Credible traceability method based on block chain

Abstract

The invention relates to the technical field of block chain credible traceability, in particular to a credible traceability method based on a block chain. The method comprises the following steps: extracting transportation and distribution data from a supply chain management log, and storing the transportation and distribution data through a block chain to ensure that the data cannot be tampered; carrying out anomaly analysis by using the stored distribution data, and calculating vehicle driving difficulty and delay time, thereby identifying potential distribution problems; constructing a supply chain distribution topological graph and planning an avoidance route of the vehicle to ensure high efficiency of the distribution process; and through aging simulation and feedback, an intelligent scheduling scheme is optimized, scheduling data is stored by using the block chain again, and the traceability and security of each link are ensured. According to the method, the intelligent level of transportation and distribution is improved. The block chain credible tracing method is optimized, so that credible tracing is more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain trusted traceability, and in particular to a trusted traceability method based on blockchain. Background Art

[0002] Traditional supply chain management relies on centralized information systems, resulting in insufficient data transparency and difficulty for all parties in the supply chain to obtain real-time and accurate information, which affects decision-making efficiency and the overall operation of the supply chain. Blockchain technology, with its decentralized, tamper-proof and transparent characteristics, provides a new approach to solving these problems. Blockchain can record data from all links of the supply chain in a shared, distributed ledger, ensuring that all participants can access the same data in real time, greatly improving information transparency. Since data on the blockchain cannot be modified once written, this provides a strong guarantee for traceability and reduces the risk of data tampering. In supply chain distribution, the use of a trusted traceability method based on blockchain can achieve real-time monitoring and recording of each distribution link. By generating a unique identity for each product and registering it on the blockchain, participants can trace the entire process of product origin, processing, transportation and final sales. However, the traditional trusted traceability method based on blockchain has the problem of inaccurate analysis of supply chain distribution anomalies and inaccurate estimation of supply chain transportation efficiency. Summary of the invention

[0003] Based on this, it is necessary to provide a trusted traceability method based on blockchain to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a blockchain-based trusted traceability method includes the following steps:

[0005] Step S1: Obtain supply chain management log data; extract transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data; store the supply chain transportation and distribution data in blockchain based on blockchain to obtain blockchain-stored transportation and distribution data;

[0006] Step S2: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal distribution data of the supply chain transportation and distribution; calculating the driving difficulty of the vehicle according to the abnormal distribution data of the supply chain transportation and distribution to obtain the driving difficulty data of the supply chain distribution vehicle; calculating the delay time of the distribution vehicle according to the driving difficulty data of the supply chain distribution vehicle to obtain the delay time data of the supply chain distribution vehicle;

[0007] Step S3: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map; plan a vehicle driving avoidance route based on the supply chain distribution topology map, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain vehicle driving avoidance route data;

[0008] Step S4: Perform time simulation based on the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data; perform intelligent scheduling on the transportation and distribution data stored in the blockchain based on the vehicle travel avoidance route time feedback data to generate transportation and distribution intelligent scheduling data; perform blockchain storage on the transportation and distribution intelligent scheduling data based on the blockchain to obtain blockchain stored transportation and distribution intelligent scheduling data.

[0009] The present invention obtains and extracts supply chain management log data to ensure that the basic data for analysis is comprehensive and accurate, providing a reliable basis for subsequent decision-making. By storing transportation and distribution data through blockchain, the immutability and transparency of the data are ensured, so that all participants can verify the authenticity of the data, thereby enhancing trust and providing a solid foundation for subsequent abnormal tracing. When tracing the source of distribution anomalies, the problem can be quickly identified and located, the efficiency of problem handling can be improved, and the losses caused by distribution anomalies can be reduced. At this time, the traceability provided by the blockchain can accurately record the processing process of each step, ensure that the responsibilities of each link are clear, and enhance the credibility of the enterprise. At the same time, the calculation of vehicle driving difficulty quantifies the potential risks in the distribution process, provides a scientific basis for delay time assessment, and helps enterprises formulate more reasonable response strategies. Constructing a supply chain distribution topology map helps to understand the distribution network globally, optimize resource allocation, and provide visual support for subsequent route planning. Combining the topology map with delay and driving difficulty data to plan vehicle driving avoidance routes can design more efficient distribution routes, reduce transportation costs, and improve distribution efficiency. Time simulation helps to estimate the transportation time of different routes and ensure the scientific nature of the decision-making basis. The analysis of time-sensitive feedback data realizes the optimization of intelligent scheduling and improves the overall distribution efficiency. At the same time, the intelligent scheduling data is stored in the blockchain, which not only ensures the security of new data, but also improves the traceability of information. This systematic data processing process ensures that each link can be traced and verified, thereby achieving the goal of trusted tracing, and ultimately promoting the intelligent and refined management of supply chain distribution. Therefore, the present invention is an optimization of a traditional blockchain-based trusted traceability method, which solves the problem of inaccurate analysis of supply chain distribution anomalies and inaccurate estimation of supply chain transportation efficiency in a traditional blockchain-based trusted traceability method, and improves the accuracy of supply chain distribution anomaly analysis and supply chain transportation efficiency estimation.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtain supply chain management log data;

[0012] Step S12: extracting transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data;

[0013] Step S13: performing normalization processing on the supply chain transportation and distribution data to obtain normalized supply chain transportation and distribution data;

[0014] Step S14: performing timestamp processing on the supply chain transportation and delivery standardized data to obtain supply chain transportation and delivery timestamp data;

[0015] Step S15: Based on the blockchain, the supply chain transportation and delivery timestamp data is stored in the blockchain to obtain the blockchain-stored transportation and delivery data.

[0016] The present invention obtains the supply chain management log data to ensure that the basic information of the analysis is comprehensive and accurate, and provides a reliable basis for subsequent data processing. Subsequently, the extraction of transportation and distribution data can concentrate the relevant information, which is convenient for in-depth analysis. On this basis, through standardization processing, the inconsistency in the data is eliminated, making the subsequent analysis more accurate and efficient, and improving the availability of the data. Timestamp processing provides time information for each data record, enhances the traceability and historicity of the data, and enables the time node of data generation to be quickly located and tracked when problems occur. This step is crucial in supply chain management, especially when anomalies occur, it can quickly trace back to specific events and track responsibilities. Then, based on blockchain technology, the timestamp data is stored to ensure the immutability and transparency of the data, and all participants can verify the authenticity and integrity of the data. This transparency not only enhances the trust of all parties, but also provides a solid foundation for subsequent traceability. Through the above steps, the entire data processing process ensures that each link can be traced, enhancing the credibility of supply chain management. Ultimately, this trusted provenance mechanism can effectively reduce the risks of information asymmetry and data tampering, improve the overall efficiency and security of the supply chain, and thus provide a more scientific basis for corporate decision-making.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: extracting the loading weight of the distribution vehicle according to the supply chain management log data to obtain the loading weight data of the supply chain distribution vehicle;

[0019] Step S22: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal data of supply chain transportation and distribution;

[0020] Step S23: performing abnormal road section complexity analysis based on supply chain transportation and distribution abnormal data to obtain abnormal road section complexity data;

[0021] Step S24: Calculate the congestion probability of the distribution section according to the abnormal section complexity data and the supply chain transportation and distribution abnormality data to obtain the congestion probability data of the supply chain distribution section;

[0022] Step S25: Calculating the driving difficulty of the vehicle according to the congestion probability data of the supply chain distribution section and the complexity data of the abnormal section and the loading weight data of the supply chain distribution vehicle to obtain the driving difficulty data of the supply chain distribution vehicle;

[0023] Step S26: Calculate the delivery vehicle delay time based on the supply chain delivery vehicle driving difficulty data and the supply chain delivery road section congestion probability data to obtain the supply chain delivery vehicle delay time data.

[0024] The present invention first extracts the loading weight data of the distribution vehicle, which can effectively understand the transportation capacity of each vehicle and ensure the reasonable distribution and loading of goods. This provides basic information for subsequent distribution planning and helps to improve transportation efficiency. Then, the transportation and distribution data stored in the blockchain is used to trace the source of distribution anomalies, which can quickly locate the problem and enhance the traceability of the data. This link ensures that when distribution anomalies occur, the company can quickly obtain relevant information, thereby reducing losses and improving response speed. Subsequently, the complexity of the abnormal road section is analyzed, so that the company can identify potential risk areas and provide data support for subsequent decision-making. By calculating the congestion probability of the distribution section, the traffic situation of the section can be quantified, providing a basis for subsequent route planning. This link is crucial to improving transportation efficiency and reducing the risk of delays. The vehicle driving difficulty is calculated in combination with the complexity of the abnormal road section, the congestion probability and the vehicle loading weight data, so that the potential challenges in the distribution process can be quantified. Finally, by combining the driving difficulty and the congestion probability to calculate the delay time of the distribution vehicle, the delay situation can be accurately predicted. This prediction ability enables enterprises to make more reasonable time arrangements when planning distribution and improve overall logistics efficiency. Through this series of steps, the overall supply chain distribution process is fully monitored and optimized, which enhances the reliability and timeliness of decision-making, thereby promoting the intelligent and efficient development of the supply chain.

