Intelligent material tracking management system and method, electronic equipment and storage medium

Abstract

The invention discloses an intelligent material tracking management system and method, electronic equipment and a storage medium. The system comprises a data acquisition module, a path tracking module and a warehousing detection module. The data acquisition module is used for acquiring carrying data of a vehicle carrying materials from a supplier origin and acquiring equipment data of the carried materials; the path tracking module is used for tracking a carrying path according to vehicle positioning in the vehicle carrying process and judging whether the carrying path and the carrying time of the vehicle are reasonable or not according to the collected carrying data when the vehicle arrives at the destination; and the warehousing detection module is used for acquiring equipment images in real time after the materials are warehoused, and judging whether the equipment is a supplier origin production product or not by comparing the equipment images acquired in real time with the equipment data. According to the invention, the problems of non-transparent information and weak quality control in the material transportation process are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics management, and in particular relates to an intelligent material tracking management system, method, electronic equipment and storage medium. Background Art

[0002] In the existing material transportation and inventory management system, most of the existing power systems still rely on traditional manual recording and management methods to track and manage materials. Traditional methods usually manually record data such as transportation routes, transportation time and vehicle information, lacking real-time data collection and dynamic monitoring. This method has many problems, mainly reflected in the following aspects:

[0003] 1. The transportation route and time cannot be monitored in real time: Traditional transportation management systems make it difficult to monitor the transportation vehicles in real time. Usually, the route information during transportation depends on the driver's report or regular manual inspection, and there is a lack of effective GPS positioning system for real-time tracking. This makes it difficult to detect problems such as deviation from the planned route and improper route selection in a timely manner, and it is impossible to effectively verify the rationality of the transportation process.

[0004] 2. Difficulty in verifying the authenticity of materials after they enter the warehouse: For materials entering the warehouse, especially those from different suppliers, traditional systems can only rely on paper documents or manual confirmation to verify the source and authenticity of the materials. The batch, production date, supplier code and other information of the materials may be missed or wrong if they are only checked manually, which may easily lead to counterfeit and inferior products entering the warehouse, affecting the accuracy and security of inventory management.

[0005] 3. Lack of accurate data collection and equipment identification: Traditional material tracking management systems often rely on manual entry or simple barcode scanning to collect data, and are unable to comprehensively collect and compare various types of material data (such as production batch numbers, product specifications, quality inspection numbers, etc.) in real time and accurately. This method is not only prone to errors, but also cannot efficiently cope with the management needs of large-scale logistics and multi-category materials.

[0006] 4. Route deviation and transportation time cannot be effectively supervised: Existing transportation management systems often cannot provide comprehensive vehicle location monitoring and route optimization suggestions. Once a deviation from the scheduled route or overtime occurs during transportation, it is difficult for managers to respond and adjust in the first time, resulting in increased transportation costs or cargo delays.

[0007] 5. Information islands and data integration difficulties: In the existing material tracking management, data from different modules often cannot be seamlessly connected and shared, resulting in the existence of information islands. It is difficult to effectively integrate information in data collection, path tracking, warehousing inspection and other links, and it is difficult to form a complete material circulation and management chain, which increases the complexity of operations and management difficulty.

[0008] The Chinese patent with publication number CN103593751A discloses an intelligent logistics management system based on RFID, which is used to manage logistics information of goods equipped with RFID tags, including a logistics control system, a storage system located in a warehouse, and a vehicle-mounted mobile module. The logistics control system connects the storage system and the vehicle-mounted mobile module through a communication network to realize mutual communication between the logistics control system and the storage system or the vehicle-mounted mobile module; the storage system and the vehicle-mounted mobile module are both equipped with RFID readers, and the RFID readers read the information of the RFID tags and transmit the information to the logistics control system. The reading distance of the RFID tags described in this invention is relatively short, and is greatly affected by environmental factors (such as interference from metal objects and liquids). If the tag is placed in an improper position or is blocked, it may not be read accurately, thereby affecting the accuracy of data collection. Summary of the invention

[0009] The present invention provides an intelligent material tracking management system, method, electronic equipment and storage medium, aiming to solve the problems of opaque information and weak quality control in the material transportation process.

[0010] In order to solve the above technical problems, the present invention provides an intelligent material tracking and management system, including a data acquisition module, a path tracking module and a warehousing detection module.

[0011] The data collection module is used to collect transportation data for vehicles transporting materials from the supplier's origin, and to collect equipment data for the transported materials.

