Logistics order transportation management and control system based on digital driving

Abstract

The invention relates to the technical field of logistics management, in particular to a logistics order transportation management and control system based on digital driving, and the system comprises a data collection module which is used for collecting order data of a logistics order, environment data of an initial path and traffic data, carrying out the preprocessing of the collected data, and carrying out the fusion construction of a basic data set after the processing; and the path evaluation module is used for carrying out trafficability risk evaluation on the initial path based on the basic data set and judging whether to adjust the initial path based on an evaluation result. According to the invention, through cooperative work of the data acquisition module, the path evaluation module and the path planning module, full-process digital management of logistics transportation is realized; by utilizing dynamic path planning and multi-dimensional risk assessment, the influence of extreme weather on logistics transportation is effectively solved, and the transportation timeliness is guaranteed, especially under the conditions of road closing, icing and the like in snowy weather.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics order transportation management and control system based on digital drive. Background Art

[0002] As the core support for modern economic development, the logistics industry fulfills the transportation needs of goods throughout their entire life cycle, from production to consumption. However, traditional logistics and transportation management and control models suffer from multiple issues. These include information flows for order processing, transportation scheduling, and logistics monitoring, which are often scattered across multiple systems and lack unified management. Furthermore, it is difficult to obtain real-time information throughout the transportation process, especially in multi-stage transport. In recent years, with the rapid development and application of digital technologies, the logistics industry has initially achieved intelligent order management and transportation control. By integrating data and business flows throughout the entire logistics life cycle, digital technologies optimize and coordinate each link in real time, gradually achieving transparency in logistics orders, precision in transportation, and efficient management.

[0003] After searching, Chinese patent number CN202410722719.X discloses a multi-terminal collaborative logistics digital management and control system, including: a server side and several driver terminals and several management terminals of various departments connected to the server side interface. The server side includes a waybill generation module, a transportation planning module and an arrival management module; the above solution connects the server side of the multi-terminal collaborative logistics digital management and control system with the driver terminal and the management terminal of each department through an interface, which can realize multi-terminal collaboration of various departments in the logistics management process, have higher real-time coordination capabilities in logistics scheduling, and thus greatly improve the execution efficiency of logistics business.

[0004] However, although the existing digital logistics management and control system has the ability to predict weather, road traffic may be affected by extreme weather conditions. For example, road closures and traffic interruptions caused by extreme snowstorms are not included in the planning in advance, which in turn causes delays in the transportation of logistics orders. In particular, for logistics orders that are sensitive to temperature, such as fresh products or pharmaceutical products, extreme snowstorms are usually accompanied by cold waves, which may cause the temperature to drop sharply or even drop below zero, thereby having a direct impact on the quality of fresh products. This will eventually cause a chain reaction in the entire supply chain system, such as insufficient supply of raw materials at production nodes, product backlogs at storage nodes, and insufficient inventory at sales nodes, which will lead to waste of resources and economic cost losses.

[0005] Therefore, a digital-driven logistics order transportation management and control system is proposed to solve the above problems. Summary of the Invention

[0006] Technical problems solved In response to the above-mentioned shortcomings of the existing technology, the present invention provides a digital-driven logistics order transportation management and control system, which can effectively solve the problem that the logistics transportation management and control system in the existing technology cannot effectively guarantee the transportation timeliness of logistics orders for transporting fresh products in extreme blizzard weather. Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a digital-driven logistics order transportation management and control system. The technical solution adopted by the present invention is as follows: The data collection module is used to collect order data of logistics orders, environmental data and traffic data of the initial path, and pre-process the collected data before fusing it into a basic data set. The path assessment module conducts a traffic risk assessment on the initial path based on the basic data set and determines whether to adjust the initial path based on the assessment results; If the initial path needs to be adjusted, the path planning module uses the assessment results of accessibility and risk and the basic data set to plan a new effective path and select the optimal path.

[0008] The environmental data include ambient temperature, ambient humidity, snowfall range, snowfall amount, wind speed and direction, and snow thickness; Order data includes shipping place, real-time location, destination, order timeliness, and cargo type; Traffic data includes road network maps and road conditions.

