Intelligent logistics processing method and device, computer device and storage medium

Abstract

This invention provides a smart logistics processing method, apparatus, computer equipment, and storage medium, comprising: collecting multi-dimensional transportation capacity data of transport vehicles and constructing a transportation capacity resource pool based on the multi-dimensional transportation capacity data; generating a dispatch instruction containing task details and route planning according to the transportation demand parameters of the order and pushing it to the driver terminal of the target driver; after dispatching the order, establishing a closed-loop data system for the entire chain based on the current location and sensor data of the target transport vehicle, monitoring the transportation trajectory in real time and issuing early warnings for trip anomalies; based on multi-dimensional data, monitoring operational indicator deviation events of the transportation and warehousing environment in real time, classifying and issuing alarms according to the event type and severity of the operational indicator deviation events, automatically generating problem work orders and distributing them to the corresponding responsible parties for collaborative processing. The technical solution of this application improves the visualization level and anomaly response efficiency of the entire transportation process.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and in particular to a smart logistics processing method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the continuous growth of logistics business volume, the complexity of the transportation process is increasing. Logistics companies generally rely on information technology to manage transport vehicles. Existing logistics capacity management systems have the following shortcomings: First, capacity dispatching mostly relies on manual methods to notify drivers; second, data from order, dispatch, waybill, and in-transit monitoring are independent of each other; third, abnormal situations such as transportation delays and inventory discrepancies depend on manual detection and handling; and fourth, the system architecture adjustment cycle is relatively long.

[0003] Therefore, improving the efficiency of logistics capacity scheduling, the visibility of the transportation process, the efficiency of abnormal event response, and the adaptability of system architecture have become urgent technical problems to be solved. Summary of the Invention

[0004] In view of this, the main objective of the present invention is to provide a smart logistics processing method, apparatus, computer equipment and storage medium, which improves the visualization level and anomaly response efficiency of the entire transportation process.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: In a first aspect, embodiments of the present invention provide a smart logistics processing method, comprising: Collect multi-dimensional transport capacity data of transport vehicles and build a transport capacity resource pool based on the multi-dimensional transport capacity data; Based on the transportation demand parameters of the order, the system selects matching target transportation vehicles and target drivers from the transportation capacity resource pool, generates a dispatch instruction containing task details and route planning, and pushes it to the driver terminal of the target driver. After dispatching an order, based on the current location and sensor data of the target transport vehicle, a closed-loop data system is established, covering the entire chain from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation. The system monitors the transport trajectory in real time and issues early warnings for abnormal trips. Customized data dashboards are provided for different roles related to the target transport vehicle, including at least one of the following roles: supplier, logistics provider, carrier, and customer. Based on multi-dimensional data collected in the end-to-end data closed loop, the system monitors operational indicator deviation events in the transportation and warehousing links in real time. It classifies and alarms according to the event type and severity of the operational indicator deviation events, automatically generates problem work orders and distributes them to the corresponding responsible parties for collaborative processing until the loop is closed. The operational indicator deviation events include inventory data deviation, transportation node delays and environmental parameter exceedances.

[0006] The multi-dimensional transportation capacity data includes real-time transportation data, warehouse sensing data, order data, driver status data, and historical transportation efficiency data; the collection of multi-dimensional transportation capacity data of transport vehicles, and the construction of a transportation capacity resource pool based on the multi-dimensional transportation capacity data, includes: Real-time transportation data is collected by the vehicle-mounted terminal, including the vehicle's speed, fuel consumption, fault alarms, and real-time location information. The warehouse's sensing devices collect information on the quantity, location, and status changes of goods within the warehouse as warehouse sensing data. By linking and integrating real-time transportation data, warehouse sensing data, order data, driver status data, and historical transportation efficiency data, a unified transportation capacity resource pool is generated. Among them, warehouse sensing data is used to dynamically track inventory changes, providing real-time information on the availability of goods for transportation capacity matching.

[0007] The order's transportation requirements parameters include: the quantity of goods, transportation distance, and timeliness requirements; the task details include: loading location, unloading location, and estimated arrival time. The process of selecting matching target transport vehicles and drivers from the transportation resource pool based on the order's transportation demand parameters, generating a dispatch instruction containing task details and route planning, and pushing it to the corresponding target driver's driver terminal includes: The rules engine loads preset capacity matching rules and selects candidate transport vehicles and drivers that meet the rule conditions from the capacity resource pool based on the order's cargo volume, transportation distance, and timeliness requirements. The algorithm model ranks and optimizes candidate transport vehicles and drivers based on the capacity allocation strategy to determine the target transport vehicles and drivers. It generates dispatch instructions that include loading and unloading locations, estimated arrival times, and route planning, and pushes them to the driver's terminal of the target driver. The weight coefficients of the capacity allocation strategy in the algorithm model are dynamically adjusted based on historical transport data.

[0008] This includes establishing a closed-loop data system covering the entire chain from sales planning, orders, dispatch orders, waybills, in-transit monitoring to receipt confirmation, including: In response to a user's document operation request, perform at least one of the following operations on the transport document: edit, modify, query, open, or cancel; Based on real-time data collected by positioning sensors and process monitoring devices, the transportation trajectory is plotted, and when path deviation or process exceeding limits is detected, a trip anomaly warning is issued. Perform electronic waybill issuance, online receipt confirmation, and digital return receipt archiving to complete the entire paperless process.

[0009] The real-time monitoring of operational indicator deviations in the transportation and warehousing processes, and the tiered alerting based on the event type and severity, includes: Construct a multi-dimensional anomaly detection model to identify inventory data deviations, transportation delays, and environmental parameter exceedances in real time. According to the preset alarm rules, alarms are classified into levels based on the type and severity of the event. The severity levels include Level 1, Level 2, and Level 3 alarms, with different response times and handling procedures corresponding to different levels.

[0010] The automatic generation of problem work orders and their distribution to the corresponding responsible parties for collaborative handling until the loop is closed includes: Issue work orders are automatically generated based on the type of abnormal event. The issue work order includes an abnormality description, the time of occurrence, related documents, and suggested handling measures. Issue work orders are distributed to at least one of the supplier, logistics provider, carrier, and customer, and closed-loop confirmation is received from at least one of the supplier, logistics provider, carrier, and customer upon completion of the handling.

[0011] The method also includes: Based on historical transportation data and real-time monitoring data, the system optimizes and analyzes vehicle routes, loading rates, and transportation frequencies to generate low-carbon transportation recommendations and compiles carbon emission data. The real-time monitoring data includes the current location and sensor data of the target transport vehicles, as well as multi-dimensional data collected in the entire data loop.

[0012] Secondly, embodiments of the present invention provide a smart logistics processing device, comprising: The multi-source acquisition module is used to collect multi-dimensional capacity data of transport vehicles and build a capacity resource pool based on the multi-dimensional capacity data; The generation module is used to filter the target transport vehicles and target drivers from the transportation resource pool according to the transportation demand parameters of the order, generate a dispatch instruction containing task details and route planning, and push it to the driver terminal of the target driver. The first processing module is used to establish a closed-loop data system for the entire chain, from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation, based on the current location and sensor data of the target transport vehicle after the order is dispatched. It also detects the transport trajectory in real time and issues early warnings for abnormal trips, and provides customized data dashboards for different roles related to the target vehicle. The second processing module is used to monitor operational indicator deviation events in the transportation and warehousing links in real time based on multi-dimensional data collected in the end-to-end data closed loop. It classifies and alarms according to the event type and severity of the operational indicator deviation events, automatically generates problem work orders, and distributes them to the corresponding responsible parties for collaborative processing until the loop is closed.

[0013] Thirdly, embodiments of the present invention provide a computer device, which includes a processor and a memory storing a computer program. When the processor runs the computer program, it implements any of the above-described intelligent logistics processing methods.

