Method for reducing cost and increasing efficiency based on supply chain system

Abstract

According to the cost-reducing and benefit-increasing method based on the supply chain system provided by the invention, real-time acquisition, processing and transmission of data in each link of the supply chain are realized, the timeliness and accuracy of the data are ensured, and technical support is provided for quickly responding to market changes; an integrated collaborative management platform module is constructed, real-time sharing and collaboration of information of all parties of the supply chain are achieved, and transparency and collaborative efficiency of the supply chain are improved. A big data analysis and artificial intelligence module is utilized to carry out deep mining and analysis on supply chain data, so that accurate decision support and trend prediction are provided, and the market insight and competitiveness of enterprises are enhanced; and a data encryption and privacy protection module is designed, so that the security of an enterprise during sensitive data processing is ensured, and the worry of the enterprise on data leakage is eliminated. According to the invention, the modular design is adopted, so that the functional modules can operate independently and work cooperatively, and meanwhile, the system has good expandability and can meet individual requirements of different enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain, and specifically relates to a method for reducing costs and increasing efficiency based on a supply chain system. Background Art

[0002] As globalization deepens, supply chain management has become one of the core competitiveness of corporate operations. With the rapid development of science and technology, especially breakthroughs in information technology, the Internet of Things, big data, artificial intelligence and other fields, supply chain management is undergoing a profound transformation. The application of these advanced technologies not only improves the transparency, efficiency and flexibility of the supply chain, but also brings broader market opportunities to enterprises.

[0003] The existing solutions for supply chain management that have been implemented mainly include the following:

[0004] Solution 1: Supply chain management solution based on traditional ERP system. This solution realizes unified management and allocation of resources by integrating various business processes within the enterprise. However, it has weak collaborative management capabilities for external supply chains and is difficult to achieve real-time information sharing and decision support.

[0005] Solution 2: Supply chain management solution based on IoT technology. This solution collects data from all links of the supply chain in real time through IoT devices, improving the accuracy and real-time nature of the data. However, it is still insufficient in data processing and analysis, making it difficult to fully tap the potential value of the data.

[0006] Solution 3: Supply chain management solution based on big data and artificial intelligence. This solution uses big data technology to conduct in-depth mining and analysis of supply chain data and makes intelligent decisions through artificial intelligence algorithms. However, it has certain risks and challenges in terms of data security and privacy protection.

[0007] The above scheme has the following defects:

[0008] (1) Insufficient real-time performance and accuracy: Although some existing supply chain management systems can realize data collection and transmission, they still have defects in processing real-time data and ensuring data accuracy. For example, in certain links of the supply chain process, data updates may be delayed, resulting in decision makers being unable to obtain the latest and most accurate information in a timely manner, thus affecting the quality and efficiency of decision-making.

[0009] (2) Limited collaborative management capabilities: Although existing supply chain management solutions are relatively good at integrating internal resources, they are still insufficient in collaborative management with external supply chains. This results in limited information sharing and collaboration between various links in the supply chain, making it difficult to form an efficient supply chain network.

[0010] (3) Lack of data processing and analysis capabilities: Although some solutions have begun to try to use big data and artificial intelligence technologies for data processing and analysis, the current level of technology still cannot meet the needs of complex supply chain management. For example, for the deep mining and intelligent analysis of massive data, existing technologies may not be able to provide sufficiently accurate and effective results, resulting in insufficient decision support for supply chain management.

[0011] (4) Data security and privacy protection issues: Supply chain management involves a large amount of sensitive data and privacy information. However, some existing technical solutions have risks and challenges in data security and privacy protection, which may lead to data leakage or abuse, causing losses and risks to enterprises.

[0012] In summary, the shortcomings of existing technologies are mainly concentrated in real-time and accuracy, collaborative management capabilities, data processing and analysis capabilities, and data security and privacy protection, etc. These shortcomings limit the effectiveness and competitiveness of supply chain management. Summary of the invention

[0013] In order to solve the above problems existing in the prior art, the present invention provides a method for reducing costs and increasing efficiency based on a supply chain system. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0014] A method for reducing costs and increasing efficiency based on the supply chain system includes:

[0015] Build a software system that integrates real-time data processing and transmission modules, collaborative management of various supply chain information platform modules, big data analysis and artificial intelligence modules, and data security and privacy protection modules;

[0016] Using the real-time data processing and transmission module, data from all links of the supply chain are acquired, processed using data processing algorithms, and the processed data are transmitted to the supply chain software platform using high-speed transmission technology;

[0017] Utilize the collaborative management of various supply chain information platform modules to connect the information systems of all parties in the supply chain to synchronize and share real-time data, and provide an online collaborative platform for collaboration and communication for all parties in the supply chain; monitor and optimize each link of the supply chain, and update it to the supply chain software platform in real time;

[0018] Using big data analysis and artificial intelligence modules to store batch data of the supply chain, and using big data analysis and artificial intelligence technologies to conduct in-depth analysis and mining of the stored data, and display it on the supply chain software platform;

[0019] The data security and privacy protection module is used to verify the supply chain software platform and the information systems of all parties in the supply chain using a two-way authentication mechanism.