[0009] The method of assessing accessibility risk is as follows: The path accessibility score of the initial path is calculated based on the basic data set. The calculation formula is: Where, is the path accessibility score of the initial path; Risk of road closure; Risk of icy roads; The risk of road congestion; Risk of road detention; is the maximum risk value; Compare the path accessibility score with the preset judgment threshold. When the path accessibility score is greater than the judgment threshold, it means that the initial path needs to be adjusted; when the path accessibility score is less than the judgment threshold, it means that the initial path does not need to be adjusted. The formula for calculating road closure risk is: Where, is the base probability of road closure; is the real-time snowfall amount of the initial path; The historical maximum snowfall for the initial path; is the weight coefficient of snowfall; WS is the real-time wind speed of the initial path; is the historical maximum wind speed of the initial path; is the weight coefficient of wind speed; The formula for calculating road icing risk is: Where, is the critical temperature of freezing; is the road surface temperature of the initial path; is the ambient temperature of the initial path; is the activation function; is the weight coefficient of road surface temperature; is the weight coefficient of ambient temperature; The calculation formula for road congestion risk is: ; Where, CI is the road congestion index; is the weight coefficient of road congestion.

[0010] The calculation formula for road detention risk is: Where, is the historical traffic delay time of the initial path; is the current estimated travel time of the initial route; is the weight coefficient of road detention.

[0011] The path assessment module constructs a dynamic avalanche risk assessment model based on snowfall and wind speed, updates the basic probability of road closure, and obtains the dynamic closure probability of road snow accumulation. The calculation formula of the dynamic avalanche risk assessment model is: ; Where, Dynamic closure probability of road covered by snow; is the snow depth influence item; H is the real-time snow depth of the initial path; is the critical snow depth; is the weight coefficient of snow depth; is the wind speed excitation influence term; is the sensitivity parameter of wind speed to avalanche triggering; is the weight coefficient of wind speed excitation; is the terrain dynamic impact item; is the terrain slope, is the activation function, ,in is the slope critical value, is the slope risk change rate; is the terrain aspect angle.

[0012] The path assessment module updates the critical snow depth based on the basic data set, and the update formula is: ; Where, is the dynamic critical snow depth; is the vehicle dynamic load factor, ;in, is the average weight of each vehicle on the initial path; is the hourly traffic volume on the initial path; is the road snow area of the initial path; is the snow structure factor; ;in, is the real-time snow density; is the reference snow density; is the correction factor for snow structure; is the environmental dynamic factor, ; Wherein, T is the real-time temperature; RH is the real-time humidity; is the sensitivity coefficient of temperature fluctuation; is the humidity sensitivity coefficient.

[0013] The method of planning a new effective path is: Regenerate multiple candidate routes starting from the real-time location of the current logistics order and calculate the path accessibility score of each candidate route. Compare the path accessibility score of each candidate route with a pre-set judgment threshold. At the same time, extract the transportation time of each candidate route and compare it with the order timeliness. Candidate routes with a path accessibility score greater than the judgment threshold and a transportation time less than the order timeliness are classified as valid routes. All valid routes are then merged to form a valid path collection. Calculate the preference score of each valid path in the valid path collection, and select the valid path with the highest preference score as the optimal path.

[0014] The calculation formula for the optimization score is: ; Where, is the optimal score of the i-th effective path; is the item affecting transportation time, is the estimated transportation time of the i-th effective path, is the shortest transportation time among the valid paths, is the weight coefficient of transportation time; is the path safety impact item, is the path accessibility score of the i-th valid path, is the weight coefficient of path security; is the temperature control loss influence term, is the temperature control loss of the i-th effective path, The minimum temperature control loss among the valid paths; Weight coefficient of temperature control loss.

[0015] The calculation formula for the temperature control loss is: ; Where n is the number of segments of the i-th valid path; is the temperature deviation of the jth segment, and the calculation formula is ,in is the real-time temperature of the jth segment, The optimal storage temperature for current logistics orders; is the transportation time of the jth segment; is the temperature sensitivity weight.

[0016] The path planning module corrects the temperature deviation based on the basic data set, and the correction formula is: ; Where, is the corrected temperature deviation of the jth segment; is the temperature change rate of the jth segment, ; is the weight coefficient of temperature change rate; is the control weight of temperature difference fluctuation; is the nonlinear correction factor, q is the frequency adjustment factor; is an exponentially decreasing term; is the time decreasing factor; is the amplification weight of the temperature change rate; is a logarithmic term; is the nonlinear exponent of the deviation of the logarithmic term.