[0025] Preferably, step S23 includes the following steps:

[0026] Step S231: distinguishing abnormal sections according to the abnormal data of supply chain transportation and distribution, and obtaining abnormal section data of distribution bridges and abnormal section data of distribution highways;

[0027] Step S232: performing complexity analysis of the abnormal section of the distribution bridge according to the abnormal section data of the distribution bridge to obtain complexity data of the distribution bridge section;

[0028] Step S233: analyzing the road condition of the abnormal section of the delivery road according to the abnormal section data of the delivery road to obtain the road condition data of the abnormal section of the delivery road;

[0029] Step S234: performing abnormal section complexity analysis based on the distribution bridge section complexity data and the distribution highway abnormal section pavement condition data to obtain abnormal section complexity data.

[0030] The method of the present invention significantly improves the safety and efficiency of supply chain distribution through in-depth analysis of abnormal sections, and strengthens the characteristics of trusted traceability. First, by distinguishing abnormal sections, the potential risk areas of distribution bridges and roads are clearly identified, providing a clear basis for subsequent analysis. This link ensures the transparency of information, allowing all participants to understand the situation of abnormal sections in real time, so as to make more reasonable decisions. When performing complexity analysis on abnormal sections of distribution bridges, the structural characteristics and passage difficulty of bridges can be quantified, providing key data for vehicle traffic safety. This transparent evaluation process ensures that each link is traceable, which helps to clarify responsibilities and quickly locate problems. The complexity analysis of abnormal sections is combined with the complexity data of distribution bridges and the road surface condition data, so that various complex factors can be comprehensively considered to ensure a comprehensive understanding of the distribution route. In this way, all analysis processes and results can be recorded on the blockchain, further enhancing the immutability and traceability of the data. This traceable mechanism not only improves the transparency of supply chain management, but also provides a reliable basis for decision-making, ensuring that problems can be quickly located and solved when encountered. Ultimately, through this series of steps, the company achieved comprehensive monitoring and precise management of abnormal road sections, ensuring that each link can be traced and verified, thereby promoting the intelligent development of the supply chain and improving overall transportation capacity and safety.

[0031] Preferably, performing complexity analysis of abnormal sections of distribution bridges according to abnormal section data of distribution bridges comprises the following steps:

[0032] According to the abnormal section data of the distribution bridge, the traffic flow characteristics and the bridge structure characteristics are extracted to obtain the bridge traffic flow characteristic data and the bridge structure characteristic data;

[0033] Calculate the bridge travel arc according to the bridge structure characteristic data to obtain the bridge travel arc data;

[0034] The average speed of bridge traffic is calculated according to the bridge traffic characteristic data to obtain the average speed data of bridge traffic;

[0035] The overload strength of the bridge is calculated based on the average speed data of the bridge traffic flow and the bridge travel arc data to obtain the overload strength data of the bridge;

[0036] According to the bridge overload strength data and bridge structure characteristic data, the bridge structure degradation trend analysis is carried out to obtain the bridge structure degradation trend data;

[0037] Calculate the vehicle speed limit based on the bridge structure degradation trend data and the bridge travel arc data to obtain the vehicle speed limit data;

[0038] The complexity analysis of abnormal sections of distribution bridges is carried out using the bridge structure degradation trend data and vehicle speed limit data to obtain the complexity data of distribution bridge sections.

[0039] First, the present invention can fully understand the use and structural characteristics of the bridge by extracting traffic flow characteristics and bridge structure characteristic data, which provides a solid foundation for subsequent risk assessment. The transparent data extraction process ensures the traceability of all relevant information, so that all parties can make judgments based on real data when making decisions. When calculating the travel arc of the bridge, the traffic conditions of the bridge can be accurately evaluated, which provides an important basis for the safe passage of vehicles. Then, the average speed calculation of the bridge traffic flow further quantifies the traffic flow, so that transportation managers can have a clearer understanding of the load capacity of the bridge. The calculation of the overweight load strength combined with the travel arc and traffic flow data can identify the pressure on the bridge and ensure safety under heavy load conditions. By analyzing the degradation trend of the bridge structure, potential safety hazards can be discovered in time. This process enhances the transparency and traceability of information, allowing managers to take preventive measures before problems occur. Subsequently, the vehicle speed limit calculated based on the degradation trend and travel arc data can reasonably control the vehicle speed and reduce the risk of accidents. This speed limit measure based on data analysis not only improves safety, but also provides a scientific basis for subsequent transportation plans. It uses the bridge structure degradation trend and speed limit data to analyze the complexity of abnormal sections of distribution bridges, ensuring a comprehensive understanding of bridge traffic conditions. This series of steps not only achieves effective data integration and analysis, but also ensures transparency and traceability of each link through blockchain technology.

[0040] Preferably, the road condition analysis of the abnormal section of the delivery highway is performed based on the abnormal section data of the delivery highway, comprising the following steps:

[0041] Extract the terrain of the section according to the abnormal section data of the distribution highway to obtain the terrain data of the abnormal section of the distribution highway;

[0042] Calculate the ground friction coefficient and obtain the elevation of the abnormal section according to the terrain data of the abnormal section of the distribution highway, and obtain the ground friction coefficient and elevation data of the abnormal section of the highway;

[0043] The steep slope of the road section is collected according to the elevation data of the abnormal road section, and the steep slope data of the abnormal road section is obtained;

[0044] According to the data of steep slope of abnormal road section and the ground friction coefficient of abnormal road section, the vehicle driving stability is simulated to obtain the driving stability data of the vehicle;

[0045] The road surface condition of abnormal sections of the highway is analyzed based on the driving stability data of the two vehicles and the data on the steep slope of abnormal sections, and the road surface condition data of abnormal sections of the distribution highway are obtained.

[0046] First, the present invention can fully understand the geographical features of the road section by extracting the terrain of the abnormal road section, which provides a solid foundation for subsequent risk assessment and decision-making. The transparent terrain data extraction process ensures the traceability of information, so that all parties can make judgments based on real data when making decisions. By calculating the ground friction coefficient and obtaining the elevation data of the abnormal road section, the driving conditions of the road surface can be more accurately evaluated. These data provide necessary information for subsequent slope analysis and ensure a comprehensive understanding of the road section conditions. Data collection of steep slopes can identify potential dangerous areas, which is crucial for transportation safety and helps managers take countermeasures in advance. When simulating the smoothness of vehicle driving, combined with the data of slope and friction coefficient, the driving stability of the vehicle in different sections can be quantified, improving safety and comfort. Subsequently, through the combined analysis of the smoothness data and the steep slope data, the road surface conditions of the abnormal sections of the highway can be deeply understood, providing important decision support for transportation managers. This comprehensive assessment based on data analysis ensures safety during transportation and reduces the risk of accidents. Through this series of steps, the company has achieved comprehensive monitoring and precise management of abnormal sections of distribution roads, ensuring that each link can be traced and verified.

[0047] Preferably, step S25 comprises the following steps:

[0048] Step S251: Obtaining the drag coefficient of the supply chain delivery vehicle;

[0049] Step S252: extracting road surface defects according to the abnormal road section complexity data to obtain abnormal road section surface defect data;

[0050] Step S253: performing defect distribution statistics and defect size collection according to the abnormal road section surface defect data to obtain an abnormal road section defect distribution data set and an abnormal road section defect size data set;

[0051] Step S254: estimating the vehicle driving inclination according to the abnormal road section defect distribution data set, the abnormal road section defect size data set and the supply chain delivery vehicle loading weight data, and obtaining the supply chain delivery vehicle driving inclination data;

[0052] Step S255: Calculating the center of gravity offset of the vehicle according to the supply chain distribution vehicle driving inclination data and the supply chain distribution vehicle loading weight data to obtain the center of gravity offset data of the vehicle;

[0053] Step S256: Calculate the vehicle rollover probability based on the vehicle center of gravity offset data and the wind resistance coefficient of the supply chain delivery vehicle to obtain the delivery vehicle rollover probability data;

[0054] Step S257: Calculate the vehicle saturation of the driving section according to the congestion probability data of the supply chain distribution section to obtain the vehicle saturation data of the driving section;

[0055] Step S258: Calculate the driving difficulty of the vehicle based on the vehicle saturation data of the driving section and the rollover probability data of the delivery vehicle to obtain the driving difficulty data of the supply chain delivery vehicle.

