An industrial lubricant traceability system

Abstract

The present invention discloses an industrial lubricant traceability system, which relates to the technical field of product delivery management, including generating a unique identification ID to record industrial lubricant production information; predicting the demand for industrial lubricants in various regions to create delivery tasks, and merging delivery tasks based on the similarity of delivery tasks; monitoring the storage environment of industrial lubricants and implementing treatment measures according to the monitoring results; uploading the collected and analyzed information data to a cloud database for storage. The present invention records industrial lubricant production information through a unique ID, predicts the demand in different regions to generate delivery tasks, merges and delivers based on the similarity of delivery tasks, monitors and warns the storage environment of industrial lubricants at the same time, and records the delivery information through blockchain throughout the delivery process, effectively improving the efficiency of industrial lubricant delivery, reducing delivery costs, and at the same time improving the accuracy and query efficiency of traceability information, greatly improving user experience.

Description

Technical Field

[0001] The invention relates to the technical field of product delivery management, in particular to an industrial lubricating oil tracing system. Background Art

[0002] With the continuous advancement of industrialization, industrial lubricants are increasingly used in mechanical equipment, automobile manufacturing, aerospace and other fields. The quality and use management of lubricants directly affect the operating efficiency and life of the equipment. Traditional industrial lubricant management mainly relies on manual records and regular maintenance. This method is not only inefficient, but also prone to data errors and omissions. In addition, with the development of the Internet of Things and big data technology, lubricant management is gradually moving towards intelligence and informatization. Many advanced monitoring equipment and data management systems have been introduced into lubricant management, realizing real-time monitoring of lubricant status and automatic data processing. However, existing technologies still have certain limitations, especially in the tracking and management of lubricants throughout their life cycle, lacking systematic and intelligent solutions. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing industrial lubricating oil traceability system, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology still has certain limitations, especially in the tracking and management of the entire life cycle of lubricants, and lacks systematic and intelligent solutions.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an industrial lubricant traceability system, which includes: a production record module, used to generate a unique identification ID to record industrial lubricant production information; a logistics and transportation module, used to predict the demand for industrial lubricants in various regions to create transportation tasks, and merge transportation tasks based on the similarity of transportation tasks; an environmental monitoring module, used to monitor the storage environment of industrial lubricants and implement treatment measures based on the monitoring results; a data storage module, used to upload the collected and analyzed information data to a cloud database for storage; a central control module, used to control the operation of all modules and remotely connect to the user terminal to integrate traceability information.

[0006] As a preferred solution of the industrial lubricant traceability system described in the present invention, the generation of a unique identification ID to record industrial lubricant production information refers to generating a unique ID through a blockchain network when the industrial lubricant is produced, collecting industrial lubricant production information including production date, production location and ingredient ratio, associating the collected production information with the unique ID, recording it through the blockchain network, printing a QR code containing the unique ID information on the industrial lubricant packaging and associating the QR code with the blockchain network.

[0007] As a preferred solution of the industrial lubricant traceability system of the present invention, the prediction of industrial lubricant demand in each region and the creation of a delivery task include:

[0008] Build a demand forecasting model using convolutional neural networks;

[0009] Obtain the forecast demand for industrial lubricants in each region based on the forecast model, and calculate the logistics transportation volume of industrial lubricants;

[0010] If the industrial lubricant logistics transportation volume is greater than 0, a transportation node is generated according to the location of the industrial lubricant production site and the logistics warehouse in the delivery area, the transportation route data between the transportation nodes is obtained, and the genetic algorithm is used to optimize the transportation route;

[0011] The fitness of each transport route is calculated as:

[0012]

[0013] Where R a is the ath transport route, is the total transportation cost of the a-th transportation route, is the fitness of the a-th transport route, K is the total number of transport vehicles, N is the total number of transport nodes, C n，ij is the transportation cost from transportation node i to node j, For vehicle k, it takes route R from transport node i to node j a The probability is 1 if it passes, otherwise it is 0, and n is the total number of transportation routes;

[0014] Update the initial population;

[0015] The transport route with the lowest fitness in the initial population is taken as the optimal transport route.