[0012] The path tracking module is used to track the transportation path according to the vehicle positioning during the vehicle transportation process. When the vehicle arrives at the destination, it determines whether the vehicle's transportation path and transportation time are reasonable based on the collected transportation data.

[0013] The warehousing detection module is used to collect equipment images in real time after materials enter the warehouse. By comparing the real-time collected equipment images with the equipment data, it is determined whether the equipment is a product produced by the supplier's origin.

[0014] Preferably, the transport data in the data acquisition module includes at least: basic vehicle information, scheduled longest transport route, scheduled transport route, vehicle transport limit, transport quantity, and transport start time; the equipment data includes at least: production batch number, equipment type, supplier code, quality inspection number, and production date.

[0015] Preferably, the path tracking module implements the transportation path tracking according to the vehicle positioning during the vehicle transportation process, and when the vehicle arrives at the destination, determines whether the transportation path and transportation time of the vehicle are reasonable according to the collected transportation data. Specifically:

[0016] Regularly obtain the vehicle's GPS positioning data, including longitude, latitude, and altitude information, compare the GPS positioning data with the scheduled transportation route, and monitor whether the transport vehicle deviates from the route.

[0017] When the vehicle arrives at the destination, the longest transportation time is calculated in combination with the transportation data to determine whether the vehicle transportation exceeds the longest transportation time and whether there is any violation. The calculation formula for the longest transportation time is as follows:

[0018]

[0019] Where, T max_i DEST is the longest transport time; i The vehicle arrives at the i-th destination. After the vehicle arrives at the i-th destination, DEST i The value is 1, otherwise DEST i Value 0; PA i is the longest scheduled transport route; v is the theoretical average speed of the vehicle; α is the transport time delay cost coefficient; β load is the load impact coefficient; γ is the multi-state delay cost coefficient; M is the total number of delay states, j∈[1,M]; FT j is the jth delay condition that occurs during the vehicle transportation process. It takes the value of 1 when the jth delay condition occurs during the vehicle transportation process, otherwise it takes the value of 0; T cost_j is the additional transportation time after the jth delay occurs; i∈[1,N], N is the total number of destinations that the vehicle needs to reach.

[0020] Preferably, the path tracking module monitors whether the transport vehicle deviates from the path specifically as follows:

[0021] Identify the supplier's place of origin and destination, and use the map to plan the scheduled transportation route. The scheduled transportation route includes the main traffic arteries, turning points and area restriction nodes along the way. Obtain GPS positioning data from the vehicle within the specified time interval, and calculate the distance between the vehicle position and each node of the scheduled transportation route based on the GPS positioning data. If the distance exceeds the allowable distance error range, the vehicle is considered to have deviated from the path.

[0022] Preferably, the warehousing detection module determines whether the equipment is a product produced at the supplier's place of origin by:

[0023] The edge image is obtained by performing edge detection on the device image collected in real time, and the image segmentation and morphological operation are performed on the obtained edge image based on threshold segmentation to obtain the ROI image.

[0024] The ROI image is searched for contours existing in the image, the minimum bounding rectangle is calculated for the found contours, and it is determined whether the length and width of the minimum bounding rectangle exceed the set length and width range, and the contours whose length and width of the minimum bounding rectangle exceed the set length and width range are filtered out.

[0025] The KNN algorithm is used to perform image recognition on the filtered contours to screen out the barcode contours that need to be identified. By comparing the identified barcode information with the device data, it is determined whether the device is a product produced by the supplier's origin.

[0026] Preferably, the storage detection module performs image segmentation and morphological operations on the edge image obtained based on threshold segmentation to obtain the ROI image specifically as follows:

[0027] The frequency of occurrence of each gray value in the edge image is counted to obtain a gray histogram, the total gray mean of the edge image is calculated, and for each gray value t∈[0,255], the edge image is divided into foreground and background.

[0028] For each gray value, the mean and inter-class variance of the background class and the foreground class are calculated, all gray values ​​are traversed, and the gray value that maximizes the inter-class variance is selected as the threshold. The obtained threshold is used to binarize the image to obtain a segmented image.

[0029] Morphological operations including dilation, erosion, opening and closing are selected for the segmented image to obtain the ROI image.

[0030] In another aspect, the present invention provides an intelligent material tracking and management method, comprising the following steps:

[0031] Transportation data is collected for vehicles that transport materials from the supplier's origin, and equipment data is collected for the transported materials.

[0032] During the transportation process, the transportation path is tracked according to the vehicle positioning. When the vehicle arrives at the destination, the reasonableness of the vehicle's transportation path and transportation time is determined based on the collected transportation data.