[0014] Fourthly, embodiments of the present invention provide a computer storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any of the above-described intelligent logistics processing methods.

[0015] This invention provides a smart logistics processing method, apparatus, computer equipment, and storage medium. It collects multi-dimensional capacity data of transport vehicles and constructs a capacity resource pool based on this data. Then, based on the transport demand parameters of the order, it selects matching target transport vehicles and drivers from the capacity resource pool, generates a dispatch instruction containing task details and route planning, and pushes it to the target driver's terminal. After dispatching the order, based on the current location and sensor data of the target transport vehicle, it establishes a closed-loop data system covering the entire chain from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation. It monitors the transport trajectory in real time and issues early warnings for trip anomalies, and provides customized data dashboards for different roles related to the target transport vehicle. Based on the multi-dimensional data collected in the closed-loop data system, it monitors operational indicator deviations in the transportation and warehousing processes in real time, classifies and alerts based on the event type and severity of these deviations, automatically generates problem work orders, and distributes them to the corresponding responsible parties for collaborative handling until the loop is closed. This application's embodiments achieve efficient scheduling and dynamic optimization of transportation resources by constructing a transportation capacity resource pool and performing precise matching and intelligent dispatch based on order demand, thereby improving vehicle utilization and dispatch response speed. By establishing a closed-loop data system across the entire chain from sales planning to receipt confirmation, it monitors transportation trajectories in real time and issues early warnings for trip anomalies. Simultaneously, it provides customized data dashboards for different roles, achieving visualization of the entire transportation process and multi-role information collaboration. Furthermore, by monitoring operational indicator deviations in real time based on multi-dimensional data and issuing graded alerts according to event type and severity, it automatically generates problem work orders and distributes them to the corresponding responsible parties for handling until the loop is closed, improving the efficiency of anomaly detection and response capabilities. In summary, this application's embodiments effectively solve the problems of low transportation capacity scheduling efficiency, opaque transportation processes, poor anomaly response, and poor system scalability in existing technologies, thereby improving the overall operational efficiency and collaborative management level of logistics transportation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a smart logistics processing method provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a smart logistics device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] It should be noted that logistics and transportation, as a core link in the modern supply chain system, directly impact a company's operating costs, customer satisfaction, and market competitiveness through efficient capacity scheduling and a high degree of visibility into the transportation process. In industries with extremely high requirements for transportation timeliness and service quality, such as e-commerce, manufacturing, and bulk commodity trading, how to schedule transportation resources in real time and accurately, monitor the transportation process, and quickly handle abnormal events has always been a key focus and challenge in the field of logistics management and intelligent technology.

[0020] Currently, logistics capacity management in the industry mainly relies on manual dispatching and basic information systems. The common practice is for dispatchers to contact drivers via phone, WeChat, etc., to inform them of loading and unloading times and locations, and to record transport documents through a separate order system. However, this approach has inherent limitations: First, capacity dispatching heavily relies on human experience. Dispatchers struggle to monitor the location, status, and availability of all vehicles in real time, often assigning vehicles based on experience, leading to inaccuracies between capacity resources and order demands, resulting in some vehicles being idle while others are underutilized. Second, the transportation process lacks end-to-end visualization; data from orders, dispatching, waybills, and in-transit monitoring are fragmented. Suppliers cannot obtain real-time shipment status, customers cannot accurately predict arrival times, and carriers cannot promptly track driver performance, highlighting information asymmetry. Third, response to abnormal events is delayed. When vehicle breakdowns, traffic congestion, or route deviations occur, the system can only record the problem, unable to proactively issue warnings, relying on manual intervention after the fact, often missing the optimal time for handling.

[0021] To obtain the real-time location of transport vehicles, some advanced solutions attempt to introduce satellite positioning technology, such as installing GPS or BeiDou positioning modules on the vehicles. However, single spatial positioning data is affected by signal blockage, tunnel blind spots, etc., and its instantaneous accuracy and reliability are difficult to consistently meet the requirements of high-precision capacity monitoring in complex urban road or mountainous scenarios. More importantly, both single positioning data and single scheduling systems only provide "one aspect" of capacity management, failing to integrate multi-dimensional data such as vehicle status, order demand, warehouse inventory, and historical efficiency, and thus cannot build a robust observation model that can comprehensively reflect capacity resources and transportation demand.

[0022] Furthermore, most existing logistics management systems remain at the "recording" and "query" level, lacking in-depth intelligent analysis capabilities. When there is insufficient capacity or transportation delays, the system can only provide basic information displays and cannot automatically diagnose the root causes of the problems (such as inappropriate capacity allocation strategies, low driver efficiency, and backlogs in loading and unloading). This makes it difficult for managers to take quick and targeted measures, often relying on personal experience for post-event remediation, which is inefficient and has uncertain results, making it difficult to achieve the leap from "passive response" to "proactive scheduling."

[0023] Therefore, there is an urgent need for a smart logistics processing method that can integrate multi-source heterogeneous data, achieve precise matching of transportation capacity, establish visualization of the entire transportation process, and intelligently diagnose the root causes of anomalies, so as to fundamentally improve the quality control capabilities and intelligence level of logistics transportation.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a smart logistics processing method, which includes the following steps: Step 101: Collect multi-dimensional transport capacity data of transport vehicles and build a transport capacity resource pool based on the multi-dimensional transport capacity data.

[0025] Optionally, multidimensional capacity data refers to a comprehensive data set that describes the capacity characteristics of transport vehicles from multiple dimensions.

[0026] Optionally, the capacity resource pool refers to a unified, searchable, and dynamically updated collection of capacity resources formed by associating and integrating the aforementioned multi-dimensional capacity data. This capacity resource pool is stored in system memory or a database in a structured data format, providing data support for subsequent capacity matching and scheduling.

[0027] In some implementations, multi-dimensional capacity data includes real-time transportation data, warehouse awareness data, order data, driver status data, and historical transportation efficiency data; multi-dimensional capacity data of transport vehicles is collected, and a capacity resource pool is constructed based on the multi-dimensional capacity data, including: Real-time transportation data is collected by the vehicle-mounted terminal, including the vehicle's speed, fuel consumption, fault alarms, and real-time location information. The warehouse's sensing devices collect information on the quantity, location, and status changes of goods within the warehouse as warehouse sensing data. By linking and integrating real-time transportation data, warehouse sensing data, order data, driver status data, and historical transportation efficiency data, a unified transportation capacity resource pool is generated. Among them, warehouse sensing data is used to dynamically track inventory changes, providing real-time information on the availability of goods for transportation capacity matching.

[0028] Optionally, real-time transportation data is vehicle status information collected in real time by an on-board terminal installed on the transport vehicle, including vehicle speed, engine fuel consumption, fault alarm status, real-time geographical location (latitude and longitude), and direction of travel.

[0029] Optionally, cargo status information can be collected through sensing devices deployed in the warehouse, such as RFID readers, infrared sensors, weight sensors, etc., including the quantity of goods in the warehouse, storage location, entry time, exit time, and cargo status change information (such as unpacking, relocation, etc.).

[0030] Optionally, the order data is obtained from the transportation task information obtained from the enterprise resource planning system or the order management system, including the order number, cargo type, cargo weight, cargo volume, loading location, unloading location, expected loading time, and expected delivery time.

[0031] Optionally, driver status data can be collected from driver terminal devices or driver management systems, including driver identity information, contact information, current working status (idle, on the road, resting), historical order records, service evaluation level, etc.

[0032] Optionally, historical transportation efficiency data refers to the system's storage of historical transportation task execution records, including the historical transportation duration, on-time rate, fuel consumption level, and failure rate of each vehicle and driver at different times and on different transportation routes.

