A logistics information management method based on big data

Abstract

The application relates to the technical field of information management and discloses a logistics information management method based on big data, which comprises the following steps: step one, collecting multi-source heterogeneous data containing recycling appointment data, vehicle real-time state data, warehouse state data and battery safety state data, and performing clock alignment processing on the multi-source heterogeneous data to generate aligned multi-source data; and step two, quantitatively calculating a power battery recycling task based on the aligned multi-source data to obtain a multi-dimensional safety risk index. The technical scheme of collecting multi-source heterogeneous data and constructing a synchronous optimization target function containing a logistics efficiency item is adopted, so that the recycling task with scattered sources and large fluctuations can be effectively batch packed and optimally routed; compared with the scheduling mode lacking the prediction ability and the packing planning in the prior art, the recycling hotspot cannot be grasped, the vehicle empty running rate is high, the waiting time is long, and the operating cost is significantly increased, and the defects are solved.

Description

Technical Field

[0001] This invention relates to the field of information management technology, specifically to a logistics information management method based on big data. Background Technology

[0002] The rapid development of the new energy vehicle industry has led to a large-scale retirement of power batteries, posing a severe recycling challenge. Reverse logistics management of power batteries—the transportation process from recycling points to processing centers—is a crucial link in the recycling chain, where management efficiency, safety, and compliance are paramount.

[0003] However, existing logistics information management systems have many shortcomings in dealing with reverse logistics of power batteries:

[0004] The existing power battery recycling logistics system lacks the ability to collect and predict reverse recycling demands from scattered and fluctuating sources in multiple dimensions. This makes it difficult to identify peak recycling periods in advance, and consequently, it is impossible to effectively package and route scattered tasks in batches, resulting in high vehicle empty running rates, long waiting times, and significantly increased operating costs.

[0005] Existing logistics scheduling methods often focus on efficiency indicators such as the shortest distance or the fastest speed, generally neglecting the safety risks of the power battery recycling process. Safety constraints, including battery temperature, vibration status, and loading thermal effects, are not easily quantified and integrated into scheduling algorithms, leading to hidden safety hazards in scheduling plans. This results in a conflict between recycling efficiency and recycling safety, making it impossible to achieve simultaneous optimization during decision-making.

[0006] In the existing power battery recycling process, the records of key actions and status information are scattered and easily tampered with. The lack of a unified, fully automated record-keeping mechanism leads to poor traceability and difficulty in assigning responsibility. When dealing with compliance requirements for battery files and process records, a lot of manual evidence must be collected. Furthermore, the lack of tamper-proof event summaries and evidence packages severely prolongs the settlement, reconciliation, and compliance declaration cycles.

[0007] To address the aforementioned issues, this invention proposes a big data-based logistics information management method. This method aggregates and aligns heterogeneous data from multiple sources, quantifies and calculates multi-dimensional security risks, and uniformly converts them into equivalent security waiting time windows. It then constructs a synchronous optimization objective function to generate a scheduling plan. During execution, it automatically generates tamper-proof event summary numbers and compliant ledgers, significantly improving the operational efficiency of reverse logistics while ensuring recycling security, and automating the entire process of tamper-proof compliance and traceability. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a logistics information management method based on big data to solve the problems mentioned in the background section.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a logistics information management method based on big data, comprising:

[0010] Step 1: Collect multi-source heterogeneous data including recycling reservation data, real-time vehicle status, warehouse status data, and battery safety status data, and perform clock alignment processing on the multi-source heterogeneous data to generate aligned multi-source data;

[0011] Step 2: Based on the aligned multi-source data, quantify and calculate the power battery recycling task to obtain multi-dimensional safety risk indicators;

[0012] Step 3: Convert the multi-dimensional security risk indicators into equivalent security waiting time windows;

[0013] Step 4: Construct an objective function to simultaneously optimize logistics efficiency and safety risks, and use an equivalent safety waiting time window as a scheduling constraint to solve the objective function to generate a batch recycling scheduling plan;

[0014] Step 5: During the execution of the batch recycling schedule, key actions are monitored to generate tamper-proof event summary numbers, and full-process compliance ledgers and settlement evidence packages are automatically generated based on the event summary numbers.

[0015] Preferably, step two includes: calculating the remaining battery risk time based on the battery's current real-time temperature, temperature change rate, and a preset battery safety temperature threshold.

