 manages the storage of materials. The receiving module 10 inspects and accepts the materials and checks whether the quantity and quality of the materials meet the requirements. The incoming inspection module 11 performs quality inspection on the materials to ensure that the materials meet the quality standards and avoid unqualified materials from entering the warehouse. The incoming shelving module 12 selects the appropriate storage location and shelving method according to the material characteristics and storage requirements to ensure the safety and traceability of material storage. The material outbound management module 7 manages the outbound delivery of materials. The outbound order processing module 13 receives and processes the customer's order information, including order confirmation, material preparation, etc. The outbound operation module 14 takes out the corresponding materials from the warehouse according to the order requirements and confirms the outbound delivery. The shipping and distribution module 15 packs and labels the outbound materials and selects the appropriate The material transfer management module 8 manages the material transfer and allocation. The transfer operation module 16 moves materials across warehouses or regions according to inventory conditions and business needs to ensure the rationality and efficiency of material distribution. The allocation operation module 17 proposes material allocation applications according to production or sales needs, and performs allocation operations after approval to ensure the timeliness and accuracy of material supply. The material inventory statistics module 9 manages material inventory. The inventory inventory module 18 regularly counts warehouse materials to ensure the accuracy and consistency of material quantities. The inventory data statistical analysis module 19 analyzes and evaluates the inventory status based on the inventory data to provide decision support for inventory management and material distribution.
[0039] Among them, in order to achieve the purpose of classification optimization, the present device is implemented by the following technical scheme: the material classification optimization module 4 includes a material level classification module 20, a material classification establishment module 21, a material classification modification and adjustment module 22 and a material review and confirmation module 23, and the material classification optimization module 4 is respectively connected with the material level classification module 20, the material classification establishment module 21, the material classification modification and adjustment module 22 and the material review and confirmation module 23. The material level classification module 20 can classify materials according to their functions, uses, materials and other characteristics. The material classification establishment module 21 sets the relevant material classification, adds new materials to the corresponding classification according to the characteristics and classification methods of the materials, and sets the attributes and labels of the materials. The material classification modification and adjustment module 22 can adjust and modify the classification of materials. When adjusting and modifying, it is necessary to classify and adjust the existing materials, and update the relevant systems and processes. The material review and confirmation module 23 reviews the classification of existing materials and confirms the classification of new materials.
[0040] Among them, in order to achieve the purpose of pushing, the present device is implemented by the following technical solutions: the material warehousing management module 6 includes a conveying frame 24, a plurality of sliding rods 26 are fixedly connected inside the conveying frame 24, support plates 29 are fixedly provided on both sides of the bottom of the conveying frame 24, a top frame 27 is fixedly provided on the top of the conveying frame 24, a camera 28 is fixedly provided at the bottom of the top frame 27, a pushing mechanism is provided at the bottom of the conveying frame 24, a material dividing mechanism is provided on one side of the conveying frame 24, and the pushing mechanism includes a screw rod 31 and a limit rod 32, both ends of the screw rod 31 are connected to the connection points of the two support plates 29 through bearings, and both ends of the limit rod 32 are fixedly connected to the two support plates 29, and one side of one of the support plates 29 is fixedly provided with a first motor 30, the output end of the first motor 30 is fixedly connected to the screw rod 31, the screw rod 31 is provided with a threaded block 33 on the outside through a threaded sleeve, a push plate 34 is fixedly provided on the top of the threaded block 33, a limit plate 42 is fixedly provided on the top of the push plate 34, a first tooth plate 35 is provided on the top of the limit plate 42, an electric push rod 36 is fixedly provided