[0020] Beneficial effects:

[0021] The present invention provides a method for reducing costs and increasing efficiency based on the supply chain system, realizes the real-time collection, processing and transmission of data from all links of the supply chain, ensures the timeliness and accuracy of the data, and provides technical support for rapid response to market changes; constructs an integrated collaborative management platform module, realizes real-time sharing and collaboration of information from all parties in the supply chain, and improves the transparency and collaborative efficiency of the supply chain; uses big data analysis and artificial intelligence modules to conduct in-depth mining and analysis of supply chain data, provides accurate decision support and trend forecasting, and enhances the market insight and competitiveness of enterprises; designs data encryption and privacy protection modules to ensure the security of enterprises when processing sensitive data, and eliminates the concerns of enterprises about data leakage. The present invention adopts modular design so that each functional module can operate independently or work in collaboration, and has good scalability, which can meet the personalized needs of different enterprises.

[0022] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for reducing costs and increasing efficiency based on a supply chain system provided by the present invention. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0025] like Figure 1 As shown, the present invention provides a method for reducing costs and increasing efficiency based on a supply chain system, including:

[0026] S100, builds a supply chain software platform that integrates real-time data processing and transmission modules, collaborative management modules for various supply chain information platforms, big data analysis and artificial intelligence modules, and data security and privacy protection modules;

[0027] The present invention constructs a supply chain software platform, which ensures that the supply chain software can reflect the status and changes of each link of the supply chain in real time and accurately by optimizing the data processing algorithm and enhancing the real-time performance of data transmission. This will help decision makers to obtain the latest information in a timely manner and make more accurate decisions. Therefore, the real-time performance and accuracy of data processing can be improved; by building a more complete collaborative management platform module, information sharing and collaboration between various links of the supply chain can be promoted. This can not only enhance the transparency of the supply chain, but also optimize the allocation of resources, reduce operating costs, improve response speed, and enhance the collaborative management capabilities of the supply chain; using advanced big data analysis and artificial intelligence technology, the supply chain data is deeply mined and analyzed to provide decision makers with more valuable insights and predictions. This will help enterprises discover potential market opportunities, formulate more reasonable sales strategies and inventory plans, and thus strengthen data processing and analysis capabilities; by adopting advanced data encryption and privacy protection technologies, it is ensured that the supply chain software can guarantee the security and privacy of data when processing sensitive data and privacy information. This will help eliminate the worries of enterprises, enhance users' trust in the software, and strengthen data security and privacy protection.

[0028] S200, using the real-time data processing and transmission module to obtain data from each link of the supply chain, and using the data processing algorithm to process the data, and using high-speed transmission technology to transmit the processed data to the supply chain software platform;

[0029] In order to improve the real-time and accuracy of data processing, this invention uses advanced data processing algorithms and high-speed data transmission technology. By optimizing the data processing process and reducing data delays, it ensures that the supply chain software can obtain, process and display data from all links of the supply chain in real time. At the same time, data verification and error correction mechanisms are used to ensure the accuracy and reliability of the data.

[0030] S300, using the collaborative management modules of various supply chain information platforms, connecting the information systems of various parties in the supply chain to synchronize and share real-time data, and providing an online collaborative platform for collaboration and communication for various parties in the supply chain; monitoring and optimizing various links of the supply chain, and updating the supply chain software platform in real time;

[0031] In order to achieve collaborative management of all links in the supply chain, this invention builds a complete collaborative management platform module, which integrates the information systems of all parties in the supply chain and realizes information sharing and intercommunication. By providing collaborative work tools and process management functions, it promotes close cooperation between all parties in the supply chain and improves the overall efficiency of the supply chain.

[0032] S400, using the big data analysis and artificial intelligence module to store the batch data of the supply chain, and using big data analysis and artificial intelligence technology to deeply analyze and mine the stored data, and display it to the supply chain software platform;

[0033] In order to fully tap the potential value of supply chain data, this invention uses big data analysis and artificial intelligence technology. Through in-depth mining and analysis of massive data, efficiency bottlenecks and potential problems in the supply chain are discovered. At the same time, machine learning algorithms and prediction models are used to predict supply chain trends, providing strong support for decision-making.