[0033] After the materials are put into storage, the equipment images are collected in real time. By comparing the real-time collected equipment images with the equipment data, it is determined whether the equipment is a product produced by the supplier's place of origin.

[0034] On the other hand, the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the intelligent material tracking and management method as described in any embodiment of the present invention is implemented.

[0035] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent material tracking and management method as described in any embodiment of the present invention.

[0036] Compared with the prior art, the present invention has the following technical effects:

[0037] 1. Full-process visualization and traceability: The transportation process of materials from the supplier's origin to the warehouse, as well as the equipment inspection after warehousing, can be monitored and recorded in real time. These data can be used for subsequent audits, analysis and optimization, improving the transparency and security of material management.

[0038] 2. Quality control and anti-counterfeiting capabilities: Through precise image processing technology, we ensure that the equipment and materials entering the warehouse meet the standards of the supplier's country of origin, reduce the risk of counterfeit and shoddy products, and prevent counterfeit and shoddy products from entering the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is an overall flow chart of the intelligent material tracking management method described in the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0041] Embodiment 1

[0042] This embodiment provides an intelligent material tracking and management system, including a data collection module, a path tracking module and a storage detection module.

[0043] The data collection module is used to collect transportation data for vehicles transporting materials from the supplier's origin, and to collect equipment data for the transported materials.

[0044] The path tracking module is used to track the transportation path according to the vehicle positioning during the vehicle transportation process. When the vehicle arrives at the destination, it determines whether the vehicle's transportation path and transportation time are reasonable based on the collected transportation data.

[0045] The warehousing detection module is used to collect equipment images in real time after materials enter the warehouse. By comparing the real-time collected equipment images with the equipment data, it is determined whether the equipment is a product produced by the supplier's origin.

[0046] As a preferred implementation of this embodiment, the transport data in the data acquisition module at least includes: basic vehicle information, the longest scheduled transport route, the scheduled transport route, the vehicle transport limit, the transport quantity, and the transport start time; the equipment data at least includes: production batch number, equipment type, supplier code, quality inspection number, and production date. The selection of equipment data can be adjusted according to the barcode data matched with the actual materials, and can also include: product specifications, customer code, certification identification code, etc.

[0047] As a preferred implementation of this embodiment, the path tracking module implements the transportation path tracking according to the vehicle positioning during the vehicle transportation process. When the vehicle arrives at the destination, it determines whether the vehicle transportation path and transportation time are reasonable according to the collected transportation data. Specifically:

[0048] Regularly obtain the vehicle's GPS positioning data, including longitude, latitude, and altitude information, compare the GPS positioning data with the scheduled transportation route, and monitor whether the transport vehicle deviates from the route.

[0049] When the vehicle arrives at the destination, the longest transportation time is calculated in combination with the transportation data to determine whether the vehicle transportation exceeds the longest transportation time and whether there is any violation. The calculation formula for the longest transportation time is as follows:

[0050]

[0051] Where, T max_i DEST is the longest transport time; i The vehicle arrives at the i-th destination. After the vehicle arrives at the i-th destination, DEST i The value is 1, otherwise DEST i Value 0; PA i is the longest scheduled transport route; v is the theoretical average speed of the vehicle; α is the transport time delay cost coefficient; β load is the load influence coefficient, which is obtained according to the ratio of the number of vehicles carried to the set number of vehicles, and takes a negative value when the number of vehicles carried exceeds the set range and affects the vehicle speed; γ is the multivariate delay cost coefficient; M is the total number of delay conditions, j∈[1,M]; FT j is the jth delay condition that occurs during the vehicle transportation process. It takes the value of 1 when the jth delay condition occurs during the vehicle transportation process, otherwise it takes the value of 0; T cost_jis the additional transportation time after the jth delay occurs; i∈[1,N], N is the total number of destinations that the vehicle needs to reach. The delay conditions may include traffic conditions, road conditions, vehicle factors, stops and inspections during transportation, and weather conditions. Among them, traffic conditions include traffic jams, traffic accidents, and traffic control; road conditions include road closures or repairs, potholes, sandy or muddy roads; vehicle factors include fuel supply and vehicle failure; stops and inspections during transportation include checkpoint inspections and regular rest stops; weather conditions include extreme weather and geological disasters.

