A traceability control method and system based on bulk commodity trading platform

Abstract

The present invention relates to the field of risk management and control technology, and in particular to a traceability management and control method and system based on a bulk commodity trading platform. The method comprises the following steps: obtaining entity transaction data from the bulk commodity trading platform and constructing a blockchain model; using the blockchain to perform data collection and anomaly detection, identifying abnormal transaction situations and dividing abnormal accounting nodes, extracting their link structure characteristics to obtain abnormal accounting node link data; obtaining logistics data through the bulk commodity trading platform, establishing a logistics sensor network, associating abnormal accounting nodes with logistics link space, and obtaining logistics link data of abnormal accounting nodes; using logistics link data and abnormal transaction data for correction, deriving transportation error data, and using the logistics sensor network to trace the transportation risk, and obtaining risk tracing data; analyzing compensation strategies based on risk tracing data, and uploading to the platform to perform compensation tasks. The present invention can improve the efficiency of bulk commodity trade management.

Description

Technical Field

[0001] The present invention relates to the technical field of risk management and control, and in particular to a traceability management and control method and system based on a bulk commodity trading platform. Background Art

[0002] With the increasing complexity of global trade and consumers' increasing requirements for product quality and safety, the demand for tracing and controlling the commodity circulation chain has gradually increased. Especially in the field of bulk commodity trade, such as agricultural products, mineral resources and industrial raw materials, ensuring the traceability of products from production to consumption has become an important challenge and market requirement. Traditional trade platforms and traceability systems often rely on a single or limited centralized database, which leads to information opacity and data silos. Different entities are responsible for recording information at different links in the supply chain, and information updates and sharing lack real-time and accuracy, making the entire traceability process difficult to effectively manage and monitor. Traditional centralized databases are vulnerable to the risk of data tampering and falsification, especially in cross-border trade and complex supply chains. The unreliability of such data not only affects market trust, but also leads to an increase in trade disputes and legal disputes, increasing the operating costs and legal risks of enterprises. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a traceability control method and system based on a bulk commodity trading platform to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a traceability control method based on a bulk commodity trading platform includes the following steps:

[0005] Step S1: Obtaining commodity physical transaction data based on the commodity trading platform; Modeling the distributed ledger blockchain based on the commodity physical transaction data to obtain the commodity trading blockchain, and uploading it to the commodity trading platform to execute the physical transaction blockchain application task;

[0006] Step S2: collecting real-time commodity trading data according to the commodity trading blockchain, thereby obtaining real-time commodity trading data, and performing abnormal commodity delivery volume detection on the real-time commodity trading data according to the commodity entity transaction data, thereby obtaining abnormal commodity delivery data;

[0007] Step S3: dividing the commodity trading blockchain into abnormal accounting blockchain nodes according to the abnormal commodity delivery data, thereby obtaining abnormal accounting blockchain node data, and extracting abnormal accounting node link structure features from the abnormal accounting blockchain node data, thereby obtaining abnormal accounting node link data;

[0008] Step S4: obtaining bulk commodity logistics data based on the bulk commodity trading platform; constructing a bulk commodity logistics sensor network based on the bulk commodity logistics data, thereby obtaining a bulk commodity logistics sensor network, and spatially correlating the bulk commodity logistics sensor network and the abnormal accounting node link data with the abnormal accounting node logistics link, thereby obtaining the abnormal accounting node logistics link data;

[0009] Step S5: Correct the bulk commodity logistics transportation loss according to the abnormal accounting node logistics link data and the abnormal bulk commodity delivery data, so as to obtain the bulk commodity logistics transportation error data, and trace the bulk commodity transportation risk according to the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, so as to obtain the bulk commodity transportation risk tracing data;

[0010] Step S6: Perform a bulk commodity compensation strategy analysis on the bulk commodity logistics transportation error data based on the bulk commodity transportation risk tracing data, thereby obtaining a bulk commodity compensation strategy, and upload it to the bulk commodity trading platform to execute the bulk commodity compensation task.

[0011] The present invention uses a distributed ledger blockchain model to ensure the transparency and immutability of transaction data. Each transaction is recorded on the blockchain, and all participants can view and verify it, thereby reducing the risk of information opacity and data tampering. Blockchain technology enables the recording and updating of transaction data to be carried out almost in real time, ensuring the accuracy and timeliness of the data. Compared with traditional centralized databases, it can reflect market changes and transaction conditions more quickly. By collecting and monitoring commodity trading data in real time, the system can quickly identify and detect abnormal delivery situations, which helps to take adjustment measures in time to reduce potential risks and losses. Combining abnormal accounting node data with logistics sensor network data can achieve full chain traceability. This not only helps to find out the cause and path of abnormal delivery, but also improves the ability to identify and manage risks and errors that may exist in logistics transportation. Through precise logistics link spatial association, the system can more accurately perform transportation loss correction and transportation error data analysis, thereby formulating more effective transportation compensation strategies and risk management plans. Through transportation risk traceability data and compensation strategy analysis, enterprises can better manage and reduce operating costs, while reducing the possibility of legal disputes caused by errors and risks. Efficient compensation strategies and risk management measures can help enhance the market's trust in enterprises and improve their competitiveness and reputation in the global trade market. In summary, this traceability system based on blockchain and the Internet of Things can not only improve information transparency and data security in commodity trade, but also effectively manage risks and optimize operations.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: Obtaining commodity entity transaction data based on the commodity trading platform;

[0014] Step S12: extracting entity transaction timestamp features from the commodity entity transaction data to obtain entity transaction timestamp data, and performing entity transaction logic structure analysis based on the entity transaction timestamp data to obtain entity transaction logic structure data;

[0015] Step S13: Designing a data structure based on the commodity entity transaction data, thereby obtaining a transaction data structure;

[0016] Step S14: Designing a smart contract according to the entity transaction logic structure data, thereby obtaining a smart contract;

[0017] Step S15: Construct a commodity trading blockchain based on the transaction data structure and the smart contract, and upload it to the commodity trading platform to execute the physical transaction blockchain application task.

[0018] The present invention can obtain real transaction information, including commodity types, transaction quantities, prices, etc., by obtaining entity transaction data from a commodity trading platform. These data are the basis for building a blockchain transaction system and ensure the authenticity and credibility of the system. Extracting timestamp features from entity transaction data can ensure the time sequence and accuracy of transactions. The addition of timestamps can help record the exact time when a transaction occurs, which is crucial for subsequent transaction analysis and contract execution. Data structure design is performed based on commodity entity transaction data to effectively store and manage commodity transaction data. Reasonable data structure design can improve data access efficiency, reduce storage space occupation, and provide the necessary basis for subsequent smart contract execution. Smart contracts are the core of the blockchain system. By designing according to the entity transaction logic structure data, the logical rigor, compliance and security of the contract can be ensured. Appropriate smart contract design can realize automated transaction execution and reduce intermediary costs, and improve transaction efficiency and transparency. After building a commodity transaction blockchain, real-time transaction records and transparent transaction history queries based on blockchain can be realized. This structure can not only improve the security and traceability of transactions, but also reduce operational risks and transaction costs, thereby promoting the development and innovation of the commodity market.

[0019] Optionally, step S12 is specifically:

[0020] Step S121: extracting entity transaction timestamp features from the commodity entity transaction data, thereby obtaining entity transaction timestamp data;

[0021] Step S122: performing transaction activity time series analysis based on entity transaction timestamp data, thereby obtaining transaction activity time distribution data;

[0022] Step S123: clustering the commodity entity transaction data by transaction type according to the transaction time distribution data, thereby obtaining commodity entity transaction type data;

[0023] Step S124: mapping the entity transaction timestamp data and the commodity entity transaction type data to the transaction activity stage, thereby obtaining the transaction activity stage time data;

[0024] Step S125: construct a transaction logic structure model according to the transaction activity stage time data.

[0025] The present invention extracts timestamp features from commodity entity transaction data. These features may include information such as the exact time, date, day of the week, and even holidays when the transaction occurs. The extraction of these features helps to provide a data basis for subsequent steps and helps to understand the time patterns and trends of trading activities. Revealing the rules of trading activities in different time periods, such as peak and trough periods, helps to analyze market behavior and participant behavior patterns. Using the extracted entity transaction timestamp data, time series analysis is performed. This includes statistics and analysis of the intensity, frequency, and changing trends of trading activities in different time periods, which helps to reveal the peaks and troughs of trading activities and determine the busy and dormant periods of the market. The periodic or irregular changes in trading activities are discovered to provide a time distribution data basis for subsequent steps. Based on the transaction time distribution data, cluster analysis of transaction types is performed on the commodity entity transaction data. This clustering can help divide the entity transaction data into different categories or types according to the transaction time and behavior pattern, understand the behavior patterns and strategies of different types of traders in the market, and provide a basis for the prediction and management of market behavior. The entity transaction timestamp data and the transaction type data are mapped to obtain the stage time data of the transaction activity. This helps determine the start, duration, and end time of different trading activity stages, forming a complete timeline of trading activities, and helps study the behavioral patterns of different stages in the market. Build a transaction logic structure model based on the obtained trading activity stage time data. By understanding the time pattern and behavioral laws of trading activities, the logical rigor, compliance, and security of subsequent smart contracts can be improved.