[0033] In this implementation, multi-dimensional capacity data from multiple transport vehicles is collected. The system collects real-time data from all registered transport vehicles in its fleet or from cooperating carriers via onboard terminals. The collection scope covers all transport vehicles connected to the system, rather than focusing on individual vehicles. For example, for a logistics company with 50 owned transport vehicles and cooperating with 20 carriers, the system simultaneously collects data from both its 50 owned vehicles and vehicles reported by cooperating carriers, forming a comprehensive capacity awareness capability.

[0034] Optionally, the construction of the capacity resource pool may include: First, the system reports real-time transportation data of the vehicle to the server via the vehicle terminal at a preset frequency (such as once every 10 seconds), including location, speed, fuel consumption, fault status, etc. Secondly, the system obtains inventory status data collected by warehouse sensing devices through data access interfaces and synchronizes transportation order data to be executed from the order management system; Then, the system constructs a driver status dataset based on the status information reported by the driver's terminal (such as idle, on the road, rest) and driver profile data; Finally, the system integrates and merges the aforementioned real-time transportation data, warehouse awareness data, order data, driver status data, and historical transportation efficiency data. For example, it uses vehicle identifiers (such as license plate numbers) and driver identifiers (such as driver IDs) as primary keys to form a unified transportation capacity resource pool. Each record in this pool corresponds to a transportation vehicle and its matched driver. The record content includes the vehicle's real-time location, current status (idle / on duty), type of cargo it can carry, weight it can carry, driver contact information, and current order acceptance status.

[0035] Through the above methods, the system has completed the unified integration of multi-source heterogeneous data and constructed a power resource pool that can be used for subsequent capacity matching and intelligent dispatch.

[0036] Step 102: Based on the transportation demand parameters of the order, select matching target transportation vehicles and target drivers from the transportation resource pool, generate a dispatch instruction containing task details and route planning, and push it to the driver terminal of the target driver.

[0037] Optionally, order transportation parameters refer to feature data extracted from the transportation order to be executed, which describes the core requirements of the transportation task.

[0038] In some implementations, order transportation demand parameters include: order quantity, transportation distance, and timeliness requirements; task details include: loading location, unloading location, and estimated arrival time. Based on the order's transportation demand parameters, the system filters matching target vehicles and drivers from the transportation resource pool, generates a dispatch instruction containing task details and route planning, and pushes it to the corresponding target driver's terminal, including: By loading preset capacity matching rules through the rules engine, target transport vehicles and drivers that meet the rule conditions are selected from the capacity resource pool based on the order's cargo volume, transportation distance, and timeliness requirements. The algorithm model optimizes the ranking of target transport vehicles and drivers based on the capacity allocation strategy, generates dispatch instructions that include loading and unloading locations, estimated arrival times, and route planning, and pushes them to the driver's terminal. The weight coefficients of the capacity allocation strategy in the algorithm model are dynamically adjusted based on historical transport data.

[0039] Optionally, the quantity of goods in an order refers to the total weight (e.g., 10 tons) and total volume (e.g., 30 cubic meters) of the goods in the order, used to match the vehicle's load capacity and loading space.

[0040] Optionally, the transportation distance refers to the estimated driving distance (e.g., 200 kilometers) between the two locations calculated through a map service interface based on the loading and unloading locations in the order, used to assess transportation time and vehicle range requirements.

[0041] Optionally, timeliness requirements refer to the expected loading time and expected delivery time (e.g., "loading before 14:00 on March 28, 2025, and delivery before 12:00 on March 29, 2025"), used to determine the time window and urgency of the task.

[0042] In some implementations, the transportation demand parameters may also include: cargo type (such as cold chain cargo, dangerous goods, large equipment, etc.) to match vehicles with the corresponding transportation qualifications; and special customer requirements (such as the need for tailgate loading and unloading, the need for truck-mounted cranes, etc.) to match vehicles with the corresponding equipment.

[0043] Optionally, the loading location is the location of the goods before transportation. Optionally, the unloading location is the destination of the goods transportation. Optionally, the estimated arrival time is calculated based on the transportation distance, estimated travel time, and the time required for loading to reach the unloading location.

[0044] Optionally, the rules engine is a built-in decision-making component used to quickly filter vehicles in the transportation capacity resource pool according to preset matching rules. The rules engine loads and executes business rules, matches the input order demand parameters with preset conditions, and outputs a candidate set that meets the conditions.

[0045] Optionally, the capacity matching rules are pre-configured filtering conditions used to filter out vehicles that meet the basic requirements of the order from the capacity resource pool.

[0046] Optionally, the capacity matching rules may include, but are not limited to, at least one of the following: The load matching rule selects vehicles whose rated load capacity is not less than the total weight of the order goods as candidate vehicles. Volume matching rule, that is, selecting vehicles whose vehicle volume is not less than the total volume of the order goods as candidate vehicles; The qualification matching rules select vehicles with corresponding qualification certificates as candidate vehicles for target transportation if the order includes cold chain or dangerous goods requirements. Location matching rules, which select vehicles whose current real-time location is within a certain range (e.g., within 50 kilometers) of the loading location as candidate vehicles; The status matching rule is to filter vehicles whose current status is "idle" or "about to complete the current task" as candidate vehicles; The driver status rule selects vehicles whose current status is "available for order taking" and whose working hours have not exceeded the limit as candidate vehicles.

[0047] In practical applications, the system loads preset capacity matching rules through the rule engine. Based on the order's cargo volume, transportation distance, and timeliness requirements, it iterates through and filters all registered vehicles in the capacity resource pool. Assuming there are 50 registered vehicles in the capacity resource pool, the rule engine filters out 10 candidate vehicles that meet the conditions and their corresponding drivers, forming a candidate set.

[0048] Optionally, the algorithm model is a built-in computational component of the system, used to comprehensively score and rank candidate vehicles and drivers in the candidate set. Based on a pre-defined capacity allocation strategy, the algorithm model determines the target transport vehicles and drivers through a weighted calculation method.

[0049] Optionally, the weighting coefficient is the proportion of each evaluation dimension in the overall score, reflecting the degree of influence of that dimension on the final selection result. For example, a distance weight of 0.30 means that the distance factor contributes 30% to the overall score.

[0050] Optionally, the capacity allocation strategy refers to the scoring mechanism that determines how to select the optimal vehicle from the candidate set. In this embodiment, the capacity allocation strategy includes the following weighting coefficients and corresponding evaluation dimensions: For example, consider candidate vehicle A and candidate vehicle B: Vehicle A: 10 km from loading point (distance score 95), historical on-time rate 98% (efficiency score 98), estimated transportation cost 280 yuan (cost score 85), driver service rating 4.8 (service score 96). Vehicle B: 30 km from loading point (distance score 85), historical on-time rate 92% (efficiency score 92), estimated transportation cost 260 yuan (cost score 90), driver service level 4.5 (service score 90).

[0051] Therefore, the overall score for vehicle A is 95×0.30 + 98×0.25 + 85×0.25 + 96×0.20 = 28.5 + 24.5 + 21.25 + 19.2 = 93.45; The overall score for vehicle B is 85×0.30 + 92×0.25 + 90×0.25 + 90×0.20 = 25.5 + 23.0 + 22.5 + 18.0 = 89.00.

[0052] As a result, vehicle A received a higher overall score and was therefore identified as the target transport vehicle and the target driver.

[0053] Optionally, dynamically adjusting the weighting coefficients using historical transportation data means that the system periodically optimizes the values ​​of each weighting coefficient based on the actual execution results of historical transportation tasks, so that the subsequent ranking results are more in line with actual operational efficiency.

[0054] For example, the system analyzes all transportation tasks completed in the past 30 days each week, and statistically analyzes the correlation between various evaluation dimensions and actual performance results (such as actual transportation time, actual fuel consumption, customer satisfaction, etc.). For instance, if the system finds that the correlation between the "distance" factor and actual transportation timeliness has decreased (i.e., vehicles that are closer are not necessarily on time), while the correlation between the "efficiency weight" and actual timeliness has increased, the system will correspondingly reduce the value of the distance weight and increase the value of the efficiency weight.