[0034] S500, using the data security and privacy protection module, adopting a two-way authentication mechanism to verify the supply chain software platform and the information systems of the supply chain parties.

[0035] To ensure the security of supply chain software when processing sensitive data and private information, this invention uses advanced data encryption and privacy protection technologies. By implementing strict data access control and authority management, unauthorized access and data leakage can be prevented. At the same time, anonymization and desensitization technologies are used to protect users' private information from being abused.

[0036] In a specific embodiment of the present invention, the real-time data processing and transmission module includes a data acquisition module, a data processing module, a data transmission module and a data display module;

[0037] The data collection module is used to collect data from each link of the supply chain in real time;

[0038] The data processing module is used to divide the collected data into multiple small batches of data and set an asynchronous processing mechanism; clean, verify, convert, and optimize the data processing logic of each small batch of data according to the asynchronous processing mechanism to obtain processed data;

[0039] The present invention divides the real-time data stream into multiple small batches for processing to reduce the delay of single processing and adopts an asynchronous processing mechanism to ensure the continuity and efficiency of data processing.

[0040] The present invention adopts advanced data processing algorithms, mainly including distributed data processing algorithms and stream processing algorithms. Distributed data processing algorithms (such as Apache Spark) can efficiently process large-scale data, and have good fault tolerance and scalability. Stream processing algorithms (such as Apache Kafka Streams) focus on the processing and analysis of real-time data streams, and can quickly process data as it arrives.

[0041] The data transmission module is used to compress the processed data and transmit the compressed data to the supply chain software platform via the TCP / IP protocol or the UDP protocol;

[0042] The data display module is used to display the real-time collected data on the supply chain software and provide visual data analysis and monitoring functions.

[0043] In a specific implementation of the present invention, the data processing module is specifically used for:

[0044] For each small batch of data, a hash algorithm is used to detect duplicate data in the small batch of data to obtain deduplicated data;

[0045] This step uses hash algorithms and other technologies to detect and delete duplicate records to ensure the uniqueness of the data.

[0046] Using data validation rules to identify and correct errors in the deduplicated data to obtain error-free data;

[0047] This step uses data validation rules (such as regular expression matching, value range checking, etc.) to identify and correct erroneous data. For example, for inventory status data, a reasonable inventory range can be set, and data outside the range will be considered an error and corrected.

[0048] Deleting or replacing data that does not conform to business logic or format in the error-free data to obtain cleaned data;

[0049] This step deletes or replaces data that does not conform to business logic or format. For example, invalid addresses or expired timestamps in logistics information will be deleted or updated.

[0050] Verify the cleaned data using a data check code or a data integrity verification algorithm to obtain verified data;

[0051] This step uses data checksums (such as CRC checksums) or data integrity verification algorithms (such as SHA-256) to ensure the integrity and accuracy of the data. The cleaned data is further verified to ensure that the data meets business requirements and quality standards.

[0052] Converting the verified data into a unified format and standard to obtain converted data;

[0053] The converted data is stored using a storage medium and a database management system, and index optimization or partition optimization is performed on the database or storage medium storing the data.

[0054] This step converts the raw data into a unified format and standard based on business needs. For example, the inventory data of different suppliers is converted into a unified inventory unit (such as pieces, boxes, pallets, etc.). The data is encoded, compressed, and processed for easy storage and transmission.

[0055] The present invention can reduce unnecessary computing and storage overheads when optimizing data processing logic. For example, for frequently queried data, cache technology can be used to improve query efficiency. The data processing process is managed in a refined manner to optimize the processing time and resource allocation of each link.

[0056] The present invention can use efficient storage media (such as SSD solid state drives) and database management systems (such as NoSQL databases) to store and process real-time data. The database is optimized for indexing and partitioning to improve data reading and writing speed and query efficiency.

[0057] Before communication begins, a stable communication link will be established between the software system and the supply chain software platform. During the communication process, the stability of the link and the quality of data transmission will be monitored in real time, and dynamic adjustments and optimizations will be made based on actual conditions.

[0058] In a specific implementation of the present invention, the data transmission module is specifically used for:

[0059] The converted data is compressed using a data compression technology, and the compressed data is encrypted using a data encryption technology to obtain encrypted data;

[0060] During data transmission, if errors or data loss occur, the error handling mechanism will be triggered. Depending on the specific situation, you can choose to retransmit the lost data packets or take other remedial measures to ensure data integrity and reliability.

[0061] The present invention encapsulates the data before data transmission and adds necessary metadata (such as timestamp, sequence number, etc.) to facilitate data analysis and verification by the receiver. At the same time, data encryption technology (such as AES encryption algorithm) is used to encrypt the data to ensure the security of the data during transmission.