[0052] As a preferred implementation of this embodiment, the path tracking module monitors whether the transport vehicle deviates from the path specifically as follows:

[0053] Identify the supplier's origin and destination, use the map to plan the scheduled transportation route, which includes the main traffic arteries, turning points and regional restriction nodes along the way. Obtain GPS positioning data from the vehicle within a specified time interval. The collection frequency can be adjusted according to transportation needs. Calculate the distance between the vehicle position and each node of the scheduled transportation route based on GPS positioning data. If the distance exceeds the allowable distance error range, the vehicle is considered to have deviated from the path. Specifically, regional restriction nodes include traffic control areas (restricted areas), speed limit or special road condition areas (such as tunnels, bridges or narrow sections, etc.), and special event restriction areas.

[0054] As a preferred implementation of this embodiment, the warehousing detection module determines whether the device is a product produced at the supplier's place of origin as follows:

[0055] Edge detection is performed on the device image collected in real time to obtain an edge image, and image segmentation and morphological operations are performed on the obtained edge image based on threshold segmentation to obtain an ROI image. Specifically, edge detection can identify edges by calculating the gradient of the image (i.e., the amount of change in the image pixels), or using Canny edge detection to smooth the image through a Gaussian filter to eliminate noise, and then calculate the gradient amplitude and direction of the image, followed by edge tracking, and finally obtain a clear edge image.

[0056] The ROI image is searched for contours existing in the image, the minimum bounding rectangle of the found contour is calculated, and it is determined whether the length and width of the minimum bounding rectangle exceed the set length and width range, and the contours whose length and width of the minimum bounding rectangle exceed the set length and width range are filtered. Specifically, the contour can be found by boundary tracking, and each pixel is traversed to check whether it is connected to the surrounding pixels and forms a boundary, so as to find the contour of the object.

[0057] The KNN algorithm is used to perform image recognition on the filtered contours. Based on the labeled training data, the distance metric is calculated to select K neighbors with the smallest distance. The contour sample category is predicted according to the categories of these neighbors. The barcode contour that needs to be identified is screened and compared with the device data to determine whether the device is a product produced by the supplier.

[0058] As a preferred implementation of this embodiment, the storage detection module performs image segmentation and morphological operations on the edge image obtained based on threshold segmentation to obtain the ROI image specifically as follows:

[0059] The frequency of occurrence of each gray value in the edge image is counted to obtain a gray histogram, the total gray mean of the edge image is calculated, and for each gray value t∈[0,255], the edge image is divided into foreground (object area) and background.

[0060] For each gray value, the mean and inter-class variance of the background class and the foreground class are calculated, all gray values ​​are traversed, and the gray value that maximizes the inter-class variance is selected as the threshold, and the obtained threshold is used to binarize the image to obtain the segmented image. The inter-class variance calculation formula is:

[0061]

[0062] In the formula, is the inter-class variance; ω1(t) is the weight of the background class; ω2(t) is the weight of the foreground class; μ1(t) is the mean of the background class; μ2(t) is the mean of the foreground class.

[0063]

[0064] Where P(i) is the probability of gray value i; L is the gray level.

[0065] Morphological operations including dilation, erosion, opening and closing are selected for the segmented image to obtain the ROI image.

[0066] Embodiment 2

[0067] Accordingly, this embodiment provides an intelligent material tracking management method, which is implemented based on the intelligent material tracking management system described in the embodiment. Figure 1 As shown, the following steps are included:

[0068] Transportation data is collected for vehicles that transport materials from the supplier's origin, and equipment data is collected for the transported materials.

[0069] During the transportation process, the transportation path is tracked according to the vehicle positioning. When the vehicle arrives at the destination, the reasonableness of the vehicle's transportation path and transportation time is determined based on the collected transportation data.

[0070] After the materials are put into storage, the equipment images are collected in real time. By comparing the real-time collected equipment images with the equipment data, it is determined whether the equipment is a product produced by the supplier's place of origin.

[0071] Embodiment 3

[0072] This embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the intelligent material tracking and management method as described in any embodiment of the present invention is implemented.

[0073] Embodiment 4

[0074] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the intelligent material tracking and management method as described in any embodiment of the present invention is implemented.

[0075] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0076] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0079] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent material tracking and management system, characterized in that: It includes data collection module, path tracking module and warehouse entry detection module; The data collection module is used to collect transportation data for vehicles carrying materials from the supplier's origin, and to collect equipment data for the transported materials; The path tracking module is used to track the transportation path according to the vehicle positioning during the transportation process. When the vehicle arrives at the destination, it determines whether the transportation path and transportation time of the vehicle are reasonable based on the collected transportation data; The warehousing detection module is used to collect equipment images in real time after materials enter the warehouse. By comparing the real-time collected equipment images with the equipment data, it is determined whether the equipment is a product produced by the supplier's origin.