on the top of the push plate 34, the output end of the electric push rod 36 is fixedly connected to the first tooth plate 35, a toggle assembly is provided on the top of the push plate 34, and the toggle assembly includes two fixed plates 39, the two fixed plates 39 are fixedly connected to the push plate 34, a rotating rod 40 is connected between the two fixed plates 39 through a bearing, a toggle plate 37 is fixedly provided on the outside of the rotating rod 40, one end of the rotating rod 40 is fixed A first gear 41 is fixedly connected, the first gear 41 is meshed with the first toothed plate 35, the first motor 30 is started, the first motor 30 controls the screw rod 31 to rotate, the screw rod 31 drives the threaded block 33 to move, the threaded block 33 drives the pushing plate 34 to move, the pushing plate 34 drives the toggle assembly to move, so that the toggle plate 37 of the toggle assembly pushes the material on the top of the slide bar 26, and stops pushing after each pushing for a certain distance, the camera 28 at the bottom of the top frame 27 scans the material on the top of the slide bar 26, and after scanning the label information of the material, the toggle plate 37 of the toggle assembly is controlled to move again through the pushing mechanism, and after the toggle plate 37 pushes the material to one side of the material distribution mechanism, the material is pushed to different conveyor belts 25 according to the material information, and the electric push rod is started 36, the electric push rod 36 controls the movement of the first tooth plate 35, the first tooth plate 35 drives the first gear 41 to rotate, the first gear 41 drives the rotating rod 40 to rotate, the rotating rod 40 drives the toggle plate 37 to rotate, so that the toggle plate 37 rotates and pushes the material to fall onto the conveyor belt 25 on one side. At this time, the toggle plate 37 is kept hidden under the conveyor frame 24, and the first motor 30 controls the screw rod 31 to rotate, the screw rod 31 drives the threaded block 33 to move, the threaded block 33 drives the push plate 34 to move, and the push plate 34 drives the toggle assembly to move, so that the toggle plate 37 moves to the other side of the material still on the slide bar 26, and the first tooth plate 35 is controlled to move by starting the electric push rod 36, the first tooth plate 35 drives the first gear 41 to rotate, and the first gear 41 drives the rotating rod 40 to rotate,The rotating rod 40 drives the toggle plate 37 to rotate, so that the toggle plate 37 rotates again to be higher than the slide bar 26, and the material is pushed continuously;
[0041] Among them, in order to achieve the purpose of classification, the present device is implemented by the following technical solutions: the material dividing mechanism includes a side groove 43, the side groove 43 is opened on one side of the conveying frame 24, a threaded rod 44 is connected to the inside of the side groove 43 through a bearing, a second motor 45 is fixedly provided on one side of the conveying frame 24, the output end of the second motor 45 is fixedly connected to the threaded rod 44, a slider 46 is provided on the outside of the threaded rod 44 through a threaded sleeve, the slider 46 is slidably connected inside the side groove 43, an empty groove 49 is opened on one side of the slider 46, a second tooth plate 48 is provided inside the empty groove 49, the second tooth plate 48 slides inside the empty groove 49, a third motor 47 is fixedly provided on one side of the slider 46, the output end of the third motor 47 is fixedly connected to a transmission rod 50, the transmission The rod 50 is connected to the side wall of the empty slot 49 through a bearing, and a second gear 51 is fixedly sleeved on the outside of the transmission rod 50, and the second gear 51 is meshed with the second tooth plate 48, and a dividing plate 38 is fixedly provided on one side of the second tooth plate 48. By starting the second motor 45, the second motor 45 controls the threaded rod 44 to rotate, and the threaded rod 44 drives the slider 46 to move, and the third motor 47 is started, and the third motor 47 controls the transmission rod 50 to rotate, and the transmission rod 50 drives the second gear 51 to rotate, and the second gear 51 drives the second tooth plate 48 to rise and fall, and the second tooth plate 48 drives the dividing plate 38 to rise and fall, and the slider 46 slides in the side groove 43 and drives the second tooth plate 48 and the dividing plate 38 to move, so that the dividing plate 38 pushes the material to move to one of the other two conveyor belts 25 for material transportation and transfer;
[0042] In order to achieve the purpose of classified transportation, the device is implemented by the following technical solution: a plurality of conveyor belts 25 are provided on one side of the material data collection and sorting module 1, and different conveyor belts 25 can transport different materials.