[0062] In the data processing module, the data verification process has been involved to ensure the accuracy and consistency of the data. In the process of data transmission and reception, the present invention also adopts the following measures to ensure the security and integrity of the data:

[0063] Data encryption: Encrypt data packets before transmission to prevent data from being stolen or tampered with during transmission.

[0064] Data integrity check: Add information such as checksums or hash values ​​to data packets to perform integrity checks when receiving data to ensure that the data is not damaged or lost during transmission.

[0065] Re-authenticating the encrypted data;

[0066] The data processing module has cleaned, verified and converted the collected data to ensure the accuracy and consistency of the data. The data will be verified again before transmission to prevent data corruption or tampering during the transmission process.

[0067] The re-authenticated encrypted data is transmitted to the supply chain software platform via TCP / IP or UDP protocol.

[0068] During the data transmission process, the present invention achieves efficient data transmission through the following steps:

[0069] Data encapsulation: encapsulate the processed data according to a specific protocol to form a data packet. The data packet contains necessary information such as data content, source address, and destination address.

[0070] Channel selection: Select a suitable communication channel for data transmission based on the current network environment and data transmission requirements. For example, when a large amount of data needs to be transmitted at high speed, high-speed channels such as optical fiber communication or 5G mobile communication can be selected.

[0071] Data transmission: The encapsulated data packets are transmitted through the selected channel. During the transmission process, advanced coding and modulation technologies are used to improve the data's anti-interference ability and transmission efficiency.

[0072] Data reception and decapsulation: After receiving the data packet, the target system performs decapsulation operations, extracts the data content, and passes it to the upper-level application for processing.

[0073] This implementation uses an optimized version of the TCP / IP protocol or the UDP protocol for data transmission to improve transmission speed and reliability. During data transmission, data compression technology is used to reduce the amount of data transmitted and improve transmission efficiency. At the same time, data encryption technology is used to ensure data security. High-speed data transmission technology is used to transmit the processed data to the supply chain software platform in real time.

[0074] The present invention is based on the traditional TCP / IP protocol, and improves the data transmission speed and reliability by optimizing the transmission control strategy, reducing the number of data packet retransmissions, etc. This technology is particularly suitable for data transmission scenarios that require high reliability. Compared with TCP, the UDP protocol has lower latency and higher throughput, and is suitable for data transmission scenarios with high real-time requirements. However, UDP does not provide mechanisms such as error retransmission and flow control. Therefore, in the present invention, it is necessary to combine the data verification and retransmission mechanism of the application layer to ensure the integrity and reliability of the data. In view of the special needs of supply chain management, the present invention may also adopt a customized dedicated transmission protocol. This protocol can be optimized according to the actual business scenario to achieve more efficient and safer data transmission.

[0075] The high-speed data transmission technologies used in the present invention mainly include [specific technology names, such as optical fiber communication, 4G / 5G mobile communication, Wi-Fi 6, etc.]. These technologies have the characteristics of high speed, low latency, high reliability, etc., and can meet the real-time and accuracy requirements of data transmission in supply chain management.

[0076] The communication process between the supply chain software system and the supply chain software platform constructed by the present invention is as follows: First, define the communication interface and protocol between the system and the supply chain software platform to ensure that both parties can exchange data in the agreed manner. Through the defined interface and protocol, data is exchanged between the system and the supply chain software platform. This includes the processes of sending, receiving, and confirming data. During the communication process, if an abnormal situation occurs (such as network failure, data loss, etc.), the present invention adopts a corresponding exception handling mechanism for recovery and retransmission to ensure the integrity and reliability of the data.

[0077] This embodiment can clearly understand the implementation mode and advantages of the present invention in terms of high-speed data transmission technology, authentication process, and communication process between the system and the supply chain software platform. These technical details not only improve the efficiency and security of data transmission, but also provide strong technical support for the practical application of the present invention.

[0078] Through the software system of this embodiment, the enterprise can grasp the latest dynamics of the supply chain in real time, discover potential problems in time, and improve decision-making efficiency and accuracy.

[0079] In a specific implementation of the present invention, the collaborative management modules of various supply chain information platforms include: an information integration module, a collaborative work module and a process management module;

[0080] The information integration module is connected with the information systems of all parties in the supply chain to synchronize and share the real-time collected data in real time;

[0081] The information integration module of the present invention is responsible for integrating the information systems of all parties in the supply chain to achieve data sharing and intercommunication. The information integration module first needs to formulate a unified data interface standard to ensure that the information systems of all parties in the supply chain can exchange data according to unified specifications. This includes standardization of data formats, transmission protocols, access rights, etc. Next, the information systems of all parties in the supply chain are integrated through technical means. This can be achieved through API interfaces, middleware, data buses, etc., to ensure that data can be smoothly transmitted and received between systems. Since the information systems of all parties in the supply chain may use different data models and encoding methods, data mapping and conversion work is required. This includes converting the data formats of different systems into a unified format, and decoding and encoding conversion of data with different encoding methods.