[0056] First, the present invention obtains the drag coefficient of the delivery vehicle to provide basic data for subsequent driving performance analysis, ensuring that all evaluations and decisions are based on real and reliable parameters. The road surface defects are extracted through the abnormal road section complexity data, and the potential problems of the road surface can be accurately identified, which provides important information for safe driving. When performing defect distribution statistics and defect size collection, the data of road conditions are comprehensively collected, so that managers can better understand the impact of road conditions and ensure the scientific nature of the transportation plan. Subsequently, the driving inclination is estimated in combination with the defect data and the vehicle loading weight, which helps to quantify the risks faced by the vehicle under different road conditions. The center of gravity offset calculation further analyzes the stability of the vehicle during driving and quantifies the risk of rollover. This rollover probability calculation based on data analysis enables transportation managers to timely identify vehicle behavior in high-risk situations and take preventive measures in advance. By calculating the vehicle saturation of the driving section, the impact of traffic flow on vehicle driving safety can be evaluated, providing a reference for the transportation plan, and the driving difficulty is calculated by combining the saturation of the driving section with the rollover probability, which comprehensively considers the impact of multiple factors on vehicle driving safety. This series of steps not only achieves comprehensive monitoring of driving conditions on delivery routes, but also ensures transparency and traceability of each link through blockchain technology.

[0057] Preferably, step S3 comprises the following steps:

[0058] Step S31: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map;

[0059] Step S32: collecting inter-node distribution paths according to the supply chain distribution topology map to obtain a multi-path dataset between distribution nodes;

[0060] Step S33: screening high-efficiency paths for the supply chain distribution topology map according to the multi-path dataset between distribution nodes, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain distribution high-efficiency path data;

[0061] Step S34: planning a vehicle travel avoidance route based on the delivery high-efficiency path data to obtain vehicle travel avoidance route data.

[0062] First, the present invention constructs a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain, which provides a basis for visual management. Through this transparent graphical display, each participant can understand the structure of the distribution network in real time, ensure the sharing and accuracy of information, and thus improve the trust of the overall management. Then, according to the topological map, the distribution paths between nodes are collected, and multiple paths between different nodes can be systematically identified, which provides data support for subsequent path optimization. When screening high-efficiency paths, combined with vehicle delay time and driving difficulty data, managers can screen out the best distribution path based on real-time traffic conditions and vehicle performance. This process ensures the scientificity and timeliness of decision-making and reduces losses caused by delays. Finally, based on the distribution high-efficiency path data, the planning of vehicle travel avoidance routes can effectively avoid congestion and potential risks, and further improve transportation efficiency. Through this comprehensive analysis, enterprises can achieve efficient and rapid response in a complex distribution environment to ensure the smoothness and safety of the transportation process.

[0063] Preferably, step S33 includes the following steps:

[0064] Step S331: Calculate the multi-path travel time according to the multi-path data set between distribution nodes and the supply chain distribution vehicle delay time data to obtain the multi-path travel time data between nodes;

[0065] Step S332: Performing a multi-path security test between nodes based on the multi-path data set between distribution nodes and the driving difficulty data of the supply chain distribution vehicles to obtain multi-path security data between nodes;

[0066] Step S333: Perform comprehensive weighted scoring according to the multi-path safety data between nodes and the multi-path travel time data between nodes to obtain a multi-path comprehensive weighted scoring data set;

[0067] Step S334: Screen the supply chain distribution topology map for high-efficiency paths based on the multi-path comprehensive weighted scoring data set to obtain distribution high-efficiency path data.

[0068] The method of the present invention effectively improves the efficiency and safety of supply chain distribution through a comprehensive analysis of multiple paths between distribution nodes, and at the same time strengthens the characteristics of trusted tracing. The travel time is calculated according to the multi-path data set and the vehicle delay time, so that the manager can obtain the estimated travel time of different paths. This information lays the foundation for subsequent path optimization and ensures the real-time and accuracy of the decision-making process. Then, the safety test of the multi-path between nodes is carried out to quantify the safety risks of different paths and provide the necessary safety assessment for the manager. This transparent data analysis process allows the safety of each path to be traced and verified, and enhances the trust of all parties in the data. The importance of this link is to ensure that not only efficiency but also driving safety is taken into account when selecting a path. When the comprehensive weighted scoring is performed, the travel time and safety data are combined to make the final path selection more scientific and reasonable. This weighted evaluation method can balance the relationship between efficiency and safety and provide a more comprehensive reference basis for decision-making. Finally, the distribution topology map is screened for high-efficiency paths according to the comprehensive scoring data to ensure that the selected path is both fast and safe, further improving the transportation efficiency and reducing the probability of delays and accidents. Overall, through this series of steps, the company has achieved comprehensive monitoring and optimization of the distribution route, ensuring that information at each link is transparent and traceable, thereby enhancing the trust in supply chain management.

[0069] Preferably, step S4 comprises the following steps:

[0070] Step S41: performing time simulation according to the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data;

[0071] Step S42: performing time efficiency feedback according to the time efficiency simulation data of the vehicle travel avoidance route to obtain time efficiency feedback data of the vehicle travel avoidance route;

[0072] Step S43: Intelligently dispatch the transportation and distribution data stored in the blockchain according to the vehicle travel avoidance route time feedback data to generate transportation and distribution intelligent dispatching data;

[0073] Step S44: Based on the blockchain, the transportation and distribution intelligent scheduling data is stored in the blockchain to obtain the transportation and distribution intelligent scheduling data stored in the blockchain.

[0074] The method of the present invention significantly improves the efficiency and intelligence level of transportation and distribution through time simulation and feedback of vehicle travel avoidance routes, and at the same time strengthens the characteristics of trusted traceability. First, time simulation can truly reflect the vehicle travel time under specific conditions and provide accurate time estimates for subsequent decisions. The transparency of this process ensures that each simulation result can be reviewed and verified, thereby enhancing the trust of all parties in the data. Subsequently, by providing feedback on the time simulation data, managers can identify and adjust the existing deficiencies and further optimize the travel route. This feedback mechanism not only improves the ability of real-time decision-making, but also ensures the timely update of information, making subsequent scheduling more scientific and effective. Based on this feedback data, intelligent scheduling of transportation and distribution data stored in the blockchain can realize data-driven dynamic scheduling, provide support for the rational allocation of transportation resources, and thus improve the overall transportation efficiency. Finally, based on the blockchain, the intelligent scheduling data is stored to ensure the security and non-tamperability of the data, so that all scheduling information can be traced.

[0075] The present invention has beneficial effects. By acquiring and extracting supply chain management log data, the present invention ensures that the basic data for analysis is comprehensive and accurate, providing a reliable basis for subsequent decision-making. By storing transportation and distribution data through blockchain, the immutability and transparency of the data are ensured, so that all participants can verify the authenticity of the data, thereby enhancing trust and providing a solid foundation for subsequent abnormal tracing. When tracing the source of distribution anomalies, the problem can be quickly identified and located, the efficiency of problem handling can be improved, and the losses caused by distribution anomalies can be reduced. By using the traceability provided by blockchain, the processing process of each step can be accurately recorded to ensure that the responsibilities of each link are clear, and the credibility of the enterprise is enhanced. At the same time, the calculation of vehicle driving difficulty quantifies the potential risks in the distribution process, provides a scientific basis for delay time assessment, and helps enterprises formulate more reasonable response strategies. Constructing a supply chain distribution topology map helps to understand the distribution network globally, optimize resource allocation, and provide visual support for subsequent route planning. Combining the topology map with delay and driving difficulty data to plan vehicle driving avoidance routes can design more efficient distribution routes, reduce transportation costs, and improve distribution efficiency. Time simulation helps to estimate the transportation time of different routes and ensure the scientific nature of the decision-making basis. The analysis of time-sensitive feedback data realizes the optimization of intelligent scheduling and improves the overall distribution efficiency. At the same time, the intelligent scheduling data is stored in the blockchain, which not only ensures the security of new data, but also improves the traceability of information. This systematic data processing process ensures that each link can be traced and verified, thereby achieving the goal of trusted tracing, and ultimately promoting the intelligent and refined management of supply chain distribution. Therefore, the present invention is an optimization of a traditional blockchain-based trusted traceability method, which solves the problem of inaccurate analysis of supply chain distribution anomalies and inaccurate estimation of supply chain transportation efficiency in a traditional blockchain-based trusted traceability method, and improves the accuracy of supply chain distribution anomaly analysis and supply chain transportation efficiency estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A flowchart of a trusted traceability method based on blockchain;

[0077] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0078] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0079] 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

[0080] 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.

