Intelligent freight platform for supply chain management

Abstract

The invention discloses a supply chain management-oriented intelligent freight platform, which integrates artificial intelligence, big data analysis and Internet of Things technologies, and aims at optimizing the transportation efficiency, reducing the cost and improving the intelligent level of supply chain management. Route planning and freight demand prediction are carried out through an intelligent algorithm, and optimal route selection of cargo distribution and optimal configuration of vehicle resources are realized; meanwhile, transportation data are collected and analyzed by using a big data technology, and business insight and supply chain optimization management decision support are provided. And due to the introduction of the Internet of Things technology, real-time freight monitoring and vehicle tracking are realized, and the transparency and safety of transportation are enhanced. Besides, the platform also has high compatibility and expandability, and can be seamlessly connected with existing ERP, WMS and other systems of an enterprise to realize data sharing and cooperative work. According to the invention, the efficiency and response speed of supply chain management are significantly improved, and significant competitive advantages are brought to enterprises.

Description

Technical Field

[0001] The present invention relates to the fields of information technology, logistics management and supply chain management, and in particular to the problems of information asymmetry, low efficiency and high cost in current international freight services. An intelligent freight platform for supply chain management that integrates intelligence, integration and high efficiency is proposed. Background Art

[0002] As the current market economy develops from production-determined to circulation-dominated, the competitiveness of enterprises increasingly depends on circulation capabilities, and logistics has become an important factor for enterprises to cope with competition. The smart logistics and transportation system mainly includes order management, loading operations, dispatching and allocation, driving management, GPS vehicle positioning system, vehicle management, personnel management, data reporting, basic information maintenance, system management and other functional modules. Realizing the informatization, digitization and intelligent management of transportation enterprises can improve operational efficiency and reduce transportation costs.

[0003] Driven by the digital economy, advanced technologies such as artificial intelligence (AI), big data and the Internet of Things (IoT) are constantly reshaping the online freight industry. These technologies have improved transportation efficiency, reduced costs, and improved user experience, thus opening a new chapter in the intelligent logistics industry. With the rapid development of science and technology, the application of these technologies has penetrated into all aspects of the online freight industry, strengthening the level of intelligence in this industry.

[0004] Although artificial intelligence (AI), big data and the Internet of Things (IoT) have shown significant advantages in freight management, there are still some drawbacks. Compared with traditional methods, the specific challenges faced by these technologies in the implementation and application process are as follows:

[0005] 1. Lack of deep integration and intelligent decision-making

[0006] Disadvantages of existing technologies: Although AI technology has been applied in route planning, vehicle scheduling and freight demand forecasting, some freight platforms may not have yet achieved deep integration of these functions, resulting in incomplete and inaccurate intelligent decision support. This may lead to insufficient utilization of transportation efficiency and high costs.

[0007] Insufficient traditional methods: Traditional logistics companies completely lack intelligent decision-making support and rely more on manual judgment and experience, making efficiency and cost control more difficult.

[0008] 2. Insufficient data utilization and privacy protection

[0009] Disadvantages of existing technologies: Although big data technology can collect and analyze a large amount of transportation data, some companies are still insufficient in data utilization and fail to fully tap the value of data. At the same time, data privacy protection is also a major challenge. How to protect user privacy while using data has become an urgent problem to be solved.

[0010] Insufficient traditional methods: Although traditional logistics companies are able to collect certain data, they often lack effective data analysis and utilization capabilities, and the value of the data is not fully utilized.

[0011] 3. Limited monitoring and transparency

[0012] Disadvantages of existing technologies: IoT technology has made some progress in cargo monitoring and transportation transparency, but the monitoring capabilities of some freight platforms are still limited, and it is difficult for companies to understand the exact location and status of goods in a timely manner. In addition, some technologies may have problems with unstable signals or incomplete coverage, which affects the monitoring effect.

[0013] Insufficient traditional methods: Traditional freight platforms have obvious deficiencies in cargo monitoring and transportation transparency. It is difficult for companies to understand the status of cargo in real time, which affects transportation safety and customer satisfaction.

[0014] 4. System compatibility and integration difficulties

[0015] Disadvantages of existing technologies: There may be information gaps between different enterprises, industries, and upstream and downstream links of logistics, which makes it difficult to link systems and share data. The compatibility issues between different systems and technologies are still prominent, and differences in data formats, interface standards, etc. need to be resolved to achieve effective integration and collaborative work.

[0016] Insufficient traditional methods: Traditional logistics companies also have problems with system compatibility, but this is mainly due to the lack of standardized data formats and interface standards, which leads to poor information transmission and inefficient collaborative work. Summary of the invention

[0017] The purpose of the present invention is to provide an intelligent freight platform for supply chain management, which solves the shortcomings of traditional freight platforms in efficiency, cost and transparency through technical means such as intelligent matching, real-time monitoring, collaborative management and predictive optimization, provides efficient and intelligent solutions for supply chain management, and enhances the visibility and flexibility of the supply chain.

[0018] To achieve the above object, the present invention adopts the following technical solutions:

[0019] An intelligent freight platform for supply chain management, the platform comprising:

[0020] a) Intelligent matching system: Obtain data on the characteristics of the goods and the conditions of the carrier, and determine the optimal transportation plan based on big data analysis and machine learning algorithms;

[0021] b) Real-time monitoring system: Integrate IoT technology to collect real-time data of goods in transit through GPS trackers and sensor devices, and use detection models to detect anomalies;

[0022] c) Collaborative management platform: It provides a cloud-based service interface, allowing upstream and downstream enterprises in the supply chain to share order information, inventory status, and transportation status in real time, and improve overall efficiency through collaborative optimization models;

[0023] d) Intelligent prediction and optimization system: Based on historical data and real-time information, use prediction models to predict transportation demand, cost change trends and potential bottlenecks in the future, and adjust strategies through optimization models.

[0024] Furthermore, in the intelligent matching system, the characteristics of the goods include the type of goods, weight, volume, destination, transportation requirements, and urgency, and the conditions of the carrier include the type of transportation tool, transportation cost, transportation time, transportation capacity, and historical reputation, and the optimal transportation plan is calculated by the following formula:

[0025]

[0026] Among them, α, β, and γ are weight coefficients preset according to user needs, representing the relative importance of cost, time, and risk respectively; cost i, time i, and risk i represent the cost, time, and risk values ​​of the i-th carrier solution respectively.

[0027] Furthermore, the intelligent matching system further includes a dynamic adjustment mechanism, which uses the following formula and logic to evaluate and adjust the transportation plan in real time:

[0028] Real-time evaluation formula:

[0029]

[0030] Among them, Δ time and Δ cost represent the increase in the estimated transportation time and cost due to factors such as traffic congestion and weather changes, respectively; original time and original cost represent the estimated transportation time and cost before adjustment; λ is the cost weight coefficient set according to user preferences;

[0031] Logical judgment: When the adjustment index exceeds the preset threshold, the intelligent matching system automatically triggers the re-matching process to find a new optimal transportation solution.

[0032] Furthermore, in the real-time monitoring system, the real-time data includes position, speed, temperature, humidity, vibration, and the following detection model is used for abnormal detection:

[0033]

[0034] Among them, xj represents the real-time measurement value of the jth parameter, μj and σj represent the historical mean and standard deviation of the parameter respectively, and n is the number of monitored parameters. When the abnormal index exceeds the preset threshold, the system triggers an alarm.