[0016] As a preferred solution of the industrial lubricant traceability system of the present invention, the merging of delivery tasks based on delivery task similarity includes:

[0017] After creating a delivery task, calculate the similarity of the optimal transportation routes in different delivery tasks, define the optimal transportation routes in different delivery tasks as route A and route B, calculate the distance from each point in route A to the nearest point in route B, and sum them up. The sum value is D;

[0018] The sum value D is converted to similarity using the normalization method:

[0019]

[0020] Where SM(A, B) is the similarity of route A to route B, Max D is the predefined maximum distance, and m is the total number of points in route A;

[0021] If the similarity of route A to route B is 1, then compare the lengths of route A and route B, merge the shorter route into the longer route to form a new transportation route, and calculate the total transportation cost based on the new transportation route;

[0022] If the total transportation cost is greater than the sum of the transportation costs of route A and route B, the delivery tasks will not be merged, and the delivery tasks of route A and route B will be carried out separately. If the total transportation cost is less than or equal to the sum of the transportation costs of route A and route B, the delivery tasks of route A and route B will be merged according to the new transportation route to generate a new delivery task and update it to the blockchain network to replace the initial delivery task;

[0023] Traverse all delivery tasks, calculate similarities, and merge them until the similarities of all delivery tasks are not 1. Then, upload a list of all merged delivery tasks to the blockchain.

[0024] As a preferred solution of the industrial lubricant traceability system of the present invention, wherein: the monitoring of the industrial lubricant storage environment and the implementation of treatment measures according to the monitoring results refer to storing the delivered industrial lubricant in a logistics warehouse after the delivery task is completed, and recording the industrial lubricant delivery information through a unique ID and updating it to the blockchain network;

[0025] Environmental sensors are deployed in logistics warehouses to collect environmental data in real time. The storage environment requirements of industrial lubricants are queried through unique IDs and input into environmental sensors as environmental data thresholds.

[0026] The environmental sensor is connected to an alarm device. When the environmental sensor detects that the environmental data exceeds the environmental data threshold, the alarm device is automatically triggered to notify the staff to adjust the industrial lubricant storage environment.

[0027] As a preferred solution of the industrial lubricant traceability system described in the present invention, the uploading of the collected and analyzed information data to the cloud database for storage refers to uploading the information data generated by the collection and analysis with attached timestamp data to the cloud database for storage, and applying security protection measures to the stored data. The cloud database backs up the stored data, stores the stored data and the backup data in different hardware, and regularly performs security and integrity checks on the stored data and generates a test report.

[0028] As a preferred solution of the industrial lubricant traceability system described in the present invention, wherein: the control of all modules running until the central control module is connected to the remaining modules through the blockchain network, and generates control commands and sends them to the remaining modules through the blockchain network to control the operation of the remaining modules, while receiving the data and feedback generated by the remaining modules.

[0029] As a preferred solution of the industrial lubricant traceability system described in the present invention, the remote connection to the user terminal to integrate the traceability information refers to the remote connection between the central control module and the user terminal through a wireless network. The user scans the QR code on the industrial lubricant package through the user terminal to associate the unique ID, and sends a traceability application to the blockchain network through the unique ID. The blockchain network queries and organizes the industrial lubricant production information and transportation information through the unique ID and sends it to the user terminal for the user to view.

[0030] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned industrial lubricant traceability system when executing the computer program.

[0031] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned industrial lubricant traceability system when executed by a processor.

[0032] The beneficial effects of the present invention are as follows: the present invention records industrial lubricant production information through a unique ID, predicts demand in different regions to generate delivery tasks, merges deliveries based on the similarity of delivery tasks, monitors and warns the storage environment of industrial lubricants, and records delivery information through blockchain throughout the entire delivery process, thereby effectively improving the efficiency of industrial lubricant delivery, reducing delivery costs, and at the same time improving the accuracy and query efficiency of traceability information, greatly improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 This is a schematic diagram of the structure of the industrial lubricant traceability system.