[0017] The path planning module corrects the transportation time based on the basic data set, and the correction formula is: ; Where, is the corrected transport time, is the time threshold; In a short period of time : Where, is the logarithmic growth term of time to weight; is the temperature deviation correction factor; are the adjustment parameters of time and temperature difference; In a long period of time : Where, is the nonlinear weight term accumulated over a long period of time, where s is the nonlinear power; is the temperature difference sensitivity adjustment parameter; is a logarithmic growth term.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. In this invention, dynamic path planning and multi-dimensional risk assessment are used to effectively address the impact of extreme weather on logistics and transportation, ensuring the timeliness of transportation, especially in situations such as road closures and icing in blizzards.

[0019] 2. In the present invention, through dynamic avalanche risk assessment and dynamic critical snow depth update, the risks of complex terrain in extreme weather are accurately predicted, and the flexibility of path planning is improved. This dynamic adjustment capability improves the scientific nature and flexibility of path planning and reduces the interference of subjective factors.

[0020] 3. In the present invention, in the planning of dynamic routes, the transportation time, route safety and temperature control loss are comprehensively considered to select the optimal route and realize multi-objective optimization; and a temperature control loss calculation model is introduced to quantify the impact of cold waves and low temperatures on fresh products or pharmaceutical products, thereby reducing the risk of quality loss.

[0021] 4. In the present invention, through the collaborative work of data collection, path evaluation and path planning modules, the whole process of logistics transportation is digitally managed, the level of intelligence is improved, the transportation timeliness and cargo quality of logistics orders are guaranteed, supply chain node problems are avoided, and resource waste and economic losses are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a simplified structural diagram of the management and control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 only used to explain the present invention and are not intended to limit the present invention. Example

[0024] Reference Figure 1 ,This case proposes a digital-driven logistics order transportation control system,,including data collection module, path evaluation module, and path planning module; The data collection module is used to collect environmental data and traffic data of the initial route, as well as order data of physical orders. The collected data is pre-processed to eliminate data problems and improve data quality. Finally, the processed data is fused and constructed into a basic data set. The route assessment module assesses the traffic risk of the initial route (generated based on the logistics order's origin and destination, combined with historical route data) based on the basic data set of the logistics order. Based on the assessment results (i.e., whether the logistics order can be delivered to the destination within the time limit), it determines whether to adjust the initial route. The path planning module, if the initial path needs to be adjusted, uses the accessibility risk assessment results and the basic data set to plan a new effective path and select the optimal path (the one with a high path accessibility score and minimized transportation time).

[0025] In this case, environmental data includes ambient temperature, ambient humidity, snowfall range, snowfall amount, wind speed and direction, and snow thickness; order data includes shipping location, real-time location, destination, order timeliness, cargo type, and cargo status; traffic data includes road network maps and road conditions.

[0026] In this case, the accessibility risk assessment method is: The path accessibility score of the initial path is calculated based on the basic data set. The calculation formula is: Where, The path accessibility score of the initial path provides a quantitative indicator for the quality of the initial path. Its value range is [0,1]. A higher value indicates better path accessibility and lower risk. Otherwise, avoidance should be considered. Road closure risk reflects the quantitative assessment result of the possibility of road closure due to heavy snow on the initial path; Road icing risk reflects the quantitative assessment result of the possibility of road icing due to heavy snow and low temperature on the initial path; The road congestion risk reflects the quantitative assessment result of the possibility of congestion on the initial path due to heavy snow; is the road detention risk, which represents the quantitative assessment result of product detention caused by traffic delays on the initial path; The maximum risk value ensures that the combined value of different risks is limited to the numerical range of [0,1], facilitating the convenient comparison of path accessibility scores. It is set based on the sum of the maximum values of road closure risk, road icing risk, and road congestion risk to avoid risk assessment distortion caused by too small a value (i.e., the path accessibility score is overestimated). By comparing the path accessibility score with the preset judgment threshold, when the path accessibility score is greater than the judgment threshold, it means that the initial path is not applicable (i.e., the current logistics order cannot be delivered within the time limit via the initial path); conversely, when the path accessibility score is less than the judgment threshold, it means that the current logistics order can be delivered within the time limit via the initial path.