[0035] Furthermore, the real-time monitoring system also includes a data visualization module, which realizes data visualization in the following ways:

[0036] Map display: Using geographic information system technology, the location of goods, transportation route, estimated arrival time and other information are marked on the map in real time;

[0037] Chart analysis: Display real-time data and historical trends of key parameters such as temperature, humidity and vibration of cargoes in the form of bar charts, line charts, pie charts, etc.

[0038] Alarm prompt: When abnormal data is monitored, an alarm message is sent to relevant personnel via pop-up windows, emails, and text messages, and the abnormal location and possible causes are marked.

[0039] Furthermore, in the collaborative management platform, upstream and downstream enterprises in the supply chain include suppliers, manufacturers, distributors, and end customers, and the overall efficiency is improved through the following collaborative optimization model:

[0040]

[0041] Among them, satisfaction k represents the satisfaction of the kth supply chain participant, response time k represents the average response time of the participant to the request, and m is the number of supply chain participants.

[0042] Furthermore, the collaborative management platform supports multiple data exchange formats and protocols, and the specific implementation methods are as follows:

[0043] Data format conversion: Provides a built-in data format converter that supports conversion between multiple data formats such as XML, JSON, and EDI;

[0044] Protocol adaptation: Integrate multiple communication protocol adapters including HTTP, FTP, and SMTP to achieve seamless connection and transmission of data between upstream and downstream enterprises in the supply chain;

[0045] Security authentication: Use SSL / TLS encryption technology to ensure security and integrity during data transmission.

[0046] Furthermore, the intelligent prediction and optimization system adjusts the strategy through the following optimization model:

[0047]

[0048] Among them, profit(s) and risk cost(s) represent the expected profit and risk cost when adopting the sth strategy, respectively.

[0049] Furthermore, the intelligent prediction and optimization system also includes a machine learning model training module, which realizes continuous optimization of the model in the following ways:

[0050] Data preprocessing: Clean and normalize the collected historical data and real-time data to improve the effect of model training.

[0051] Model training: Use supervised learning or unsupervised learning algorithms to train prediction models and optimization strategy models based on preprocessed data;

[0052] Model evaluation and selection: Evaluate the performance of the model through cross-validation and A / B testing, and select the optimal model for deployment;

[0053] Continuous learning: Set up regular or triggered model update mechanisms to continuously optimize model parameters and structure based on newly collected data.

[0054] The intelligent freight platform for supply chain management of the present invention aims to solve the problems existing in traditional freight platforms by integrating advanced technologies such as artificial intelligence, big data and the Internet of Things, and brings the following beneficial effects:

[0055] Improve transportation efficiency: Use artificial intelligence algorithms for intelligent route planning to achieve the best path selection for cargo distribution, thereby reducing transportation time and costs.

[0056] Enhance data-driven capabilities: Collect and analyze massive amounts of transportation data through big data technology, provide business insights and supply chain optimization management, help companies make more accurate operational decisions, and improve market competitiveness.

[0057] Improve monitoring and transparency: Use IoT technology to achieve real-time freight monitoring and vehicle tracking to ensure cargo safety and transportation transparency, while monitoring vehicle health, preventing breakdowns and reducing accidents.

[0058] Optimize user experience: The intelligent freight platform provides comprehensive decision support to help companies optimize inventory management, predict market fluctuations, and respond to changes in customer demand, thereby improving customer satisfaction and service quality.

[0059] Promoting intelligent and digital transformation: The construction of an intelligent freight platform will help promote the intelligent and digital transformation of the logistics industry, improve overall logistics efficiency and service levels, and lay the foundation for the sustainable development of the logistics industry.

[0060] To sum up, the intelligent freight platform for supply chain management can solve the problems existing in traditional freight platforms by integrating advanced technologies, bring many beneficial effects, and promote the intelligent and digital transformation of the logistics industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a structural diagram of an intelligent freight platform for supply chain management according to the present invention. DETAILED DESCRIPTION

[0062] 1. Technical solution content

[0063] The intelligent freight platform for supply chain management proposed in this invention is a comprehensive logistics solution that integrates advanced technologies such as big data, artificial intelligence, the Internet of Things and cloud computing. The platform aims to improve transportation efficiency, reduce costs, optimize the supply chain and provide customers with a better service experience through functions such as real-time data collection, intelligent matching, real-time monitoring and collaborative management.

[0064] 2. Implementation Method

[0065] 1. Intelligent matching system

[0066] The intelligent matching system uses big data analysis and machine learning algorithms to automatically calculate and recommend the optimal transportation plan based on the characteristics of the goods and the conditions of the carrier.

[0067] Data collection: IoT devices collect real-time information such as cargo weight, volume, destination, transportation requirements, and carriers’ transportation tool types, transportation costs, transportation time, and other data;

[0068] Matching algorithm: The optimal transportation solution is calculated using the following formula:

[0069]

[0070] Among them, α, β, and γ are weight coefficients preset according to user needs, representing the relative importance of cost, time, and risk respectively; cost i, time i, and risk i represent the cost, time, and risk values ​​of the i-th carrier solution respectively;

[0071] Dynamic adjustment: Based on real-time traffic information, weather changes and other factors, the dynamic adjustment mechanism is used to evaluate and adjust the transportation plan in real time. The dynamic evaluation formula is:

[0072]

[0073] Among them, Δ time and Δ cost represent the increase in the estimated transportation time and cost due to factors such as traffic congestion and weather changes, respectively; original time and original cost represent the estimated transportation time and cost before adjustment; λ is the cost weight coefficient set according to user preferences;

[0074] Logical judgment: When the adjustment index exceeds the preset threshold, the intelligent matching system automatically triggers the re-matching process to find a new optimal transportation solution.

[0075] The specific implementation process is:

[0076] 1.1 Data Collection Phase

[0077] Step 1.1.1: IoT device deployment

[0078] Deploy IoT devices such as sensors, RFID tags, and GPS trackers in warehouses, transport vehicles, and key logistics nodes;

[0079] Ensure that these devices can collect information on the weight, volume, destination, and transportation requirements of goods in real time and accurately;

[0080] Step 1.1.2: Enter carrier information

[0081] Establish a carrier database and enter data on the carrier's transportation tool type (such as truck, ship, airplane), transportation cost, transportation time, and historical reputation;

[0082] Regularly update and maintain carrier information to ensure data accuracy and timeliness.

[0083] 1.2 Matching algorithm application stage

[0084] Step 1.2.1: Weight coefficient setting

[0085] According to the transportation needs and preferences of enterprises, the weight coefficients α, β, γ of cost, time and risk are set;

[0086] These coefficients can be determined through expert evaluation, historical data analysis, etc., and can be adjusted according to actual conditions.

[0087] Step 1.2.2: Calculate the optimal solution

[0088] Using matching algorithms, we calculate the cost, time and risk of each carrier’s transportation solution based on the characteristics of the goods and the carrier’s conditions;

[0089] Substituting these values ​​into the optimal solution formula, we can calculate the total score for each carrier;

[0090] The carrier with the lowest score is selected as the provider of the best transportation solution.

[0091] 1.3 Dynamic Adjustment Phase

[0092] Step 1.3.1: Real-time information collection

[0093] Collect traffic information, weather changes and other data in real time through IoT devices, traffic information service platforms, weather forecast systems and other channels;

[0094] These data are processed and analyzed to obtain factors that may affect the transportation plan.