[0035] Figure 2 This is a schematic diagram of the implementation process of the industrial lubricant traceability system. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0038] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0039] Example 1

[0040] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an industrial lubricant oil traceability system, the industrial lubricant oil traceability system comprising:

[0041] S1, production record module, used to generate a unique ID to record industrial lubricant production information;

[0042] Specifically, generating a unique identification ID to record industrial lubricant production information means generating a unique ID through a blockchain network when the industrial lubricant is produced, collecting industrial lubricant production information including production date, production location, and ingredient ratio, associating the collected production information with the unique ID, recording it through the blockchain network, printing a QR code containing the unique ID information on the industrial lubricant packaging, and associating the QR code with the blockchain network.

[0043] The blockchain technology generates a unique identification ID and records production information, ensuring the immutability and transparency of the data. Users can obtain accurate production and traceability information by scanning the QR code, which improves the transparency and security of the information. Industrial lubricants have a unique identification ID, which is associated with the detailed production information in the blockchain network. Users only need to scan the QR code to quickly obtain relevant information, which improves the efficiency and accuracy of traceability. The existing technology for obtaining lubricant production and traceability information is relatively complicated. The present invention simplifies this process through QR code technology. Users only need to scan the QR code to obtain comprehensive information, which greatly improves the user experience. By recording production information through the blockchain network, enterprises can monitor and manage the production process of industrial lubricants in real time, and promptly discover and handle abnormal situations. Regulatory authorities can also supervise through blockchain data to ensure the compliance and transparency of the production process. The use of blockchain technology and QR code technology reduces the workload of manual recording and management, reduces management costs, and improves work efficiency.

[0044] S2, logistics and delivery module, used to predict the demand for industrial lubricants in various regions, create delivery tasks, and merge delivery tasks based on the similarity of delivery tasks;

[0045] Specifically, predicting the demand for industrial lubricants in various regions and creating a delivery task means collecting the historical demand data for industrial lubricants in various regions and the inventory of industrial lubricants in logistics warehouses, and cleaning and filtering the collected historical demand data to form a historical demand data set;

[0046] Use convolutional neural networks to build a demand forecasting model, including input layer, convolution layer, pooling layer, and output layer;

[0047] Use the demand training set to input the demand forecasting model for iterative training, define the loss function and Adam optimizer to iteratively optimize the model parameters. When the loss of the demand forecasting model no longer decreases significantly during the continuous iteration process, stop iterating and output the model parameters to update the demand forecasting model.

[0048] Input the historical demand data set into the demand forecasting model to obtain the forecasted demand for industrial lubricants in each region, and subtract the industrial lubricant inventory in the logistics warehouse of the region to obtain the logistics transportation volume of industrial lubricants;

[0049] If the industrial lubricant logistics transportation volume is less than or equal to 0, no transportation task will be created in the area;

[0050] If the industrial lubricant logistics transportation volume is greater than 0, then the transportation nodes are generated according to the location of the industrial lubricant production site and the logistics warehouse in the transportation area, the transportation route data between the transportation nodes are obtained, and the genetic algorithm is used to optimize the transportation route. The transportation routes are randomly generated according to the transportation route data to form the initial population:

[0051] P = {R1, R2, ..., R n};

[0052] Where P is the initial population, R n is the nth transport route;

[0053] Determine the total number of transport vehicles based on the transport volume and obtain the transport cost C of each transport route n , define the transportation optimization model as:

[0054]

[0055] Where K is the total number of transport vehicles, N is the total number of transport nodes, and C n，ij is the transportation cost from transportation node i to transportation node j, For vehicle k, it takes route R from node i to node j n Probability, 1 if passed, 0 otherwise;

[0056] The optimization constraints are defined as:

[0057]

[0058] in It means that all parameters are valid. The constraint condition means that each transport node i and transport node j can only be used by vehicle k through route R. n Serve once;

[0059] The fitness of each transport route is calculated as:

[0060]

[0061]

[0062] Where R a is the ath transport route, is the total transportation cost of the a-th transportation route, is the fitness of the a-th transport route, For vehicle k, it takes route R from transport node i to node j a The probability is 1 if it passes, otherwise it is 0, and n is the total number of transportation routes;