[0081] 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.

[0082] 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.

[0083] To achieve this, please refer to Figures 1 to 3 , a trusted traceability method based on blockchain, comprising the following steps:

[0084] Step S1: Obtain supply chain management log data; extract transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data; store the supply chain transportation and distribution data in blockchain based on blockchain to obtain blockchain-stored transportation and distribution data;

[0085] Step S2: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal distribution data of the supply chain transportation and distribution; calculating the driving difficulty of the vehicle according to the abnormal distribution data of the supply chain transportation and distribution to obtain the driving difficulty data of the supply chain distribution vehicle; calculating the delay time of the distribution vehicle according to the driving difficulty data of the supply chain distribution vehicle to obtain the delay time data of the supply chain distribution vehicle;

[0086] Step S3: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map; plan a vehicle driving avoidance route based on the supply chain distribution topology map, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain vehicle driving avoidance route data;

[0087] Step S4: Perform time simulation based on the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data; perform intelligent scheduling on the transportation and distribution data stored in the blockchain based on the vehicle travel avoidance route time feedback data to generate transportation and distribution intelligent scheduling data; perform blockchain storage on the transportation and distribution intelligent scheduling data based on the blockchain to obtain blockchain stored transportation and distribution intelligent scheduling data.

[0088] In the embodiment of the present invention, reference Figure 1 The above is a flowchart of a trusted source tracing method based on blockchain of the present invention. In this example, the trusted source tracing method based on blockchain includes the following steps:

[0089] Step S1: Obtain supply chain management log data; extract transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data; store the supply chain transportation and distribution data in blockchain based on blockchain to obtain blockchain-stored transportation and distribution data;

[0090] In the embodiment of the present invention, the supply chain management log data is first acquired, and the transport-related log information is extracted from different management systems using efficient data collection tools. These logs contain detailed records of each distribution link, such as order generation, shipment, transportation status and delivery time. Through data cleaning and standardization, effective transportation and distribution data are extracted to ensure the consistency and integrity of the information. Subsequently, the extracted supply chain transportation and distribution data is stored in the blockchain, and the change history and access rights of each data are automatically recorded through smart contracts, thereby achieving data security, transparency and non-tamperability, and forming a reliable transportation and distribution data chain.

[0091] Step S2: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal distribution data of the supply chain transportation and distribution; calculating the driving difficulty of the vehicle according to the abnormal distribution data of the supply chain transportation and distribution to obtain the driving difficulty data of the supply chain distribution vehicle; calculating the delay time of the distribution vehicle according to the driving difficulty data of the supply chain distribution vehicle to obtain the delay time data of the supply chain distribution vehicle;

[0092] In the embodiment of the present invention, at this stage, based on the stored transportation and delivery data, data analysis tools are used to trace the abnormal delivery events and identify the factors that cause the delivery anomaly. The analysis results generate supply chain transportation and delivery anomaly data, providing detailed information about the cause of the delay and accident records. On this basis, an algorithm is used to calculate the driving difficulty of the vehicle under different transportation environments, including factors such as road conditions, weather, and cargo weight, to form a vehicle driving difficulty data set. Finally, combined with the driving difficulty data, the delay time is predicted, and the delivery vehicle delay time data is obtained through statistical analysis methods.

[0093] Step S3: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map; plan a vehicle driving avoidance route based on the supply chain distribution topology map, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain vehicle driving avoidance route data;

[0094] In the embodiment of the present invention, the transportation and distribution data stored in the blockchain is used to construct a supply chain distribution topology map. This topology map uses visualization tools to display distribution nodes and their interconnected relationships, and identifies each distribution route and its related data, such as transportation timeliness and driving difficulty. With the help of GIS technology, the distribution topology map is analyzed, and the vehicle delay time data and driving difficulty data are combined to develop the best vehicle driving avoidance route. The planning process uses the optimization algorithm in operations research to ensure that the selected route reduces potential risks and delays while meeting timeliness.

[0095] Step S4: Perform time simulation based on the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data; perform intelligent scheduling on the transportation and distribution data stored in the blockchain based on the vehicle travel avoidance route time feedback data to generate transportation and distribution intelligent scheduling data; perform blockchain storage on the transportation and distribution intelligent scheduling data based on the blockchain to obtain blockchain stored transportation and distribution intelligent scheduling data.

[0096] In the embodiment of the present invention, a detailed time-effect simulation is performed for the vehicle avoidance route data. The driving path, traffic flow, weather and other data are input into the simulation software to generate the time-effect simulation data of the vehicle avoidance route. By analyzing the simulation results, feedback information is collected to evaluate the gap between the actual driving situation and the expectation. These feedback data will be used in the intelligent scheduling system, and the system will automatically adjust the transportation plan according to the real-time situation and optimize the resource allocation. Finally, the generated intelligent scheduling data for transportation and distribution is stored using blockchain technology to ensure the transparency and traceability of all scheduling information, and to achieve complete recording and management of the distribution process, thereby enhancing the credibility and efficiency of the supply chain.

[0097] Preferably, step S1 comprises the following steps:

[0098] Step S11: Obtain supply chain management log data;

[0099] Step S12: extracting transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data;

[0100] Step S13: performing normalization processing on the supply chain transportation and distribution data to obtain normalized supply chain transportation and distribution data;

[0101] Step S14: performing timestamp processing on the supply chain transportation and delivery standardized data to obtain supply chain transportation and delivery timestamp data;

[0102] Step S15: Based on the blockchain, the supply chain transportation and delivery timestamp data is stored in the blockchain to obtain the blockchain-stored transportation and delivery data.

[0103] In the embodiment of the present invention, when acquiring the supply chain management log data, the data acquisition tool is used to directly extract the relevant logs from the supply chain management system. These logs contain various types of information, including order generation time, shipment time, transportation status update and delivery time. In the data extraction process, the ETL (extraction, transformation, loading) tool is used to ensure seamless acquisition and integration of data from different systems. The data is preliminarily screened to remove redundant and invalid information to ensure the data quality of subsequent processing. Through database query, the integration of the original supply chain management log data is formed, and the transportation and distribution data is extracted by using the data parsing tool according to the extracted supply chain management log data. In the parsing process, the data is first structured to convert the unstructured log information into analyzable structured data. Through regular expressions and data matching technology, fields related to transportation are extracted, such as shipping location, destination, cargo type and transportation mode. After the extraction is completed, a complete supply chain transportation and distribution data set is formed. When the supply chain transportation and distribution data is standardized, the data set is sorted by using data cleaning technology. The specific operations include eliminating duplicates, filling missing values ​​and unifying data formats. By using data standardization methods, such as unifying the date format to YYYY-MM-DD and converting address information into a standardized geocoding format, all data can be compared on the same dimension. After normalization, clear and consistent supply chain transportation and distribution normalized data is formed. When performing timestamp processing, the time field related to the transportation process is first extracted. The time information in the obtained normalized data is converted into a timestamp format for time series analysis. Use the date and time library in the programming language to parse the date and time string into a UNIX timestamp for subsequent processing. Ensure that all transportation events have clear timestamp records to form supply chain transportation and distribution timestamp data, providing a time basis for subsequent data storage and traceability. When storing supply chain transportation and distribution timestamp data based on blockchain, first set up blockchain nodes and configure relevant parameters. Use smart contracts to write timestamp data and related information (such as transportation status and node information) into the blockchain. Cryptography technology is used to ensure the security of data during storage, and to ensure the immutability and transparency of data. After storage is completed, the generated blockchain storage transportation and distribution data will be traceable, and any access and changes to the data can be recorded on the blockchain, thereby ensuring the credibility of the information. Implementation of this step ensures a complete history of every shipping event.

[0104] Preferably, step S2 comprises the following steps:

[0105] Step S21: extracting the loading weight of the distribution vehicle according to the supply chain management log data to obtain the loading weight data of the supply chain distribution vehicle;

[0106] Step S22: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal data of supply chain transportation and distribution;

[0107] Step S23: performing abnormal road section complexity analysis based on supply chain transportation and distribution abnormal data to obtain abnormal road section complexity data;

[0108] Step S24: Calculate the congestion probability of the distribution section according to the abnormal section complexity data and the supply chain transportation and distribution abnormality data to obtain the congestion probability data of the supply chain distribution section;

[0109] Step S25: Calculating the driving difficulty of the vehicle according to the congestion probability data of the supply chain distribution section and the complexity data of the abnormal section and the loading weight data of the supply chain distribution vehicle to obtain the driving difficulty data of the supply chain distribution vehicle;

[0110] Step S26: Calculate the delivery vehicle delay time based on the supply chain delivery vehicle driving difficulty data and the supply chain delivery road section congestion probability data to obtain the supply chain delivery vehicle delay time data.