2. The intelligent material tracking and management system according to claim 1 is characterized in that: The transport data in the data acquisition module includes at least: basic vehicle information, scheduled longest transport route, scheduled transport route, vehicle transport limit, transport quantity, and transport start time; the equipment data includes at least: production batch number, equipment type, supplier code, quality inspection number, and production date.

3. The intelligent material tracking and management system according to claim 2 is characterized in that: The path tracking module implements the transportation path tracking according to the vehicle positioning during the vehicle transportation process. When the vehicle arrives at the destination, it determines whether the vehicle transportation path and transportation time are reasonable according to the collected transportation data. Specifically: Regularly obtain the vehicle's GPS positioning data, including longitude, latitude, and altitude information, compare the GPS positioning data with the scheduled transportation route, and monitor whether the transportation vehicle deviates from the route; When the vehicle arrives at the destination, the longest transportation time is calculated in combination with the transportation data to determine whether the vehicle transportation exceeds the longest transportation time and whether there is any violation. The calculation formula for the longest transportation time is as follows: Where, T max_i DEST is the longest transport time; i The vehicle arrives at the i-th destination. After the vehicle arrives at the i-th destination, DEST i The value is 1, otherwise DEST i Value 0; PA i is the longest scheduled transport route; v is the theoretical average speed of the vehicle; α is the transport time delay cost coefficient; β load is the load influence coefficient; γ is the multivariate delay cost coefficient; M is the total number of delay conditions, j∈[1,M]; FT j is the jth delay condition that occurs during the vehicle transportation process. It takes the value of 1 when the jth delay condition occurs during the vehicle transportation process, otherwise it takes the value of 0; T cost_j is the additional transportation time after the jth delay occurs; i∈[1,N], N is the total number of destinations that the vehicle needs to reach.

4. The intelligent material tracking and management system according to claim 3 is characterized in that: The path tracking module monitors whether the transport vehicle deviates from the path specifically as follows: Identify the supplier's place of origin and destination, and use the map to plan the scheduled transportation route. The scheduled transportation route includes the main traffic arteries, turning points and area restriction nodes along the way. Obtain GPS positioning data from the vehicle within the specified time interval, and calculate the distance between the vehicle position and each node of the scheduled transportation route based on the GPS positioning data. If the distance exceeds the allowable distance error range, the vehicle is considered to have deviated from the path.

5. The intelligent material tracking and management system according to claim 2 is characterized in that: The specific method for judging whether the equipment is a product produced in the supplier's place of origin in the warehousing detection module is as follows: Perform edge detection on the real-time acquired device image to obtain an edge image, and perform image segmentation and morphological operations on the obtained edge image based on threshold segmentation to obtain a ROI image; Search the ROI image for the contours in the image, calculate the minimum bounding rectangle for the found contours and determine whether the length and width of the minimum bounding rectangle exceed the set length and width range, and filter out the contours whose length and width of the minimum bounding rectangle exceed the set length and width range; Use the KNN algorithm to perform image recognition on the filtered contours, filter out the barcode contours that need to be identified, and compare the identified barcode information with the device data to determine whether the device is from the supplier's country of origin. Produce products.

6. The intelligent material tracking and management system according to claim 5, characterized in that: In the storage detection module, the edge image obtained by performing image segmentation and morphological operation based on threshold segmentation to obtain the ROI image is specifically: Count the frequency of each gray value in the edge image to obtain the gray histogram, calculate the total gray mean of the edge image, and divide the edge image into foreground and background for each gray value t∈[0,255]; For each gray value, calculate the mean and inter-class variance of the background class and the foreground class, traverse all gray values, select the gray value that maximizes the inter-class variance as the threshold, and use the obtained threshold to binarize the image to obtain the segmented image; Morphological operations including dilation, erosion, opening and closing are selected for the segmented image to obtain the ROI image.

7. An intelligent material tracking and management method, characterized in that: The method is implemented based on the intelligent material tracking management system according to any one of claims 1 to 6, and comprises the following steps: Collect transportation data for vehicles carrying materials from the supplier's origin, and collect equipment data for the transported materials; During the transportation process, the transportation path is tracked according to the vehicle positioning. When the vehicle arrives at the destination, the transportation path and transportation time of the vehicle are judged to be reasonable according to the collected transportation data. After the materials are put into storage, the equipment images are collected in real time. By comparing the real-time collected equipment images with the equipment data, it is determined whether the equipment is a product produced by the supplier's place of origin.

8. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intelligent material tracking and management method as described in claim 7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent material tracking and management method as claimed in claim 7 is implemented.

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