[0063] Sort all the transport routes in the initial population by fitness from high to low, select the two transport routes with the lowest fitness to cross and generate a new transport route, perform random mutation on the new transport route, add the randomly mutated transport route to the initial population, mark the crossed transport routes, select the two with the highest fitness from the unmarked transport routes, and perform the above operations in sequence to update the initial population, and re-optimize through the transport optimization model after each update, until the output value of the transport optimization model no longer decreases significantly after several consecutive updates, then stop updating the initial population;

[0064] The transport route with the lowest fitness in the initial population is taken as the optimal transport route, and a delivery task is created based on the optimal transport route, transport cost, transport volume, transport vehicle information and transport node information and sent to the blockchain network for recording.

[0065] By comprehensively collecting the historical demand data of industrial lubricants in various regions and the inventory data of logistics warehouses, the collected data is cleaned and filtered to form a high-quality historical demand data set. Through data cleaning, abnormal data and erroneous data can be removed to further improve the effect of model training and prediction accuracy. At the same time, high-quality data sets can also be used for other related analysis and optimization tasks to provide more extensive data support. A convolutional neural network (CNN) is used to build a demand forecasting model. Through the design of input layer, convolution layer, pooling layer and output layer, complex features in the data are extracted and learned, loss function and Adam optimizer are defined, and model parameters are iteratively optimized. Through the deep learning ability of convolutional neural network, the long-term and short-term trends in historical demand data can be captured, and the ability to predict future demand changes can be improved. At the same time, the optimized prediction model has better generalization ability and can adapt to demand forecasts in different regions and different time periods. The historical demand data set is input into the demand forecasting model to obtain industrial The predicted demand for lubricants is deducted from the inventory of the logistics warehouse in the area to calculate the logistics transportation volume of industrial lubricants. By accurately calculating the logistics transportation volume, unnecessary transportation tasks can be reduced and transportation costs can be reduced. At the same time, the optimized transportation task arrangement can also improve resource utilization and reduce operational risks caused by insufficient or excessive inventory. By merging transportation tasks, the empty rate of transportation vehicles can be reduced and vehicle utilization can be improved. At the same time, the optimized transportation route can reduce the total distance traveled by vehicles, reduce fuel consumption and carbon emissions, and has important environmental protection significance. The optimal transportation route, transportation cost, transportation volume, transportation vehicle information and transportation node information are generated into a transportation task and sent to the blockchain network for recording to ensure the transparency and immutability of the transportation task. The application of blockchain technology can also realize the full process tracking of transportation tasks. Users can query the transportation status and historical records at any time to improve user experience. At the same time, the distributed ledger technology of blockchain can improve the system's risk resistance and prevent single point failures.

[0066] Furthermore, merging delivery tasks based on delivery task similarity means that after creating a delivery task, the similarity of the optimal transportation routes in different delivery tasks is calculated, the optimal transportation routes in different delivery tasks are defined as route A and route B, and the distance from each point in route A to the nearest point in route B is calculated:

[0067]

[0068] Where d is the distance from each point in route A to the nearest point in route B, (x A ,y A ) is the coordinate of the point in route A, (x B ,y B ) are the coordinates of the points in route B;

[0069] Traverse each point in route A, calculate the distance to the nearest point on route B and sum them up:

[0070]

[0071] Where D is the sum of the distances from all points on route A to the nearest point on route B, d i is the distance from the i-th point in route A to the nearest point in route B, and m is the total number of points in route A;

[0072] Convert D to similarity using normalization method:

[0073]

[0074] Where SM(A, B) is the similarity of route A to route B, and Max D is the predefined maximum distance;

[0075] If the similarity of route A to route B is 1, then compare the lengths of route A and route B, merge the shorter route into the longer route to form a new transportation route, and calculate the total transportation cost based on the new transportation route;

[0076] If the total transportation cost is greater than the sum of the transportation costs of route A and route B, the delivery tasks will not be merged, and the delivery tasks of route A and route B will be carried out separately. If the total transportation cost is less than or equal to the sum of the transportation costs of route A and route B, the delivery tasks of route A and route B will be merged according to the new transportation route to generate a new delivery task and update it to the blockchain network to replace the initial delivery task;

[0077] Traverse all delivery tasks, calculate similarities, and merge them until the similarities of all delivery tasks are not 1. Upload a list of all merged delivery tasks to the blockchain. Before all delivery tasks begin, load industrial lubricants onto transport vehicles, and bind the unique ID information of the industrial lubricants to the delivery tasks on the blockchain network.