[0026] Optionally, step S2 specifically includes:

[0027] Step S21: collecting real-time commodity trading data according to the commodity trading blockchain, thereby obtaining real-time commodity trading data;

[0028] Step S22: extracting the characteristics of commodity inventory changes and transaction delivery volume from the commodity entity transaction data, thereby obtaining commodity transaction inventory change data and transaction delivery volume data;

[0029] Step S23: Calculate the commodity delivery volume error based on the commodity trading inventory change data and the transaction delivery volume data, thereby obtaining the commodity delivery volume error data;

[0030] Step S24: performing reasonable error statistical analysis on the commodity delivery quantity error data, thereby obtaining reasonable error data of the commodity delivery quantity;

[0031] Step S25: classifying and calculating unreasonable error commodity transactions on the real-time commodity transaction data according to the reasonable error data of commodity delivery volume, thereby obtaining unreasonable error commodity transaction data;

[0032] Step S26: construct an abnormal commodity delivery volume detection model based on the unreasonable error commodity transaction data, and perform abnormal commodity delivery volume detection on the real-time bulk commodity transaction data through the abnormal commodity delivery volume detection model, so as to obtain abnormal bulk commodity delivery data.

[0033] The present invention realizes real-time data collection through blockchain technology to ensure the security, transparency and non-tamperability of data. This method can eliminate the information asymmetry problem that may exist in the traditional transaction process and improve the transparency and trust of market participants in the market situation. It is used to monitor the real-time transaction situation of bulk commodities during transportation, including transaction direction, information of both parties to the transaction, etc., to provide accurate basic data for subsequent data analysis. By analyzing the inventory changes and transaction delivery volume in bulk commodity transactions, the supply and demand dynamics of bulk commodities in the market can be identified. The extraction of these features helps to track the actual situation of bulk commodities during transportation and trading, and provides detailed data support for subsequent error analysis and anomaly detection. By comparing the difference between actual transactions and expected transactions, the commodity delivery error is calculated, which helps to find the delivery error problem that may exist in the commodity transaction process, and is used to identify the transaction error problem of possible quantity errors. The commodity delivery error data is statistically analyzed to determine the level of transportation loss in normal commodity transportation. This helps to distinguish normal transportation loss from possible abnormal loss. Based on reasonable error data, the real-time bulk commodity transaction data is classified and calculated to identify unreasonable error commodity transactions. This helps to find transaction data with abnormal transportation loss as early as possible. Based on the unreasonable error commodity transaction data, an abnormal commodity delivery volume detection model is constructed. This model can use machine learning or rule engine and other technologies to monitor abnormal loss behavior in transactions in real time.

[0034] Optionally, step S3 specifically includes:

[0035] Step S31: extracting commodity quantity accounting features based on the abnormal bulk commodity delivery data, thereby obtaining bulk commodity quantity accounting data;

[0036] Step S32: Calculate the transport accounting quantity difference on the bulk commodity quantity accounting data, thereby obtaining the transport accounting quantity difference data;

[0037] Step S33: performing statistics on the high accounting difference of the transportation accounting volume difference data in the accounting stage, thereby obtaining high difference accounting stage data;

[0038] Step S34: associate the transaction blockchain nodes according to the high difference accounting stage data and the commodity trading blockchain, so as to obtain abnormal accounting blockchain node data;

[0039] Step S35: extracting the post-calculation nodes of the commodity trading blockchain according to the abnormal accounting blockchain node data, thereby obtaining the post-calculation node data;

[0040] Step S36: Perform transaction node link integration on the abnormal accounting blockchain node data and the post-accounting node data to obtain the abnormal accounting node link data.

[0041] The present invention extracts commodity quantity accounting features from abnormal bulk commodity delivery data. This means identifying and recording the transaction volume of each bulk commodity. By doing so, basic data for commodity transactions can be established, thereby providing accurate quantitative information for subsequent analysis and comparison. The quantity difference in the transportation process of bulk commodities is calculated, that is, the difference between the actual quantity in transportation and the planned or standard quantity. By calculating the quantity difference, the transportation efficiency and accuracy can be evaluated, thereby discovering potential transportation problems or irregular operations. The transportation accounting quantity difference data is statistically analyzed, with special attention paid to those with large differences, that is, high accounting differences. These high differences indicate abnormalities or areas that need improvement in a specific stage, helping to improve the efficiency and reliability of the overall transportation process. Using the commodity transaction blockchain data, especially the association analysis of the transaction blockchain nodes, the abnormal accounting node data is identified and extracted. These abnormal nodes indicate potential abnormal commodity quantity accounting situations, which require further investigation and processing. According to the abnormal accounting blockchain node data, the relevant post-accounting node data is extracted. These post-accounting nodes are key transaction links or involve specific transaction conditions or contract terms. Further analysis of these data can reveal details or inconsistencies in the transaction process. The process of integrating and analyzing abnormal accounting node link data. By integrating data, a complete abnormal transaction link can be established to help understand the possible loopholes, risks or problems in the transportation accounting process, so as to take appropriate measures to intervene or improve the transportation mechanism.

[0042] Optionally, step S4 specifically includes:

[0043] Step S41: acquiring bulk commodity logistics data based on the bulk commodity trading platform, and extracting transportation sensor data from the bulk commodity logistics data, thereby obtaining logistics transportation sensor data;

[0044] Step S42: performing logistics transportation node link space division on the logistics transportation sensor data, thereby obtaining logistics transportation node link space data;

[0045] Step S43: constructing a logistics transportation space network according to the logistics transportation node link space data, and performing sensor data fusion on the logistics transportation space network according to the logistics transportation sensor data, thereby obtaining a bulk commodity logistics sensor network;

[0046] Step S44: Perform node-link spatial association on the bulk commodity logistics sensor network and the abnormal accounting node link data, thereby obtaining the abnormal accounting node logistics link data.

[0047] The present invention obtains logistics data through a bulk commodity trading platform and extracts transportation sensor data from it. These data include the real-time location, transportation status, environmental conditions (such as temperature, humidity, etc.) of the goods, and event records during transportation. Acquiring these data can provide comprehensive monitoring and real-time management capabilities for the logistics process, help optimize transportation efficiency and reduce operating costs. The logistics transportation sensor data is analyzed and processed to divide the space of logistics transportation nodes and links. Logistics nodes are key locations or intersections in the logistics network, and links represent the connections between nodes. Through spatial division, a structural model of the logistics network can be established, which is helpful for subsequent network optimization, path planning and risk analysis. The logistics transportation node link space data is used to construct a logistics transportation space network. This network structure can better reflect the complexity and relevance of actual logistics transportation. By fusing sensor data, such as combining real-time location data with environmental sensor data, a more accurate bulk commodity logistics sensor network can be generated. This helps to achieve intelligent monitoring, real-time decision-making and rapid response capabilities. The abnormal accounting node link data is associated with the logistics sensor network. The location and impact path of abnormal accounting nodes in the logistics network can be identified, thereby helping to determine the logistics transmission path and possible associated factors of abnormal transactions. This analysis helps to reveal the potential patterns of abnormal transaction behavior, the influencing factors of the transaction link, and support further investigation and monitoring measures.

[0048] Optionally, step S5 specifically includes:

[0049] Step S51: extracting the difference of the transport accounting quantity of the abnormal bulk commodity delivery data, thereby obtaining the transport accounting quantity difference data;

[0050] Step S52: according to the transport accounting volume difference data, the logistics link data of the abnormal accounting node is associated with the logistics transportation node, so as to obtain the abnormal transaction logistics transportation node accounting difference data;

[0051] Step S53: Correct the bulk commodity logistics transportation loss difference of the abnormal transaction logistics transportation node accounting difference data according to the reasonable error data of the commodity delivery volume, so as to obtain the bulk commodity logistics transportation error data;

[0052] Step S54: tracing the bulk commodity transportation risk based on the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, thereby obtaining bulk commodity transportation risk tracing data.

[0053] The present invention extracts the difference of transportation accounting volume from abnormal bulk commodity delivery data. Generally, the transaction of bulk commodities often involves transportation and logistics links, and the difference of transportation accounting volume means that there is abnormal transportation loss. Extracting these differences can help analysts identify possible abnormal loss situations, and then take corresponding investigation and control measures. The extracted transportation accounting volume difference data is used to associate the logistics link data of the abnormal accounting node. The purpose of this is to determine the logistics transportation node associated with the abnormal transaction. By associating the logistics transportation node, the location or path of the abnormal transaction can be more accurately located, which is helpful for further analysis and investigation. The reasonable error data of the commodity delivery volume is used to correct the accounting difference data of the logistics transportation node of the abnormal transaction. Bulk commodities will cause losses due to various reasons during transportation, such as transportation losses, errors, etc. By correcting these loss differences, the actual transportation loss of bulk commodities can be more accurately calculated, so as to better evaluate the efficiency and cost of transportation. The obtained bulk commodity transportation error data is traced using the bulk commodity logistics sensor network. This means tracing the risk factors that commodities may face during transportation, such as accidental losses and abnormal situations during transportation. By tracing these risks, it can help relevant departments or companies develop more effective risk management strategies and reduce losses and uncertainties during transportation.