[0055] In this way, by dynamically adjusting the weight coefficients of the capacity allocation strategy in the algorithm model through historical transportation data, it can automatically adapt to external factors such as seasonal changes, road condition changes, and fleet composition changes without human intervention; as historical data accumulates, the matching accuracy of the algorithm model can be continuously improved, and the capacity allocation becomes more and more reasonable; it can also be personalized, that is, the weight configuration of different enterprises and different business scenarios can be optimized differently according to actual operating data to achieve customized capacity scheduling strategies.

[0056] Furthermore, after optimizing the ranking, the system generates dispatch instructions for the selected target vehicles and drivers. For example, the system pushes the dispatch instructions to the target driver's terminal (such as a mobile app) via a 4G / 5G wireless network. Upon receiving the notification, the driver's terminal displays task details and route planning, and the driver can click "Confirm Order" to accept the order. If the driver does not respond within a preset time (e.g., 5 minutes), the system automatically reassigns the task to the vehicle and driver with the second-highest rating in the candidate set.

[0057] Through the above implementation methods, the system has completed the automatic screening, sorting optimization and dispatch instruction push of order demand parameters, target transportation vehicles and target drivers, realizing the intelligent and automated scheduling of transportation capacity.

[0058] Step 103: After dispatching the order, based on the current location and sensor data of the target transport vehicle, establish a closed-loop data system covering the entire chain from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation. Monitor the transport trajectory in real time and issue early warnings for abnormal trips. Provide customized data dashboards for different roles related to the target transport vehicle. These roles include at least one of the following: supplier, logistics provider, carrier, and customer.

[0059] In some implementations, a closed-loop data system is established, encompassing the entire data chain from sales planning, orders, dispatch orders, waybills, in-transit monitoring to receipt confirmation, including: In response to a user's document operation request, perform at least one of the following operations on the transport document: edit, modify, query, open, or cancel; Based on real-time data collected by positioning sensors and environmental monitoring devices, the transportation trajectory is plotted, and when path deviation or environmental exceedance is detected, a trip anomaly warning is issued. Perform electronic waybill issuance, online receipt confirmation, and digital return receipt archiving to complete the entire paperless process.

[0060] Alternatively, the end-to-end data closed loop can be understood as connecting the data generated in each link of the entire transportation business process according to business logic to form a complete, traceable, and real-time updatable data chain.

[0061] Optionally, during the sales planning stage, when a company formulates a sales plan, the system automatically generates a sales plan record, which includes information such as planned delivery time, planned delivery quantity, and target customers. The sales plan is the source of subsequent orders, and the system uses it as the starting point of the entire supply chain.

[0062] Optionally, during the order process, when a sales plan is converted into an actual order, the system creates an order record. The order record includes the order number, goods type, goods weight, goods volume, loading location, unloading location, expected loading time, and expected delivery time. The system links the order record with the sales plan record, forming a data link from sales plan to order.

[0063] Optionally, in the dispatch order stage, after the system completes capacity matching and generates a dispatch instruction based on the order information, it automatically generates a dispatch order record. The dispatch order record includes the dispatch order number, associated order number, target vehicle information, target driver information, estimated loading time, and estimated delivery time. The system associates the dispatch order record with the order record, forming a data link from order to dispatch order.

[0064] Optionally, during the waybill process, once the driver confirms acceptance of the order, the system automatically generates a waybill record. The waybill record includes the waybill number, associated dispatch order number, actual loading time, actual departure time, current vehicle location, and estimated arrival time. The system associates the waybill record with the dispatch order record, forming a data link from dispatch order to waybill.

[0065] Optionally, during the in-transit monitoring phase, the system continuously receives location and sensor data reported by the vehicle, and updates the vehicle location, speed, and estimated arrival time in the waybill record in real time. The system links the in-transit monitoring data with the waybill record, forming a data link from waybill to in-transit monitoring.

[0066] Optionally, during the receipt confirmation process, after the vehicle arrives at the unloading location, the driver uploads unloading completion confirmation information via a terminal device and can take a photo of the receipt or obtain the consignee's electronic signature. Upon receiving the receipt confirmation information, the system generates a receipt record, including the receipt time, the person signing, the receipt method, and a return receipt. The system links the receipt record with the waybill record, forming a data link from in-transit monitoring to receipt confirmation.

[0067] Through the above methods, the system has established a complete data chain from sales planning to receipt confirmation, with data at each stage being interconnected and synchronized in real time, forming a closed-loop data chain.

[0068] Optionally, in response to a user's document operation request, the system performs at least one of the following operations on the transport document: edit, modify, query, open, or cancel. It is understood that the system provides different document operation entry points for different users based on their role permissions. For example: Dispatchers can perform operations such as editing (modifying loading time and unloading location), modifying (adjusting vehicle type requirements), querying (viewing historical dispatch records), starting (initiating the dispatch process), or canceling (canceling the dispatch task) on dispatch orders that have not been dispatched. Customer service personnel can query generated waybills to view waybill details and execution status, but they do not have the authority to modify or cancel them; Administrators can perform query and archiving operations on completed task documents.

[0069] It should be noted that all document operations are recorded in the system's operation log, including the operator, operation time, operation type, and data comparison before and after the operation, to ensure that the operation is traceable.

[0070] Optionally, based on real-time data collected by positioning sensors and environmental monitoring devices, a transportation trajectory is plotted, and when path deviation or environmental exceedances are detected, a trip anomaly warning is issued, including: Data collected through positioning sensors includes: the vehicle's onboard terminal uses a Beidou / GPS dual-mode positioning module to collect the vehicle's real-time geographical location at a preset frequency (e.g., once every 10 seconds), including longitude, latitude, driving direction, driving speed, altitude, etc. Real-time data collection through process monitoring devices includes: for cold chain transport vehicles, temperature and humidity sensors installed on the vehicle collect real-time temperature and humidity data inside the compartment and upload it to the system via an onboard terminal. For general cargo transport, vibration sensors, door magnetic sensors, etc., can be configured as needed; Transportation trajectory mapping includes: after receiving location data, the system draws the vehicle's driving trajectory in real time on the map service module. The system connects consecutive location points in chronological order to form a complete transportation trajectory line, which is then visualized on an electronic map. Trip anomaly alerts include: the system issues trip anomaly alerts based on monitoring results.

[0071] Optionally, the warning types and triggering conditions include the following: Optionally, the system performs electronic waybill issuance, online receipt confirmation, and digital return receipt archiving, completing the entire paperless process, including: Electronic waybill issuance includes: the system automatically generates an electronic waybill simultaneously with the dispatch instruction. The electronic waybill contains all information about the transportation task (order number, cargo information, loading location, unloading location, vehicle information, driver information, etc.) and is stored in the system database in a structured data format. The electronic waybill can be exported as a PDF for printing or downloading. Online delivery confirmation includes: after the vehicle arrives at the unloading location, the driver performs an online delivery confirmation operation through the driver's terminal. The specific process includes: after arriving at the unloading location, the driver clicks the "Arrived at Unloading Point" button on the driver's terminal; the system automatically records the arrival time and location; the consignee confirms the delivery by electronically signing through the driver's terminal screen, or the driver takes a photo of the delivery receipt and uploads it; the system generates a delivery confirmation record, including the delivery time, the signatory's information, and the delivery receipt (signature photo or delivery receipt photo). Digital receipt archiving includes the system automatically linking electronic waybills, delivery confirmation records, transportation trajectory data, and abnormal event records to form a complete digital receipt. The digital receipt is stored in the system database as structured data and supports retrieval by time, order number, license plate number, and other criteria. Digital receipts can serve as settlement evidence, dispute resolution documents, and audit traceability materials, eliminating the need for paper documents.