[0111] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0112] Step S21: extracting the loading weight of the distribution vehicle according to the supply chain management log data to obtain the loading weight data of the supply chain distribution vehicle;

[0113] In the embodiment of the present invention, in the process of extracting the loading weight of the delivery vehicle, the supply chain management log data is first analyzed to identify the fields related to vehicle loading. The loading weight information in these fields is extracted through data analysis tools. In the specific operation, data cleaning and conversion techniques are used to ensure that redundant information is removed from the original data and the loading weight of each delivery record is retained. The extracted weight information is integrated into a structured data set to form the supply chain delivery vehicle loading weight data.

[0114] Step S22: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal data of supply chain transportation and distribution;

[0115] In the embodiment of the present invention, when tracing the source of distribution anomalies, relevant information is first extracted from the transportation and distribution data stored in the blockchain, focusing on the records of abnormal events. In this process, data analysis tools are used to screen abnormal events and identify abnormal situations such as delays, damage or loss. Through smart contracts and event-driven mechanisms, the time, location and cause of each abnormal event are automatically recorded to form supply chain transportation and distribution abnormal data. The transparency and immutability of this data ensure the reliability of subsequent analysis and can clearly trace the root cause of each abnormal event.

[0116] Step S23: performing abnormal road section complexity analysis based on supply chain transportation and distribution abnormal data to obtain abnormal road section complexity data;

[0117] In the embodiment of the present invention, when performing complexity analysis of abnormal road sections, firstly, the abnormal event data is combined with the transportation path data to identify the road sections involved. The complexity analysis model is adopted to calculate the complexity index of each abnormal road section by considering the terrain, traffic conditions and historical event data of the road section. In the specific operation, data mining technology is used to analyze various data related to the road section to form the complexity data of the abnormal road section.

[0118] Step S24: Calculate the congestion probability of the distribution section according to the abnormal section complexity data and the supply chain transportation and distribution abnormality data to obtain the congestion probability data of the supply chain distribution section;

[0119] In the embodiment of the present invention, in the process of calculating the congestion probability of the distribution section, the traffic flow model and historical traffic data are first used in combination with the complexity data of the abnormal section to evaluate the congestion probability of each section. In specific implementation, a statistical analysis method is used to analyze the traffic flow of the section in different time periods, and combined with the influence of weather and events to form comprehensive congestion probability data. Ultimately, the congestion probability data of the supply chain distribution section obtained can help optimize the distribution route and reduce the risk of delays.

[0120] Step S25: Calculating the driving difficulty of the vehicle according to the congestion probability data of the supply chain distribution section and the complexity data of the abnormal section and the loading weight data of the supply chain distribution vehicle to obtain the driving difficulty data of the supply chain distribution vehicle;

[0121] In the embodiment of the present invention, in the calculation of vehicle driving difficulty, the distribution vehicle loading weight data, abnormal road section complexity data and congestion probability data are first combined. These data are integrated using a weighted algorithm to calculate the driving difficulty of each road section. In the specific implementation process, a mathematical model is applied to consider the impact of the vehicle load on driving stability, as well as the complexity and congestion of the road section. After calculation, the driving difficulty data of the supply chain distribution vehicle is formed.

[0122] Step S26: Calculate the delivery vehicle delay time based on the supply chain delivery vehicle driving difficulty data and the supply chain delivery road section congestion probability data to obtain the supply chain delivery vehicle delay time data.

[0123] In the embodiment of the present invention, when calculating the delay time of the delivery vehicle, the vehicle driving difficulty data and the congestion probability data of the delivery section are first combined, and the linear regression model is used to analyze the impact of the two on the delay time. Through the regression analysis of historical data, the weights and relationships of each influencing factor are obtained. In the specific operation, statistical software is used to train and verify the model to ensure the accuracy of the calculation. The final generated supply chain delivery vehicle delay time data.

[0124] Preferably, step S1 comprises the following steps:

[0125] Step S231: distinguishing abnormal sections according to the abnormal data of supply chain transportation and distribution, and obtaining abnormal section data of distribution bridges and abnormal section data of distribution highways;

[0126] Step S232: performing complexity analysis of the abnormal section of the distribution bridge according to the abnormal section data of the distribution bridge to obtain complexity data of the distribution bridge section;

[0127] Step S233: analyzing the road condition of the abnormal section of the delivery road according to the abnormal section data of the delivery road to obtain the road condition data of the abnormal section of the delivery road;

[0128] Step S234: performing abnormal section complexity analysis based on the distribution bridge section complexity data and the distribution highway abnormal section pavement condition data to obtain abnormal section complexity data.

[0129] In the embodiment of the present invention, when distinguishing abnormal sections, firstly, the abnormal data of supply chain transportation and distribution is imported into the data analysis platform. The data cleaning tool is used to screen the abnormal event data and identify the involved sections. By setting conditions, the abnormal events are classified into two categories: bridges and roads. In the specific operation, the geographic information system (GIS) technology is used to perform spatial analysis on the abnormal events according to the geographical characteristics of the sections, so as to accurately identify the abnormal section data of the distribution bridge and the abnormal section data of the distribution highway. When analyzing the complexity of the abnormal section of the distribution bridge, the abnormal section data of the distribution bridge obtained in step S231 is first extracted. Using the complexity analysis algorithm, considering the structural characteristics, traffic flow and historical transportation data of the bridge, each abnormal section is evaluated. In the specific operation, by collecting the design parameters of the bridge (such as span, height and material) and the relevant traffic flow data, the weighted scoring method is applied to calculate the complexity index. Finally, the complexity data of the distribution bridge section is obtained. In the analysis of the pavement condition of the abnormal section of the distribution highway, the abnormal section data of the distribution highway obtained in step S231 is first imported into the data analysis system. Through the pavement detection tools, data related to the pavement condition, such as cracks, potholes, and friction coefficients, are collected. The pavement condition is evaluated by machine learning algorithms, and historical detection data and real-time monitoring information are analyzed. In the specific operation, the road sections are divided into different condition levels by setting thresholds and standards, and the pavement condition data of abnormal sections of the distribution highway are formed. When conducting the complexity analysis of abnormal sections, the complexity data of the distribution bridge sections are first combined with the pavement condition data of abnormal sections of the distribution highway, and a comprehensive evaluation model is used. In the specific operation, by setting multiple influencing factors (such as bridge complexity, pavement condition, and traffic flow), the hierarchical analysis method (AHP) is used to weight each factor. Through data calculation, the overall complexity data of abnormal sections are formed.

[0130] Preferably, performing complexity analysis of abnormal sections of distribution bridges according to abnormal section data of distribution bridges comprises the following steps:

[0131] According to the abnormal section data of the distribution bridge, the traffic flow characteristics and the bridge structure characteristics are extracted to obtain the bridge traffic flow characteristic data and the bridge structure characteristic data;

[0132] Calculate the bridge travel arc according to the bridge structure characteristic data to obtain the bridge travel arc data;

[0133] The average speed of bridge traffic is calculated according to the bridge traffic characteristic data to obtain the average speed data of bridge traffic;

[0134] The overload strength of the bridge is calculated based on the average speed data of the bridge traffic flow and the bridge travel arc data to obtain the overload strength data of the bridge;

[0135] According to the bridge overload strength data and bridge structure characteristic data, the bridge structure degradation trend analysis is carried out to obtain the bridge structure degradation trend data;

[0136] Calculate the vehicle speed limit based on the bridge structure degradation trend data and the bridge travel arc data to obtain the vehicle speed limit data;

[0137] The complexity analysis of abnormal sections of distribution bridges is carried out using the bridge structure degradation trend data and vehicle speed limit data to obtain the complexity data of distribution bridge sections.