[0054] Optionally, step S54 is specifically:

[0055] Step S541: collecting the logistics transportation segment sensor data of the bulk commodity logistics transportation error data based on the bulk commodity logistics sensor network, thereby obtaining the logistics transportation segment sensor data;

[0056] Step S542: performing physical inertial sensing feature extraction and vibration sensing feature extraction on the sensing data of the logistics transport section, thereby obtaining inertial sensing data of the logistics transport section and vibration sensing data of the logistics transport section;

[0057] Step S543: performing transportation segment acceleration statistics according to the inertial sensor data of the logistics transportation segment, thereby obtaining high-acceleration transportation segment data and low-acceleration transportation segment data;

[0058] Step S544: performing statistics on the vibration level of the transport section according to the vibration sensor data of the logistics transport section, thereby obtaining data of the transport section with a high vibration level and data of the transport section with a low vibration level;

[0059] Step S545: performing a transport segment intersection operation on the high-value acceleration transport segment data and the high-vibration level transport segment data, thereby obtaining high-risk transport segment data; performing a transport segment intersection operation on the low-value acceleration transport segment data and the low-vibration level transport segment data, thereby obtaining low-risk transport segment data;

[0060] Step S546: Merge the high-risk transport segment data and the low-risk transport segment data in a transport segment space to obtain bulk commodity transport risk traceability data.

[0061] The present invention collects sensor data of the logistics transport section through the bulk commodity logistics sensor network. These sensor data can cover a variety of information, such as temperature, humidity, light, vibration, etc., which are very important for analyzing the environmental conditions and transportation status of bulk commodities during transportation. Physical inertial sensing features and vibration sensing features are extracted from the logistics transport section sensor data. The physical inertial sensing features can help analyze the acceleration data of the logistics transport section, while the vibration sensing features are used to evaluate the vibration level during transportation. The data extracted from these features are helpful for further transportation quality analysis and risk assessment. Acceleration statistics are performed using the inertial sensing data of the logistics transport section. Through this step, transportation sections with high acceleration during transportation can be identified, and these sections will be exposed to higher transportation risks, such as collisions, falls, etc. The vibration sensing data of the logistics transport section is used to perform vibration level statistics. Through this step, transportation sections with high vibration levels during transportation can be determined, and these sections will face the risk of damage or deformation of goods. Intersection operations are performed on the data of high acceleration transport sections and high vibration levels, as well as the data of low acceleration transport sections and low vibration levels. Through these intersection operations, transport sections with both high acceleration and high vibration levels (high-risk transport sections) and transport sections with both low acceleration and low vibration levels (low-risk transport sections) can be found. The data of high-risk transport sections and low-risk transport sections are spatially merged. This means integrating different types of risky transport sections to form complete traceability data for bulk commodity transportation risks. These data can help enterprises or relevant departments identify and manage high-risk sections in the transportation process, so as to take necessary measures to reduce losses and improve transportation efficiency.

[0062] Optionally, step S6 specifically includes:

[0063] Step S61: extracting the high-risk transport segment transport difference from the bulk commodity logistics transport error data according to the bulk commodity transport risk tracing data, thereby obtaining the tracing transport segment transport difference data;

[0064] Step S62: performing transport section difference compensation calculation on the traceable transport section difference data, thereby obtaining bulk commodity compensation quantity data;

[0065] Step S63: selecting a low-risk logistics transportation path for the bulk commodity transportation risk tracing data according to the bulk commodity logistics sensor network, thereby obtaining optimized logistics transportation path data;

[0066] Step S64: Perform a bulk commodity compensation strategy analysis based on the bulk commodity compensation quantity data and the optimized logistics transportation route data to obtain the bulk commodity compensation strategy, and upload it to the bulk commodity trading platform to execute the bulk commodity compensation task.

[0067] The present invention uses the bulk commodity transportation risk traceability data to analyze and extract the bulk commodity logistics transportation error data. In particular, the transportation difference of the high-risk transportation section is concerned, and these differences reflect the difference between the actual and ideal state during the transportation process. Through this analysis, it is possible to accurately locate which transportation sections have large errors or risks, providing data support for subsequent compensation and optimization. The transportation section difference compensation calculation is to evaluate the specific compensation amount of bulk commodities based on the transportation difference data of the transportation section. The calculation of the compensation amount is usually based on the degree of error and the value of the loss, which can help enterprises or relevant institutions understand the possible losses during transportation and provide a basis for the formulation of compensation strategies. Using the bulk commodity logistics sensor network and transportation risk traceability data, a low-risk logistics transportation path is selected. Using existing risk data and real-time sensor information, high-risk transportation sections are avoided, thereby reducing cargo losses and transportation costs. The optimized logistics transportation path usually takes into account the stability, safety and efficiency of the route. Based on the compensation amount data of bulk commodities and the optimized logistics transportation path data, an in-depth analysis of the compensation strategy is carried out. This includes evaluating the cost-benefit ratio of compensation, selecting appropriate compensation methods (such as insurance claims, cargo replacement or refund, etc.), and specific measures for compensation implementation. The analysis results are usually uploaded to commodity trading platforms or related platforms to ensure that compensation tasks can be carried out in a timely and effective manner.

[0068] Optionally, the present invention further provides a traceability control system based on a bulk commodity trading platform, which is used to execute the traceability control method based on a bulk commodity trading platform as described above. The traceability control system based on a bulk commodity trading platform includes:

[0069] A distributed ledger blockchain modeling module is used to obtain commodity entity transaction data based on the commodity trading platform; based on the commodity entity transaction data, a distributed ledger blockchain modeling is performed to obtain a commodity transaction blockchain, and the blockchain is uploaded to the commodity trading platform to execute the entity transaction blockchain application task;

[0070] An abnormal commodity delivery volume detection module is used to collect real-time commodity transaction data based on the commodity transaction blockchain, thereby obtaining real-time commodity transaction data, and to detect abnormal commodity delivery volumes on the real-time commodity transaction data based on the commodity entity transaction data, thereby obtaining abnormal commodity delivery data;

[0071] The transaction blockchain node division module is used to divide the commodity transaction blockchain into abnormal accounting blockchain nodes according to the abnormal commodity delivery data, thereby obtaining abnormal accounting blockchain node data, and extract abnormal accounting node link structure features from the abnormal accounting blockchain node data, thereby obtaining abnormal accounting node link data;

[0072] The logistics link space association module is used to obtain bulk commodity logistics data based on the bulk commodity trading platform; construct a bulk commodity logistics sensor network based on the bulk commodity logistics data, thereby obtaining the bulk commodity logistics sensor network, and perform abnormal accounting node logistics link space association on the bulk commodity logistics sensor network and abnormal accounting node link data, thereby obtaining abnormal accounting node logistics link data;

[0073] The transportation risk tracing module is used to correct the bulk commodity logistics transportation loss according to the abnormal accounting node logistics link data and the abnormal bulk commodity delivery data, so as to obtain the bulk commodity logistics transportation error data, and to trace the bulk commodity transportation risk according to the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, so as to obtain the bulk commodity transportation risk tracing data;

[0074] The bulk commodity compensation strategy analysis module is used to perform bulk commodity compensation strategy analysis on bulk commodity logistics transportation error data based on bulk commodity transportation risk traceability data, so as to obtain bulk commodity compensation strategy and upload it to the bulk commodity trading platform to execute bulk commodity compensation tasks.

[0075] The traceability control system based on the bulk commodity trading platform of the present invention can implement any traceability control method based on the bulk commodity trading platform of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the traceability control method based on the bulk commodity trading platform. The internal modules of the system cooperate with each other, thereby improving the efficiency of bulk commodity trading management. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0077] Figure 1 It is a schematic diagram of the steps of the traceability control method based on the bulk commodity trading platform of the present invention;

[0078] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0079] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0080] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0083] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides a traceability control method based on a bulk commodity trading platform, the method comprising the following steps:

[0085] Step S1: Obtaining commodity physical transaction data based on the commodity trading platform; Modeling the distributed ledger blockchain based on the commodity physical transaction data to obtain the commodity trading blockchain, and uploading it to the commodity trading platform to execute the physical transaction blockchain application task;

[0086] In this embodiment, on the commodity trading platform, it is first necessary to obtain the physical transaction data of commodities, such as crude oil, metals, etc., through an API or data provider interface. These data include information such as price, quantity, quality, etc. Then, distributed ledger technology (such as blockchain) is used to model and record these data. This technical feature usually involves the characteristics of blockchain such as decentralization, immutability and transparency. For example, blockchain platforms such as Hyperledger Fabric or Ethereum can be used to write each commodity transaction into the blockchain as a transaction record. Smart contracts can be used to verify the legitimacy of transactions and enforce specific transaction rules, such as contract execution, payment confirmation, etc.