[0095] Step 1.3.2: Dynamic evaluation and adjustment

[0096] Calculate the adjustment index using the dynamic evaluation formula;

[0097] When the adjustment index exceeds the preset threshold, the rematching process is triggered;

[0098] During the rematching process, the characteristics of the goods and the conditions of the carrier are updated based on real-time information, and the optimal transportation plan is recalculated.

[0099] Step 1.3.3: Plan implementation and feedback

[0100] Send the recalculated optimal transportation plan to the carrier and the shipper;

[0101] Track the transportation process and collect real-time data during transportation, such as cargo location, transportation time, and cost;

[0102] Compare these data with expected values ​​and analyze the performance of the transportation plan;

[0103] Based on the execution results, the matching algorithm and dynamic adjustment mechanism are optimized and improved.

[0104] 1.4 Continuous Optimization Phase

[0105] Step 1.4.1: Historical data analysis

[0106] Regularly organize and analyze historical transportation data and summarize lessons learned during transportation;

[0107] According to the analysis results, adjust the weight coefficient, optimize the matching algorithm and dynamic adjustment mechanism.

[0108] Step 1.4.2: New technology application

[0109] Pay attention to new technologies and development trends in the logistics industry, such as artificial intelligence, blockchain, etc.;

[0110] Apply new technologies to intelligent matching systems to improve the intelligence and efficiency of the system.

[0111] Step 1.4.3: User feedback and communication

[0112] Establish a user feedback mechanism to collect users’ opinions and suggestions on the intelligent matching system;

[0113] Based on user feedback, the system is continuously improved and optimized to enhance user satisfaction.

[0114] Through the above implementation process, the intelligent matching system can calculate and recommend the optimal transportation plan in real time and accurately based on the characteristics of the goods and the conditions of the carrier; at the same time, through the dynamic adjustment mechanism, the system can evaluate and adjust the transportation plan in real time to adapt to the influence of real-time traffic information and weather changes.

[0115] 2. Real-time monitoring system

[0116] The real-time monitoring system collects real-time data of goods in transit through the Internet of Things technology, and performs anomaly detection and alarm prompts.

[0117] Data collection: Use GPS trackers and sensor devices to collect information about the location, speed, temperature, and humidity of goods;

[0118] Anomaly Detection: The following models are used for anomaly detection:

[0119]

[0120] Among them, xj represents the real-time measurement value of the jth parameter, μj and σj represent the historical average and standard deviation of the parameter respectively, and n is the number of monitored parameters. When the abnormal index exceeds the preset threshold, the system triggers an alarm;

[0121] Data visualization: Display the transportation path and location of goods through maps, and analyze the key parameters of goods through charts.

[0122] The specific implementation process is:

[0123] 2.1 System Preparation

[0124] 2.1.1 Clearly define monitoring objectives: Before implementation, it is necessary to clearly define the monitoring objectives of the real-time monitoring system, such as ensuring cargo safety, improving transportation efficiency, and reducing losses;

[0125] 2.1.2 System architecture design: Design a reasonable system architecture based on the monitoring objectives, including hardware equipment selection (such as GPS trackers, sensors), network layout, and software platform selection;

[0126] 2.1.3 Develop implementation plan: Develop a detailed implementation plan, including project schedule, personnel division of labor, and budget arrangements.

[0127] 2.2 Hardware deployment and data collection

[0128] 2.2.1 Hardware selection and procurement: According to the system architecture design, select appropriate hardware equipment, such as high-precision GPS trackers, temperature and humidity sensors, and purchase them;

[0129] 2.2.2 Equipment installation: Install GPS trackers and sensor devices on cargo, transport vehicles or key logistics nodes to ensure that the equipment can work stably and collect data in real time;

[0130] 2.2.3 Data collection and transmission: Configure the data transmission function of the device to ensure that the collected data can be transmitted to the monitoring center or cloud server in real time and accurately.

[0131] 2.3 Anomaly Detection Model Establishment and Testing

[0132] 2.3.1 Historical data analysis: Collect and organize historical transportation data, including the location, speed, temperature, and humidity information of the goods, for training and optimization of anomaly detection models;

[0133] 2.3.2 Model establishment: According to the anomaly detection formula, an anomaly detection model is established, where xj represents a parameter of the goods at a certain moment (such as temperature, humidity), μj represents the historical mean of the parameter, and σj represents the historical standard deviation of the parameter. By calculating the anomaly index, it can be determined whether the current state of the goods is abnormal;

[0134] 2.3.3 Threshold setting: According to historical data and business needs, set a reasonable abnormal index threshold. When the abnormal index exceeds the threshold, an alarm is triggered;

[0135] 2.3.4 Model testing and optimization: Use historical data to test the model, evaluate the accuracy and stability of the model, and optimize and adjust the model based on the test results to improve the performance of the model.

[0136] 2.4 Data visualization and alarm prompts

[0137] 2.4.1 Data visualization platform development: Develop a data visualization platform to display the transportation route, location and real-time changes of key parameters (such as temperature and humidity) of the goods. The platform can be displayed in various forms such as maps and charts to facilitate users to intuitively understand the status of the goods;

[0138] 2.4.2 Implementation of alarm prompt function: Integrate the alarm prompt function in the data visualization platform. When the anomaly detection model triggers an alarm, the platform can display the alarm information in real time and notify relevant personnel through SMS and email;

[0139] 2.4.3 User authority management: Set the authority of different user roles according to business needs. For example, the administrator can view the transportation status of all goods, while ordinary users can only view the goods they are responsible for.

[0140] 2.5 System Testing and Optimization

[0141] 2.5.1 Functional testing: Test various functions of the real-time monitoring system, including data collection, anomaly detection, data visualization, and alarm prompts, to ensure that the system can operate normally and meet business needs;

[0142] 2.5.2 Performance testing: Test the performance of the system, including response speed, stability, and reliability. Based on the test results, optimize and adjust the system to improve system performance and stability.

[0143] 2.6 Operation and maintenance support and continuous improvement

[0144] 2.6.1 User training: Provide training to system users, including operation methods and precautions, to ensure that users can use the system proficiently and give full play to its functions;

[0145] 2.6.2 Operation and maintenance support: Provide system operation and maintenance support services, including troubleshooting, system upgrades, and regular maintenance of system hardware equipment to ensure that the equipment can work stably in the long term;

[0146] 2.6.3 Continuous Improvement: Continuously improve and optimize the system based on user feedback and actual operation conditions, for example, optimize anomaly detection models, add new monitoring parameters, and continuously improve the performance and functionality of the system.

[0147] Through the above implementation process, the real-time monitoring system can realize real-time data collection, anomaly detection and alarm prompts of goods in transit, providing strong guarantees for the company's logistics and transportation.

[0148] 3. Collaborative management platform

[0149] The collaborative management platform supports data sharing and collaborative work between upstream and downstream companies in the supply chain.

[0150] Data exchange: Provides data format converters and protocol adapters, supports conversion between multiple data formats such as XML, JSON, and EDI, and adaption to multiple communication protocols;

[0151] Information sharing: Cloud computing technology is used to achieve information sharing between upstream and downstream enterprises in the supply chain, including order information, inventory status, and transportation status;

[0152] Collaborative optimization: Use collaborative optimization model to improve overall efficiency. The model formula is:

[0153]

[0154] Among them, satisfaction k represents the satisfaction of the kth supply chain participant, response time k represents the average response time of the participant to the request, and m is the number of supply chain participants.