[0027] Specifically: The formula for calculating road closure risk is: Where, The base probability of road closure (based on real-time traffic data and historical closure data, such as the closure record of a certain route under historical snowfall or wind speed conditions); is the real-time snowfall amount of the initial path; The historical maximum snowfall for the initial path; is the weight coefficient of snowfall; WS is the real-time wind speed of the initial path; is the historical maximum wind speed of the initial path; is the weight coefficient of wind speed; The formula for calculating road icing risk is: Where, is the critical temperature of freezing; is the road surface temperature of the initial path; is the ambient temperature of the initial path; is an activation function whose purpose is to calculate the risk only in negative temperature environments (i.e., when ice may form); is the weight coefficient of road surface temperature; is the weight coefficient of ambient temperature; The calculation formula for road congestion risk is: ; Where CI is the road congestion index, which reflects the real-time road congestion status on the initial path and ranges from [0,1]; is the weight coefficient of road congestion, which reflects the impact of the road congestion index on the road congestion risk.

[0028] The calculation formula for road detention risk is: Where, is the historical traffic delay time of the initial path, which represents the average delay time of the initial path caused by congestion, accidents, etc. due to heavy snow in historical data; The current estimated travel time of the initial route, that is, the estimated travel time of the current logistics order under the current traffic conditions; is the weight coefficient of road detention.

[0029] It is worth noting that when obtaining the basic probability of road closure based on historical data, the historical data may not include current extreme weather conditions, resulting in an inability to accurately predict the basic probability of road closure. Especially on mountain roads or canyon roads, heavy snow weather can cause snow to accumulate rapidly on both sides of the road. When the snow exceeds a certain amount and is dynamically stimulated by wind speed, it is easy to trigger avalanches, directly closing the road or threatening the safety of passing vehicles. The path assessment module constructs an avalanche dynamic risk assessment model based on snowfall and wind speed, updates the basic probability of road closure, and obtains the dynamic closure probability of road snow accumulation. The calculation formula of the avalanche dynamic risk assessment model is: ; Where, The dynamic closure probability of snowy roads, that is, the dynamic closure probability of the initial route under the current blizzard weather, accurately links the complex influences of meteorology and terrain to route planning, improves the scientific nature of decision-making, reduces deviations from subjective factors, and returns the dynamic closure probability of snowy roads to the calculation formula of road closure risk, so that the road closure risk can dynamically adapt to changing meteorological conditions, thereby providing a more accurate judgment for the initial route's accessibility risk assessment; The snow depth influencing term is one of the core factors for avalanche triggering. It quantifies whether the snow poses a high risk by comparing the real-time snow thickness to the critical snow depth. H is the real-time snow depth of the initial path. is the critical snow depth, that is, the snow depth threshold that triggers an avalanche on the initial path, which is usually obtained based on historical data statistics; is the weight coefficient of snow depth, which is used to adjust the impact of snow depth on the overall avalanche risk; The wind speed excitation influences the snowpack. Strong winds not only directly move the snowpack, but also disrupt the internal structure of the snowpack, stimulating snow instability and leading to avalanches. It is the sensitivity parameter of wind speed to avalanche triggering, which controls the effect of wind speed; is the weight coefficient of wind speed excitation, which adjusts the importance of wind speed to the overall avalanche excitation risk assessment; The terrain dynamic impact term combines the slope and aspect, which provide the physical conditions for snow sliding. The interaction between wind direction and slope aspect determines whether the wind directly pushes the sliding snow layer. Together, they describe the terrain risk of the current path. is the terrain slope, is the activation function used to quantify the risk level of the slope, ,in is the slope critical value, is the slope risk change rate; is the terrain aspect angle (i.e., the angle with the wind direction), which determines the effect of wind pressure on the sliding snow layer; By building a comprehensive avalanche dynamic risk assessment model based on three core dimensions: snow depth, wind speed excitation, and terrain dynamics, we can conduct a detailed quantitative risk assessment of the initial path along mountainous and canyon roads. By combining historical data and real-time environmental information, we can predict whether the initial path is likely to be affected by avalanches, flexibly and dynamically improve the assessment of the initial path, and provide an effective data basis for the subsequent re-planning of effective paths.