[0087] Step S2: collecting real-time commodity trading data according to the commodity trading blockchain, thereby obtaining real-time commodity trading data, and performing abnormal commodity delivery volume detection on the real-time commodity trading data according to the commodity entity transaction data, thereby obtaining abnormal commodity delivery data;

[0088] In this embodiment, real-time transaction data is collected based on the established commodity transaction blockchain. Through blockchain technology, transaction data can be recorded and updated in real time to ensure the timeliness and accuracy of transaction information. At the same time, real-time transaction data is used to detect abnormal delivery volume using commodity entity transaction data. This includes using machine learning algorithms or rule engines to analyze abnormal transportation loss patterns in transaction data. For example, based on abnormal pattern recognition technology, transportation losses that are inconsistent with normal transaction and transportation conditions can be quickly discovered.

[0089] Step S3: dividing the commodity trading blockchain into abnormal accounting blockchain nodes according to the abnormal commodity delivery data, thereby obtaining abnormal accounting blockchain node data, and extracting abnormal accounting node link structure features from the abnormal accounting blockchain node data, thereby obtaining abnormal accounting node link data;

[0090] In this embodiment, the commodity trading blockchain is node-divided based on the detected abnormal transaction data. This involves identifying and separating transaction links or nodes that may be abnormal for further investigation and analysis. Using graph analysis methods, such as community discovery algorithms or shortest path algorithms, abnormal nodes and their connections are extracted from the blockchain. These data can be used for subsequent link structure feature extraction, such as node degree centrality, closeness, etc.

[0091] Step S4: obtaining bulk commodity logistics data based on the bulk commodity trading platform; constructing a bulk commodity logistics sensor network based on the bulk commodity logistics data, thereby obtaining a bulk commodity logistics sensor network, and spatially correlating the bulk commodity logistics sensor network and the abnormal accounting node link data with the abnormal accounting node logistics link, thereby obtaining the abnormal accounting node logistics link data;

[0092] In this embodiment, logistics data, including transportation routes, transportation time, etc., are obtained through the commodity trading platform. These data are used to build a logistics sensor network, that is, to describe the logistics routes and nodes of commodities from production to delivery. Geographic Information System (GIS) technology can be used to visualize and model the logistics routes of commodities. Combined with abnormal accounting node link data, their spatial association relationships are analyzed, such as through path analysis on a map or spatial clustering algorithms to identify potential logistics bottlenecks or abnormal routes.

[0093] Step S5: Correct the bulk commodity logistics transportation loss according to the abnormal accounting node logistics link data and the abnormal bulk commodity delivery data, so as to obtain the bulk commodity logistics transportation error data, and trace the bulk commodity transportation risk according to the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, so as to obtain the bulk commodity transportation risk tracing data;

[0094] In this embodiment, the logistics transportation errors of bulk commodities are corrected and analyzed based on the logistics link data of abnormal accounting nodes. The logistics sensor network is used to trace the transportation risks on the logistics link, such as cargo damage, loss or delay. For example, statistical analysis and spatiotemporal data mining techniques are used to conduct in-depth analysis of logistics transportation error data. Through time series analysis or event tracing methods, the key factors and nodes that may cause transportation errors are revealed, providing a basis for formulating subsequent compensation strategies.

[0095] Step S6: Perform a bulk commodity compensation strategy analysis on the bulk commodity logistics transportation error data based on the bulk commodity transportation risk tracing data, thereby obtaining a bulk commodity compensation strategy, and upload it to the bulk commodity trading platform to execute the bulk commodity compensation task.

[0096] In this embodiment, based on the above analysis results, a compensation strategy for commodity transportation risks is formulated. This includes determining the object, method and amount of compensation, as well as how to execute these compensation tasks on the commodity trading platform. The compensation strategy can be programmed and executed on the blockchain through smart contracts to ensure the transparency and traceability of the compensation process. The smart contract function of the commodity trading blockchain is used to automatically process and confirm compensation requests to ensure that the rights and responsibilities of all parties are fully protected.

[0097] Optionally, step S1 specifically includes:

[0098] Step S11: Obtaining commodity entity transaction data based on the commodity trading platform;

[0099] In this embodiment, the commodity entity transaction data is obtained through the cooperative commodity trading platform. These platforms provide a wide range of commodity transaction data, including metals, agricultural products, energy and other types. The real-time or historical transaction data of commodities can be obtained through the platform's API interface or directly from the data file provided by the platform. For example, the API can be used to obtain the transaction data of gold futures, including the price, volume, buying and selling direction, transportation route and other information of each transaction.

[0100] Step S12: extracting entity transaction timestamp features from the commodity entity transaction data to obtain entity transaction timestamp data, and performing entity transaction logic structure analysis based on the entity transaction timestamp data to obtain entity transaction logic structure data;

[0101] In this embodiment, the entity transaction timestamp is extracted from the acquired commodity transaction data for subsequent logical analysis and smart contract design. The entity transaction timestamp refers to the exact time point when the transaction occurs, usually accurate to the second or millisecond level. Data processing tools such as Python's Pandas library or professional data processing software can be used to extract and parse timestamp information from the original data. For example, the date and hour, minute and second are extracted from the time field of the transaction data and converted into a machine-readable time format. On the basis of analyzing the timestamp data, the logical structure of the entity transaction is further analyzed. Time series analysis methods and graph theory are used to understand the association between transactions, the order of transaction execution, possible parallel or serial transaction logic, etc. The logical structure data of the entity transaction is obtained from the analysis. These data can be graphical representations, logical flow charts, timelines or other forms to show and understand the relationship and execution order between transactions.

[0102] Step S13: Designing a data structure based on the commodity entity transaction data, thereby obtaining a transaction data structure;

[0103] In this embodiment, it involves designing a data structure suitable for storing and processing commodity entity transaction data. The data structure design needs to take into account storage efficiency, data retrieval speed, and the needs of subsequent smart contracts. You can choose a suitable database technology such as MySQL or NoSQL database to create a database table or data set containing fields such as transaction timestamp, commodity type, transaction volume, price, etc. For example, use a relational database management system (RDBMS) to design a transaction data storage solution containing standardized tables.

[0104] Step S14: Designing a smart contract according to the entity transaction logic structure data, thereby obtaining a smart contract;

[0105] In this embodiment, based on the entity transaction logic structure data, a smart contract is designed to execute and verify the transaction. A smart contract is an automated contract based on blockchain that can ensure the security and transparency of transactions. The contract code can be written using the Solidity language of a smart contract platform such as Ethereum to verify the validity of the transaction according to a pre-set logical structure. For example, a contract is written to check whether the timestamp of each transaction is within the predetermined transaction time range and execute the corresponding transaction logic.

[0106] Step S15: Construct a commodity trading blockchain based on the transaction data structure and the smart contract, and upload it to the commodity trading platform to execute the physical transaction blockchain application task.

[0107] In this embodiment, the designed transaction data structure and smart contract are deployed on the blockchain platform to achieve safe and efficient trading of commodities. Blockchain technology can ensure the immutability and transparency of transactions, and improve the trust and efficiency of transactions. For example, a suitable blockchain platform such as Hyperledger Fabric or Ethereum can be selected, and the designed data structure and smart contract can be deployed on the blockchain node. For example, the smart contract function of Ethereum is used to create a blockchain application that can be used for gold trading, ensuring the security and traceability of each transaction.

[0108] Optionally, step S12 is specifically:

[0109] Step S121: extracting entity transaction timestamp features from the commodity entity transaction data, thereby obtaining entity transaction timestamp data;

[0110] In this embodiment, timestamp features are extracted from commodity entity transaction data. Timestamp features may include the exact time point or time range when the transaction occurred, as well as other time information related to the transaction. Commodity transaction data can be processed by data processing and analysis tools (such as the Pandas library in Python) to extract timestamp information for each transaction. For example, the transaction time, date, or time period is extracted from the transaction data to ensure the consistency and accuracy of the data format.

[0111] Step S122: performing transaction activity time series analysis based on entity transaction timestamp data, thereby obtaining transaction activity time distribution data;

[0112] In this embodiment, the extracted entity transaction timestamp data is used to perform time series analysis of transaction activities. This is intended to understand the occurrence pattern and frequency of transaction activities, as well as the distribution characteristics over time. Statistical methods or time series analysis techniques are used, such as average transaction intervals, seasonal or periodic analysis of transaction activities, etc. For example, the distribution of transaction activities can be visualized by drawing a time distribution graph or frequency graph of transaction activities.