[0138] In the embodiment of the present invention, the traffic flow data of the bridge in a specific time period is first collected by using the traffic monitoring system, including information such as the number of vehicles passing, the speed and the type of vehicles. Then, the structural detection sensor is used to collect the structural parameters of the bridge, such as the span, the support type and the material properties. These data are imported into the data processing platform, and the statistical analysis method is used to generate the bridge traffic flow characteristic data and the bridge structure characteristic data. When calculating the bridge travel arc, the bridge structure characteristic data is used to determine the geometric shape and design parameters of the bridge. By analyzing the longitudinal section and the cross section of the bridge, the minimum turning radius required for the traveling vehicle is calculated by applying the geometric calculation formula. These calculation results generate the bridge travel arc data. When calculating the average speed of the bridge traffic flow, the total time and the number of vehicles passing through are first extracted from the bridge traffic flow characteristic data. The average speed data is obtained by dividing the total traffic flow by the total time of the passing vehicles. This process uses a data analysis tool to ensure the accuracy of the calculation. The generated bridge traffic flow average speed data is used for calculating the bridge overload load strength, and the bridge traffic flow average speed data and the bridge travel arc data are used. Combined with the vehicle weight and the bridge design load standard, the mechanical formula is used to calculate the load strength of the bridge under specific conditions. This calculation result generates the bridge overload strength data. When analyzing the degradation trend of the bridge structure, the bridge overload strength data and the bridge structure characteristic data are used to apply the degradation model to evaluate the structural health of the bridge under different use conditions. Collect historical monitoring data and current load data, and use the trend analysis method to generate the bridge structure degradation trend data. When calculating the vehicle speed limit based on the bridge structure degradation trend data and the bridge travel arc data, the structural health of the bridge and the impact of the vehicle passing on the structure are first considered. Using the calculation model, combined with the bending stress and load limit conditions, the safe vehicle speed limit data is obtained. When analyzing the complexity of the abnormal section of the distribution bridge, the bridge structure degradation trend data and the vehicle speed limit data are used. By setting weights and influencing factors, the two are combined for comprehensive evaluation. The complexity scoring system is used to generate the complexity data of the distribution bridge section.

[0139] Preferably, the road condition analysis of the abnormal section of the delivery highway is performed based on the abnormal section data of the delivery highway, comprising the following steps:

[0140] Extract the terrain of the section according to the abnormal section data of the distribution highway to obtain the terrain data of the abnormal section of the distribution highway;

[0141] Calculate the ground friction coefficient and obtain the elevation of the abnormal section according to the terrain data of the abnormal section of the distribution highway, and obtain the ground friction coefficient and elevation data of the abnormal section of the highway;

[0142] The steep slope of the road section is collected according to the elevation data of the abnormal road section, and the steep slope data of the abnormal road section is obtained;

[0143] According to the data of steep slope of abnormal road section and the ground friction coefficient of abnormal road section, the vehicle driving stability is simulated to obtain the driving stability data of the vehicle;

[0144] The road surface condition of abnormal sections of the highway is analyzed based on the driving stability data of the two vehicles and the data on the steep slope of abnormal sections, and the road surface condition data of abnormal sections of the distribution highway are obtained.

[0145] In the embodiment of the present invention, when extracting the road section terrain of the abnormal section data of the distribution highway, firstly, the abnormal section of the highway is mapped in detail using high-resolution satellite images and terrain measuring instruments. Through GIS (Geographic Information System) technology, the collected terrain data is converted into a digital elevation model (DEM) to obtain the terrain characteristics of the abnormal section of the highway, including information such as slope, curvature and ground conditions. The data is processed using a spatial analysis tool to generate the terrain data of the abnormal section of the distribution highway. After obtaining the terrain data of the abnormal section of the distribution highway, the ground friction coefficient is calculated. Using the field test method, the friction of the road surface is measured by a sliding mode tester under different weather and traffic conditions. The test results are combined with the vehicle speed and downforce, and the ground friction coefficient is calculated using a formula. At the same time, the elevation information of the abnormal section is extracted using the terrain data, and the highest and lowest points of the section are recorded, thereby generating the ground friction coefficient and elevation data of the abnormal section of the highway. When collecting the steep slope of the section, the elevation data analysis software is used to calculate the slope change of the abnormal section of the highway. By setting a slope threshold (for example, more than 15%), the sections with too steep slopes are screened out. Combined with the terrain features, a database containing slope information is generated to clearly identify all areas with too steep slopes. When simulating the vehicle driving stability based on the data of the steep slope of abnormal sections and the ground friction coefficient of abnormal sections of highways, a vehicle dynamic model is first established to consider the driving conditions of different types of vehicles on the section. Through the numerical simulation method, the slope and friction coefficient data are input to simulate the driving behavior of the vehicle on the steep slope section, including acceleration, braking and steering. Generate vehicle driving stability data. When analyzing the road surface conditions of abnormal sections of highways, combine the vehicle driving stability data and the data of the steep slope of abnormal sections, and first set the analysis indicators, including the degree of road damage, driving safety and traffic capacity. By comparing the driving stability data of different sections and combining real-time monitoring data, a comprehensive evaluation report is generated to evaluate the overall condition of the road surface.

[0146] Preferably, step S25 comprises the following steps:

[0147] Step S251: Obtaining the drag coefficient of the supply chain delivery vehicle;

[0148] Step S252: extracting road surface defects according to the abnormal road section complexity data to obtain abnormal road section surface defect data;

[0149] Step S253: performing defect distribution statistics and defect size collection according to the abnormal road section surface defect data to obtain an abnormal road section defect distribution data set and an abnormal road section defect size data set;

[0150] Step S254: estimating the vehicle driving inclination according to the abnormal road section defect distribution data set, the abnormal road section defect size data set and the supply chain delivery vehicle loading weight data, and obtaining the supply chain delivery vehicle driving inclination data;

[0151] Step S255: Calculating the center of gravity offset of the vehicle according to the supply chain distribution vehicle driving inclination data and the supply chain distribution vehicle loading weight data to obtain the center of gravity offset data of the vehicle;

[0152] Step S256: Calculate the vehicle rollover probability based on the vehicle center of gravity offset data and the wind resistance coefficient of the supply chain delivery vehicle to obtain the delivery vehicle rollover probability data;

[0153] Step S257: Calculate the vehicle saturation of the driving section according to the congestion probability data of the supply chain distribution section to obtain the vehicle saturation data of the driving section;

[0154] Step S258: Calculate the driving difficulty of the vehicle based on the vehicle saturation data of the driving section and the rollover probability data of the delivery vehicle to obtain the driving difficulty data of the supply chain delivery vehicle.

[0155] In the embodiment of the present invention, the delivery vehicle model is placed in a wind tunnel, and the wind speed is adjusted to record the wind resistance data at different vehicle speeds. The resistance of the model in the airflow is measured by a force sensor, and the geometric parameters such as the front face shape and the side shape of the vehicle are recorded at the same time. The wind resistance coefficient is calculated by applying computational fluid dynamics (CFD) analysis based on the test data at different wind speeds. When the road surface defects are extracted according to the abnormal road section complexity data, the road surface is firstly comprehensively inspected by a high-resolution camera and a laser scanner. The acquired image data is analyzed by an image processing algorithm to identify defects such as cracks and potholes. Combined with the road surface complexity data, data mining technology is used to extract the surface defect features related to the abnormal road section, and the abnormal road section surface defect data including the defect type, location and severity is generated. When performing defect distribution statistics and defect size collection of the abnormal road section surface defect data, GIS (geographic information system) technology is used to combine the extracted defect information with the road network. The position of each defect is marked, and the total number of defects and their distribution are calculated. Statistical analysis methods are applied to measure the size and distribution characteristics of defects, and form a defect distribution data set and defect size data set for abnormal sections containing the number of defects and defect sizes. When estimating the vehicle driving tilt, first collect the abnormal section defect distribution data set, defect size data set and vehicle loading weight data. Combined with the vehicle's center of gravity height and wheel distance, the vehicle's tilt angle under different defect conditions is calculated using geometric principles. By establishing a mathematical model and combining it with actual driving environment data, the supply chain distribution vehicle driving tilt data is generated. When calculating the vehicle's center of gravity offset, first analyze the load distribution of each tire of the vehicle based on the vehicle's loading weight and driving tilt data using the static balance principle. The vehicle's center of gravity offset data is obtained by calculating the change of the center of gravity during driving. When calculating the vehicle rollover probability, a rollover risk assessment model is established by combining the vehicle's center of gravity offset data and the drag coefficient. Through simulation technology, the stability of the vehicle at different speeds and tilt angles is analyzed to obtain the rollover probability data of the distribution vehicle. When calculating the vehicle saturation of the driving section based on the supply chain distribution section congestion probability data, first collect traffic flow monitoring data and analyze the relationship between the traffic flow and the section's traffic capacity in a specific time period. Using the principles of traffic engineering, the vehicle saturation of each road section is calculated to generate vehicle saturation data for the driving section. When calculating the difficulty of vehicle driving, the vehicle saturation data of the driving section and the rollover probability data of the delivery vehicle are combined to calculate the overall difficulty of vehicle driving through comprehensive evaluation indicators. Using a weighted algorithm, the congestion level, rollover risk and influencing factors are quantified to generate driving difficulty data for supply chain delivery vehicles.