[0082] In order to ensure the real-time and accuracy of data, a real-time synchronization and update mechanism for data needs to be implemented. This can be achieved through timed polling, event triggering, etc., to ensure that the information systems of all parties in the supply chain can obtain the latest data in a timely manner. In the process of data sharing and intercommunication, attention should also be paid to data security and privacy protection. Therefore, a strict data permission management strategy needs to be implemented to ensure that only authorized users or systems can access and modify data.

[0083] The collaborative work module provides an online collaboration platform for all parties in the supply chain, so that all parties in the supply chain can communicate and collaborate synchronously on the online collaboration platform;

[0084] The present invention constructs an integrated collaborative management platform module, which integrates online meetings, task allocation and other functions to form a unified interface and process. In this way, all parties in the supply chain can complete all collaborative work on one platform, improving work efficiency and convenience.

[0085] The process management module uses Internet of Things technology, big data analysis technology and artificial intelligence technology to monitor and optimize various processes in the supply chain.

[0086] In addition to the basic online meeting and task allocation functions, the present invention may also introduce intelligent technologies, such as machine learning, natural language processing, etc., to enhance the collaborative effect. For example, the system can automatically analyze meeting records and task progress, identify potential problems and risks, and propose corresponding solutions or suggestions.

[0087] In order to achieve closer collaboration, the present invention may provide a real-time communication and feedback mechanism. All parties in the supply chain can exchange information, share progress, and obtain timely feedback on the platform in real time. This helps speed up the decision-making process and reduce misunderstandings and conflicts.

[0088] The present invention may use big data analysis and artificial intelligence technology to conduct in-depth mining and analysis of supply chain data, providing more accurate and comprehensive information support for collaborative decision-making. In this way, all parties in the supply chain can make more informed decisions based on data and optimize resource allocation and process management.

[0089] Taking into account the special needs and business scenarios of different supply chain enterprises, the present invention may provide a personalized collaboration solution. Enterprises can choose appropriate collaboration tools and functions according to their own circumstances to achieve a more practical collaboration effect.

[0090] The present invention conducts a comprehensive analysis and diagnosis of each process of the supply chain, including identifying bottlenecks, redundant steps, information islands and other issues in the process, and evaluating the impact of these issues on the overall performance.

[0091] Based on process analysis and diagnosis, streamline and reorganize the process. The following is a specific example:

[0092] Example: Streamlining order processing

[0093] Original process: Customer places order → Customer service confirms order information → Warehouse prepares goods → Logistics delivers goods → Customer signs for goods → Customer service returns visit

[0094] Optimized process: Customer places order (system automatically verifies information) → Warehouse stocking (system synchronizes order information in real time) → Logistics delivery (system automatically assigns the optimal route) → Customer sign-off (system automatically sends confirmation notification) → Customer service return visit (targeted return visit based on system data analysis)

[0095] The present invention eliminates the step of customer service confirming order information and replaces it with automatic verification by the system; at the same time, the logistics delivery link also reduces human intervention and decision-making time by automatically allocating the optimal path through the system. By streamlining the process, unnecessary steps and waiting time are reduced, and the efficiency and accuracy of order processing are improved.

[0096] In a specific embodiment of the present invention, the information integration module uses a standardized interface to connect the information systems of all parties in the supply chain, and collects data from the information systems in real time; and performs data mapping and conversion on the data collected in real time to form a unified data format, and decodes and encodes the data collected in real time; synchronizes and updates the supply chain data in real time and publishes it to the information systems of all parties in the supply chain; and sets data permissions to enable authorized supply chain users or systems to access or modify data.

[0097] In a specific embodiment of the present invention, the big data analysis and artificial intelligence module includes a data storage module and a data analysis module;

[0098] The data storage module stores real-time data and historical data of each link of the supply chain collected by various data sources;

[0099] The data analysis module uses a distributed computing framework to process the stored data, and uses a data mining algorithm to mine patterns and association rules in the data to obtain supply chain characteristics, uses a machine learning algorithm to build a prediction model, and uses the supply chain characteristics to train the prediction model to obtain a trained prediction model, and uses the prediction model to predict supply chain trends to obtain prediction results; specifies decisions based on the prediction results, and uses an optimization algorithm to solve optimization problems in the supply chain; uses an intelligent recommendation algorithm to formulate personalized procurement and sales suggestions; collects real-time data fed back according to optimization problems, personalized procurement and sales suggestions through a feedback mechanism, and continuously improves the data mining algorithm and prediction model based on the fed back real-time data.