[0043] A material classification optimization method based on a big data model, the specific steps are as follows:
[0044] S1. Collect basic property information and material inventory information of materials through material data collection and sorting module 1, extract, convert and load data through data integration technology to achieve data integration and unified storage, clean the integrated data and pre-process the data, including data standardization and normalization, so as to eliminate the dimensional differences between different data features and improve the accuracy of subsequent analysis;
[0045] S2. Through the material data extraction module 2, the data is deeply analyzed to extract the attributes that can reflect the essential characteristics of the material and the classification requirements as features. Then, the feature selection technology is used to screen out the feature subset that has the greatest impact on the classification results, laying the foundation for the subsequent classification model construction;
[0046] S3, material data model module 3 builds a model suitable for material classification based on the extracted and selected features, uses the cleaned and preprocessed data to train the classification model, optimizes the performance of the model by continuously adjusting the parameters and structure of the model, and uses strategies such as ensemble learning to combine multiple models together to improve the accuracy and stability of classification;
[0047] S4, material classification optimization module 4 evaluates the classification results through confusion matrix, accuracy, recall rate, F1 value and other indicators, formulates corresponding optimization strategies based on the evaluation results of the classification results, and proposes more targeted optimization solutions in combination with business scenarios and actual needs. With the development of business and the accumulation of data, the classification model is continuously iterated and optimized to adapt to new needs and scenarios, and the inventory, procurement, production and other links of materials are optimized. The classification results are visualized to help users better understand the data distribution and classification;
[0048] S5. The classification results and optimization suggestions are output to the user through the material data output module 5. The user can make decisions and adjustments based on this information to improve the efficiency and accuracy of material management.
[0049] The use process of the present invention is as follows: when using the present invention, the first motor 30 is started, the first motor 30 controls the screw rod 31 to rotate, the screw rod 31 drives the threaded block 33 to move, the threaded block 33 drives the pushing plate 34 to move, and the pushing plate 34 drives the toggle assembly to move, so that the toggle plate 37 of the toggle assembly pushes the material on the top of the slide rod 26, and after each pushing distance, the pushing is stopped, and the camera 28 at the bottom of the top frame 27 scans the material on the top of the slide rod 26. After scanning the label information of the material, the toggle assembly is controlled again through the pushing mechanism. The toggle plate 37 of the component moves, and after the toggle plate 37 pushes the material to move to one side of the material distribution mechanism, the material is pushed to different conveyor belts 25 according to the material information, and the electric push rod 36 is started, and the electric push rod 36 controls the first tooth plate 35 to move, and the first tooth plate 35 drives the first gear 41 to rotate, and the first gear 41 drives the rotating rod 40 to rotate, and the rotating rod 40 drives the toggle plate 37 to rotate, so that the toggle plate 37 rotates and pushes the material to fall onto the conveyor belt 25 on one side. At this time, the toggle plate 37 is kept hidden under the conveyor frame 24, and the first electric push rod 36 is used to control the movement of the first tooth plate 35 ... The machine 30 controls the screw rod 31 to rotate, the screw rod 31 drives the threaded block 33 to move, the threaded block 33 drives the pushing plate 34 to move, the pushing plate 34 drives the toggle assembly to move, so that the toggle plate 37 moves to the other side of the material still on the slide bar 26, and the electric push rod 36 is started to control the first tooth plate 35 to move, the first tooth plate 35 drives the first gear 41 to rotate, the first gear 41 drives the rotating rod 40 to rotate, the rotating rod 40 drives the toggle plate 37 to rotate, so that the toggle plate 37 rotates again to be higher than the slide bar 26, and the material is pushed continuously, through Start the second motor 45, the second motor 45 controls the threaded rod 44 to rotate, the threaded rod 44 drives the slider 46 to move, start the third motor 47, the third motor 47 controls the transmission rod 50 to rotate, the transmission rod 50 drives the second gear 51 to rotate, the second gear 51 drives the second tooth plate 48 to rise and fall, the second tooth plate 48 drives the dividing plate 38 to rise and fall, the slider 46 slides in the side groove 43 and drives the second tooth plate 48 and the dividing plate 38 to move, so that the dividing plate 38 pushes the material to move to one of the other two conveyor belts 25 for material transportation and transfer.