[0016] Preferably, step two includes: calculating the loading thermal influence coefficient, which characterizes the risk of thermal runaway due to the aggregation of multiple battery boxes, based on the planned number of battery boxes and the thermal influence factor.

[0017] Preferably, step five specifically includes: automatically collecting timestamp, location, operator, and battery serial number information when a key action occurs, and compressing the information to generate a fixed-length string.

[0018] Preferably, step five specifically includes: calling and concatenating strings of fixed length in chronological order, with the strings serving as indexes and verification criteria, and combining them to generate ledgers and evidence packages.

[0019] Preferably, step one further includes:

[0020] Sub-step Based on battery safety status data from aligned multi-source data, the remaining battery risk time is calculated using the following formula:

[0021] ,

[0022] in, Indicates the remaining time of battery risk. This indicates the preset battery safety temperature threshold. This indicates the current real-time temperature of the battery. This indicates the rate of temperature change calculated based on historical data. To prevent extremely small positive numbers with a denominator of zero;

[0023] Sub-step Based on real-time vehicle status and road condition information from aligned multi-source data, the environmental risk value is calculated using the following environmental risk formula:

[0024] ,

[0025] in, Indicates the environmental risk value. , , These are the weighting coefficients for different risk factors. This represents the quantified road condition risk value. This represents the quantified weather risk value. This represents the quantified slope risk value;

[0026] Sub-step Based on the recycling reservation data from the aligned multi-source data, the planned number of battery boxes to be loaded is obtained, and the loading heat impact coefficient is calculated using the following loading heat impact coefficient formula:

[0027] ,

[0028] in, Indicates the loading thermal influence coefficient. Indicates the thermal influence factor. This indicates the planned number of battery boxes to be loaded. Indicates the standard reference base;

[0029] Sub-step Environmental risk value Integrate with other external risks into a comprehensive risk coefficient Together with the remaining time of the battery risk and the loading thermal influence coefficient Together, they serve as the input for step three.

[0030] Preferably, step three includes: calculating the equivalent safety waiting time window based on the remaining battery risk time, the loading thermal impact coefficient, and the comprehensive risk coefficient using a preset conversion formula.

[0031] Preferably, step three further includes:

[0032] Sub-step The obtained multi-dimensional safety risk indicators include battery risk remaining time. Comprehensive risk coefficient and loading thermal influence coefficient ;

[0033] Sub-step The remaining battery risk time is calculated using the following equivalent safety waiting time window formula. Comprehensive risk coefficient and loading thermal influence coefficient All are uniformly converted into equivalent safety waiting time windows. :

[0034] ,

[0035] in, This represents the equivalent safety waiting time window. Indicates the remaining time of battery risk. This represents the overall risk coefficient. Indicates the thermal influence coefficient of the load;

[0036] Sub-step Based on equivalent safety waiting time window Establish safety scheduling criteria for the objective function. The algorithmic formula for the safety scheduling criteria is to calculate the penalty value for safety violations. : ,

[0037] in, This indicates the penalty value for safety violations. Indicates the estimated time to arrive for the recovery mission;

[0038] The security scheduling determination condition is as follows:

[0039] When the estimated time for the recovery mission is reached Larger than the equivalent safety waiting time window At that time, it was determined that the recovery mission violated safety constraints;

[0040] Sub-step The equivalent safety waiting time window Together with the safety scheduling decision conditions, they serve as the scheduling constraints for constructing the objective function and are output.

[0041] Preferably, step four further includes:

[0042] Sub-step Based on security violation penalty values The calculation method involves constructing an objective function to simultaneously optimize logistics efficiency and safety risks. The algorithm formula for the objective function is as follows:

[0043] ,

[0044] in, This represents the overall optimization objective. This represents a logistics efficiency term calculated based on total mileage. For high-weight security penalty coefficients, For the first The security violation penalty value for the recovery task The sum of the security violation penalty values ​​for all recycling tasks;

[0045] Sub-step The overall optimization objective is obtained by solving the defined objective function through a scheduling algorithm. The batch recycling schedule corresponding to the minimum value.