[0155] The specific implementation process is:

[0156] 3.1 Demand Analysis and Planning

[0157] 3.1.1 Demand Research:

[0158] Conduct in-depth communication with upstream and downstream companies in the supply chain to understand their business needs, data sharing and specific scenarios of collaborative work;

[0159] Collect the requirements of each enterprise on data format, communication protocol, and information sharing content;

[0160] 3.1.2 Goal Setting:

[0161] Clarify the construction goals of the collaborative management platform, such as improving collaborative efficiency, reducing operating costs, and enhancing supply chain transparency;

[0162] Set corresponding performance indicators and evaluation criteria according to the goals;

[0163] 3.1.3 Technical planning:

[0164] Determine the technical architecture of the collaborative management platform, including the data exchange layer, information sharing layer, and collaborative optimization layer;

[0165] Choose appropriate technical solutions, such as data format converters, protocol adapters, and cloud computing platforms.

[0166] 3.2 Platform design and development

[0167] 3.2.1 Data exchange layer design:

[0168] Design data format converters to support conversion between multiple data formats including XML, JSON, and EDI;

[0169] Develop protocol adapters to adapt to various communication protocols to ensure smooth data transmission;

[0170] 3.2.2 Information sharing layer design:

[0171] Build a cloud computing platform to achieve information sharing between upstream and downstream enterprises in the supply chain;

[0172] Design information sharing interface and interaction logic to ensure the accuracy and timeliness of information;

[0173] 3.2.3 Collaborative optimization layer design:

[0174] According to the collaborative optimization model formula, design algorithms and logic to achieve the calculation and optimization of collaborative efficiency;

[0175] Determine metrics and data collection methods for satisfaction and response time;

[0176] 3.2.3 System development and integration:

[0177] Develop various modules and functions of the collaborative management platform according to the design plan;

[0178] Carry out system integration and testing to ensure the coordination between modules and the accuracy of data.

[0179] 3.3 Platform deployment and training

[0180] 3.3.1 Platform deployment:

[0181] Deploy the collaborative management platform on a suitable server to ensure the stability and security of the system;

[0182] Perform system configuration and debugging to ensure the normal operation of various functions;

[0183] 3.3.2 User training:

[0184] Provide training to users of upstream and downstream enterprises in the supply chain, including how to use the platform, data sharing and collaborative work processes;

[0185] Provide operation manuals and online help documents for users to refer to at any time.

[0186] 3.4 Platform operation and optimization

[0187] 3.4.1 Daily operation:

[0188] Monitor the daily operation of the collaborative management platform and handle faults and exceptions in a timely manner;

[0189] Back up data regularly to ensure data security and integrity;

[0190] 3.4.2 Performance evaluation and optimization:

[0191] Evaluate the performance of the collaborative management platform based on the set performance indicators and evaluation criteria;

[0192] Based on the evaluation results, optimize and improve the platform to improve collaboration efficiency;

[0193] 3.4.3 Continuous update and maintenance:

[0194] Continuously update the functions and performance of the collaborative management platform according to the needs and technological development of upstream and downstream enterprises in the supply chain;

[0195] Provide technical support and maintenance services to ensure the stable operation and sustainable development of the platform.

[0196] 3.5 Evaluation and feedback of synergy effects

[0197] 3.5.1 Evaluation of synergistic effects:

[0198] Evaluate the effectiveness of the collaborative management platform by regularly collecting and analyzing feedback from upstream and downstream companies in the supply chain;

[0199] Adjust and optimize the strategies and functions of the collaborative management platform based on the evaluation results;

[0200] 3.5.2 Continuous improvement and innovation:

[0201] Pay attention to industry dynamics and technological development trends, and constantly explore and innovate new models and methods of collaborative management;

[0202] Strengthen cooperation and communication with upstream and downstream enterprises in the supply chain to jointly promote the optimization and development of the supply chain.

[0203] Through the above implementation process, the collaborative management platform can realize data sharing and collaborative work between upstream and downstream enterprises in the supply chain, improve overall efficiency, reduce operating costs, and enhance the transparency and competitiveness of the supply chain.

[0204] 4. Intelligent prediction and optimization system

[0205] The intelligent forecasting and optimization system predicts future demand and optimizes strategies based on historical data and real-time information.

[0206] Data preprocessing: cleaning, normalization and other preprocessing operations on historical data and real-time data;

[0207] Prediction model: Use time series analysis, regression analysis and other prediction models to predict transportation demand and cost change trends in the future;

[0208] Optimization strategy: Use the optimization model to adjust the strategy. The model formula is:

[0209]

[0210] Among them, profit (s) and risk cost (s0) represent the expected profit and risk cost when adopting the sth strategy;

[0211] Model training: Set up a machine learning model training module to continuously optimize the prediction model and optimization strategy model based on new data.

[0212] The specific implementation process is:

[0213] 4.1 Data Preprocessing

[0214] 4.1.1 Data Collection:

[0215] Collect historical data and real-time information from upstream and downstream supply chain companies, logistics systems, marketing departments and other channels;

[0216] Ensure the integrity, accuracy and timeliness of data;

[0217] 4.1.2 Data cleaning:

[0218] Identify and eliminate abnormal data, duplicate data, and missing data;

[0219] De-noise the data to improve data quality;

[0220] 4.1.3 Data Normalization:

[0221] Convert data to a uniform format and range to eliminate dimensional differences;

[0222] Improve the consistency and comparability of data.

[0223] 4.2 Prediction model construction

[0224] 4.2.1 Model selection:

[0225] Select appropriate time series analysis models (such as ARIMA, LSTM) or regression analysis models (such as linear regression, random forest) based on business needs and data characteristics;

[0226] 4.2.1.1 Taking the LSTM model to predict freight demand as an example

[0227] Model selection

[0228] Taking into account that freight demand data may have time series characteristics, that is, historical data has a certain impact on future data, and considering the complexity and nonlinear relationship of the data, the long short-term memory network (LSTM) is selected as the prediction model. LSTM is a special recurrent neural network (RNN) that is particularly suitable for processing and predicting long-term dependencies in time series data.

[0229] 4.2.1.2 Specific analysis implementation method

[0230] Data preparation

[0231] Collect historical freight demand data, including time series of freight volume, cargo type, shipping destination, etc.

[0232] Preprocess the data, including missing value filling, outlier processing and data normalization.

[0233] Feature Engineering

[0234] According to business needs, select features that have an impact on freight demand, such as seasonal factors (such as holidays, quarterly changes), economic indicators (such as GDP growth rate), transportation costs, etc.

[0235] The features are encoded and transformed to meet the input requirements of the LSTM model.

[0236] Model building

[0237] Build an LSTM model using a deep learning framework such as TensorFlow or PyTorch.

[0238] Set the input layer, hidden layer, and output layer of the model, and choose appropriate activation functions and optimization algorithms.

[0239] Adjust the model's hyperparameters, such as the number of hidden layers, number of neurons, learning rate, etc., according to the size and characteristics of the data.

[0240] Model Training

[0241] The preprocessed data is divided into training set and test set.

[0242] Use the training set data to train the LSTM model and monitor the model's loss function and evaluation indicators.

[0243] Based on the training results, adjust the model's hyperparameters and optimization algorithms to improve the model's predictive performance.

[0244] Model Evaluation

[0245] Use the test set data to evaluate the trained LSTM model and calculate the prediction error and evaluation indicators (such as mean square error MSE, mean absolute error MAE, etc.).