[0113] Step S123: clustering the commodity entity transaction data by transaction type according to the transaction time distribution data, thereby obtaining commodity entity transaction type data;

[0114] In this embodiment, based on the obtained transaction time distribution data, a transaction type cluster analysis of the commodity entity transaction data is performed. The purpose is to identify and group transaction types with similar transaction time distribution characteristics. Cluster analysis algorithms, such as K-means clustering or hierarchical clustering, can be used to divide transaction data into different categories. For example, transactions can be divided into high-frequency transactions, low-frequency transactions, and other types based on the time distribution characteristics of transaction activities.

[0115] Step S124: mapping the entity transaction timestamp data and the commodity entity transaction type data to the transaction activity stage, thereby obtaining the transaction activity stage time data;

[0116] In this embodiment, the extracted entity transaction timestamp data and the obtained commodity entity transaction type data are used to perform a mapping analysis of the transaction activity stages. This is intended to determine the specific execution stages of different transaction types in time. A mapping table of time periods or stages can be established to map each transaction to a different transaction activity stage based on its timestamp and type. For example, high-frequency transactions are mapped to the "fast transaction stage" and low-frequency transactions are mapped to the "long-cycle transaction stage".

[0117] Step S125: construct a transaction logic structure model according to the transaction activity stage time data.

[0118] In this embodiment, a logical structure model of commodity physical transactions is constructed based on the obtained transaction activity stage time data. This model can reveal the association and logical flow between transaction activities. The logical structure of transaction activities can be presented using methods such as flow charts, state transition diagrams, or network diagrams. For example, the transition conditions and sequence between different transaction activity stages can be described.

[0119] Optionally, step S2 specifically includes:

[0120] Step S21: collecting real-time commodity trading data according to the commodity trading blockchain, thereby obtaining real-time commodity trading data;

[0121] In this embodiment, it is necessary to establish an efficient commodity trading blockchain network. The network should be highly secure and scalable to ensure the real-time collection and transmission of transaction data. Distributed ledger technology (DLT) such as blockchain is used to ensure the transparency and immutability of transactions. For example, a private chain or consortium chain is built using platforms such as Hyperledger Fabric or Ethereum to ensure the rights of participants and data confidentiality. At the same time, in order to achieve real-time data collection, smart contracts need to be developed to automatically perform the recording and verification of transaction data. Smart contracts can be automatically triggered when a transaction occurs, record transaction details and synchronize data to the blockchain. For example, a smart contract can be written to monitor the price changes and transaction volume of a specific commodity to ensure the real-time and accuracy of the data. Of course, real-time pending commodity transaction data can also be obtained from smart contracts in the commodity trading blockchain that has been uploaded and applied to the commodity trading platform. This type of data is transaction data that customers or platforms have questions about delivery.

[0122] Step S22: extracting the characteristics of commodity inventory changes and transaction delivery volume from the commodity entity transaction data, thereby obtaining commodity transaction inventory change data and transaction delivery volume data;

[0123] In this embodiment, machine learning and data mining techniques are used to extract inventory change characteristics and transaction delivery volume characteristics of bulk commodities from physical transaction data. Time series analysis and statistical methods are used to extract supply and demand changes, actual delivery volume and other characteristics from the historical transaction data of bulk commodity physical transaction data.

[0124] Step S23: Calculate the commodity delivery volume error based on the commodity trading inventory change data and the transaction delivery volume data, thereby obtaining the commodity delivery volume error data;

[0125] In this embodiment, the real-time commodity trading inventory change data and transaction delivery volume data are compared and analyzed to calculate the error of commodity delivery volume. By comparing the difference between the actual delivery volume and the expected delivery volume, the degree of error in the transaction process can be evaluated. For example, a mathematical model such as the root mean square error (RMSE) can be applied to quantify the error level, thereby accurately measuring the gap between the actual delivery volume and the expected delivery volume.

[0126] Step S24: performing reasonable error statistical analysis on the commodity delivery quantity error data, thereby obtaining reasonable error data of the commodity delivery quantity;

[0127] In this embodiment, the reasonable error data of the commodity delivery volume is statistically analyzed to determine the distribution and probability characteristics of the error. Statistical methods such as normal distribution test or skewness and kurtosis analysis are used to ensure the rationality and reliability of the error data. For example, by drawing an error distribution graph and calculating a confidence interval, the statistical characteristics and credibility of the commodity delivery volume error data are evaluated to determine a reasonable error range and probability distribution. For example, the normal distribution assumption or non-parametric statistical methods are used to ensure a comprehensive understanding and scientific evaluation of the error data.

[0128] Step S25: classifying and calculating unreasonable error commodity transactions on the real-time commodity transaction data according to the reasonable error data of commodity delivery volume, thereby obtaining unreasonable error commodity transaction data;

[0129] In this embodiment, based on the reasonable error data of commodity delivery, a machine learning classification algorithm such as a decision tree or a support vector machine is used to classify and calculate unreasonable error commodity transactions for real-time commodity transaction data. The training model can be used to identify abnormal situations that may exist in the transaction process, such as delivery volume far exceeding expectations or delivery delays. For example, historical data is used to train a classification model to predict whether there is a potential unreasonable error in the current transaction.

[0130] Step S26: construct an abnormal commodity delivery volume detection model based on the unreasonable error commodity transaction data, and perform abnormal commodity delivery volume detection on the real-time bulk commodity transaction data through the abnormal commodity delivery volume detection model, so as to obtain abnormal bulk commodity delivery data.

[0131] In this embodiment, an abnormal commodity delivery volume detection model is constructed based on unreasonable error commodity transaction data. Supervised learning or unsupervised learning methods are used, for example, anomaly detection algorithms such as isolation forest or cluster analysis are used to identify and mark abnormal bulk commodity delivery data. These models can automatically monitor and warn of potential transaction anomalies, helping market participants to adjust strategies and risk management measures in a timely manner. Finally, the constructed abnormal commodity delivery volume detection model is used to detect abnormal commodity delivery volume on real-time bulk commodity transaction data. If abnormal commodity delivery is detected in the transaction, the corresponding real-time bulk commodity transaction data is marked as abnormal bulk commodity delivery data; if abnormal commodity delivery is not detected, the corresponding real-time bulk commodity transaction data is marked as normal transaction data, and the normal transaction data is uploaded to the bulk commodity trading platform, thereby determining that the real-time bulk commodity transaction data is a delivery abnormality.

[0132] Optionally, step S3 specifically includes:

[0133] Step S31: extracting commodity quantity accounting features based on the abnormal bulk commodity delivery data, thereby obtaining bulk commodity quantity accounting data;

[0134] In this embodiment, key commodity quantity accounting features are extracted from abnormal bulk commodity delivery data. These features include, but are not limited to, commodity type, transaction time, delivery location, delivery method, etc. Technically, data mining and machine learning techniques, such as cluster analysis or time series analysis, can be used to extract these features from a large amount of delivery data. For example, cluster analysis is used to identify typical transaction behaviors under different delivery modes, or time series analysis is used to capture seasonal or cyclical changes in delivery volume. After these features are extracted, bulk commodity quantity accounting data can be constructed for subsequent transportation accounting volume difference calculations.

[0135] Step S32: Calculate the transport accounting quantity difference on the bulk commodity quantity accounting data, thereby obtaining the transport accounting quantity difference data;

[0136] In this embodiment, the difference in transportation accounting volume between the bulk commodity volume accounting data is calculated. The purpose of this step is to analyze the difference in commodity volume between different delivery nodes and accounting nodes, which may involve comparisons in terms of transportation time, transportation cost, cargo loss rate, etc. Mathematical modeling and optimization algorithms, such as linear programming or constraint-based optimization models, can be used to calculate the optimal transportation path and related costs. For example, the best solution to minimize transportation costs or minimize transportation time can be determined through the optimization model, and quantitative analysis can be performed based on actual data to obtain transportation accounting volume difference data.

[0137] Step S33: performing statistics on the high accounting difference of the transportation accounting volume difference data in the accounting stage, thereby obtaining high difference accounting stage data;

[0138] In this embodiment, the transport accounting volume difference data is further analyzed to identify accounting stages with high accounting differences. This involves abnormal cost increases or abnormal time extensions, and specific stages in the transportation process need to be analyzed in detail. Data visualization and statistical analysis methods, such as anomaly detection based on box plots or abnormal event identification based on time series, can be used to discover and quantify high accounting difference stages in the transportation process. For example, a box plot can be used to intuitively view the distribution of accounting differences in different transportation stages and identify abnormal situations that are higher than expected.

[0139] Step S34: associate the transaction blockchain nodes according to the high difference accounting stage data and the commodity trading blockchain, so as to obtain abnormal accounting blockchain node data;

[0140] In this embodiment, the identified high-difference accounting stage data is associated with the commodity trading blockchain to obtain blockchain node data related to abnormal accounting. The immutability and traceability of blockchain technology are used to track and confirm specific transaction nodes involved in abnormal accounting. Blockchain analysis tools and smart contract programming can be used, such as using smart contracts to monitor transaction events in a specific accounting stage to automatically identify and record abnormal events. For example, by writing smart contracts to monitor transaction records related to the high-difference accounting stage, the integrity and transparency of each transaction can be ensured.