[0100] Use distributed computing frameworks (such as Hadoop, Spark, etc.) to efficiently process massive data.

[0101] Use data mining algorithms (such as association rule mining, cluster analysis, etc.) to discover patterns and association rules in the data and reveal potential problems and opportunities in the supply chain. Display analysis results through visualization tools so that decision makers can intuitively understand the meaning behind the data. Apply machine learning algorithms (such as supervised learning, unsupervised learning, reinforcement learning, etc.) to conduct deep learning and training on supply chain data. Build prediction models (such as time series prediction, demand prediction, etc.) to predict supply chain trends and provide a scientific basis for corporate decision-making. Use natural language processing technology to perform sentiment analysis and opinion mining on text data in the supply chain to understand customer feedback and market dynamics. Provide intelligent decision support for supply chain management based on the results of big data analysis and artificial intelligence technology. Solve optimization problems in the supply chain, such as inventory optimization and path optimization, through optimization algorithms (such as linear programming, genetic algorithms, etc.). Use intelligent recommendation systems to provide personalized suggestions for procurement, sales and other links to improve decision-making efficiency and accuracy. Establish a mechanism for continuous learning and iteration so that big data analysis and artificial intelligence technology can continuously adapt to changes in the supply chain environment. Collect actual operating results through feedback mechanisms and continuously optimize and improve analysis models and algorithms. Prediction model module: Build a prediction model based on historical data and real-time data to predict supply chain trends.

[0102] In the process of process optimization, the present invention actively introduces advanced technologies to improve efficiency. For example, the Internet of Things technology is used to track the status and location of goods in real time to reduce uncertainty in the logistics process. Big data analysis is used to deeply mine and analyze supply chain data to discover potential problems and optimization opportunities. Artificial intelligence technology is used to predict market demand and supply chain trends through machine learning algorithms to provide more accurate support for decision-making.

[0103] In order to achieve continuous optimization of the process, the present invention also establishes a real-time monitoring and feedback mechanism. Through integrated monitoring tools, real-time monitoring and data analysis are performed on each link of the supply chain. Once an abnormality or bottleneck link is found, feedback and adjustment are immediately made to ensure that the process is always in the optimal state.

[0104] Finally, each process of the supply chain is regularly evaluated and improved. This includes collecting user feedback, analyzing process data, evaluating optimization effects, etc. Based on the evaluation results, the process is further adjusted and optimized to adapt to market changes and business development needs.

[0105] Through the supply chain software of this embodiment, all parties in the supply chain can understand each other's work progress and needs in real time, strengthen communication and collaboration, and improve the overall efficiency of the supply chain.

[0106] In a specific embodiment of the present invention, using a data mining algorithm to mine patterns and association rules in data to obtain supply chain characteristics includes:

[0107] Use association rules to mine patterns and association rules in data, and use clustering algorithms to cluster the same type of data to obtain group characteristics;

[0108] The present invention has preprocessed the collected supply chain data. This includes steps such as data cleaning, deduplication, missing value processing, and data conversion. Through data cleaning, invalid and erroneous data are removed; deduplication and missing value processing ensure the accuracy and completeness of the data; data conversion converts the data into a format suitable for processing by machine learning algorithms. Next, feature extraction and selection are performed. This is a key step in machine learning, which aims to extract features from raw data that are useful for prediction or classification tasks. For example, in supply chain data, features such as order quantity, inventory, shipping time, and customer demand can be extracted.

[0109] In terms of feature selection, the present invention automatically screens out the features that have the greatest impact on the prediction results by introducing a feature importance evaluation algorithm (such as feature importance evaluation in random forests, gradient-based feature selection, etc.), thereby improving the accuracy and efficiency of the model.

[0110] Compare and analyze the group characteristics of different groups to obtain the difference characteristics and common characteristics between groups;

[0111] The difference characteristics and common characteristics are taken as supply chain characteristics.

[0112] In a specific embodiment of the present invention, the prediction model is constructed by using a machine learning algorithm, and the prediction model is trained by using supply chain characteristics to obtain a trained prediction model, and the prediction result is obtained by using the prediction model to predict the supply chain trend, including:

[0113] A prediction model is constructed using a machine learning algorithm, and the prediction model is trained using supply chain characteristics, and hyperparameters are optimized during the training process to obtain a trained prediction model;

[0114] Using the forecasting model to forecast supply chain trends to obtain forecasting results;

[0115] The prediction results are used to calculate the performance parameters of the prediction model. If the performance parameters do not meet the performance requirements, the hyperparameters of the prediction model are adjusted to retrain it.