[0246] Compare the prediction performance of different models (such as ARIMA, linear regression, etc.) to verify the superiority of the LSTM model.

[0247] Model deployment

[0248] Deploy the trained LSTM model into the freight management system to achieve real-time freight demand forecasting.

[0249] Based on the forecast results, optimize decisions such as transportation planning, vehicle scheduling and inventory management.

[0250] 4.2.1.3 Analysis process and analysis results

[0251] Analysis process:

[0252] Data collection and preprocessing: Freight demand data for the past year was collected, and missing values ​​and outliers were processed. At the same time, the data was normalized to improve the training efficiency and prediction performance of the model.

[0253] Feature selection and encoding: Based on business requirements and data characteristics, features such as seasonal factors, economic indicators, and transportation costs were selected as model inputs, and these features were encoded and converted to meet the input requirements of the LSTM model.

[0254] Model training and evaluation: We built an LSTM model and trained it using the training set data. During the training process, we continuously adjusted the model’s hyperparameters and optimized the algorithm to improve the model’s prediction performance. Finally, we evaluated the model using the test set data, and calculated the prediction error and evaluation indicators.

[0255] Analysis results:

[0256] By comparing the prediction performance of different models, it is found that the LSTM model has higher accuracy in predicting freight demand. Specifically, the mean square error (MSE) and mean absolute error (MAE) of the LSTM model are lower than those of other models (such as ARIMA, linear regression, etc.), which shows that the LSTM model can better capture the time series characteristics and nonlinear relationships in freight demand data, thereby improving the accuracy of the prediction.

[0257] In addition, it is found that the LSTM model has a high sensitivity in predicting seasonal changes and long-term trends, which helps to better understand the changing patterns of freight demand and formulate corresponding transportation plans and inventory management strategies.

[0258] In summary, by building an LSTM model for freight demand forecasting, we can improve the accuracy of the forecast and optimize decisions such as transportation planning and inventory management, thereby bringing greater economic benefits to the enterprise.

[0259] 4.2.2 Model training:

[0260] Use historical data to train the prediction model and adjust the model parameters;

[0261] Optimize model performance through cross-validation, grid search and other methods;

[0262] 4.2.3 Prediction Verification:

[0263] Use validation set data to validate the prediction model and evaluate the prediction accuracy and generalization ability of the model;

[0264] Adjust and optimize the model based on the verification results.

[0265] 4.3 Optimization strategy formulation

[0266] 4.3.1 Strategy set construction:

[0267] Build a variety of possible strategy sets based on business needs and market environment;

[0268] Each strategy set contains multiple specific strategy solutions;

[0269] 4.3.2 Optimize model construction:

[0270] According to the optimization strategy model formula, build a calculation model for profit and risk cost;

[0271]

[0272] Among them, profit (s) and risk cost (s) represent the expected profit and risk cost when adopting the sth strategy respectively;

[0273] Ensure the accuracy and reliability of the model;

[0274] 4.3.3 Strategy evaluation and optimization:

[0275] Use the optimization model to evaluate each strategy in the strategy set and calculate its profit and risk cost;

[0276] Select the optimal strategy based on the evaluation results, and make adjustments and optimizations.

[0277] 4.4 Model Training and Optimization

[0278] 4.4.1 Machine Learning Module Settings:

[0279] Set up machine learning model training modules, including data input, model training, model evaluation and other modules;

[0280] Ensure collaboration and data flow between modules;

[0281] 4.4.2 Continuous model optimization:

[0282] Continuously train and optimize the prediction model and optimization strategy model based on new data;

[0283] Monitor the performance changes of the model and adjust the model parameters and structure in a timely manner;

[0284] 4.4.3 Model verification and testing:

[0285] Use the test set data to verify and test the optimized model;

[0286] Ensure the stability and reliability of the model in practical applications.

[0287] Specifically,

[0288] When building a machine learning model training module, you need to ensure the coordination and data flow between modules such as data input, model training, and model evaluation. The following is a detailed setting plan:

[0289] 1. Data input module

[0290] Data source: Historical freight demand data is extracted from the database, including time series freight volume, cargo type, transportation destination, etc.

[0291] Data preprocessing: Clean the raw data, including processing missing values, outliers, data normalization, etc., to ensure the quality and consistency of the data.

[0292] Feature Engineering: Select and encode features based on business needs, such as seasonal factors, economic indicators, etc., to suit the input requirements of the model.

[0293] 2. Model training module

[0294] Model selection: Select appropriate machine learning models based on business needs and data characteristics, such as LSTM (Long Short-Term Memory Network) for time series prediction.

[0295] Hyperparameter setting: Adjust the model’s hyperparameters, such as the number of hidden layers, number of neurons, learning rate, etc., to optimize the model’s performance.

[0296] Training process: Use the preprocessed data to train the model and monitor the loss function and evaluation indicators during the training process.

[0297] 3. Model Evaluation Module

[0298] Evaluation Metrics: Choose appropriate evaluation metrics such as mean squared error (MSE), mean absolute error (MAE), etc. to measure the predictive performance of the model.

[0299] Cross-validation: Use cross-validation to evaluate the model to reduce the risk of overfitting.

[0300] Performance analysis: Analyze the model’s predictions, identify potential sources of error, and propose improvements.

[0301] 4. Data circulation and collaborative work

[0302] Data pipeline: Establish a data pipeline to ensure smooth flow of data between modules.

[0303] Module collaboration: Ensure the collaboration between modules such as data input, model training, and model evaluation to achieve an automated and efficient model training process.

[0304] Continuous model optimization

[0305] In order to maintain the accuracy and adaptability of the model, we need to continuously train and optimize the prediction model and optimization strategy model based on new data. The following is a continuous optimization plan:

[0306] Data update: Regularly collect new freight demand data and perform preprocessing and feature engineering.

[0307] Model retraining: Retrain the model using new data to update the model’s weights and parameters.

[0308] Performance monitoring: Monitor the performance changes of the model, including prediction accuracy and computational efficiency.

[0309] Parameter adjustment: According to the performance monitoring results, timely adjust the model's hyperparameters and structure to improve the model's performance.

[0310] Model Validation and Testing

[0311] After model optimization, we need to use the test set data to verify and test the model to ensure the stability and reliability of the model in practical applications. The following is a verification and testing plan:

[0312] Test set selection: Select test set data independent of the training set and validation set.

[0313] Prediction performance evaluation: Use the test set data to make predictions for the model and calculate evaluation metrics to measure the prediction performance of the model.

[0314] Stability analysis: Analyze the prediction results of the model in different scenarios to ensure the stability and reliability of the model under different conditions.

[0315] Error rate monitoring: Monitor the error rate of the model and adjust the model as needed to improve prediction accuracy.

[0316] Model examples before and after training

[0317] Pre-training model instance:

[0318] Model structure: Simple linear regression model.

[0319] Hyperparameters: No specific hyperparameter settings.

[0320] Prediction performance: There is a large deviation between the prediction results and the actual data, and the mean square error (MSE) is high.

[0321] Model instance after training:

[0322] Model structure: LSTM model, consisting of two hidden layers, each with 100 neurons.

[0323] Hyperparameters: learning rate is set to 0.001, batch size is set to 32, and number of training rounds is set to 100.

[0324] Prediction performance: The prediction results are close to the actual data, the mean square error (MSE) is significantly reduced, and the prediction accuracy and stability of the model are significantly improved.