[0072] Through the above paperless processing, the system has achieved full electronic processing from waybill issuance to return receipt archiving, eliminating the risk of delays and loss in the transmission of paper documents and improving the efficiency of cross-entity collaboration.

[0073] Optionally, customized data dashboards are provided for different roles. For the supplier role, i.e., the shipper, the supplier's (i.e., the shipper's) data dashboard mainly focuses on the shipment status and transportation progress, including: List of orders awaiting shipment and their current processing status; The shipping trajectory of shipped orders is displayed in real time; Estimated arrival time and current progress; Alerts for abnormal events (such as delays, route deviations, and environmental limitations). Historical transportation task statistics and on-time rate analysis.

[0074] For logistics providers (i.e., logistics company managers), the data dashboard primarily focuses on transportation capacity resources and overall operational status, including: Real-time location map of all vehicles on the road; Vehicle status statistics (idle, en route, under repair, resting); Driver order statistics and on-time rate ranking; Real-time list and handling status of abnormal events; Analysis of transportation task completion and costs.

[0075] For carrier roles, such as fleets or drivers, the data dashboard for the carrier (i.e., the driver or fleet manager actually performing the transportation task) primarily focuses on individual tasks and vehicle status, including: Detailed information about the current task (loading location, unloading location, estimated time); Transportation trajectory and real-time location display; Abnormal warning alerts (route deviation, speeding, delays, environmental over-limits); Historical mission records and revenue statistics; Vehicle status information (fuel consumption, fault alarms, maintenance reminders).

[0076] For the customer role, i.e., the consignee, the customer's (i.e., the consignee's) data dashboard primarily focuses on the estimated arrival time and transportation status of the goods, including: Real-time location and transportation trajectory of goods in transit; Estimated arrival time as a percentage of current progress; Driver contact information and real-time communication portal; Historical order inquiry and receipt download; Notification of abnormal events (such as delays, abnormal cargo status, and exceeding environmental limits).

[0077] Through the above methods, the system enables real-time monitoring and visualization of the entire transportation process, providing customized data dashboards for different roles that match their responsibilities, effectively improving information transparency and collaboration efficiency for all participants in the supply chain.

[0078] Step S104: Based on the multi-dimensional data collected in the full-link data closed loop, monitor the operational indicator deviation events in the transportation and warehousing links in real time, and issue graded alarms according to the event type and severity of the operational indicator deviation events. Automatically generate problem work orders and distribute them to the corresponding responsible parties for collaborative handling until the loop is closed. The operational indicator deviation events include inventory data deviation, transportation node delays, and environmental parameter exceedances.

[0079] In some implementations, operational metric deviations in the transportation and warehousing processes are monitored in real time, and tiered alerts are issued based on the severity of the event type, including: Construct a multi-dimensional anomaly detection model to identify inventory data deviations, transportation delays, and environmental parameter exceedances in real time. According to the preset alarm rules, alarms are classified into levels based on the type and severity of the event. The severity levels include Level 1, Level 2, and Level 3 alarms, with different response times and handling procedures corresponding to different levels.

[0080] Optionally, operational metric deviation events refer to events where the system detects discrepancies between actual operational data and preset standard or planned values ​​that exceed acceptable limits when monitoring transportation and warehousing processes. These events reflect abnormal conditions in logistics operations and may affect transportation efficiency, cargo quality, or customer satisfaction.

[0081] Optionally, the types of indicator deviations mainly include the following: Inventory data deviation refers to the difference between the inventory quantity recorded by the system and the actual inventory quantity. The data comes from warehouse sensing devices (RFID, weight sensors) and WMS system.

[0082] Transportation node delays refer to the actual time of a transportation task at key nodes (loading, departure, arrival, and unloading) being later than the planned time. The data comes from the timestamp records in the closed-loop data of the entire chain.

[0083] Exceeding environmental parameters means that parameters such as temperature, humidity, and vibration in the transportation or storage environment exceed the safe range for goods. The data comes from environmental monitoring devices (temperature and humidity sensors, vibration sensors). It should be noted that trip anomaly warnings are real-time, micro-level, and driver-oriented immediate reminders, focusing on "anomalies during the current task execution process," such as route deviations, speeding, and excessive stops. Their goal is to allow drivers to make immediate adjustments and prevent problems from escalating. Operational metric deviation events, on the other hand, are comprehensive, macro-level, and management-oriented systemic anomaly identifications, focusing on "deviations in overall operational metrics," such as inaccurate inventory data, consecutive delays in multiple transport tasks, and persistent exceedances of cold chain environmental standards. Their goal is to trigger cross-departmental collaborative handling to solve problems at their root. For example, when a vehicle deviates from its route during transport: step 103 detects the deviation and immediately sends a route deviation warning (trip anomaly warning) to the driver; if the route deviation causes a delay of more than 30 minutes in the estimated arrival time, step 104 identifies it as a "transportation node delay" event (operational metric deviation event) and triggers tiered alarms based on severity, notifying customer service personnel to communicate with the customer in advance.

[0084] It should be noted that the system constructs a multi-dimensional anomaly detection model to identify inventory data deviations, transportation delays, and environmental parameter exceedances in real time.

[0085] Optionally, the multidimensional anomaly detection model is an anomaly recognition engine that integrates multi-source data and multi-dimensional indicators. Its construction and operation process is as follows: A multidimensional anomaly detection model can consist of the following modules: Optionally, the detection rules are configured by pre-setting detection rules for various operational indicator deviation events in the multi-dimensional anomaly detection model: ① Inventory data deviation detection rules The system performs an inventory data comparison every 30 minutes. Retrieve the inventory quantity recorded in the WMS system; Obtain the actual scanned inventory quantity from warehouse sensing devices (such as RFID readers and weight sensors); Calculate the discrepancy rate as follows: |System Inventory - Actual Inventory| / System Inventory × 100%; If the discrepancy rate is greater than 0.5%, the "Inventory Data Deviation" event will be triggered.

[0086] For example, the system records an inventory of 1,000 units for a certain SKU, while the RFID scan result shows 980 units, a difference rate of 2%, which exceeds the 0.5% threshold and triggers an inventory data deviation event.

[0087] ② Rules for detecting delays at transportation nodes The system monitors the following nodes of each transportation task in real time: Loading time: The difference between the actual loading time and the planned loading time; Departure time: The difference between the actual departure time and the planned departure time; Arrival point: The difference between the actual arrival time at the unloading location and the estimated arrival time; Receipt time: The difference between the actual receipt time and the promised delivery time.

[0088] If the delay of any node exceeds 30 minutes, the "Transportation Node Delay" event is triggered.

[0089] For example, if an order is scheduled to be loaded at 14:00, but the driver actually arrives at the loading location at 14:45, a delay of 45 minutes, exceeding the 30-minute threshold, a transportation node delay event is triggered.

[0090] ③ Environmental parameter exceedance detection rules The system monitors environmental sensor data in real time within transport vehicles and warehouses: For cold chain transportation, monitor whether the temperature inside the vehicle compartment is within the range of 2-8℃; For temperature-controlled warehouses, monitor whether the temperature inside the warehouse is within the set range; For the transportation of precision instruments, monitor whether the vibration sensor data exceeds the threshold.

[0091] If environmental parameters exceed the preset safety range for more than 30 seconds, an "environmental parameter exceeded" event will be triggered.

[0092] For example, during the transportation of a cold chain vehicle, the temperature sensor showed that the temperature inside the vehicle rose to 10°C and remained there for more than 30 seconds, triggering an event where environmental parameters exceeded the standard.