[0030] It is worth mentioning that the critical snow depth obtained based on historical data is usually static and cannot reflect the dynamic fluctuations under current extreme snowstorm weather. For example, vehicle movement can affect the stability of the snow layer on the road, increase the instability of the snow, compact the snow layer, and may cause stress transfer, thereby triggering avalanches in advance. The path assessment module updates the critical snow depth based on the basic data set. The update formula is: ; Where, The dynamic critical snow depth takes into account important factors such as the spatial distribution of snow depth, vehicle disturbance, and environmental fluctuations. This allows for dynamic assessment of avalanche risk under different conditions, more accurately reflecting the snow state and induced risk in complex scenarios, and providing an effective data basis for initial path assessment. is the vehicle dynamic load factor, which indicates the instability caused by load and flow on snow when vehicles pass through the road; ;in, is the average weight of each vehicle on the initial path (including vehicle pressure and additional load caused by driving); is the hourly traffic volume on the initial path; is the snow-covered area of the road on the initial path; when the traffic volume or the average weight of vehicles increases, the degree of snow compaction and the accumulated pressure will be significantly improved, resulting in a lower upper limit of the dynamic critical snow depth, thereby increasing the probability of dynamic snow closure of the road; is the snow structure factor, reflecting the stability of snow; ;in, is the real-time snow density; is the reference snow density; is a correction factor for snow structure, reflecting the characteristics of snow type (wet snow is more slippery, icy snow is more stable). When the snow density increases (compaction increases) or the snow becomes more stable, the upper limit of the dynamic critical snow depth decreases, thereby increasing the probability of dynamic snow closure of the road. is the environmental dynamic factor, which indicates the impact of temperature and humidity changes on snow stability; Where T is the real-time temperature. When the difference between the real-time temperature and the critical temperature for snow to freeze is positive, it means that the snow is melting. Otherwise, it means that the snow structure is more stable. RH is the real-time humidity. The higher the humidity, the more difficult it is for the snow to maintain a stable state. Its internal force decreases, the shear surface becomes more slippery, and it is more likely to collapse. is the sensitivity coefficient of temperature fluctuation; is the humidity sensitivity coefficient; The relative suppression effect of the vehicle dynamic load factor and the environmental dynamic factor on the snow structure factor. That is, when the environmental conditions and vehicle disturbances are strong, their combined effect will weaken the structure and bearing capacity of the snow itself. For example, under conditions of high temperature, high humidity, and strong vehicle loads, the snow will be relatively loose, indicating that the stability of the snow is greatly weakened. is the relative impact of vehicle dynamic load factor and snow structure factor on environmental dynamic factor, that is, when vehicles increase the compaction effect of snow and the snow structure is more stable, the destructive effect of environmental disturbance will be reduced; is the relative influence of environmental dynamic factors and snow structure factors on vehicle dynamic load factors; the final dynamic critical snow depth is affected by these three interaction terms. The larger the relative value, the worse the stability of the snow, and the dynamic critical snow depth will also drop significantly, resulting in higher risks.