[0046] Preferably, step five further includes:

[0047] Sub-step The system acquires and executes a batch recycling schedule, while setting key action judgment conditions. When the execution status of the batch recycling schedule meets the key action judgment conditions, it automatically collects all node information, including timestamps, locations, operators, and battery serial numbers.

[0048] Sub-step Using all collected node information as input, the event digest number is calculated using the following anti-tampering digest algorithm formula:

[0049] ,

[0050] in, This indicates an event summary number with a fixed length. For the preset hash function, This contains all node information;

[0051] Sub-step Set task completion criteria. When the batch recycling schedule meets the task completion criteria, automatically call and concatenate all generated event summary numbers in chronological order. And by event summary number As an index and verification basis, all node information is combined to generate a full-process compliance ledger and settlement evidence package.

[0052] This invention provides a logistics information management method based on big data. It has the following beneficial effects:

[0053] 1. This invention adopts a technical solution of multi-source heterogeneous data collection and construction of a synchronous optimization objective function that includes logistics efficiency terms, so as to realize effective batch packaging and optimal route planning for recycling tasks with dispersed and fluctuating sources; compared with the scheduling method in the prior art that lacks predictive ability and packaging planning, it solves the shortcomings of high vehicle empty running rate, long waiting time and significantly increased operating costs caused by the inability to grasp recycling hotspots.

[0054] 2. This invention quantifies multi-dimensional safety risk indicators and uniformly converts them into equivalent safety waiting time windows, constructing a target function that includes safety violation penalty values. This achieves simultaneous optimization of logistics efficiency and recovery safety during the scheduling decision-making stage. Compared with existing technologies that ignore safety constraints or make it difficult to quantify and integrate safety information, this invention solves the problem of hidden safety hazards in scheduling plans, which lead to conflicts between efficiency and safety and the inability to optimize them simultaneously.

[0055] 3. This invention adopts a technical solution that automatically collects all node information when key actions occur, generates tamper-proof event summary numbers, and automatically generates a full-process compliance ledger and settlement evidence package based on the event summary numbers, thereby achieving full-process automated traceability and tamper-proofing. Compared with the existing technology where information records are scattered, easily tampered with, and lack a unified traceability mechanism, this invention solves the shortcomings of poor traceability throughout the process, difficulty in assigning responsibilities, and the long settlement reconciliation and compliance declaration cycle caused by a large amount of manual evidence processing. Attached Figure Description

[0056] Figure 1 This is a flowchart of the present invention;

[0057] Figure 2 This is a detailed flowchart of steps two and three in this invention. Detailed Implementation

[0058] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0059] The present invention will now be described in detail with reference to the accompanying drawings:

[0060] Example:

[0061] Please see the appendix Figure 1 and attached Figure 2This invention provides a logistics information management method based on big data, comprising:

[0062] Step 1: Collect multi-source heterogeneous data including recycling reservation data, real-time vehicle status, warehouse status data, and battery safety status data, and perform clock alignment processing on the multi-source heterogeneous data to generate aligned multi-source data;

[0063] Step 2: Based on the aligned multi-source data, quantify and calculate the power battery recycling task to obtain multi-dimensional safety risk indicators;

[0064] Step 3: Convert the multi-dimensional security risk indicators into equivalent security waiting time windows;

[0065] Step 4: Construct an objective function to simultaneously optimize logistics efficiency and safety risks, and use an equivalent safety waiting time window as a scheduling constraint to solve the objective function to generate a batch recycling scheduling plan;

[0066] Step 5: During the execution of the batch recycling schedule, key actions are monitored to generate tamper-proof event summary numbers, and full-process compliance ledgers and settlement evidence packages are automatically generated based on the event summary numbers.

[0067] Step one further includes:

[0068] Sub-step Based on battery safety status data from aligned multi-source data, the remaining battery risk time is calculated using the following formula:

[0069] ,

[0070] in, Indicates the remaining time of battery risk. This indicates the preset battery safety temperature threshold. This indicates the current real-time temperature of the battery. This indicates the rate of temperature change calculated based on historical data. To prevent extremely small positive numbers with a denominator of zero;

[0071] Sub-step Based on real-time vehicle status and road condition information from aligned multi-source data, the environmental risk value is calculated using the following environmental risk formula:

[0072] ,

[0073] in, Indicates the environmental risk value. , , These are the weighting coefficients for different risk factors. This represents the quantified road condition risk value. This represents the quantified weather risk value. This represents the quantified slope risk value;