[0093] (3) Real-time recognition process Taking a shipping order as an example, the real-time identification process of the multidimensional anomaly detection model is as follows: Optionally, the system can issue alarms in a tiered manner according to preset alarm rules, based on the event type and severity. The severity levels include Level 1, Level 2, and Level 3 alarms, with different levels corresponding to different response times and handling procedures.

[0094] For example, severity grading criteria can be defined according to the following rules: Take transportation node delay events as an example: Level 3 Alarm: An order is delayed by 35 minutes. The system classifies this as a Level 3 alarm and sends a reminder message to the dispatcher: "Order TK240328001 is delayed by 35 minutes and is expected to arrive at 18:35. Please pay attention." Level 2 Alarm: The same order is delayed by 1.5 hours. The system determines this to be a Level 2 alarm and sends an alarm notification to the transportation manager: "Order TK240328001 is delayed by 1.5 hours and is expected to arrive at 19:30. Please verify the cause and formulate a response plan." At the same time, a problem work order is generated.

[0095] Level 1 Alarm: The same order is delayed by 2.5 hours. The system has determined this to be a Level 1 alarm and has sent an alarm notification to both the customer service supervisor and the transportation manager: "Order TK240328001 is delayed by 2.5 hours and is expected to arrive at 20:30. Please activate the emergency plan immediately, notify the customer, and coordinate resources." The following example, using a complete cold chain transportation order, illustrates the entire process of identifying, tiered alerting, and collaboratively handling operational performance deviation events: Order information: Order Number: LD240328001 Type of goods: Vaccines (cold chain goods, temperature requirement 2-8℃) Quantity of goods: 500 cases Loading location: A pharmaceutical warehouse in Beijing Unloading location: A hospital in Tianjin Planned loading time: 10:00 Planned departure time: 11:00 Planned arrival time: 14:00 Execution process and exception identification: Work order generation and closed-loop handling, including: During the exception identification process, the system automatically generates problem work orders. Taking the above environmental parameter overrun as an example: Work order number: WO20240328001 Event type: Environmental parameter overrun Severity level: Second-level alarm Occurrence time: 2025-03-28 13:00 Related document: LD240328001 Exception description: When the cold-chain vehicle (Beijing A12345) was driving on the Beijing-Tianjin Expressway, the temperature sensor showed that the temperature in the carriage was 8.5°C, exceeding the safe range (2-8°C) for 30 seconds.

[0096] Suggested handling measures: Contact the driver to confirm the operating status of the refrigeration equipment, check the door seal, and if necessary, guide the vehicle to the nearest service area for repair.

[0097] The system automatically distributes the work order to the customer service team. After receiving the work order, the customer service staff immediately contacts the driver to confirm the situation and guides the driver to adjust the refrigeration equipment parameters. At 13:15, the temperature returned to normal. The customer service staff records the handling result in the system and updates the work order status to "closed-loop". The system records the complete handling process to form a traceable exception handling record.

[0098] Through the above implementation methods, the system realizes the real-time identification, hierarchical alarm and collaborative handling of operation index deviation events, effectively improving the discovery efficiency of exception events and the handling response ability.

[0099] In some implementation methods, automatically generating problem work orders and distributing them to the corresponding responsible parties for collaborative handling until closed-loop, including: Automatically generating problem work orders according to the exception event type, and the problem work orders include exception descriptions, occurrence times, related documents and suggested handling measures; Distributing the problem work orders to at least one of the supplier, logistics party, carrier and customer, and receiving the closed-loop confirmation sent by at least one of the supplier, logistics party, carrier and customer after the handling is completed.

[0100] In this implementation, by automatically generating problem work orders that include anomaly description, occurrence time, related documents, and suggested handling measures, and accurately distributing them to the corresponding responsible parties among suppliers, logistics providers, carriers, and customers, standardized transmission and rapid response of abnormal event information are achieved, improving the efficiency of cross-departmental collaborative handling. By receiving closed-loop confirmation from the corresponding responsible party after handling is completed, a complete closed loop for handling abnormal events is formed, ensuring that the problem is effectively resolved and that the handling process is traceable.

[0101] It should be noted that the multi-dimensional transportation capacity data is used to solve the technical problem of matching weight, volume and vehicle in cargo transportation; the full-link data closed loop is used to solve the technical problem of information coordination among multiple roles in the supply chain; and the operational indicator deviation events are used to solve the technical problem of abnormal linkage handling in warehousing and transportation links.

[0102] In summary, this application embodiment collects multi-dimensional capacity data of transport vehicles and constructs a capacity resource pool based on this data. Then, according to the transport demand parameters of the order, it selects matching target transport vehicles and target drivers from the capacity resource pool, generates a dispatch instruction containing task details and route planning, and pushes it to the target driver's terminal. After dispatching the order, based on the current location and sensor data of the target transport vehicle, it establishes a closed-loop data system covering the entire chain from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation. It monitors the transport trajectory in real time and issues early warnings for abnormal trips, and provides customized data dashboards for different roles related to the target transport vehicle. Based on the multi-dimensional data collected in the closed-loop data system, it monitors operational indicator deviation events in the transportation and warehousing links in real time, classifies and alarms according to the event type and severity of the operational indicator deviation events, automatically generates problem work orders, and distributes them to the corresponding responsible parties for collaborative handling until the loop is closed. This application's embodiments achieve efficient scheduling and dynamic optimization of transportation resources by constructing a transportation capacity resource pool and performing precise matching and intelligent dispatch based on order demand, thereby improving vehicle utilization and dispatch response speed. By establishing a closed-loop data system across the entire chain from sales planning to receipt confirmation, it monitors transportation trajectories in real time and issues early warnings for trip anomalies. Simultaneously, it provides customized data dashboards for different roles, achieving visualization of the entire transportation process and multi-role information collaboration. Furthermore, by monitoring operational indicator deviations in real time based on multi-dimensional data and issuing graded alerts according to event type and severity, it automatically generates problem work orders and distributes them to the corresponding responsible parties for handling until the loop is closed, improving the efficiency of anomaly detection and response capabilities. In summary, this application's embodiments effectively solve the problems of low transportation capacity scheduling efficiency, opaque transportation processes, poor anomaly response, and poor system scalability in existing technologies, thereby improving the overall operational efficiency and collaborative management level of logistics transportation.

[0103] In some embodiments, the method further includes: Based on historical transportation data and real-time monitoring data, vehicle routes, loading rates, and transportation frequencies are optimized and analyzed to generate low-carbon transportation suggestions and to collect carbon emission data. The real-time monitoring data includes the current location and sensor data of the target transportation vehicle, as well as multi-dimensional data collected in the end-to-end data closed loop.

[0104] Optionally, real-time monitoring data refers to data collected by the system in real time during the execution of the current transportation task, including: Current location and sensor data of the target transport vehicle: vehicle location, driving speed, engine speed, instantaneous fuel consumption, fault status, etc., collected in real time through the vehicle terminal; Multi-dimensional data collected in the end-to-end data closed loop includes order execution progress, in-transit monitoring data, inventory status data, and environmental monitoring data (temperature, humidity, vibration, etc.).

[0105] Optionally, optimization analysis can be performed from the following three dimensions: One dimension: The system analyzes transportation tasks with the same or similar origin and destination locations in historical transportation data to identify the optimal driving route.

[0106] The specific analysis method is as follows: Collect, for example, driving records for all transport missions from loading point A to unloading point B within the past 90 days; Extract the driving route, mileage, driving time, and fuel consumption for each task; Different routes are comprehensively scored, with scoring dimensions including mileage, driving time, fuel consumption, and road condition stability. The route with the highest overall score is identified as the recommended route.

[0107] Another dimension: The system analyzes the loading rate in historical transportation data, identifies tasks with low loading rates, and proposes optimization suggestions.