[0156] Preferably, step S3 comprises the following steps:

[0157] Step S31: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map;

[0158] Step S32: collecting inter-node distribution paths according to the supply chain distribution topology map to obtain a multi-path dataset between distribution nodes;

[0159] Step S33: screening high-efficiency paths for the supply chain distribution topology map according to the multi-path dataset between distribution nodes, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain distribution high-efficiency path data;

[0160] Step S34: planning a vehicle travel avoidance route based on the delivery high-efficiency path data to obtain vehicle travel avoidance route data.

[0161] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0162] Step S31: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map;

[0163] In the embodiment of the present invention, when constructing the supply chain distribution topology map, the transportation and distribution data stored in the blockchain is first extracted, including node locations, transportation routes, vehicle information, etc. The adjacency matrix method in graph theory is used to model each distribution node and connection path. The nodes are regarded as vertices in the graph, and the paths are regarded as edges to form a directed graph, which represents the distribution relationship between the nodes. The topology map is visualized through data visualization tools (such as Gephi) to ensure its accuracy and completeness.

[0164] Step S32: collecting inter-node distribution paths according to the supply chain distribution topology map to obtain a multi-path dataset between distribution nodes;

[0165] In the embodiment of the present invention, when collecting the distribution paths between nodes, the information of each distribution node is first extracted from the constructed supply chain distribution topology map. The connection relationship between the nodes is traversed by an algorithm to identify the available distribution paths. The Dijkstra algorithm or the A* algorithm is used to optimize the path to ensure that the selected path is optimal in terms of transportation time and cost. Finally, a multi-path data set containing multiple distribution paths is generated.

[0166] Step S33: screening high-efficiency paths for the supply chain distribution topology map according to the multi-path dataset between distribution nodes, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain distribution high-efficiency path data;

[0167] In the embodiment of the present invention, when screening the high-efficiency path of the supply chain distribution topology, first collect the multi-path data set between distribution nodes, vehicle delay time data and driving difficulty data. These data are integrated through data fusion technology to form a comprehensive evaluation model. Combined with the weighted algorithm, the timeliness of each path is evaluated, focusing on the impact of delay time and driving difficulty. Through screening, the distribution path with the best timeliness is selected to generate the distribution high-efficiency path data.

[0168] Step S34: planning a vehicle travel avoidance route based on the delivery high-efficiency path data to obtain vehicle travel avoidance route data.

[0169] In the embodiment of the present invention, when planning the vehicle travel avoidance route, the distribution high-efficiency path data is first analyzed to determine the potential traffic bottlenecks and risk areas. The simulated annealing algorithm is applied in combination with real-time traffic flow data to calculate the congestion points and accident risks encountered during the driving process. By comparing the safety and efficiency of different routes, the travel route that avoids these unfavorable factors is optimized and planned, and finally the vehicle travel avoidance route data is generated.

[0170] Preferably, step S33 includes the following steps:

[0171] Step S331: Calculate the multi-path travel time according to the multi-path data set between distribution nodes and the supply chain distribution vehicle delay time data to obtain the multi-path travel time data between nodes;

[0172] Step S332: Performing a multi-path security test between nodes based on the multi-path data set between distribution nodes and the driving difficulty data of the supply chain distribution vehicles to obtain multi-path security data between nodes;

[0173] Step S333: Perform comprehensive weighted scoring according to the multi-path safety data between nodes and the multi-path travel time data between nodes to obtain a multi-path comprehensive weighted scoring data set;

[0174] Step S334: Screen the supply chain distribution topology map for high-efficiency paths based on the multi-path comprehensive weighted scoring data set to obtain distribution high-efficiency path data.

[0175] In the embodiment of the present invention, when calculating the multi-path travel time, all the distribution paths are first extracted from the multi-path data set between distribution nodes. The travel time of each path is calculated by considering factors such as real-time traffic flow, section restrictions and vehicle performance. GPS data and historical driving records are used in combination with traffic flow analysis tools to update the delay time data in real time to ensure accuracy. The basic travel time and delay time of each path are added by a formula to finally obtain the multi-path travel time data between nodes. When performing the multi-path safety test between nodes, the relevant information of each path is first extracted from the multi-path data set between distribution nodes, and the safety of each path is evaluated in combination with the vehicle driving difficulty data. A safety scoring model is used to calculate the safety score of each path by considering the road conditions, traffic density and past accident data. The score is standardized to facilitate comparison between different paths. Finally, the multi-path safety data between nodes is obtained. When performing a comprehensive weighted score, the multi-path safety data between nodes and the multi-path travel time data are first integrated. The weighted average method is applied to determine the weights of safety and travel time to ensure that important factors are reasonably reflected in the score. Calculate the comprehensive score for each path, consider the balance between high timeliness and security, and form a multi-path comprehensive weighted scoring data set. When screening high-timeliness paths, extract path information from the supply chain distribution topology map based on the multi-path comprehensive weighted scoring data set. Set a threshold, sort the paths according to the comprehensive score, and select the path with the highest score as the high-timeliness path. This process is achieved through automated screening tools to ensure the speed and accuracy of the screening results and generate distribution high-timeliness path data.

[0176] Preferably, step S4 comprises the following steps:

[0177] Step S41: performing time simulation according to the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data;

[0178] Step S42: performing time efficiency feedback according to the time efficiency simulation data of the vehicle travel avoidance route to obtain time efficiency feedback data of the vehicle travel avoidance route;

[0179] Step S43: Intelligently dispatch the transportation and distribution data stored in the blockchain according to the vehicle travel avoidance route time feedback data to generate transportation and distribution intelligent dispatching data;

[0180] Step S44: Based on the blockchain, the transportation and distribution intelligent scheduling data is stored in the blockchain to obtain the transportation and distribution intelligent scheduling data stored in the blockchain.

[0181] In an embodiment of the present invention, when performing a time-efficiency simulation of a vehicle travel avoidance route, all relevant route information is first extracted from the vehicle travel avoidance route data. The driving conditions in different time periods are simulated by a traffic simulation tool, and factors including road conditions, traffic signals, real-time traffic flow, etc. are considered. Historical data is used to evaluate the driving time of each route, and dynamic adjustments are made based on the current traffic conditions. Finally, the simulation results generate vehicle travel avoidance route time-efficiency simulation data. When performing time-efficiency feedback, the actual driving conditions and feedback information are collected based on the vehicle travel avoidance route time-efficiency simulation data. Through a data analysis tool, the simulation results are compared with the actual driving time to identify potential causes of deviation. This process includes data cleaning and standardization to ensure the accuracy of the feedback data. Through comprehensive analysis, the vehicle travel avoidance route time-efficiency feedback data is obtained. When performing intelligent scheduling generation, the effectiveness and priority of each distribution route are evaluated based on the vehicle travel avoidance route time-efficiency feedback data. The scheduling optimization algorithm is applied, combined with the transportation and distribution data stored in the blockchain, and the current position, loading situation and delay history of the vehicle are considered to perform intelligent scheduling. This process includes prioritizing each delivery task, generating the optimal delivery plan, and forming intelligent dispatching data for transportation and delivery. When storing in blockchain, the dispatching information is encrypted and stored using blockchain technology based on the generated intelligent dispatching data for transportation and delivery. This process ensures the immutability and traceability of the data. By establishing a new block, the intelligent dispatching data is attached to the blockchain. Blockchain storage uses a decentralized network to enhance data security and transparency. Finally, the intelligent dispatching data for transportation and delivery stored in the blockchain is obtained, which provides a reliable basis for subsequent inquiries and audits, and ensures the transparency and trust of the supply chain.

[0182] 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 trusted traceability method based on blockchain, characterized in that: The following steps are involved: Step S1: Acquire supply chain management log data; extract transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data; Based on the blockchain, the supply chain transportation and distribution data is stored in the blockchain to obtain the blockchain-stored transportation and distribution data; Step S2: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal distribution data of the supply chain transportation and distribution; calculating the driving difficulty of the vehicle according to the abnormal distribution data of the supply chain transportation and distribution to obtain the driving difficulty data of the supply chain distribution vehicle; calculating the delay time of the distribution vehicle according to the driving difficulty data of the supply chain distribution vehicle to obtain the delay time data of the supply chain distribution vehicle; Step S3: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map; plan a vehicle driving avoidance route based on the supply chain distribution topology map, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain vehicle driving avoidance route data; Step S4: performing time simulation according to the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data; According to the vehicle driving avoidance route time feedback data, the transportation and distribution data stored in the blockchain is intelligently scheduled to generate transportation and distribution intelligent scheduling data; based on the blockchain, the transportation and distribution intelligent scheduling data is stored in the blockchain to obtain the transportation and distribution intelligent scheduling data stored in the blockchain.