[0031] In this case, when the initial path needs to be adjusted, the method for planning a new effective path is: Regenerate multiple candidate routes starting from the real-time location of the current logistics order and calculate the path accessibility score of each candidate route. Compare the path accessibility score of each candidate route with a pre-set judgment threshold. At the same time, extract the transportation time of each candidate route and compare it with the order timeliness. Candidate routes with a path accessibility score greater than the judgment threshold and a transportation time less than the order timeliness are classified as valid routes. All valid routes are then merged to form a valid path collection. The path planning module calculates the optimal score of each valid path based on the valid path collection. The calculation formula is: ; Where, is the preferred score of the i-th valid path, which is used to measure the comprehensive performance of the valid path in three dimensions: transportation time, path safety, and temperature control loss. The higher the value, the better the path in the comprehensive evaluation. The path with the highest score will be selected as the optimal path. is the item affecting transportation time, is the estimated transportation time of the i-th effective path, is the shortest transportation time among the valid paths, is the weight coefficient of transportation time, which indicates the importance of transportation time in the comprehensive evaluation; is the path safety impact item, is the path accessibility score of the i-th valid path, reflecting the comprehensive security of the path, is the weight coefficient of path safety, which indicates the importance of path safety in the comprehensive evaluation; is the temperature control loss influence term, is the temperature control loss of the i-th effective path, reflecting the impact of cold waves and low temperatures on the quality of fresh products. The minimum temperature control loss among the valid paths; The weight coefficient of temperature control loss indicates the importance of temperature control loss in the comprehensive evaluation; Finally, the valid path with the highest optimization score is selected from the collection of valid paths as the optimal path. This path comprehensively considers the impact of cold waves and low temperatures on fresh products, introduces temperature control loss evaluation, and can quantify the impact of cold waves and low temperatures on the quality of fresh products, and give it priority consideration in path optimization; at the same time, comprehensive optimization is achieved between transportation time, path safety and temperature control loss to ensure that the selection of the optimal path is more scientific and reasonable; and in actual applications, it can be dynamically adjusted according to different logistics order types and environmental conditions to improve the applicability and accuracy of the solution.

[0032] The calculation formula for temperature control loss is: ; Where n is the number of segments of the i-th valid path; is the temperature deviation of the jth segment, and the calculation formula is ,in is the real-time temperature of the jth segment, The optimal storage temperature for the current logistics order. The greater the temperature deviation, the greater the gap between the ambient temperature and the optimal storage temperature for fresh products, and the more serious the impact on product quality. is the transportation time of the jth segment, calculated based on real-time traffic data and historical traffic data; Temperature sensitivity weight, which reflects the sensitivity of different types of fresh products to temperature changes and is determined based on the type of goods; By accumulating all segments of the i-th effective path, the formula can fully reflect the temperature control loss of the entire path on fresh products, providing a scientific basis for path optimization and dynamic adjustment.

[0033] It's worth noting that when calculating temperature control losses, while cold snaps brought on by extreme snowstorms can cause overall temperatures to drop in the transportation environment, exposing cargo to a persistent cold threat, cold snaps are not evenly distributed. The effects of terrain and the environment can lead to localized "reverse temperature drifts." This refers to the development of "abnormally warm zones" in certain areas (such as those with sheltered terrain, areas affected by wind direction, or areas with unusual snow depth), resulting in a brief rise in temperature that contradicts the overall cold snap trend. During transportation, logistics vehicles can enter these small, transiently warm areas, causing the ambient temperature to "reverse drift" (i.e., a sudden rise followed by a rapid drop). This, in turn, leads to significant high-frequency fluctuations in temperature deviations, preventing temperature control losses from reflecting the additional damage these fluctuations may cause to products. The path planning module corrects the temperature deviation based on the basic data set. The correction formula is: ; Where, is the corrected temperature deviation of the jth segment, which represents the weighted impact value of the temperature deviation of segment j in the effective path, that is, the result obtained by comprehensively considering the temperature difference itself, temperature change rate, time dynamic influence and other weights; is the temperature change rate of the jth segment, The temperature change rate captures the dynamic behavior of temperature differences (for example, in a cold wave environment, when the temperature drops suddenly, the temperature change rate will increase significantly, which has a greater impact on product quality than stable temperature deviations); is the weight coefficient of the temperature change rate, which is used to adjust the importance of the temperature difference change rate in the loss; is the control weight of temperature difference fluctuation, which indicates the importance of the overall effect of temperature difference fluctuation; is a nonlinear correction factor that describes the periodic or oscillatory characteristics of the error when it deviates from the optimal storage temperature state, and q is a frequency adjustment factor; is an exponentially decreasing term, which describes the weakening trend of the impact of temperature fluctuations as time goes by; is the time-decreasing factor, which controls the weakening effect of temperature deviation on product loss over time; is the amplification weight of the temperature change rate; is a logarithmic term; is the deviation nonlinearity index of the logarithmic term, which is used to adjust the response amplitude to dynamic fluctuations; By updating and calculating the temperature control loss through corrected temperature deviations, we can accurately simulate the complex periodic temperature fluctuations, especially the impact of drastic fluctuations in short-term warm areas along cold wave areas on the loss of fresh products. This can provide more accurate predictions for the priority scores of different logistics orders, effectively evaluate the optimal path, and optimize transportation routes to reduce the risk of cargo loss.