[0325] Through the above settings and optimization schemes, the accuracy and reliability of machine learning models in freight demand forecasting can be ensured, providing strong support for the company's logistics management and optimization.

[0326] 4.5 System Implementation and Operation and Maintenance

[0327] 4.5.1 System deployment:

[0328] Deploy the intelligent prediction and optimization system on a suitable server to ensure the stability and security of the system;

[0329] Perform system configuration and debugging to ensure the normal operation of various functions;

[0330] 4.5.2 User training and support:

[0331] Provide training to users of upstream and downstream enterprises in the supply chain, including how to use the system, forecasting and optimization processes, etc.;

[0332] Provide operation manuals and online help documents for users to check at any time;

[0333] Set up a technical support team to promptly resolve problems encountered by users during use;

[0334] 4.5.3 System operation and monitoring:

[0335] Monitor the daily operation of the system and handle faults and anomalies in a timely manner;

[0336] Back up data regularly to ensure data security and integrity;

[0337] Continuously update and optimize system functions according to business needs and technological development.

[0338] 4.6 Effect Evaluation and Feedback

[0339] 4.6.1 Effect evaluation:

[0340] Evaluate the effectiveness of the intelligent forecasting and optimization system by regularly collecting and analyzing feedback from upstream and downstream companies in the supply chain;

[0341] Adjust and optimize the system's strategies and functions based on the evaluation results;

[0342] 4.6.2 Continuous improvement and innovation:

[0343] Pay attention to industry dynamics and technology development trends, and continuously explore and innovate new models and methods for intelligent prediction and optimization systems;

[0344] Strengthen cooperation and communication with upstream and downstream enterprises in the supply chain to jointly promote the optimization and development of the supply chain.

[0345] Through the above implementation process, the intelligent forecasting and optimization system can achieve accurate forecasting of future demand and optimal adjustment of strategies, thereby improving the collaborative efficiency and overall benefits of the supply chain.

[0346] 3. Implementation Effect:

[0347] 1. Transportation efficiency is significantly improved

[0348] Path Optimization:

[0349] Through intelligent algorithms, the platform can automatically plan the optimal transportation route, reduce transportation time and mileage, and thus improve transportation efficiency;

[0350] Real-time scheduling:

[0351] Monitor vehicle location and transportation status in real time, dynamically adjust transportation plans based on demand, ensure on-time delivery of goods, and reduce waiting and delay time;

[0352] Collaborative work:

[0353] Promote information sharing and collaborative operations between upstream and downstream enterprises in the supply chain, improve overall transportation efficiency and reduce resource waste.

[0354] 2. Effective cost reduction

[0355] Reduced fuel consumption:

[0356] By optimizing transportation routes and driving behaviors, unnecessary fuel consumption can be reduced, thus lowering transportation costs;

[0357] Reduced idle rate:

[0358] Monitor vehicle status in real time, arrange transportation tasks reasonably, reduce empty driving rate, and improve vehicle utilization rate;

[0359] Human Resources Optimization:

[0360] Automation and intelligent management reduce dependence on manual labor and reduce labor costs.

[0361] 3. Supply Chain Optimization

[0362] Inventory Management:

[0363] Through data analysis, we can predict future demand, optimize inventory management, and reduce inventory backlogs and out-of-stock situations;

[0364] Order Processing:

[0365] Automate the order processing process, increase the order processing speed and reduce the error rate;

[0366] Supplier Management:

[0367] Evaluate supplier performance, optimize supplier selection, and improve supply chain stability and reliability.

[0368] 4. Improve customer service

[0369] Real-time tracking:

[0370] Provide customers with real-time cargo tracking services to improve customer satisfaction and trust;

[0371] Personalized service:

[0372] Provide personalized transportation solutions and services according to customer needs to enhance customer stickiness;

[0373] Complaints handling:

[0374] Efficiently handle customer complaints and feedback to improve customer service quality and response speed.

[0375] 5. Sustainable development and green transformation

[0376] Energy saving and emission reduction:

[0377] Reduce carbon emissions and energy consumption by optimizing transportation routes and driving behaviors, and promote green logistics;

[0378] Environmentally friendly materials:

[0379] Encourage the use of environmentally friendly packaging materials and renewable resources to reduce the impact on the environment;

[0380] Social Responsibility:

[0381] Actively participate in social welfare activities, fulfill corporate social responsibilities, and enhance corporate image.

[0382] 6. Logistics Industry Change

[0383] Technological innovation:

[0384] Promote technological innovation and development in the logistics industry and improve the overall level of the industry;

[0385] Model innovation:

[0386] Explore new logistics and business models, such as shared logistics and unmanned delivery;

[0387] Talent cultivation:

[0388] The platform will cultivate and attract high-end talents who can significantly improve the efficiency of the logistics and transportation industry. It will also support the sustainable development and green transformation of enterprises and promote new changes in the logistics industry.

[0389] Specific application example 1:

[0390] 1. Platform Overview

[0391] XX Smart Logistics Supply Chain Collaboration Platform is a highly integrated and intelligent logistics management system that aims to achieve seamless connection and efficient collaboration between upstream and downstream enterprises in the supply chain through advanced technologies such as big data, cloud computing, and the Internet of Things. The platform not only covers traditional logistics management functions such as transportation scheduling and warehouse management, but also introduces advanced functions such as intelligent prediction, optimized decision-making, and real-time monitoring to comprehensively improve the overall efficiency of the supply chain.

[0392] 2. Specific Application

[0393] Data preprocessing

[0394] Data collection: The platform collects a large amount of transportation data from multiple data sources (such as GPS devices, ERP systems, WMS systems, etc.) every day, including transportation volume, transportation cost, transportation time, cargo type, vehicle information, etc. For example, the transportation volume data collected every day may reach 5,000, each of which contains information such as transportation date, cargo type (such as electronic products, fresh food), transportation volume (such as 20 tons), and transportation cost (such as 16,000 yuan).

[0395] Data cleaning: Clean the collected data to remove abnormal data, duplicate data, etc. For example, for data with abnormally high transportation costs (such as a certain transportation cost exceeding 50,000 yuan), manual verification will be carried out to confirm whether there are input errors or special circumstances (such as special goods, emergency transportation, etc.).

[0396] Data normalization: converting data of different dimensions into a unified range to improve the consistency and comparability of data. For example, converting the transportation cost into the cost per ton of goods (such as 800 yuan per ton) and converting the transportation time into hours (such as 24 hours).

[0397] Smart prediction

[0398] Transportation demand forecasting: Use time series analysis models (such as ARIMA models) and regression analysis models (such as multivariate linear regression models, random forest models) to forecast transportation demand. By inputting historical transportation data and market demand change data (such as seasonal demand changes, promotional activities, etc.), the model can predict transportation demand in the future. For example, it is predicted that the monthly transportation volume in the next three months will be 7,000 tons, 7,500 tons, and 8,000 tons respectively, and the distribution of cargo types will be 40% electronic products, 30% fresh food, and 30% other goods.

[0399] Transportation cost prediction: Based on factors such as oil price changes, vehicle maintenance costs, and labor costs, machine learning algorithms (such as LSTM models and gradient boosting tree models) are used to predict transportation costs. By inputting historical data of relevant factors, the model can predict transportation costs in the future. For example, it is predicted that the transportation cost per ton of goods in the next three months will be 85 yuan, 90 yuan, and 95 yuan respectively.