[0141] Step S35: extracting the post-calculation nodes of the commodity trading blockchain according to the abnormal accounting blockchain node data, thereby obtaining the post-calculation node data;

[0142] In this embodiment, based on the abnormal accounting blockchain node data obtained, node data related to post-accounting is further extracted. These post-accounting nodes include subsequent accounting nodes at the spatial level and in the transaction process to confirm the accuracy and compliance of the accounting. Blockchain data mining technology and audit analysis tools, such as data pattern recognition algorithms or audit log recording systems, can be used to identify and extract post-accounting node data. For example, by analyzing the association patterns and transaction characteristics of abnormal accounting blockchain nodes through data mining technology, post-accounting nodes that may have abnormalities are automatically extracted.

[0143] Step S36: Perform transaction node link integration on the abnormal accounting blockchain node data and the post-accounting node data to obtain the abnormal accounting node link data.

[0144] In this embodiment, the obtained abnormal accounting blockchain node data and post-accounting node data are integrated to construct complete abnormal accounting node link data. The purpose of this step is to analyze and display the complete transaction link from the occurrence of abnormal accounting to the subsequent accounting node, to help identify and prevent similar accounting anomalies. Technically, data integration and link analysis tools can be used, such as using a relational database management system or link analysis software, to integrate and visualize the transaction links between abnormal accounting nodes. For example, by establishing a relationship map or path analysis between transaction nodes, the process of abnormal accounting and possible related factors can be clearly displayed to provide support for subsequent risk management and control.

[0145] Optionally, step S4 specifically includes:

[0146] Step S41: acquiring bulk commodity logistics data based on the bulk commodity trading platform, and extracting transportation sensor data from the bulk commodity logistics data, thereby obtaining logistics transportation sensor data;

[0147] In this embodiment, on the commodity trading platform, logistics and transportation data can be obtained through API interfaces or data subscription services. These data usually include information such as the real-time location, transportation status, and estimated arrival time of the goods. For example, through cooperation with logistics companies, sensor data on different transportation vehicles can be obtained, such as GPS location, temperature sensor data, etc. The extraction of these data can be achieved through real-time query or regular batch download to ensure the timeliness and integrity of the data.

[0148] Step S42: performing logistics transportation node link space division on the logistics transportation sensor data, thereby obtaining logistics transportation node link space data;

[0149] In this embodiment, after the logistics and transportation sensor data is collected, the logistics and transportation network needs to be spatially divided. This can be achieved through geographic information system (GIS) technology, and the logistics nodes (such as ports, warehouses, transportation hubs, etc.) and links (paths connecting these nodes) are spatially analyzed and divided. For example, GIS software can be used to spatially visualize and analyze nodes and links based on geographic coordinates, transportation networks, cargo flow directions, and other factors to facilitate subsequent network construction and optimization.

[0150] Step S43: constructing a logistics transportation space network according to the logistics transportation node link space data, and performing sensor data fusion on the logistics transportation space network according to the logistics transportation sensor data, thereby obtaining a bulk commodity logistics sensor network;

[0151] In this embodiment, based on the obtained spatial data of logistics transportation node links, a logistics transportation spatial network can be constructed. This involves connecting and modeling various logistics nodes and links through graph theory or network analysis methods. Logistics transportation sensor data such as real-time location, transportation status, etc. can be added to the network through data fusion technology (such as data mining, machine learning models), thereby forming a more complex and dynamic logistics sensor network. For example, the real-time GPS data is matched with the preset logistics network model through an algorithm to build a logistics sensor network with real-time monitoring capabilities.

[0152] Step S44: Perform node-link spatial association on the bulk commodity logistics sensor network and the abnormal accounting node link data, thereby obtaining the abnormal accounting node logistics link data.

[0153] In this embodiment, after the logistics sensor network is built, it is necessary to associate the link data of the abnormal accounting nodes. These anomalies include the loss of goods, and the corresponding node link data needs to be identified and associated through data mining or statistical analysis methods. The spatial information of the accounting nodes on the abnormal accounting node link data is associated with the spatial information of the commodity logistics sensor network. These associated data can help with subsequent logistics efficiency optimization and risk management.

[0154] Optionally, step S5 specifically includes:

[0155] Step S51: extracting the difference of the transport accounting quantity of the abnormal bulk commodity delivery data, thereby obtaining the transport accounting quantity difference data;

[0156] In this embodiment, it is necessary to conduct detailed analysis and extraction of the difference in accounting volume for abnormal bulk commodity delivery data. This process involves the use of advanced data analysis techniques and algorithms to ensure accurate extraction of transportation accounting volume difference data. Using mathematical statistical methods or machine learning algorithms, the difference in accounting volume for each delivery node is calculated. This may involve data comparison and difference analysis between different transportation nodes. Assume that a batch of bulk commodity delivery data is being processed, including the original delivery volume and the actual accounting volume. By comparing the actual accounting volume of each node with the expected accounting volume, we can identify abnormal nodes that have delivery errors or do not meet the expected accounting volume.

[0157] Step S52: according to the transport accounting volume difference data, the logistics link data of the abnormal accounting node is associated with the logistics transportation node, so as to obtain the abnormal transaction logistics transportation node accounting difference data;

[0158] In this embodiment, based on the previously extracted transport accounting volume difference data, we will conduct a detailed analysis of the logistics link data of the abnormal accounting nodes and the association of the logistics transportation nodes. Use network analysis or relationship analysis technology to establish the relationship between each abnormal accounting node and its related logistics transportation nodes. Combine the logistics link data and the accounting volume difference data to integrate the specific accounting volume difference of each associated logistics node, and then analyze its impact on abnormal transactions.

[0159] Step S53: Correct the bulk commodity logistics transportation loss difference of the abnormal transaction logistics transportation node accounting difference data according to the reasonable error data of the commodity delivery volume, so as to obtain the bulk commodity logistics transportation error data;

[0160] In this embodiment, it involves correcting the difference in bulk commodity logistics transportation loss for the difference in accounting data of abnormal transaction logistics transportation nodes based on reasonable error data of commodity delivery. It aims to correct the accounting difference caused by loss or error in logistics transportation to obtain more accurate error data of bulk commodity logistics transportation. A loss correction model can be established based on historical data or real-time monitoring data to analyze the probability and influencing factors of loss. Mathematical statistical methods or machine learning algorithms are used to make error adjustments to the accounting differences of abnormal nodes, taking into account possible losses in logistics transportation. Assuming that a batch of bulk commodities is found to have a high loss rate during transportation, the loss correction model can be used to adjust the accounting differences of relevant nodes based on the historical data of logistics transportation losses to reduce errors and ensure the accuracy of the accounting data.

[0161] Step S54: tracing the bulk commodity transportation risk based on the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, thereby obtaining bulk commodity transportation risk tracing data.

[0162] In this embodiment, it involves tracing the risk of bulk commodity transportation based on the error data of bulk commodity logistics transportation according to the bulk commodity logistics sensor network. The risk factors in the transportation process of bulk commodities are traced through the logistics sensor network, so as to obtain the transportation risk tracing data. The environmental parameters and transportation conditions of bulk commodities during transportation are monitored in real time by using the Internet of Things technology and sensor networks. The sensor data is analyzed to identify the risk factors that may affect the safety and quality of bulk commodity transportation, such as temperature changes, humidity, vibration, etc. Assuming that the temperature fluctuations of a batch of bulk commodities during transportation are large, the sensor data can be traced back to the abnormal temperature events during transportation by analyzing the sensor data, and timely measures can be taken to reduce the loss of commodity quality.

[0163] Optionally, step S54 is specifically:

[0164] Step S541: collecting the logistics transportation segment sensor data of the bulk commodity logistics transportation error data based on the bulk commodity logistics sensor network, thereby obtaining the logistics transportation segment sensor data;

[0165] In this embodiment, in the bulk commodity logistics sensor network, the collection of logistics transportation error data is achieved by deploying various sensor devices. For example, GPS sensors, accelerometers and temperature sensors are installed on freight vehicles or freight containers to monitor the location, running speed and environmental conditions of logistics transportation in real time. These sensors transmit data to the central database through wireless communication technology for subsequent data analysis and processing. The sensor data of each transportation segment in the bulk commodity physical transportation error data is collected.

[0166] Step S542: performing physical inertial sensing feature extraction and vibration sensing feature extraction on the sensing data of the logistics transport section, thereby obtaining inertial sensing data of the logistics transport section and vibration sensing data of the logistics transport section;

[0167] In this embodiment, feature extraction of sensor data of the logistics transportation section is performed to analyze the inertia and vibration conditions in logistics transportation. Physical inertial sensor feature extraction can record the acceleration changes of the transportation section through an accelerometer, and then evaluate the transportation stability and possible risks of the goods. Vibration sensor feature extraction focuses on detecting the vibration amplitude caused during transportation, such as using a vibration sensor to detect the vibration frequency and amplitude in cargo transportation.