[0034] Furthermore, during a cold wave reverse temperature drift, a section of the road may experience a brief warming phenomenon (e.g., lasting only ten minutes, with the temperature rising by five degrees), but due to the transportation time Simple linear superposition may amplify the impact of this brief warm zone in the corrected temperature deviation calculation, leading to an overestimation of losses. In reality, such short-term warm fluctuations may have limited damage to cargo, especially when the cargo has a large heat capacity, where short-term temperature fluctuations will not significantly affect its core temperature. Furthermore, the temperature on one section of road remained at -30°C (extremely cold) for two hours, but the simple linear superposition of transportation time underestimated the cumulative damage to cargo caused by the prolonged extreme cold. Consequently, the linear transportation time calculation failed to capture the nonlinear characteristics of the cumulative effects of short-term drastic temperature excursions and prolonged extreme cold. The path planning module corrects the transportation time based on the basic data set. The correction formula is: ; Where, is the corrected transport time, The time threshold is used as the dividing point for the corrected transport time, and the short time periods are processed separately. and long periods of time The transportation time, describing the different effects of time on cargo loss; In a short period of time: Where, It is the logarithmic growth term of time to weight, which is used to control the transportation time increment in a short period of time; The temperature deviation correction factor is used to dynamically adjust the impact of temperature deviation on transportation time in a short period of time; The time and temperature difference adjustment parameters are used to control the impact of temperature difference on transportation time; Over a long period of time: Where, It is a nonlinear weight term accumulated over a long period of time, where s is a nonlinear power, which is used to enhance the impact of long time on transportation time; It is the temperature difference sensitivity adjustment parameter, and its value is positive, which is used to control the amplification effect of temperature difference on transportation time; It is a logarithmic growth term that smoothly adds the impact of temperature difference to the transportation time to avoid excessive surge in transportation time when the temperature difference is large. By incorporating the corrected transport time into the calculation of the corrected temperature deviation, we can accurately address the overestimation of short-term, drastic temperature excursions while precisely capturing the cumulative effects of prolonged extreme cold. This allows the solution to dynamically adapt to different temperature excursion patterns, increasing its flexibility and, in turn, improving the accuracy and reliability of cargo loss assessments and determining the transport route most suitable for the current physical order.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A digital-driven logistics order transportation control system, characterized by: include: The data collection module is used to collect order data of logistics orders, environmental data and traffic data of the initial path, and pre-process the collected data before fusing it into a basic data set. The path assessment module performs a risk assessment on the initial path based on the basic data set and obtains a path accessibility score to determine whether to adjust the initial path. The evaluation formula is: Where, is the path accessibility score of the initial path; Risk of road closure; Risk of icy roads; The risk of road congestion; Risk of road detention; is the maximum risk value; Then, a dynamic avalanche risk assessment model is constructed based on the basic data set. The basic probability of road closure is updated using snow depth, wind speed, and terrain to obtain the dynamic probability of road closure by snow accumulation. If the initial path needs to be adjusted, the path planning module uses the accessibility risk assessment results and the basic data set to plan a new effective path and select the optimal path.

2. The digital-driven logistics order transportation management and control system according to claim 1, characterized in that: The environmental data include ambient temperature, ambient humidity, snowfall range, snowfall amount, wind speed and direction, and snow thickness; Order data includes shipping place, real-time location, destination, order timeliness, and cargo type; Traffic data includes road network maps and road conditions.

3. The digital-driven logistics order transportation control system according to claim 2, characterized in that: The method of accessibility risk assessment is: The formula for calculating road closure risk based on the basic data set is: Where, is the base probability of road closure; is the real-time snowfall amount of the initial path; The historical maximum snowfall for the initial path; is the weight coefficient of snowfall; WS is the real-time wind speed of the initial path; is the historical maximum wind speed of the initial path; is the weight coefficient of wind speed; The formula for calculating road icing risk is: Where, is the critical temperature of freezing; is the road surface temperature of the initial path; is the ambient temperature of the initial path; is the activation function; is the weight coefficient of road surface temperature; is the weight coefficient of ambient temperature; The calculation formula for road congestion risk is: ; Where, CI is the road congestion index; is the weight coefficient of road congestion. The calculation formula for road detention risk is: Where, is the historical traffic delay time of the initial path; is the current estimated travel time of the initial route; is the weight coefficient of road detention.