[0078] Through scientific similarity calculation and normalization processing, the similarity of different delivery tasks can be accurately quantified, providing a reliable basis for task merging. The optimized transportation route reduces redundancy and improves the overall efficiency of logistics transportation. The blockchain technology is used to record and manage delivery tasks to ensure the immutability and transparency of data. The application of blockchain technology improves the security and credibility of data management, enabling users and managers to query and track delivery tasks in real time, improving data management efficiency and transparency. Through similarity calculation and cost comparison, it is ensured that tasks are merged only when the cost is reduced after the merger, thereby optimizing transportation routes and reducing transportation costs. The scientific task merging method improves the rationality and execution efficiency of transportation tasks. By binding the unique ID information of industrial lubricants to the delivery tasks, the full traceability of each batch of lubricants is ensured. Users can quickly obtain the production, transportation and storage information of lubricants through ID information, which improves the transparency of logistics management and user experience. By scientifically quantifying and optimizing delivery tasks, repeated and redundant transportation tasks are reduced, and resource utilization efficiency is improved. The optimized transportation routes reduce the empty rate of transportation vehicles, improve vehicle utilization, and reduce logistics costs. By optimizing transportation routes and enhancing data management, more efficient and transparent logistics services are provided, which significantly improves user experience and satisfaction. Users can query the status of transportation tasks in real time through blockchain technology to ensure the transparency and controllability of the transportation process.

[0079] S3, environmental monitoring module, used to monitor the storage environment of industrial lubricants and implement treatment measures based on the monitoring results;

[0080] Specifically, monitoring the storage environment of industrial lubricants and implementing treatment measures based on the monitoring results means that after the delivery task is completed, the delivered industrial lubricants are stored in the logistics warehouse, and the industrial lubricant delivery information is recorded through a unique ID and updated to the blockchain network;

[0081] Environmental sensors are deployed in logistics warehouses to collect environmental data in real time. The storage environment requirements of industrial lubricants are queried through unique IDs and input into environmental sensors as environmental data thresholds.

[0082] The environmental sensor is connected to an alarm device. When the environmental sensor detects that the environmental data exceeds the environmental data threshold, the alarm device is automatically triggered to notify the staff to adjust the industrial lubricant storage environment.

[0083] Real-time monitoring and data recording of environmental sensors can form a detailed historical record of environmental data, which is convenient for subsequent analysis and improvement of storage strategies, and improves the scientificity and systematicness of lubricant storage management. The storage environment requirements of industrial lubricants are queried through a unique ID, and these requirements are input into the environmental sensor as the threshold of environmental data. When environmental data exceeds the preset threshold, the system will automatically record the abnormal situation. The automated setting and query of environmental data thresholds reduces manual intervention, reduces the error rate, and improves management efficiency. At the same time, this method also facilitates the personalized management of different batches of lubricants, ensuring the accuracy and pertinence of their storage environment. The automated alarm system improves the response speed of environmental monitoring, ensuring that environmental parameters can be quickly handled when they exceed the standard. By adjusting the storage environment in a timely manner, the service life of the lubricant is extended, the maintenance and replacement costs are reduced, and the overall management efficiency is improved.

[0084] S4, data storage module, used to upload the collected and analyzed information data to the cloud database for storage;

[0085] Specifically, uploading the collected and analyzed information data to the cloud database for storage means uploading the collected and analyzed information data with attached timestamp data to the cloud database for storage, and applying security protection measures to the stored data. The cloud database will back up the stored data, and store the stored data and the backup data in different hardware. At the same time, the security and integrity of the stored data and the backup data will be regularly checked and a check report will be generated.