[0074] Sub-step Based on the recycling reservation data from the aligned multi-source data, the planned number of battery boxes to be loaded is obtained, and the loading heat impact coefficient is calculated using the following loading heat impact coefficient formula:

[0075] ,

[0076] in, Indicates the loading thermal influence coefficient. Indicates the thermal influence factor. This indicates the planned number of battery boxes to be loaded. Indicates the standard reference base;

[0077] Sub-step Environmental risk value Integrate with other external risks into a comprehensive risk coefficient Together with battery risk remaining time and loading thermal influence coefficient Together, they serve as the input for step three.

[0078] Step three further includes:

[0079] Sub-step The obtained multi-dimensional safety risk indicators include battery risk and remaining time. Comprehensive risk coefficient and loading thermal influence coefficient ;

[0080] Sub-step The remaining battery risk time is calculated using the following equivalent safety waiting time window formula. Comprehensive risk coefficient and loading thermal influence coefficient All are uniformly converted into equivalent safety waiting time windows. :

[0081] ,

[0082] in, This represents the equivalent safety waiting time window. Indicates the remaining time of battery risk. This represents the overall risk coefficient. Indicates the thermal influence coefficient of the load;

[0083] Sub-step Based on equivalent safety waiting time window Establish safety scheduling criteria for the objective function. The algorithmic formula for the safety scheduling criteria is to calculate the penalty value for safety violations. : ,

[0084] in, This indicates the penalty value for safety violations. Indicates the estimated time to arrive for the recovery mission;

[0085] The conditions for determining safe dispatching are:

[0086] When the estimated time for the recovery mission is reached Larger than the equivalent safety waiting time window At that time, it was determined that the recovery mission violated safety constraints;

[0087] Sub-step The equivalent safety waiting time window Together with the safety scheduling decision conditions, they serve as the scheduling constraints for constructing the objective function and are output.

[0088] Step four further includes:

[0089] Sub-step Based on security violation penalty values The calculation method involves constructing an objective function to simultaneously optimize logistics efficiency and safety risks. The algorithm formula for the objective function is as follows:

[0090] ,

[0091] in, This represents the overall optimization objective. This represents a logistics efficiency term calculated based on total mileage. For high-weight security penalty coefficients, For the first The security violation penalty value for the recovery task The sum of the security violation penalty values ​​for all recycling tasks;

[0092] Sub-step The overall optimization objective is obtained by solving the defined objective function through a scheduling algorithm. The batch recycling schedule corresponding to the minimum value.

[0093] Step five further includes:

[0094] Sub-step The system acquires and executes a batch recycling schedule, while setting key action judgment conditions. When the execution status of the batch recycling schedule meets the key action judgment conditions, it automatically collects all node information, including timestamps, locations, operators, and battery serial numbers.

[0095] Sub-step Using all collected node information as input, the event digest number is calculated using the following anti-tampering digest algorithm formula:

[0096] ,

[0097] in, This indicates an event summary number with a fixed length. For the preset hash function, This contains all node information;

[0098] Sub-step Set task completion criteria. When the batch recycling schedule meets the task completion criteria, automatically call and concatenate all generated event summary numbers in chronological order. And by event summary number As an index and verification basis, all node information is combined to generate a full-process compliance ledger and settlement evidence package.

[0099] Building a unified and synchronized data foundation is a prerequisite for achieving quantification and optimization. This is to solve the problem of data silos caused by scattered data sources and different time bases, and to ensure that all subsequent security risk calculations and scheduling decisions are based on reliable data from the same time segment.

[0100] This technology transforms abstract security risks into calculable and measurable multi-dimensional security risk indicators, solving the problem that security factors are not easy to quantify in existing technologies. It makes "security" no longer a vague concept, but a specific value that can be input into the algorithm model.

[0101] All security risk indicators from different dimensions are uniformly converted into a single scheduling constraint based on "time," solving the problem of difficulty in comparing and uniformly using multi-dimensional risks, and utilizing security violation penalty values. The formula directly links safety constraints with logistics efficiency, serving as a bridge between safety risks and scheduling algorithms.