4. The digital-driven logistics order transportation control system according to claim 3, characterized in that: The calculation formula of the avalanche dynamic risk assessment model is: ； Where, Dynamic closure probability of road covered by snow; is the snow depth influence item; H is the real-time snow depth of the initial path; is the critical snow depth; is the weight coefficient of snow depth; is the wind speed excitation influence term; is the sensitivity parameter of wind speed to avalanche triggering; is the weight coefficient of wind speed excitation; is the terrain dynamic impact item; is the terrain slope, is the activation function, ,in is the slope critical value, is the slope risk change rate; is the terrain aspect angle.

5. The digital-driven logistics order transportation control system according to claim 4, characterized in that: The path assessment module updates the critical snow depth based on the basic data set, and the update formula is: ； Where, is the dynamic critical snow depth; is the vehicle dynamic load factor, ;in, is the average weight of each vehicle on the initial path; is the hourly traffic volume on the initial path; is the road snow area of the initial path; is the snow structure factor; ;in, is the real-time snow density; is the reference snow density; is the correction factor for snow structure; is the environmental dynamic factor, ; Wherein, T is the real-time temperature; RH is the real-time humidity; is the sensitivity coefficient of temperature fluctuation; is the humidity sensitivity coefficient.

6. The digital-driven logistics order transportation control system according to claim 3, characterized in that: The method of planning a new effective path is: Regenerate multiple candidate routes starting from the real-time location of the current logistics order and calculate the path accessibility score of each candidate route; Compare the path accessibility score of each candidate path with the pre-set judgment threshold; extract the transportation time of each candidate path and compare it with the order timeliness; Candidate paths whose path accessibility scores are greater than the judgment threshold and whose transportation time is less than the order validity period are classified as valid paths; and all valid paths are merged to form a valid path set; Calculate the preference score of each valid path in the valid path collection, and select the valid path with the highest preference score as the optimal path.

7. The digital-driven logistics order transportation control system according to claim 6, characterized in that: The calculation formula of the optimization score is: ； Where, is the optimal score of the i-th effective path; is the item affecting transportation time, is the estimated transportation time of the i-th effective path, is the shortest transportation time among the valid paths, is the weight coefficient of transportation time; is the path safety impact item, is the path accessibility score of the i-th valid path, is the weight coefficient of path security; is the temperature control loss influence term, is the temperature control loss of the i-th effective path, The minimum temperature control loss among the valid paths; Weight coefficient of temperature control loss.

8. The digital-driven logistics order transportation control system according to claim 7, characterized in that: The calculation formula of the temperature control loss is: ; Where n is the number of segments of the i-th valid path; is the temperature deviation of the jth segment, and the calculation formula is ,in is the real-time temperature of the jth segment, The optimal storage temperature for current logistics orders; is the transportation time of the jth segment; is the temperature sensitivity weight.

9. The digital-driven logistics order transportation control system according to claim 8, characterized in that: The path planning module corrects the temperature deviation based on the basic data set, and the correction formula is: ； Where, is the corrected temperature deviation of the jth segment; is the temperature change rate of the jth segment, ; is the weight coefficient of temperature change rate; is the control weight of temperature difference fluctuation; is the nonlinear correction factor, q is the frequency adjustment factor; is an exponentially decreasing term; is the time decreasing factor; is the amplification weight of the temperature change rate; is a logarithmic term; is the nonlinear exponent of the deviation of the logarithmic term.

10. The digital-driven logistics order transportation control system according to claim 9, characterized in that: The path planning module corrects the transportation time based on the basic data set. The correction formula is: ； Where, is the corrected transport time, is the time threshold; In a short period of time : Where, is the logarithmic growth term of time to weight; is the temperature deviation correction factor; are the adjustment parameters of time and temperature difference; In a long period of time : Where, is the nonlinear weight term accumulated over a long period of time, where s is the nonlinear power; is the temperature difference sensitivity adjustment parameter; is a logarithmic growth term.

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