2. The blockchain-based trusted traceability method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtain supply chain management log data; Step S12: extracting transportation and distribution data according to the supply chain management log data to obtain supply chain transportation and distribution data; Step S13: performing normalization processing on the supply chain transportation and distribution data to obtain normalized supply chain transportation and distribution data; Step S14: performing timestamp processing on the supply chain transportation and delivery standardized data to obtain supply chain transportation and delivery timestamp data; Step S15: Based on the blockchain, the supply chain transportation and delivery timestamp data is stored in the blockchain to obtain the blockchain-stored transportation and delivery data.

3. The blockchain-based trusted traceability method according to claim 2 is characterized in that: Step S2 includes the following steps: Step S21: extracting the loading weight of the distribution vehicle according to the supply chain management log data to obtain the loading weight data of the supply chain distribution vehicle; Step S22: Tracing the source of the abnormal distribution according to the transportation and distribution data stored in the blockchain to obtain the abnormal data of supply chain transportation and distribution; Step S23: performing abnormal road section complexity analysis based on supply chain transportation and distribution abnormal data to obtain abnormal road section complexity data; Step S24: Calculate the congestion probability of the distribution section according to the abnormal section complexity data and the supply chain transportation and distribution abnormality data to obtain the congestion probability data of the supply chain distribution section; Step S25: Calculating the driving difficulty of the vehicle according to the congestion probability data of the supply chain distribution section and the complexity data of the abnormal section and the loading weight data of the supply chain distribution vehicle to obtain the driving difficulty data of the supply chain distribution vehicle; Step S26: Calculate the delivery vehicle delay time based on the supply chain delivery vehicle driving difficulty data and the supply chain delivery road section congestion probability data to obtain the supply chain delivery vehicle delay time data.

4. The blockchain-based trusted traceability method according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: distinguishing abnormal sections according to the abnormal data of supply chain transportation and distribution, and obtaining abnormal section data of distribution bridges and abnormal section data of distribution highways; Step S232: performing complexity analysis of the abnormal section of the distribution bridge according to the abnormal section data of the distribution bridge to obtain complexity data of the distribution bridge section; Step S233: analyzing the road condition of the abnormal section of the delivery road according to the abnormal section data of the delivery road to obtain the road condition data of the abnormal section of the delivery road; Step S234: performing abnormal section complexity analysis based on the distribution bridge section complexity data and the distribution highway abnormal section pavement condition data to obtain abnormal section complexity data.

5. The blockchain-based trusted traceability method according to claim 4 is characterized in that: The complexity analysis of abnormal sections of distribution bridges is performed based on the abnormal section data of distribution bridges, including the following steps: According to the abnormal section data of the distribution bridge, the traffic flow characteristics and the bridge structure characteristics are extracted to obtain the bridge traffic flow characteristic data and the bridge structure characteristic data; Calculate the bridge travel arc according to the bridge structure characteristic data to obtain the bridge travel arc data; The average speed of bridge traffic flow is calculated according to the bridge traffic flow characteristic data to obtain the average speed data of bridge traffic flow; The overload strength of the bridge is calculated based on the average speed data of the bridge traffic flow and the bridge travel arc data to obtain the overload strength data of the bridge; According to the bridge overload strength data and bridge structure characteristic data, the bridge structure degradation trend analysis is carried out to obtain the bridge structure degradation trend data; Calculate the vehicle speed limit based on the bridge structure degradation trend data and the bridge travel arc data to obtain the vehicle speed limit data; The complexity analysis of abnormal sections of distribution bridges is carried out using the bridge structure degradation trend data and vehicle speed limit data to obtain the complexity data of distribution bridge sections.

6. The blockchain-based trusted traceability method according to claim 4 is characterized in that: The road condition analysis of abnormal sections of the distribution highway is performed based on the abnormal section data of the distribution highway, including the following steps: Extract the terrain of the section according to the abnormal section data of the distribution highway to obtain the terrain data of the abnormal section of the distribution highway; Calculate the ground friction coefficient and obtain the elevation of the abnormal section according to the terrain data of the abnormal section of the distribution highway, and obtain the ground friction coefficient and elevation data of the abnormal section of the highway; The steep slope of the road section is collected according to the elevation data of the abnormal road section, and the steep slope data of the abnormal road section is obtained; According to the data of steep slope of abnormal road section and the ground friction coefficient of abnormal road section, the vehicle driving stability is simulated to obtain the driving stability data of the vehicle; The road surface condition of abnormal sections of the highway is analyzed based on the driving stability data of the two vehicles and the data on the steep slope of abnormal sections, and the road surface condition data of abnormal sections of the distribution highway are obtained.

7. The blockchain-based trusted traceability method according to claim 3 is characterized in that: Step S25 includes the following steps: Step S251: Obtaining the drag coefficient of the supply chain delivery vehicle; Step S252: extracting road surface defects according to the abnormal road section complexity data to obtain abnormal road section surface defect data; Step S253: performing defect distribution statistics and defect size collection according to the abnormal road section surface defect data to obtain an abnormal road section defect distribution data set and an abnormal road section defect size data set; Step S254: estimating the vehicle driving inclination according to the abnormal road section defect distribution data set, the abnormal road section defect size data set and the supply chain delivery vehicle loading weight data, and obtaining the supply chain delivery vehicle driving inclination data; Step S255: Calculating the center of gravity offset of the vehicle according to the supply chain distribution vehicle driving inclination data and the supply chain distribution vehicle loading weight data to obtain the center of gravity offset data of the vehicle; Step S256: Calculate the vehicle rollover probability based on the vehicle center of gravity offset data and the wind resistance coefficient of the supply chain delivery vehicle to obtain the delivery vehicle rollover probability data; Step S257: Calculate the vehicle saturation of the driving section according to the congestion probability data of the supply chain distribution section to obtain the vehicle saturation data of the driving section; Step S258: Calculate the driving difficulty of the vehicle based on the vehicle saturation data of the driving section and the rollover probability data of the delivery vehicle to obtain the driving difficulty data of the supply chain delivery vehicle.

8. The blockchain-based trusted traceability method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: construct a supply chain distribution topology map based on the transportation and distribution data stored in the blockchain to obtain a supply chain distribution topology map; Step S32: collecting inter-node distribution paths according to the supply chain distribution topology map to obtain a multi-path dataset between distribution nodes; Step S33: screening high-efficiency paths for the supply chain distribution topology map according to the multi-path dataset between distribution nodes, the supply chain distribution vehicle delay time data, and the supply chain distribution vehicle driving difficulty data to obtain distribution high-efficiency path data; Step S34: planning a vehicle travel avoidance route based on the delivery high-efficiency path data to obtain vehicle travel avoidance route data.

9. The blockchain-based trusted traceability method according to claim 8, characterized in that: Step S33 includes the following steps: Step S331: Calculate the multi-path travel time according to the multi-path data set between distribution nodes and the supply chain distribution vehicle delay time data to obtain the multi-path travel time data between nodes; Step S332: Performing a multi-path security test between nodes based on the multi-path data set between distribution nodes and the driving difficulty data of the supply chain distribution vehicles to obtain multi-path security data between nodes; Step S333: Perform comprehensive weighted scoring according to the multi-path safety data between nodes and the multi-path travel time data between nodes to obtain a multi-path comprehensive weighted scoring data set; Step S334: Screen the supply chain distribution topology map for high-efficiency paths based on the multi-path comprehensive weighted scoring data set to obtain distribution high-efficiency path data.

10. The blockchain-based trusted traceability method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing time simulation according to the vehicle travel avoidance route data to obtain vehicle travel avoidance route time simulation data; Step S42: performing time efficiency feedback according to the time efficiency simulation data of the vehicle travel avoidance route to obtain time efficiency feedback data of the vehicle travel avoidance route; Step S43: Intelligently dispatch the transportation and distribution data stored in the blockchain according to the vehicle travel avoidance route time feedback data to generate transportation and distribution intelligent dispatching data; Step S44: Based on the blockchain, the transportation and distribution intelligent scheduling data is stored in the blockchain to obtain the transportation and distribution intelligent scheduling data stored in the blockchain.

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