[0400] Optimization strategy

[0401] Multi-objective optimization model: Build a multi-objective optimization model, combine factors such as transportation network, vehicle resources, cargo type, etc., comprehensively consider factors such as profit, cost, risk, etc., use optimization algorithms (such as genetic algorithm, particle swarm algorithm) to evaluate each strategy, and select the optimal transportation strategy.

[0402] The model formula is: The model formula is:

[0403] Among them, profit (s) and risk cost (s) represent the expected profit and risk cost when adopting the sth strategy respectively;

[0404] Hypothetical strategy evaluation: Assume there are two strategies: Strategy A has a profit of 600,000 yuan and a risk cost of 150,000 yuan; Strategy B has a profit of 580,000 yuan, but a lower risk cost of 100,000 yuan. Through the optimization model, strategy B is selected as the optimal strategy.

[0405] Model training and updating: Set up a machine learning model training module to continuously update the prediction model and optimize the strategy model based on the newly collected data. Introduce an online learning mechanism to achieve real-time updating and adaptive adjustment of the model. For example, update the model once a week to adapt to market changes and business development needs.

[0406] Real-time monitoring and risk management

[0407] Cargo tracking: Using GPS devices and IoT technology, the location and transportation status of cargo can be monitored in real time. Users can view the real-time location, estimated arrival time and other information of cargo on the platform. For example, when a user needs to inquire about the transportation status of a certain cargo, the platform can display the location and transportation progress of the cargo in real time, including the current location, estimated arrival time, transportation speed and other information.

[0408] Risk warning: Through data analysis, potential transportation risks such as weather changes and traffic congestion can be identified and early warning can be issued. For example, when it is predicted that heavy rain will occur in a certain area in the future, the platform will notify the relevant transportation department in advance so that corresponding countermeasures can be taken, such as adjusting the transportation route and adding transportation vehicles. At the same time, the platform can also issue early warnings and alarms for abnormal situations in the transportation process (such as vehicle failures, cargo damage, etc.) based on historical data and real-time data.

[0409] Data Analysis and Visualization

[0410] Data analysis: Conduct in-depth analysis of transportation data to explore potential value and trends. For example, analyze the differences in transportation costs and transportation time of different types of goods to provide a basis for formulating more accurate transportation strategies; analyze abnormal situations during transportation, find out the reasons and take corresponding improvement measures. At the same time, the data of upstream and downstream companies in the supply chain can also be integrated and analyzed to achieve more accurate supply chain collaboration and optimization.

[0411] Data visualization: Use charts, reports and other forms to intuitively display analysis results to users. For example, a bar chart can be used to display the changes in transportation volume in different months; a line chart can be used to display the trend of transportation costs; a heat map can be used to display transportation demand and cargo type distribution in different regions. This can help users better understand the data and analysis results and make more informed decisions.

[0412] Supply Chain Collaboration System

[0413] Information sharing: Establish a supply chain information sharing platform to achieve real-time information sharing and collaborative work between upstream and downstream companies. Through API interfaces, data exchange platforms, etc., seamless data connection and exchange can be achieved. Introduce blockchain technology to ensure data security and immutability. For example, suppliers can share inventory information and production plans in real time so that the procurement department can adjust procurement plans in a timely manner; the sales department can share sales data and customer demand information in real time so that the production department can adjust production plans and inventory strategies in a timely manner.

[0414] Collaborative work: Automatically adjust production plans, procurement plans, etc. according to the needs and plans of upstream and downstream enterprises in the supply chain. Through technical means such as smart contracts, automatic settlement and payment between upstream and downstream enterprises in the supply chain can be realized. Introduce collaborative optimization algorithms (such as collaborative filtering algorithms, reinforcement learning algorithms, etc.) to optimize the overall efficiency of the supply chain. For example, when a supplier's inventory is insufficient, the platform can automatically trigger a procurement plan and notify relevant suppliers; when the sales data of a sales area is abnormal, the platform can automatically adjust the sales strategy and inventory plan.

[0415] 3. Implementation Effect

[0416] Improved transportation efficiency

[0417] By optimizing transportation routes and scheduling strategies, the average transportation time was shortened from 3.5 days to 2.8 days, and transportation efficiency was improved by approximately 20%.

[0418] Through real-time monitoring and early warning mechanisms, transportation delays caused by weather, traffic and other reasons have been effectively reduced. For example, in heavy rain weather, more than 90% of transportation delays have been successfully avoided by adjusting transportation routes in advance and adding transportation vehicles.

[0419] Cost reduction

[0420] By optimizing transportation strategies and refined management, the average transportation cost was reduced from 85 yuan per ton to 78 yuan, a cost reduction of approximately 8%.

[0421] Carbon emissions and energy consumption costs have been further reduced by adopting more environmentally friendly transportation methods and vehicle types, optimizing vehicle scheduling and other measures.

[0422] Supply Chain Optimization

[0423] Through data analysis and visualization, the collaborative efficiency of upstream and downstream enterprises in the supply chain has been improved. For example, by sharing transportation data and forecast results with suppliers, more accurate procurement plans and inventory management have been achieved; by sharing transportation data and customer demand information with the sales department, more accurate sales forecasts and order processing have been achieved.

[0424] Shipping data example

[0425] The following is a simulated supply chain transportation data example to illustrate how to optimize the supply chain through data analysis:

[0426] date Transportation routes Quantity of goods Shipping time (days) Cargo loss rate (%) 2024-12-01 A-B 1000 pieces 3 0.1 2024-12-03 B-C 1500 items 4 0.2 2024-12-05 A-D 800 items 2 0.05 ... ... ... ... ...

[0427] In this example, the data records the quantity, transportation time, and loss rate of goods on different dates and transportation routes. By analyzing these data, we can find abnormalities in transportation time and loss rate, and take corresponding optimization measures.

[0428] Example of prediction results

[0429] Based on the above transportation data, as well as historical sales data, market demand forecasts and other information, demand forecasts and transportation plan optimization can be performed through data analysis and algorithm models. The following is an example of a simulated forecast result:

[0430] Product A: The estimated demand is 5,000 pieces, mainly distributed in locations B and C.

[0431] Product B: The estimated demand is 3,000 pieces, mainly concentrated in location D.

[0432] Optimized transportation plan:

[0433] For product A, considering the large demand in places B and C, the transportation route can be optimized, the number of transfers can be reduced, and the transportation time can be shortened.

[0434] For product B, since it is mainly concentrated in location D, inventory management can be optimized and the goods can be shipped to a warehouse near location D in advance in order to quickly respond to market demand.

[0435] Specific forecast data and optimization effects:

[0436] By optimizing the transportation routes, it is estimated that the transportation time of Product A can be shortened by 10% and the cargo loss rate can be reduced by 5%.

[0437] By optimizing inventory management, it is expected that the inventory turnover rate of Product B can be increased by 20%, reducing the occurrence of inventory backlogs and out-of-stock situations.

[0438] Specific manifestations of improved collaborative efficiency

[0439] By sharing these transportation data and forecast results with suppliers, more accurate procurement planning and inventory management can be achieved. The specific performance is as follows:

[0440] Supplier collaboration: Suppliers can adjust production plans in advance based on demand forecast results to ensure on-time delivery. At the same time, by sharing transportation data, suppliers can understand transportation bottlenecks and delays and take measures in advance to avoid affecting supply.