[0050] The above is only a preferred embodiment of the present invention. Any person skilled in the art may modify the present invention by using the above technical solution or modify it into an equivalent technical solution. Therefore, any simple modification or equivalent replacement made according to the technical solution of the present invention shall fall within the scope of protection claimed by the present invention.
Claims
1. A material classification optimization system based on a big data model, comprising a material data collection and sorting module (1), characterized in that: The material data collection and sorting module (1) is provided with a material data extraction module (2) at a connection end, the material data extraction module (2) is provided with a material data model module (3) at a connection end, the material data model module (3) is provided with a material classification optimization module (4) at a connection end, the material classification optimization module (4) is provided with a material data output module (5) at a connection end, the material data collection and sorting module (1) is data-connected with the material data extraction module (2), the material data extraction module (2) is data-connected with the material data model module (3), the material data model module (3) is data-connected with the material classification optimization module (4), and the material classification optimization module (4) is data-connected with the material data output module (5).
2. The material classification optimization system based on the big data model according to claim 1 is characterized by: The material data extraction module (2) comprises a material warehousing management module (6), a material outbound management module (7), a material transfer management module (8) and a material inventory counting module (9); the material warehousing management module (6) comprises an inbound receiving module (10), an inbound detection module (11) and an inbound shelf module (12); the material outbound management module (7) comprises an outbound order processing module (13), an outbound operation module (14) and a shipping and delivery module (15); the material transfer management module (8) comprises a transfer operation module (16) and a transfer operation module (17); and the material inventory counting module (9) comprises an inventory counting module (18) and an inventory data statistical analysis module (19).
3. The material classification optimization system based on big data model according to claim 1 is characterized by: The material classification optimization module (4) comprises a material level classification module (20), a material classification establishment module (21), a material classification modification and adjustment module (22) and a material review and confirmation module (23).
4. The material classification optimization system based on big data model according to claim 2 is characterized by: The material data extraction module (2) is respectively connected to the material warehousing management module (6), the material outbound management module (7), the material transfer management module (8) and the material inventory statistics module (9); the material warehousing management module (6) is respectively connected to the warehousing receiving module (10), the warehousing detection module (11) and the warehousing shelving module (12); the material outbound management module (7) is respectively connected to the outbound order processing module (13), the outbound operation module (14) and the shipping and delivery module (15); the material transfer management module (8) is respectively connected to the transfer operation module (16) and the transfer operation module (17); the material inventory statistics module (9) is respectively connected to the inventory counting module (18) and the inventory data statistical analysis module (19).
5. The material classification optimization system based on big data model according to claim 3 is characterized by: The material classification optimization module (4) is respectively data-connected with the material level classification module (20), the material classification establishment module (21), the material classification modification and adjustment module (22) and the material review and confirmation module (23).