[0108] Load factor refers to the ratio of the actual amount of cargo loaded to the vehicle's rated load capacity or volume. The calculation formula is as follows: Weight loading rate = Actual load / Rated load × 100% Volumetric loading rate = Actual volume / Cargo compartment volume × 100% The system analyzes the characteristics of tasks with low loading rates, identifies common causes and their proportions, and provides optimization suggestions, such as small order cargo volume, dispatching a separate vehicle, oversized vehicle selection, irregular cargo shape, and low space utilization. Based on the above analysis, the system can generate loading rate optimization suggestions, such as: "This week, there are 3 small orders from Tianjin to Beijing (cargo volumes of 5 tons, 8 tons, and 7 tons respectively). It is recommended to combine them into one vehicle, which can save 2 trips and is expected to reduce fuel consumption by 40%." Another dimension: The system analyzes the transportation frequency in historical transportation data, identifies duplicate transportation tasks for the same route and the same customer, and evaluates the feasibility of combined transportation.

[0109] The optimization analysis of transportation frequency includes: Identifying multiple orders of the same customer within the same time period (such as one week); Analyzing the time limit requirements of the orders to determine whether combined transportation is possible; Calculating the reduction in the number of trips, the savings in mileage, and the savings in fuel consumption after combined transportation.

[0110] Optionally, based on the above optimization analysis results, the system automatically generates low-carbon transportation suggestions. Low-carbon transportation suggestions refer to optimization plans aimed at reducing energy consumption and carbon emissions during transportation, including the following types: Route optimization suggestions, such as suggesting that vehicle Jing A12345 should preferentially choose the Beijing-Tianjin Expressway for the Tianjin to Beijing task, which is expected to save 3L of fuel compared to the Beijing-Shanghai Expressway; Vehicle matching suggestions, such as for the current order with a cargo volume of 8 tons, suggesting using a light truck with a load capacity of 10 tons (Jing B56789) instead of a heavy truck with a load capacity of 20 tons (Jing C12345), which is expected to reduce fuel consumption by 30%; Carpooling suggestions, such as detecting 3 carpooling orders (with cargo volumes of 5 tons, 6 tons, and 4 tons respectively) from Tianjin to Beijing on March 28, suggesting combined dispatching of vehicles, which is expected to reduce 2 trips and reduce carbon emissions by approximately 120 kg; Loading optimization suggestions, such as for the current order containing irregular-shaped equipment, suggesting using pallets for loading to improve space utilization, which is expected to increase the loading rate by 15%; Driving behavior suggestions, such as for driver Master Li with a relatively high average fuel consumption recently, suggesting maintaining a constant speed (60 - 80 km / h) and avoiding sudden acceleration and sudden braking, which is expected to reduce fuel consumption by 10%.

[0111] Optionally, the system statistically analyzes the carbon emission data during transportation, including the carbon emissions of a single transportation task, the total monthly / annual carbon emissions, and the carbon emission intensity (carbon emissions per unit cargo volume or per unit mileage).

[0112] Specifically, the system adopts a carbon emission calculation method based on fuel consumption, and the formula is as follows: Carbon emissions (kg) = Fuel consumption (L) × Carbon emission coefficient (kg / L) Among them, the fuel consumption can be obtained through the following methods: Accumulating the instantaneous fuel consumption data collected by the on-vehicle terminal; Calculating based on refueling records and mileage data; Standard fuel consumption model estimation for vehicle model and mileage.

[0113] Carbon emission coefficients are determined based on fuel type: For example, diesel fuel: approximately 2.63 kg CO2 / L; For example, gasoline: approximately 2.30 kg CO2 / L.

[0114] Specifically, the following example, using a logistics company, illustrates the application of this step: Company Background: A logistics company owns 30 transport vehicles and handles an average of 500 transport orders per month, mainly serving the Beijing-Tianjin-Hebei region.

[0115] The optimization analysis process includes: Route optimization: The system analyzed 100 transport records from Tianjin to Beijing and identified the Beijing-Tianjin Expressway as the optimal route, recommending that all Tianjin-Beijing missions prioritize this route. After adoption, the average fuel consumption per trip decreased from 35L to 32L, a reduction of 8.6%.

[0116] Loading rate optimization: System analysis revealed that 15% of transport tasks had a loading rate below 50%, primarily due to small orders being dispatched separately. The system generated carpooling suggestions, combining small orders traveling in the same direction and at similar times. After this suggestion was adopted, the percentage of tasks with a loading rate below 50% decreased to 5%.

[0117] Driving behavior optimization: System analysis revealed that driver Zhang's average fuel consumption was 15% higher than that of similar tasks. The system generated driving behavior suggestions, and after Zhang adjusted his driving habits, his fuel consumption decreased by 12%.

[0118] Low-carbon transportation recommendations include: The system automatically generates a low-carbon transportation recommendation report every week, which includes: the best route recommendation for the week (3 routes); carpooling order suggestions (5 groups); high-emission vehicle reminders (2 vehicles); and driving behavior optimization suggestions (3 items).

[0119] Carbon emission statistics include: The system compiles the company's carbon emission data for March: for example, total mileage: 45,000 kilometers; Total fuel consumption: 9,800 liters; Total carbon emissions: for example, 25,774 kg CO2; Carbon emission intensity: for example, 0.57 kg CO2 / ton-km.

[0120] Compared to last month: Total fuel consumption decreased by 8%; Total carbon emissions decreased by 8%; Carbon intensity decreased by 5%.

[0121] In addition, in some implementations, the system displays carbon emission statistics to enterprise managers through a data dashboard and generates monthly carbon emission reports for enterprises to use for carbon accounting and ESG disclosure.

[0122] This application embodiment optimizes and analyzes vehicle routes, loading rates, and transportation frequencies based on historical transportation data and real-time monitoring data, generating actionable low-carbon transportation suggestions and achieving accurate statistics on carbon emission data. This effectively reduces energy consumption and carbon emission levels during transportation, providing quantifiable emission reduction decision support for logistics companies.

[0123] In another embodiment, such as Figure 2 As shown, a smart logistics processing device is also provided, comprising: The multi-source acquisition module 21 is used to collect multi-dimensional transportation capacity data of transport vehicles and build a transportation capacity resource pool based on the multi-dimensional transportation capacity data; The generation module 22 is used to filter the target transport vehicles and target drivers from the transportation resource pool according to the transportation demand parameters of the order, generate a dispatch instruction containing task details and route planning and push it to the driver terminal of the target driver; The first processing module 23 is used to establish a closed-loop data system for the entire chain from sales plan, order, dispatch order, waybill, on-the-way monitoring to receipt confirmation based on the current location and sensor data of the target transport vehicle after the order is dispatched. It also monitors the transport trajectory in real time and issues early warnings for abnormal trips, and provides customized data dashboards for different roles related to the target vehicle. The second processing module 24 is used to monitor operational indicator deviation events in the transportation and warehousing links in real time based on multi-dimensional data collected in the full-link data closed loop, classify and issue alarms according to the event type and severity of the operational indicator deviation events, automatically generate problem work orders and distribute them to the corresponding responsible parties for collaborative processing until the loop is closed.

[0124] The intelligent logistics processing device provided in the above embodiments is illustrated only by the division of the above-described program modules. In practical applications, the above steps can be assigned to different program modules as needed. That is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. Furthermore, the intelligent logistics processing device and the intelligent logistics processing method embodiments provided in the above embodiments belong to the same concept. For details of their specific implementation, please refer to the method embodiments, which will not be repeated here.

[0125] To achieve the above objectives, embodiments of the present invention also provide a computer device, such as... Figure 3As shown, the rotary drilling rig includes a computing device with the aforementioned processing capabilities. The computing device may include a processor 301 and a memory 303 connected to the processor 301 via a communication bus 302. The memory 303 is used for a smart logistics processing program. The processor 301 is used to execute the smart logistics processing program to implement the smart logistics processing method described in any of the above schemes.