[0116] After feature extraction and selection are completed, a suitable machine learning algorithm is selected for model training. Depending on the specific task (such as demand forecasting, inventory optimization, etc.), regression algorithms, classification algorithms, clustering algorithms, etc. can be selected. In the model training process, the present invention adopts a variety of optimization strategies. For example, cross-validation is used to avoid overfitting, hyperparameters are optimized through grid search or random search, and the generalization ability of the model is improved through integrated learning methods (such as Bagging, Boosting, etc.).

[0117] In supply chain data, there are often the same types of data features, such as delivery time of different suppliers, inventory turnover rate of different products, etc. In order to deeply explore the laws behind these features, this paper adopts the following strategies:

[0118] Use clustering algorithms (such as K-Means, DBSCAN, etc.) to cluster data features of the same type and discover potential group characteristics. Compare and analyze the characteristics of different groups to find out the differences and commonalities between groups. For example, by comparing the delivery time of different suppliers, you can find out which suppliers are more reliable and efficient.

[0119] In order to better understand the data and the output results of the model, the present invention also uses feature analysis and visualization methods. For example, heat maps are used to show the correlation between features, and box plots are used to show the distribution of data. These methods not only help to find outliers and potential problems in the data, but also improve the understanding and acceptance of decision makers.

[0120] Finally, the trained model is evaluated. The performance of the model is evaluated by calculating indicators such as accuracy, recall, and F1 score. If the model performance is poor, it returns to the feature extraction and selection or model training and optimization steps for adjustment. This iterative process ensures continuous improvement and optimization of the model.

[0121] In a specific embodiment of the present invention, there is a two-way authentication mechanism between the data security and privacy protection module and the supply chain software platforms of all parties. The data security and privacy protection module authenticates the identity of users accessing the software system and performs data integrity authentication on the received data, and displays the accessed data to the user after the identity authentication is passed.

[0122] A two-way authentication mechanism is adopted between the supply chain software platform and the system of the present invention. The specific steps are as follows:

[0123] The supply chain software platform sends an authentication request to the software system of the present invention, which includes the identity information and public key of the platform. After receiving the authentication request, the system of the present invention uses the public key of the platform to verify the request and confirm the authenticity of the request. After the verification is passed, the system of the present invention sends its own identity information and public key to the supply chain software platform for the platform to perform reverse authentication. After receiving the authentication information of the system of the present invention, the supply chain software platform uses the public key of the system to verify and confirm the identity of the system. After the two-way authentication is successful, the two parties establish a secure communication channel and start transmitting data.

[0124] In the process of data sharing and intercommunication, attention should also be paid to data security and privacy protection. Therefore, strict data permission management strategies need to be implemented to ensure that only authorized users or systems can access and modify data.

[0125] The present invention realizes the real-time collection, processing and transmission of data from all links of the supply chain, ensures the timeliness and accuracy of the data, and provides technical support for rapid response to market changes. An integrated collaborative management platform module is constructed to realize real-time sharing and collaboration of information among all parties in the supply chain, and improve the transparency and collaborative efficiency of the supply chain. By using big data analysis and artificial intelligence technology, deep mining and analysis of supply chain data are carried out, providing accurate decision support and trend forecasting, and enhancing the market insight and competitiveness of enterprises. Advanced data encryption and privacy protection technologies are designed to ensure the security of the supply chain software platform when processing sensitive data, eliminating the company's concerns about data leakage. The technical solution of the present invention adopts a modular design, and each functional module can run independently or work together. At the same time, it has good scalability and can meet the personalized needs of different enterprises.

[0126] It is worth noting that the terms "first" and "second" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0127] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of components or steps.

[0128] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for reducing costs and increasing efficiency based on a supply chain system, characterized in that: include: Build a software system that integrates real-time data processing and transmission modules, collaborative management of various supply chain information platform modules, big data analysis and artificial intelligence modules, and data security and privacy protection modules; Using the real-time data processing and transmission module, data from all links of the supply chain are acquired, processed using data processing algorithms, and the processed data are transmitted to the supply chain software platform using high-speed transmission technology; Utilize the collaborative management of various supply chain information platform modules to connect the information systems of all parties in the supply chain to synchronize and share real-time data, and provide an online collaborative platform for collaboration and communication for all parties in the supply chain; monitor and optimize each link of the supply chain, and update it to the supply chain software platform in real time; Using big data analysis and artificial intelligence modules to store batch data of the supply chain, and using big data analysis and artificial intelligence technologies to conduct in-depth analysis and mining of the stored data, and display it on the supply chain software platform; The data security and privacy protection module is used to verify the supply chain software platform and the information systems of all parties in the supply chain using a two-way authentication mechanism.

2. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 1 is characterized in that: The real-time data processing and transmission module includes a data acquisition module, a data processing module, a data transmission module and a data display module; The data collection module is used to collect data from each link of the supply chain in real time; The data processing module is used to divide the collected data into multiple small batches of data and set an asynchronous processing mechanism; clean, verify, convert, and optimize the data processing logic of each small batch of data according to the asynchronous processing mechanism to obtain processed data; The data transmission module is used to compress the processed data and transmit the compressed data to the supply chain software platform via the TCP / IP protocol or the UDP protocol; The data display module is used to display the real-time collected data on the supply chain software platform and provide visual data analysis and monitoring functions.

3. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 2 is characterized in that: The data processing module is specifically used for: For each small batch of data, a hash algorithm is used to detect duplicate data in the small batch of data to obtain deduplicated data; Using data validation rules to identify and correct errors in the deduplicated data to obtain error-free data; Deleting or replacing data that does not conform to business logic or format in the error-free data to obtain cleaned data; Verify the cleaned data using a data check code or a data integrity verification algorithm to obtain verified data; Converting the verified data into a unified format and standard to obtain converted data; The converted data is stored using a storage medium and a database management system, and index optimization or partition optimization is performed on the database or storage medium storing the data.

4. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 2 is characterized in that: The data transmission module is specifically used for: The converted data is compressed using a data compression technology, and the compressed data is encrypted using a data encryption technology to obtain encrypted data; Re-authenticating the encrypted data; The re-authenticated encrypted data is transmitted to the supply chain software platform via TCP / IP or UDP protocol.

5. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 1 is characterized in that: The collaborative management modules of each supply chain information platform include: information integration module, collaborative work module and process management module; The information integration module is connected with the information systems of all parties in the supply chain to synchronize and share the real-time collected data in real time; The collaborative work module provides an online collaboration platform for all parties in the supply chain, so that all parties in the supply chain can communicate and collaborate synchronously on the online collaboration platform; The process management module uses Internet of Things technology, big data analysis technology and artificial intelligence technology to monitor and optimize various processes in the supply chain.

6. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 5 is characterized in that: The information integration module uses standardized interfaces to connect the information systems of all parties in the supply chain and collects data from the information systems in real time; And map and convert the real-time collected data to form a unified data format, and decode and encode the real-time collected data; Synchronize and update supply chain data in real time and publish it to the information systems of all parties in the supply chain; Set data permissions to allow authorized supply chain users or systems to access or modify data.

7. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 1 is characterized in that: The big data analysis and artificial intelligence module includes a data storage module and a data analysis module; The data storage module stores real-time data and historical data of each link of the supply chain collected by various data sources; The data analysis module uses a distributed computing framework to process the stored data, and uses a data mining algorithm to mine patterns and association rules in the data to obtain supply chain characteristics, uses a machine learning algorithm to build a prediction model, and uses the supply chain characteristics to train the prediction model to obtain a trained prediction model, and uses the prediction model to predict the supply chain trend to obtain a prediction result; specifies a decision based on the prediction result, and uses an optimization algorithm to solve the optimization problem in the supply chain; uses an intelligent recommendation algorithm to formulate personalized procurement and sales suggestions; Through the feedback mechanism, real-time data based on optimization problems, personalized procurement and sales suggestions are collected, and data mining algorithms and prediction models are continuously improved based on the real-time feedback data.

8. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 7 is characterized in that: Using data mining algorithms to mine patterns and association rules in data to obtain supply chain characteristics include: Use association rules to mine patterns and association rules in data, and use clustering algorithms to cluster the same type of data to obtain group characteristics; Compare and analyze the group characteristics of different groups to obtain the difference characteristics and common characteristics between groups; The difference characteristics and common characteristics are taken as supply chain characteristics.

9. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 7 is characterized in that: The method of using a machine learning algorithm to construct a prediction model, using supply chain characteristics to train the prediction model to obtain a trained prediction model, and using the prediction model to predict supply chain trends to obtain prediction results includes: A prediction model is constructed using a machine learning algorithm, and the prediction model is trained using supply chain characteristics, and hyperparameters are optimized during the training process to obtain a trained prediction model; Using the forecasting model to forecast supply chain trends to obtain forecasting results; The prediction results are used to calculate the performance parameters of the prediction model. If the performance parameters do not meet the performance requirements, the hyperparameters of the prediction model are adjusted to retrain it.

10. The method for reducing costs and increasing efficiency based on a supply chain system according to claim 1, characterized in that: There is a two-way authentication mechanism between the data security and privacy protection module and the supply chain software platforms of all parties. The data security and privacy protection module authenticates the identity of users accessing the software system and the data integrity of the received data, and displays the accessed data to the user after the identity authentication is passed.

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