[0168] Step S543: performing transportation segment acceleration statistics according to the inertial sensor data of the logistics transportation segment, thereby obtaining high-acceleration transportation segment data and low-acceleration transportation segment data;

[0169] In this embodiment, the logistics transport segment acceleration statistics utilize physical inertial sensor data to quantitatively analyze and count the acceleration of the transport segment. For example, the average acceleration, maximum acceleration, and frequency distribution of acceleration in each time period are calculated to determine the transport segment data with high and low acceleration.

[0170] Step S544: performing statistics on the vibration level of the transport section according to the vibration sensor data of the logistics transport section, thereby obtaining data of the transport section with a high vibration level and data of the transport section with a low vibration level;

[0171] In this embodiment, the vibration level statistics of the logistics transport section utilizes the vibration sensor data to quantify and count the vibration level of the transport section. For example, the vibration sensor data is collected, and the vibration amplitude, vibration frequency, and vibration duration in each time period are analyzed to identify the transport section data with high vibration level and low vibration level.

[0172] Step S545: performing a transport segment intersection operation on the high-value acceleration transport segment data and the high-vibration level transport segment data, thereby obtaining high-risk transport segment data; performing a transport segment intersection operation on the low-value acceleration transport segment data and the low-vibration level transport segment data, thereby obtaining low-risk transport segment data;

[0173] In this embodiment, the data of the high acceleration transport segment and the high vibration level transport segment are intersected, and the transport segment under high risk conditions can be identified through data processing algorithms and logical operations. Similarly, the data of the low acceleration and low vibration level transport segment are intersected to identify the transport segment under low risk conditions.

[0174] Step S546: Merge the high-risk transport segment data and the low-risk transport segment data in a transport segment space to obtain bulk commodity transport risk traceability data.

[0175] In this embodiment, in the stage of spatial merging of transport segments, the data of high-risk and low-risk transport segments are merged and analyzed to reveal potential sources of risk in the transportation of bulk commodities. For example, the spatial distribution of high-risk and low-risk transport segments is superimposed and analyzed using geographic information system (GIS) technology to help identify specific geographical areas or sections that cause transportation risks.

[0176] Optionally, step S6 specifically includes:

[0177] Step S61: extracting the high-risk transport segment transport difference from the bulk commodity logistics transport error data according to the bulk commodity transport risk tracing data, thereby obtaining the tracing transport segment transport difference data;

[0178] In this embodiment, we rely on the commodity transportation risk tracing data, which can accurately identify the high-risk transportation sections in the logistics transportation process. For example, data collected by the sensor network shows that in certain specific transportation sections, due to sudden weather changes or poor road conditions, large errors occurred in the transportation process. After analyzing these data, we can extract transportation difference data, which shows the deviation between the actual transportation situation and the expected transportation situation.

[0179] Step S62: performing transport section difference compensation calculation on the traceable transport section difference data, thereby obtaining bulk commodity compensation quantity data;

[0180] In this embodiment, compensation calculation is performed on these traceable transport segment difference data. For example, for high errors detected on a specific transport segment, we can use an algorithm to perform transport segment difference compensation calculation. This may include using mathematical models or machine learning algorithms to predict and correct errors to obtain more accurate commodity compensation data.

[0181] Step S63: selecting a low-risk logistics transportation path for the bulk commodity transportation risk tracing data according to the bulk commodity logistics sensor network, thereby obtaining optimized logistics transportation path data;

[0182] In this embodiment, the data of the commodity logistics sensor network can be used to select low-risk logistics transportation routes. For example, by combining real-time road condition information and historical transportation data, the selection of logistics routes can be optimized to reduce the risks in the logistics transportation process. This involves the application of intelligent algorithms to dynamically adjust the transportation route to ensure that the goods can reach the destination under the safest and most efficient conditions.

[0183] Step S64: Perform a bulk commodity compensation strategy analysis based on the bulk commodity compensation quantity data and the optimized logistics transportation route data to obtain the bulk commodity compensation strategy, and upload it to the bulk commodity trading platform to execute the bulk commodity compensation task.

[0184] In this embodiment, the aforementioned bulk commodity compensation volume data and optimized logistics transportation route data are combined to perform bulk commodity compensation strategy analysis. For example, different compensation strategies can be formulated based on the data analysis results, and specific measures can be formulated for different logistics risks and transportation needs. These strategies include adjusting the insurance amount, optimizing the logistics supply chain, and even improving the transportation agreement. Finally, the compensation strategy is uploaded to the bulk commodity trading platform to execute specific compensation tasks.

[0185] Optionally, the present invention further provides a traceability control system based on a bulk commodity trading platform, which is used to execute the traceability control method based on a bulk commodity trading platform as described above. The traceability control system based on a bulk commodity trading platform includes:

[0186] A distributed ledger blockchain modeling module is used to obtain commodity entity transaction data based on the commodity trading platform; based on the commodity entity transaction data, a distributed ledger blockchain modeling is performed to obtain a commodity transaction blockchain, and the blockchain is uploaded to the commodity trading platform to execute the entity transaction blockchain application task;

[0187] An abnormal commodity delivery volume detection module is used to collect real-time commodity transaction data based on the commodity transaction blockchain, thereby obtaining real-time commodity transaction data, and to detect abnormal commodity delivery volumes on the real-time commodity transaction data based on the commodity entity transaction data, thereby obtaining abnormal commodity delivery data;

[0188] The transaction blockchain node division module is used to divide the commodity transaction blockchain into abnormal accounting blockchain nodes according to the abnormal commodity delivery data, thereby obtaining abnormal accounting blockchain node data, and extract abnormal accounting node link structure features from the abnormal accounting blockchain node data, thereby obtaining abnormal accounting node link data;

[0189] The logistics link space association module is used to obtain bulk commodity logistics data based on the bulk commodity trading platform; construct a bulk commodity logistics sensor network based on the bulk commodity logistics data, thereby obtaining the bulk commodity logistics sensor network, and perform abnormal accounting node logistics link space association on the bulk commodity logistics sensor network and abnormal accounting node link data, thereby obtaining abnormal accounting node logistics link data;

[0190] The transportation risk tracing module is used to correct the bulk commodity logistics transportation loss according to the abnormal accounting node logistics link data and the abnormal bulk commodity delivery data, so as to obtain the bulk commodity logistics transportation error data, and to trace the bulk commodity transportation risk according to the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, so as to obtain the bulk commodity transportation risk tracing data;

[0191] The bulk commodity compensation strategy analysis module is used to perform bulk commodity compensation strategy analysis on bulk commodity logistics transportation error data based on bulk commodity transportation risk traceability data, so as to obtain bulk commodity compensation strategy and upload it to the bulk commodity trading platform to execute bulk commodity compensation tasks.

[0192] The traceability control system based on the bulk commodity trading platform of the present invention can implement any traceability control method based on the bulk commodity trading platform of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the traceability control method based on the bulk commodity trading platform. The internal modules of the system cooperate with each other, thereby improving the efficiency of bulk commodity trading management.

[0193] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0194] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A traceability control method based on a bulk commodity trading platform, characterized in that: The following steps are involved: Step S1: Obtaining commodity physical transaction data based on the commodity trading platform; Modeling the distributed ledger blockchain based on the commodity physical transaction data to obtain the commodity trading blockchain, and uploading it to the commodity trading platform to execute the physical transaction blockchain application task; Step S2: collecting real-time commodity trading data according to the commodity trading blockchain, thereby obtaining real-time commodity trading data, and performing abnormal commodity delivery volume detection on the real-time commodity trading data according to the commodity entity transaction data, thereby obtaining abnormal commodity delivery data; Step S3: dividing the commodity trading blockchain into abnormal accounting blockchain nodes according to the abnormal commodity delivery data, thereby obtaining abnormal accounting blockchain node data, and extracting abnormal accounting node link structure features from the abnormal accounting blockchain node data, thereby obtaining abnormal accounting node link data; Step S4: obtaining bulk commodity logistics data based on the bulk commodity trading platform; constructing a bulk commodity logistics sensor network based on the bulk commodity logistics data, thereby obtaining a bulk commodity logistics sensor network, and spatially correlating the bulk commodity logistics sensor network and the abnormal accounting node link data with the abnormal accounting node logistics link, thereby obtaining the abnormal accounting node logistics link data; Step S5: Correct the bulk commodity logistics transportation loss according to the abnormal accounting node logistics link data and the abnormal bulk commodity delivery data, so as to obtain the bulk commodity logistics transportation error data, and trace the bulk commodity transportation risk according to the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, so as to obtain the bulk commodity transportation risk tracing data; Step S6: Perform a bulk commodity compensation strategy analysis on the bulk commodity logistics transportation error data based on the bulk commodity transportation risk tracing data, thereby obtaining a bulk commodity compensation strategy, and upload it to the bulk commodity trading platform to execute the bulk commodity compensation task.

2. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Obtaining commodity entity transaction data based on the commodity trading platform; Step S12: extracting entity transaction timestamp features from the commodity entity transaction data to obtain entity transaction timestamp data, and performing entity transaction logic structure analysis based on the entity transaction timestamp data to obtain entity transaction logic structure data; Step S13: Designing a data structure based on the commodity entity transaction data, thereby obtaining a transaction data structure; Step S14: Designing a smart contract according to the entity transaction logic structure data, thereby obtaining a smart contract; Step S15: Construct a commodity trading blockchain based on the transaction data structure and the smart contract, and upload it to the commodity trading platform to execute the physical transaction blockchain application task.

3. The traceability control method based on the bulk commodity trading platform according to claim 2 is characterized in that: Step S12 is specifically as follows: Step S121: extracting entity transaction timestamp features from the commodity entity transaction data, thereby obtaining entity transaction timestamp data; Step S122: performing transaction activity time series analysis based on entity transaction timestamp data, thereby obtaining transaction activity time distribution data; Step S123: clustering the commodity entity transaction data by transaction type according to the transaction time distribution data, thereby obtaining commodity entity transaction type data; Step S124: mapping the entity transaction timestamp data and the commodity entity transaction type data to the transaction activity stage, thereby obtaining the transaction activity stage time data; Step S125: construct a transaction logic structure model according to the transaction activity stage time data.

4. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: collecting real-time commodity trading data according to the commodity trading blockchain, thereby obtaining real-time commodity trading data; Step S22: extracting the characteristics of commodity inventory changes and transaction delivery volume from the commodity entity transaction data, thereby obtaining commodity transaction inventory change data and transaction delivery volume data; Step S23: Calculate the commodity delivery volume error based on the commodity trading inventory change data and the transaction delivery volume data, thereby obtaining the commodity delivery volume error data; Step S24: performing reasonable error statistical analysis on the commodity delivery quantity error data, thereby obtaining reasonable error data of the commodity delivery quantity; Step S25: classify and calculate the unreasonable error commodity transactions of the real-time bulk commodity transaction data according to the reasonable error data of the commodity delivery volume, so as to obtain the unreasonable error commodity transaction data; Step S26: construct an abnormal commodity delivery volume detection model based on the unreasonable error commodity transaction data, and perform abnormal commodity delivery volume detection on the real-time bulk commodity transaction data through the abnormal commodity delivery volume detection model, so as to obtain abnormal bulk commodity delivery data.

5. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: extracting commodity quantity accounting features based on the abnormal bulk commodity delivery data, thereby obtaining bulk commodity quantity accounting data; Step S32: Calculate the transport accounting quantity difference on the bulk commodity quantity accounting data, thereby obtaining the transport accounting quantity difference data; Step S33: performing statistics on the high accounting difference of the transportation accounting volume difference data in the accounting stage, thereby obtaining high difference accounting stage data; Step S34: associate the transaction blockchain nodes according to the high difference accounting stage data and the commodity trading blockchain, so as to obtain abnormal accounting blockchain node data; Step S35: extracting the post-calculation nodes of the commodity trading blockchain according to the abnormal accounting blockchain node data, thereby obtaining the post-calculation node data; Step S36: Perform transaction node link integration on the abnormal accounting blockchain node data and the post-accounting node data to obtain the abnormal accounting node link data.

6. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: acquiring bulk commodity logistics data based on the bulk commodity trading platform, and extracting transportation sensor data from the bulk commodity logistics data, thereby obtaining logistics transportation sensor data; Step S42: performing logistics transportation node link space division on the logistics transportation sensor data, thereby obtaining logistics transportation node link space data; Step S43: constructing a logistics transportation space network according to the logistics transportation node link space data, and performing sensor data fusion on the logistics transportation space network according to the logistics transportation sensor data, thereby obtaining a bulk commodity logistics sensor network; Step S44: Perform node-link spatial association on the bulk commodity logistics sensor network and the abnormal accounting node link data, thereby obtaining the abnormal accounting node logistics link data.

7. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S5 is specifically as follows: Step S51: extracting the difference of the transport accounting quantity of the abnormal bulk commodity delivery data, thereby obtaining the transport accounting quantity difference data; Step S52: according to the transport accounting volume difference data, the logistics link data of the abnormal accounting node is associated with the logistics transportation node, so as to obtain the abnormal transaction logistics transportation node accounting difference data; Step S53: Correct the bulk commodity logistics transportation loss difference of the abnormal transaction logistics transportation node accounting difference data according to the reasonable error data of the commodity delivery volume, so as to obtain the bulk commodity logistics transportation error data; Step S54: tracing the bulk commodity transportation risk based on the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, thereby obtaining bulk commodity transportation risk tracing data.

8. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S54 is specifically as follows: Step S541: collecting the logistics transportation segment sensor data of the bulk commodity logistics transportation error data based on the bulk commodity logistics sensor network, thereby obtaining the logistics transportation segment sensor data; Step S542: performing physical inertial sensing feature extraction and vibration sensing feature extraction on the sensing data of the logistics transport section, thereby obtaining inertial sensing data of the logistics transport section and vibration sensing data of the logistics transport section; Step S543: performing transportation segment acceleration statistics according to the inertial sensor data of the logistics transportation segment, thereby obtaining high-acceleration transportation segment data and low-acceleration transportation segment data; Step S544: performing statistics on the vibration level of the transport section according to the vibration sensor data of the logistics transport section, thereby obtaining data of the transport section with a high vibration level and data of the transport section with a low vibration level; Step S545: performing a transport segment intersection operation on the high-acceleration transport segment data and the high-vibration level transport segment data, thereby obtaining high-risk transport segment data; performing a transport segment intersection operation on the low-acceleration transport segment data and the low-vibration level transport segment data, thereby obtaining low-risk transport segment data; Step S546: Merge the high-risk transport segment data and the low-risk transport segment data in a transport segment space to obtain bulk commodity transport risk traceability data.

9. The traceability control method based on the bulk commodity trading platform according to claim 1 is characterized in that: Step S6 is specifically as follows: Step S61: extracting the high-risk transport segment transport difference from the bulk commodity logistics transport error data according to the bulk commodity transport risk tracing data, thereby obtaining the tracing transport segment transport difference data; Step S62: performing transport section difference compensation calculation on the traceable transport section difference data, thereby obtaining bulk commodity compensation quantity data; Step S63: selecting a low-risk logistics transportation path for the bulk commodity transportation risk tracing data according to the bulk commodity logistics sensor network, thereby obtaining optimized logistics transportation path data; Step S64: Perform a bulk commodity compensation strategy analysis based on the bulk commodity compensation quantity data and the optimized logistics transportation route data to obtain the bulk commodity compensation strategy, and upload it to the bulk commodity trading platform to execute the bulk commodity compensation task.

10. A traceability control system based on a bulk commodity trading platform, characterized in that: Used to execute the traceability control method based on the bulk commodity trading platform as claimed in claim 1, the traceability control system based on the bulk commodity trading platform includes: A distributed ledger blockchain modeling module is used to obtain commodity entity transaction data based on the commodity trading platform; based on the commodity entity transaction data, a distributed ledger blockchain modeling is performed to obtain a commodity transaction blockchain, and the blockchain is uploaded to the commodity trading platform to execute the entity transaction blockchain application task; An abnormal commodity delivery volume detection module is used to collect real-time commodity transaction data based on the commodity transaction blockchain, thereby obtaining real-time commodity transaction data, and to detect abnormal commodity delivery volumes on the real-time commodity transaction data based on the commodity entity transaction data, thereby obtaining abnormal commodity delivery data; The transaction blockchain node division module is used to divide the commodity transaction blockchain into abnormal accounting blockchain nodes according to the abnormal commodity delivery data, thereby obtaining abnormal accounting blockchain node data, and extract abnormal accounting node link structure features from the abnormal accounting blockchain node data, thereby obtaining abnormal accounting node link data; The logistics link space association module is used to obtain bulk commodity logistics data based on the bulk commodity trading platform; construct a bulk commodity logistics sensor network based on the bulk commodity logistics data, thereby obtaining the bulk commodity logistics sensor network, and perform abnormal accounting node logistics link space association on the bulk commodity logistics sensor network and the abnormal accounting node link data, thereby obtaining the abnormal accounting node logistics link data; The transportation risk tracing module is used to correct the bulk commodity logistics transportation loss according to the abnormal accounting node logistics link data and the abnormal bulk commodity delivery data, so as to obtain the bulk commodity logistics transportation error data, and to trace the bulk commodity transportation risk according to the bulk commodity logistics transportation error data according to the bulk commodity logistics sensor network, so as to obtain the bulk commodity transportation risk tracing data; The bulk commodity compensation strategy analysis module is used to perform bulk commodity compensation strategy analysis on bulk commodity logistics transportation error data based on bulk commodity transportation risk traceability data, so as to obtain bulk commodity compensation strategy and upload it to the bulk commodity trading platform to execute bulk commodity compensation tasks.

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