[0126] Optionally, the processor 301 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. Here, the program executed by the processor 301 may be stored in a memory 303 connected to the processor 301 via a communication bus 302. The memory 303 may be volatile memory or non-volatile memory, or may include both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache.By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Sync Link Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM). The memory 303 described in this embodiment is intended to include, but is not limited to, these and any other suitable types of memory 303. The memory 303 in this embodiment is used to store various types of data to support the operation of the processor 301. Examples of this data include: any computer programs operated by the processor 301, such as operating systems and applications; contact data; phonebook data; messages; pictures; videos, etc. The operating system contains various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks.

[0127] In some embodiments of the present invention, the memory 303 may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 303 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0128] The processor 301 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 301 or by software instructions. The processor 301 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 303, and the processor 301 reads the information in memory 303 and, in conjunction with its hardware, completes the steps of the above method. In some embodiments, the embodiments described herein can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0129] For software implementation, the techniques described herein can be achieved through modules (e.g., procedures, functions, etc.) that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or externally.

[0130] Another embodiment of the present invention provides a computer storage medium storing an executable program, which, when executed by a processor 301, can implement the steps of a smart logistics processing method applied to the computing device. For example, as... Figure 1 One or more of the methods shown.

[0131] In some embodiments, the computer storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0132] It should be noted that the technical solutions described in the embodiments of the present invention can be combined arbitrarily without conflict.

[0133] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A smart logistics processing method, characterized in that, include: Collect multi-dimensional transport capacity data of transport vehicles, and construct a transport capacity resource pool based on the multi-dimensional transport capacity data; Based on the transportation demand parameters of the order, the system selects matching target transportation vehicles and target drivers from the transportation capacity resource pool, generates a dispatch instruction containing task details and route planning, and pushes it to the driver terminal of the target driver. After dispatching an order, based on the current location and sensor data of the target transport vehicle, a closed-loop data system is established, encompassing the entire chain from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation. The system monitors the transport trajectory in real time and issues early warnings for abnormal journeys. It also provides customized data dashboards for different roles related to the target transport vehicle, including at least one of the following roles: supplier, logistics provider, carrier, and customer. Based on the multi-dimensional data collected in the full-link data closed loop, operational indicator deviation events in the transportation and warehousing links are monitored in real time. According to the event type and severity of the operational indicator deviation events, graded alarms are issued, and problem work orders are automatically generated and distributed to the corresponding responsible parties for collaborative handling until the loop is closed. The operational indicator deviation events include inventory data deviation, transportation node delays, and environmental parameter exceedances.

2. The method according to claim 1, characterized in that, The multi-dimensional transportation capacity data includes real-time transportation data, warehouse sensing data, order data, driver status data, and historical transportation efficiency data; the collection of multi-dimensional transportation capacity data of transport vehicles, and the construction of a transportation capacity resource pool based on the multi-dimensional transportation capacity data, includes: The vehicle-mounted terminal collects the vehicle's speed, fuel consumption, fault alarms, and real-time location information in real time as the real-time transportation data. The warehouse sensing data is obtained by collecting information on the quantity, storage location, and status changes of goods in the warehouse through sensing devices. The real-time transportation data, the warehouse sensing data, the order data, the driver status data, and the historical transportation efficiency data are correlated and integrated to generate a unified transportation capacity resource pool. The warehouse sensing data is used to dynamically track inventory changes and provide real-time information on the availability of goods for transportation capacity matching.

3. The method according to claim 1, characterized in that, The shipping requirements parameters for an order include: the quantity of goods, the shipping distance, and the time requirements; the task details include: the loading location, the unloading location, and the estimated arrival time. The step of selecting matching target transport vehicles and drivers from the transportation resource pool based on the order's transportation demand parameters, generating a dispatch instruction containing task details and route planning, and pushing it to the corresponding target driver's driver terminal includes: The system loads preset capacity matching rules through the rules engine, and selects candidate transport vehicles and candidates drivers that meet the rule conditions from the capacity resource pool based on the order's cargo volume, transportation distance, and timeliness requirements. The algorithm model sorts and optimizes the candidate transport vehicles and candidate drivers based on the capacity allocation strategy to determine the target transport vehicles and target drivers, and generates a dispatch instruction that includes loading location, unloading location, estimated arrival time and route planning, and pushes it to the driver terminal of the target driver; wherein, the weight coefficient of the capacity allocation strategy in the algorithm model is dynamically adjusted according to historical transport data.

4. The method according to claim 1, characterized in that, The establishment of a closed-loop data system encompassing the entire chain from sales planning, orders, dispatch orders, waybills, in-transit monitoring to receipt confirmation includes: In response to a user's document operation request, perform at least one of the following operations on the transport document: edit, modify, query, open, or cancel; Based on real-time data collected by positioning sensors and environmental monitoring devices, the transportation trajectory is plotted, and when path deviation or environmental exceedance is detected, a trip anomaly warning is issued. Perform electronic waybill issuance, online receipt confirmation, and digital return receipt archiving to complete the entire paperless process.

5. The method according to claim 1, characterized in that, The system monitors operational indicator deviations in the transportation and warehousing processes in real time, and issues tiered alerts based on the event type and severity of these deviations, including: Construct a multi-dimensional anomaly detection model to identify inventory data deviations, transportation delays, and environmental parameter exceedances in real time. According to the preset alarm rules, alarms are classified into levels based on the type and severity of the event. The severity levels include Level 1, Level 2, and Level 3 alarms, with different response times and handling procedures corresponding to different levels.

6. The method according to claim 5, characterized in that, The automatic generation of problem work orders and their distribution to the corresponding responsible parties for collaborative handling until the loop is closed includes: Issue work orders are automatically generated based on the type of abnormal event. Each issue work order includes an abnormality description, the time of occurrence, related documents, and suggested handling measures. The problem work order is distributed to at least one of the supplier, logistics provider, carrier, and customer, and a closed-loop confirmation is received from at least one of the supplier, logistics provider, carrier, and customer after the issue is resolved.

7. The method according to claim 1, characterized in that, The method further includes: Based on historical transportation data and real-time monitoring data, vehicle routes, loading rates, and transportation frequencies are optimized and analyzed to generate low-carbon transportation suggestions and to collect carbon emission data. The real-time monitoring data includes the current location and sensor data of the target transportation vehicle, as well as multi-dimensional data collected in the end-to-end data closed loop.

8. A smart logistics processing device, characterized in that, include: The multi-source acquisition module is used to collect multi-dimensional capacity data of transport vehicles and build a capacity resource pool based on the multi-dimensional capacity data; The generation module is used to filter the target transport vehicles and target drivers from the transportation resource pool according to the transportation demand parameters of the order, generate a dispatch instruction containing task details and route planning, and push it to the driver terminal of the target driver. The first processing module is used to establish a closed-loop data system for the entire chain from sales plan, order, dispatch order, waybill, in-transit monitoring to receipt confirmation based on the current location and sensor data of the target transport vehicle after the order is dispatched. It also monitors the transport trajectory in real time and issues early warnings for abnormal trips, and provides customized data dashboards for different roles related to the target transport vehicle. The second processing module is used to monitor operational indicator deviation events in the transportation and warehousing links in real time based on multi-dimensional data collected in the end-to-end data closed loop. It classifies and alarms according to the event type and severity of the operational indicator deviation events, automatically generates problem work orders, and distributes them to the corresponding responsible parties for collaborative processing until the loop is closed.

9. A computer device, characterized in that, The computer device includes a processor and a memory storing a computer program, wherein when the processor runs the computer program, it implements the intelligent logistics processing method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, characterized in that the computer program, when executed by a processor, implements the intelligent logistics processing method according to any one of claims 1 to 7.

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