[0102] To achieve simultaneous optimization of logistics efficiency and safety risks, and to resolve the conflict between traditional scheduling that prioritizes efficiency over safety or vice versa, a unified objective function is used to find the optimal balance between the two, generating a truly safe and efficient batch recycling scheduling plan.

[0103] It provides end-to-end, automated, tamper-proof compliance traceability capabilities. Utilizing the characteristics of hash functions, it ensures that all node information for key actions cannot be tampered with once generated. The automatically generated compliance ledgers and settlement evidence packages are highly reliable, significantly shortening the cycle of compliance declaration and settlement reconciliation.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A logistics information management method based on big data, characterized in that, include: Step 1: Collect multi-source heterogeneous data including recycling reservation data, real-time vehicle status, warehouse status data, and battery safety status data, and perform clock alignment processing on the multi-source heterogeneous data to generate aligned multi-source data; Step two involves quantifying and calculating the power battery recycling task based on aligned multi-source data to obtain multi-dimensional safety risk indicators; wherein, step two includes: Based on battery safety status data from aligned multi-source data, the remaining battery risk time is calculated using the following formula: ， in, Indicates the remaining time of battery risk. This indicates the preset battery safety temperature threshold. This indicates the current real-time temperature of the battery. This indicates the rate of temperature change calculated based on historical data. To prevent extremely small positive numbers with a denominator of zero; Based on real-time vehicle status and road condition information from aligned multi-source data, the environmental risk value is calculated using the following environmental risk formula: ， in, Indicates the environmental risk value. , , These are the weighting coefficients for different risk factors. This represents the quantified road condition risk value. This represents the quantified weather risk value. This represents the quantified slope risk value; Based on the recycling reservation data from the aligned multi-source data, the planned number of battery boxes to be loaded is obtained, and the loading thermal impact coefficient is calculated using the following formula: ， in, Indicates the loading thermal influence coefficient. Indicates the thermal influence factor. This indicates the planned number of battery boxes to be loaded. Indicates the standard reference base; Environmental risk value Integrate with other external risks into a comprehensive risk coefficient And the remaining time of the battery risk Comprehensive risk coefficient and loading thermal influence coefficient These were identified as the multi-dimensional security risk indicators. Step 3: Convert the multi-dimensional security risk indicators into equivalent security waiting time windows; Step three includes: The remaining battery risk time is calculated using the equivalent safety waiting time window formula. Comprehensive risk coefficient and loading thermal influence coefficient Unified conversion to equivalent safety waiting time window : ， Based on equivalent safety waiting time window Calculate the penalty value for safety violations: ， in, This indicates the penalty value for safety violations. Indicates the estimated time to arrive for the recovery mission; when Greater than At that time, it was determined that the recovery mission violated safety constraints; Step 4: Construct an objective function to simultaneously optimize logistics efficiency and safety risks, and use an equivalent safety waiting time window as a scheduling constraint to solve the objective function to generate a batch recycling scheduling plan; Step four includes: Based on security violation penalty values Construct an objective function to simultaneously optimize logistics efficiency and safety risks: , in, This represents the overall optimization objective. This represents a logistics efficiency term calculated based on total mileage. For high-weight security penalty coefficients, For the first The security violation penalty value for the recovery task The sum of security violation penalty values ​​for all recycling tasks; The objective function is solved using a scheduling algorithm to obtain the overall optimization objective. The batch recycling schedule corresponding to the minimum value; Step 5: During the execution of the batch recycling schedule, key actions are monitored to generate tamper-proof event summary numbers, and full-process compliance ledgers and settlement evidence packages are automatically generated based on the event summary numbers.

2. The logistics information management method based on big data according to claim 1, characterized in that, In step five, when a critical action occurs, timestamp, location, operator, and battery serial number information are automatically collected, and the information is used to generate a fixed-length event summary number.

3. The logistics information management method based on big data according to claim 2, characterized in that, Using all collected node information as input, the event digest number is calculated using the following anti-tampering digest algorithm formula: ， in, This indicates an event summary number with a fixed length. For the preset hash function, This contains all node information.

4. The logistics information management method based on big data according to claim 3, characterized in that, Once the batch recycling schedule meets the task completion criteria, it automatically invokes and concatenates all generated event summary numbers in chronological order. And by event summary number As an index and verification basis, all node information is combined to generate a full-process compliance ledger and settlement evidence package.

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