6. The material classification optimization system based on big data model according to claim 2 is characterized by: The material storage management module (6) comprises a conveying frame (24), wherein a plurality of sliding rods (26) are fixedly connected inside the conveying frame (24), support plates (29) are fixedly provided on both sides of the bottom of the conveying frame (24), a top frame (27) is fixedly provided on the top of the conveying frame (24), a camera (28) is fixedly provided on the bottom of the top frame (27), a pushing mechanism is provided on the bottom of the conveying frame (24), a material dividing mechanism is provided on one side of the conveying frame (24), the pushing mechanism comprises a screw rod (31) and a limit rod (32), the two ends of the screw rod (31) are connected to the connection points of the two support plates (29) through bearings, the two ends of the limit rod (32) are fixedly connected to the two support plates (29), a first motor (30) is fixedly provided on one side of one of the support plates (29), the output end of the first motor (30) is fixedly connected to the screw rod (31), the screw rod (31) is fixedly connected to the output end of the first motor (30), and the screw rod (31) is fixedly connected to the output end of the first motor (30). ) is provided with a threaded block (33) on the outside through a threaded sleeve, a push plate (34) is fixedly provided on the top of the threaded block (33), a limit plate (42) is fixedly provided on the top of the push plate (34), a first tooth plate (35) is provided on the top of the limit plate (42), an electric push rod (36) is fixedly provided on the top of the push plate (34), an output end of the electric push rod (36) is fixedly connected to the first tooth plate (35), a toggle assembly is provided on the top of the push plate (34), the toggle assembly comprises two fixed plates (39), the two fixed plates (39) are fixedly connected to the push plate (34), a rotating rod (40) is connected between the two fixed plates (39) through a bearing, a toggle plate (37) is fixedly sleeved on the outside of the rotating rod (40), one end of the rotating rod (40) is fixedly connected to a first gear (41), and the first gear (41) is meshed with the first tooth plate (35).
7. The material classification optimization system based on big data model according to claim 6 is characterized by: The material distribution mechanism comprises a side groove (43), the side groove (43) is opened on one side of the conveying frame (24), a threaded rod (44) is connected inside the side groove (43) through a bearing, a second motor (45) is fixedly provided on one side of the conveying frame (24), an output end of the second motor (45) is fixedly connected to the threaded rod (44), a slider (46) is provided on the outside of the threaded rod (44) through a threaded sleeve, the slider (46) is slidably connected inside the side groove (43), an empty groove (49) is opened on one side of the slider (46), and the empty groove (49) A second tooth plate (48) is provided inside the slider (49), and the second tooth plate (48) slides inside the empty slot (49). A third motor (47) is fixedly provided on one side of the slider (46), and a transmission rod (50) is fixedly connected to the output end of the third motor (47). The transmission rod (50) is connected to the side wall of the empty slot (49) through a bearing. A second gear (51) is fixedly sleeved outside the transmission rod (50), and the second gear (51) is meshed with the second tooth plate (48). A material dividing plate (38) is fixedly provided on one side of the second tooth plate (48).
8. The material classification optimization system based on big data model according to claim 1 is characterized by: A plurality of conveyor belts (25) are provided on one side of the material data collection and sorting module (1).
9. A material classification optimization method based on a big data model, characterized in that: The specific steps are as follows: S1. Collecting basic property information and material inventory information of materials through the material data collection and sorting module (1), extracting, converting and loading data through data integration technology to achieve data integration and unified storage, cleaning the integrated data and preprocessing the data, including data standardization and normalization, so as to eliminate dimensional differences between different data features and improve the accuracy of subsequent analysis; S2. Analyze the data in depth through the material data extraction module (2) to extract the attributes that can reflect the essential characteristics of the material and the classification requirements as features. Then, use the feature selection technology to screen out the feature subset that has the greatest impact on the classification results, laying the foundation for the subsequent classification model construction; S3, material data model module (3) constructs a model suitable for material classification based on the extracted and selected features, uses the cleaned and preprocessed data to train the classification model, optimizes the performance of the model by continuously adjusting the parameters and structure of the model, and combines multiple models together using strategies such as ensemble learning to improve the accuracy and stability of classification; S4, Material Classification Optimization Module (4) evaluates the classification results through confusion matrix, accuracy, recall rate, F1 value and other indicators, formulates corresponding optimization strategies based on the evaluation results of the classification results, and proposes more targeted optimization solutions in combination with business scenarios and actual needs. With the development of business and the accumulation of data, the classification model is continuously iterated and optimized to adapt to new needs and scenarios, and the inventory, procurement, production and other links of materials are optimized. The classification results are visualized to help users better understand the data distribution and classification situation; S5. The classification results and optimization suggestions are output to the user through the material data output module (5). The user can make decisions and adjustments based on this information to improve the efficiency and accuracy of material management.