[0086] The generation and processing time of each piece of data is recorded by attaching a timestamp, and the data is uploaded to the cloud database for storage. The use of timestamps ensures the timing and traceability of the data, which is helpful for subsequent data analysis and processing. After the data is uploaded to the cloud database, a series of security protection measures are imposed, such as data encryption, access control, and security auditing, to ensure the security of the data during storage and transmission. After receiving the data, the cloud database automatically backs up the data and stores data copies in different physical or logical locations to improve data availability and recovery capabilities. The stored data and backup data are distributed in different hardware devices to avoid the complete loss or damage of data due to a single point of failure, thereby improving the reliability and risk resistance of the data storage system. The stored data and backup data are regularly tested for security and integrity to ensure that the data has not been tampered with or damaged during storage, and a test report is generated to provide transparency and traceability of data management.

[0087] S5, central control module, used to control the operation of all modules and remotely connect to the user terminal to integrate traceability information;

[0088] Specifically, all modules are controlled to run until the central control module is connected to the remaining modules through the blockchain network, and control commands are generated and sent to the remaining modules through the blockchain network to control the operation of the remaining modules, while receiving data and feedback generated by the remaining modules.

[0089] The decentralized nature of the blockchain network also improves the scalability and flexibility of the system. When adding or modifying modules, it is only necessary to register and connect through the blockchain network. There is no need to make major adjustments to the entire system architecture, which improves the maintainability of the system. The central control module generates control commands based on the system's operating status and needs and sends them to other modules through the blockchain network. The sending and execution of control commands are recorded and verified through the blockchain network to ensure the accuracy and non-tamperability of the instructions. Sending control commands through the blockchain network can also achieve efficient and real-time command transmission. The central control module can monitor the system's operating status in real time, generate and send control commands in a timely manner, and improve the system's response speed and operating efficiency.

[0090] Furthermore, remote connection to the user terminal to integrate traceability information means that the central control module is remotely connected to the user terminal through a wireless network, and the user scans the QR code on the industrial lubricant package to associate a unique ID through the user terminal, and sends a traceability application to the blockchain network through the unique ID. The blockchain network queries and organizes the industrial lubricant production information and transportation information through the unique ID and sends it to the user terminal for the user to view.