[0441] Inventory management optimization: Based on demand forecast results, companies can optimize inventory management strategies to reduce inventory backlogs and stockouts. By monitoring inventory levels in real time, replenishment plans can be adjusted in a timely manner to ensure that inventory remains at a reasonable level.

[0442] Reduced transportation costs: By optimizing transportation routes and reducing the number of transfers, transportation costs and time can be reduced. At the same time, through data analysis, waste and bottleneck problems in the transportation process can be identified and corresponding measures can be taken to improve them.

[0443] In summary, data analysis and visualization technology can significantly improve the collaborative efficiency of upstream and downstream enterprises in the supply chain. Although it is impossible to provide specific real data, the simulated examples and logical analysis can still illustrate the importance and effectiveness of this process.

[0444] By optimizing inventory management strategies, we have reduced inventory backlogs and stockouts. For example, by real-time monitoring of inventory levels and sales trends, we can adjust inventory plans in a timely manner; by adopting advanced inventory management systems and forecasting models, we can achieve more accurate inventory control and replenishment strategies.

[0445] Improved customer service

[0446] It provides real-time cargo tracking services and personalized transportation solutions. Users can view the location and transportation progress of goods in real time on the platform, and learn about the estimated arrival time of goods and other information; at the same time, it provides users with personalized transportation solutions and services based on their needs and preferences.

[0447] Through data analysis, timely discover and solve problems encountered by customers during transportation. For example, by analyzing customer complaints and feedback data, find out the problems and improvement directions in the transportation process; through communication and consultation with customers, formulate and implement improvement measures. This helps to improve customer service quality and customer satisfaction.

[0448] Sustainable development and green transformation

[0449] Carbon emissions and energy consumption are reduced by optimizing transportation routes and driving behaviors. For example, carbon emissions and energy consumption are reduced by adopting more environmentally friendly transportation methods and vehicle types, optimizing transportation routes and driving speeds, and other measures.

[0450] The use of environmentally friendly packaging materials and renewable resources is encouraged. For example, the amount of packaging waste generated is reduced by adopting degradable packaging materials and reducing the amount of packaging materials used; at the same time, the recycling and conservation of resources is achieved through measures such as recycling and reusing waste packaging materials.

[0451] In summary, the application examples of the XX Smart Logistics Supply Chain Collaboration Platform demonstrate the great potential and practical application effects of the smart freight platform in supply chain management. By implementing this platform, enterprises can significantly improve transportation efficiency, reduce costs, optimize the supply chain, and provide customers with a better service experience. At the same time, the platform also supports the sustainable development and green transformation of enterprises, providing strong support for the new changes in the logistics industry.

[0452] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An intelligent freight platform for supply chain management, characterized in that: The platform includes: a) Intelligent matching system: Obtain data on the characteristics of the goods and the conditions of the carrier, and determine the optimal transportation plan based on big data analysis and machine learning algorithms; b) Real-time monitoring system: Integrate IoT technology to collect real-time data of goods in transit through GPS trackers and sensor devices, and use detection models to detect anomalies; c) Collaborative management platform: It provides a cloud-based service interface, allowing upstream and downstream enterprises in the supply chain to share order information, inventory status, and transportation status in real time, and improve overall efficiency through collaborative optimization models; d) Intelligent prediction and optimization system: Based on historical data and real-time information, use prediction models to predict transportation demand, cost change trends and potential bottlenecks in the future, and adjust strategies through optimization models.

2. The intelligent freight platform for supply chain management according to claim 1, characterized in that: In the intelligent matching system, the characteristics of the goods include the type of goods, weight, volume, destination, transportation requirements, and urgency, and the conditions of the carrier include the type of transportation tool, transportation cost, transportation time, transportation capacity, and historical reputation. The optimal transportation plan is calculated by the following formula: Among them, α, β, and γ are weight coefficients preset according to user needs, representing the relative importance of cost, time, and risk respectively; cost i ,time i ,risk i They represent the cost, time and risk value of the i-th carrier plan respectively.

3. The intelligent freight platform for supply chain management according to claim 1, characterized in that: The intelligent matching system further includes a dynamic adjustment mechanism that uses the following formula and logic to evaluate and adjust the transportation plan in real time: Real-time evaluation formula: Among them, Δ time and Δ cost represent the increase in the expected transportation time and cost due to factors such as traffic congestion and weather changes, respectively; original time and original cost represent the expected transportation time and cost before adjustment; λ is the cost weight coefficient set according to user preferences; Logical judgment: When the adjustment index exceeds the preset threshold, the intelligent matching system automatically triggers the re-matching process to find a new optimal transportation solution.

4. The intelligent freight platform for supply chain management according to claim 1, characterized in that: In the real-time monitoring system, real-time data includes location, speed, temperature, humidity, vibration, and the following detection models are used for anomaly detection: Among them, x j represents the real-time measurement value of the jth parameter, μ j and σ j They represent the historical mean and standard deviation of the parameter respectively, n is the number of monitored parameters, and when the abnormal index exceeds the preset threshold, the system triggers an alarm.

5. The intelligent freight platform for supply chain management according to claim 1, characterized in that: The real-time monitoring system also includes a data visualization module, which realizes data visualization in the following ways: Map display: Using geographic information system technology, the location of goods, transportation route, estimated arrival time and other information are marked on the map in real time; Chart analysis: Display real-time data and historical trends of key parameters such as temperature, humidity and vibration of cargoes in the form of bar charts, line charts, pie charts, etc. Alarm prompt: When abnormal data is monitored, an alarm message is sent to relevant personnel via pop-up windows, emails, and text messages, and the abnormal location and possible causes are marked.

6. The intelligent freight platform for supply chain management according to claim 1, characterized in that: In the collaborative management platform, upstream and downstream enterprises in the supply chain include suppliers, manufacturers, distributors, and end customers, and the overall efficiency is improved through the following collaborative optimization model: Among them, satisfaction k represents the satisfaction of the kth supply chain participant, the response time k It represents the average response time of the participant to the request, and m is the number of supply chain participants.

7. The intelligent freight platform for supply chain management according to claim 1, characterized in that: The collaborative management platform supports multiple data exchange formats and protocols, and the specific implementation methods are as follows: Data format conversion: Provides a built-in data format converter that supports conversion between multiple data formats such as XML, JSON, and EDI; Protocol adaptation: Integrate multiple communication protocol adapters including HTTP, FTP, and SMTP to achieve seamless connection and transmission of data between upstream and downstream enterprises in the supply chain; Security authentication: Use SSL / TLS encryption technology to ensure security and integrity during data transmission.

8. The intelligent freight platform for supply chain management according to claim 1, characterized in that: The intelligent prediction and optimization system adjusts the strategy through the following optimization model: Among them, profit(s) and risk cost(s) represent the expected profit and risk cost when adopting the sth strategy, respectively.

9. The intelligent freight platform for supply chain management according to claim 1, characterized in that: The intelligent prediction and optimization system also includes a machine learning model training module, which realizes continuous optimization of the model in the following ways: Data preprocessing: Clean and normalize the collected historical data and real-time data to improve the effect of model training. Model training: Use supervised learning or unsupervised learning algorithms to train prediction models and optimization strategy models based on preprocessed data; Model evaluation and selection: Evaluate the performance of the model through cross-validation and A / B testing, and select the optimal model for deployment; Continuous learning: Set up regular or triggered model update mechanisms to continuously optimize model parameters and structure based on newly collected data.

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