[0091] Users scan the QR code on the industrial lubricant packaging through terminal devices to obtain the product's unique ID. QR code technology has the advantages of large information capacity and easy scanning. By scanning the QR code, users can quickly obtain the product's unique identification information. QR code technology not only facilitates users to scan and obtain information, but also embeds anti-counterfeiting logos in the QR code to enhance the product's anti-counterfeiting ability, prevent the circulation of counterfeit and shoddy products, and improve the product's safety and credibility. The application of blockchain technology also enhances the transparency and traceability of traceability information. Users can query the unique ID to obtain detailed information on the entire process of the product from production to transportation, which improves users' trust and satisfaction with the product. The real-time query and transmission of traceability information not only improves the user experience, but also helps companies to trace quality and track problems, promptly discover and solve problems in the production and transportation process, and improve the company's management efficiency and product quality.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An industrial lubricant traceability system, characterized by: include: Production record module, used to generate a unique ID to record industrial lubricant production information; The logistics and delivery module is used to predict the demand for industrial lubricants in various regions, create delivery tasks, and merge delivery tasks based on their similarity; Environmental monitoring module, used to monitor the storage environment of industrial lubricants and implement treatment measures based on the monitoring results; A data storage module is used to upload the collected and analyzed information data to a cloud database for storage; Central control module, used to control the operation of all modules and remotely connect to user terminals to integrate traceability information; Generating a unique identification ID to record industrial lubricant production information refers to generating a unique ID through a blockchain network when the industrial lubricant is produced, collecting industrial lubricant production information including production date, production location, and ingredient ratio, associating the collected production information with the unique ID, recording it through the blockchain network, printing a QR code containing the unique ID information on the industrial lubricant packaging, and associating the QR code with the blockchain network; The forecast of industrial lubricant demand in each region and the creation of delivery tasks include: Build a demand forecasting model using convolutional neural networks; Obtain the forecast demand for industrial lubricants in each region based on the forecast model, and calculate the logistics transportation volume of industrial lubricants; If the industrial lubricant logistics transportation volume is greater than 0, a transportation node is generated according to the location of the industrial lubricant production site and the logistics warehouse in the delivery area, the transportation route data between the transportation nodes is obtained, and the genetic algorithm is used to optimize the transportation route; The fitness of each transport route is calculated as: Where R a is the ath transport route, is the total transportation cost of the a-th transportation route, is the fitness of the a-th transport route, K is the total number of transport vehicles, N is the total number of transport nodes, C n，ij is the transportation cost from transportation node i to node j, For vehicle k, it takes route R from transport node i to node j a The probability is 1 if it passes, otherwise it is 0, and n is the total number of transportation routes; Update the initial population; The transport route with the lowest fitness in the initial population is taken as the optimal transport route; The merging of the transport tasks based on the similarity of the transport tasks includes: After creating a delivery task, calculate the similarity of the optimal transportation routes in different delivery tasks, define the optimal transportation routes in different delivery tasks as route A and route B, calculate the distance from each point in route A to the nearest point in route B, and sum them up. The sum value is D; The sum value D is converted to similarity using the normalization method: Where SM(A, B) is the similarity of route A to route B, Max D is the predefined maximum distance, and m is the total number of points in route A; If the similarity of route A to route B is 1, then compare the lengths of route A and route B, merge the shorter route into the longer route to form a new transportation route, and calculate the total transportation cost based on the new transportation route; If the total transportation cost is greater than the sum of the transportation costs of route A and route B, the delivery tasks will not be merged, and the delivery tasks of route A and route B will be carried out separately. If the total transportation cost is less than or equal to the sum of the transportation costs of route A and route B, the delivery tasks of route A and route B will be merged according to the new transportation route to generate a new delivery task and update it to the blockchain network to replace the initial delivery task; Traverse all delivery tasks, calculate similarities, and merge them until the similarities of all delivery tasks are not 1. Then, upload a list of all merged delivery tasks to the blockchain.

2. The industrial lubricant oil traceability system according to claim 1, characterized in that: The monitoring of the industrial lubricant storage environment and implementation of treatment measures based on the monitoring results means that after the delivery task is completed, the delivered industrial lubricant is stored in the logistics warehouse, and the industrial lubricant delivery information is recorded through a unique ID and updated to the blockchain network; Environmental sensors are deployed in logistics warehouses to collect environmental data in real time. The storage environment requirements of industrial lubricants are queried through unique IDs and input into environmental sensors as environmental data thresholds. The environmental sensor is connected to an alarm device. When the environmental sensor detects that the environmental data exceeds the environmental data threshold, the alarm device is automatically triggered to notify the staff to adjust the industrial lubricant storage environment.

3. The industrial lubricant oil traceability system according to claim 2, characterized in that: The uploading of the collected and analyzed information data to the cloud database for storage refers to uploading the collected and analyzed information data with attached timestamp data to the cloud database for storage, and applying security protection measures to the stored data. The cloud database will back up the stored data, and store the stored data and the backup data in different hardware. At the same time, the security and integrity of the stored data and the backup data will be regularly checked and a check report will be generated.

4. The industrial lubricant oil traceability system according to claim 3, characterized in that: The control module controls all modules to run until the central control module is connected to the remaining modules through the blockchain network, and generates control commands and sends them to the remaining modules through the blockchain network to control the operation of the remaining modules, while receiving the data and feedback generated by the remaining modules.

5. The industrial lubricant oil traceability system according to claim 4, characterized in that: The remote connection to the user terminal to integrate the traceability information means that the central control module is remotely connected to the user terminal through a wireless network. The user scans the QR code on the industrial lubricant package through the user terminal to associate with the unique ID, and sends a traceability application to the blockchain network through the unique ID. The blockchain network queries and organizes the industrial lubricant production information and transportation information through the unique ID and sends it to the user terminal for the user to view.

6. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the industrial lubricant traceability system according to any one of claims 1 to 5 when executing the computer program.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the industrial lubricant traceability system according to any one of claims 1 to 5 are implemented.

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