Delivery order timeout management method and system

Abstract

The invention discloses a delivery order timeout management method and system, and the method comprises the steps: obtaining delivery data related to a delivery order in real time, the delivery data comprising a current traffic road condition level, weather information, order priority information and current deliveryman real-time position information; according to the delivery data, a timeout threshold value of each delivery order is dynamically calculated based on a preset intelligent algorithm, and the timeout threshold value is a system allowed timeout duration when the delivery order exceeds the standard delivery time; the delivery progress of each delivery order is monitored in real time, and the predicted delivery time is calculated; and comparing the predicted arrival time with a corresponding overtime threshold value, and when the predicted arrival time exceeds the overtime threshold value, reminding a current deliveryman and / or a manager that an overtime risk exists. By dynamically adjusting the overtime threshold, a complex distribution scene can be effectively handled, the threshold is increased or decreased according to reasonable factors such as traffic and weather changes, and unreasonable overtime judgment caused by external factors is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution management, and particularly relates to a method and system for managing delivery order timeouts. Background Art

[0002] With the booming development of the e-commerce industry and the increasing expectations of consumers for delivery services, delivery timeliness has become a key indicator for measuring the quality of logistics services. In the management of delivery timeouts, the setting and adjustment of timeout thresholds have a profound impact on delivery efficiency and service quality.

[0003] Traditional delivery timeout management often uses fixed timeout thresholds, and this approach has many drawbacks. First of all, it cannot adapt to complex and changeable delivery environments. In special situations such as traffic congestion and bad weather, delivery personnel face great difficulties in delivery, but the fixed timeout threshold will not change due to these external factors. For example, in heavy rain weather, road flooding causes slow vehicle travel and a significant extension of delivery time, while the fixed timeout threshold remains unchanged. Even if the delivery personnel make efforts, they are very likely to be judged as timed out, which is neither reasonable nor motivating for the delivery personnel.

[0004] Secondly, it ignores the individual differences of delivery personnel and the characteristics of delivery areas. The work efficiencies and experiences of different delivery personnel vary, and the delivery difficulties in different areas also differ greatly. If a unified timeout threshold is set for all delivery personnel and areas, it lacks incentives for highly efficient delivery personnel and is unfair to delivery personnel responsible for remote or traffic-complex areas. For example, an experienced and highly efficient delivery personnel is judged as timed out because of occasional extensions in delivery time due to fluctuations in the order volume in the area where they are located; in remote areas, the delivery distance is long and the road conditions are poor, and the delivery personnel spend more time but also face the risk of being timed out, which is not conducive to the stability of the delivery team and the overall improvement of delivery efficiency.

[0005] Moreover, it is difficult to meet the diverse needs of users and items. Users may change the delivery time due to personal arrangements, and special items such as fresh food and high-value items have different requirements for delivery timeliness. Fixed timeout thresholds cannot respond flexibly to these needs, which may cause inconvenience for users to receive goods and damage to the quality or value of special items during delivery. For example, users hope to postpone the delivery time, but due to the fixed timeout threshold, the delivery personnel still deliver according to the original plan, resulting in the goods not being received; for fresh food items, they may deteriorate due to too long delivery time under the fixed threshold. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a method and system for managing delivery order timeouts. By dynamically adjusting the timeout threshold, it can effectively cope with complex delivery scenarios, increase or decrease the threshold according to reasonable factors such as traffic and weather changes, and avoid unreasonable timeout judgments caused by external factors.

[0007] To solve the above technical problems, the first aspect of the embodiments of the present invention provides a method for managing delivery order timeouts, including the following steps:

[0008] Obtain in real time the delivery data related to the delivery order, where the delivery data includes: the current traffic condition level, weather information, order priority information, and the real-time location information of the current delivery person;

[0009] Based on the delivery data, dynamically calculate the timeout threshold for each delivery order based on a preset intelligent algorithm, where the timeout threshold is the system-allowed overtime duration for the delivery order to exceed the standard delivery time;

[0010] Monitor in real time the delivery progress of each delivery order and calculate the estimated delivery time;

[0011] Compare the estimated delivery time with the corresponding timeout threshold, and when the estimated delivery time exceeds the timeout threshold, remind the current delivery person and / or the management that there is a timeout risk.

[0012] Further, the dynamically calculating the timeout threshold for each delivery order based on a preset intelligent algorithm includes:

[0013] Detect in real time the traffic condition information corresponding to the delivery order, rate the current traffic condition of the delivery route, and the current traffic condition level includes: smooth, slightly congested, moderately congested, and severely congested;

[0014] If the current traffic condition level is smooth, keep the current timeout threshold of the delivery order unchanged;

[0015] If the current traffic condition level is slightly congested, moderately congested, or severely congested, increase the timeout threshold duration accordingly according to the congestion level.

[0016] Further, the dynamically calculating the timeout threshold for each delivery order based on a preset intelligent algorithm includes:

[0017] Obtain the weather type of the corresponding area of the delivery order, where the weather type includes: sunny weather, rain and snow weather, and extreme weather;

[0018] If the weather type is sunny weather, keep the current timeout threshold of the delivery order unchanged;

[0019] If the weather type is rain and snow weather or extreme weather, increase the timeout threshold duration accordingly according to the weather type.

[0020] Further, the dynamically calculating the timeout threshold for each delivery order based on a preset intelligent algorithm further includes:

[0021] Obtain historical delivery time data, where the historical delivery time data includes: the historical delivery time data of the current delivery person and the historical delivery time data of the corresponding region of the delivery order;

[0022] If the historical delivery time duration of the current delivery person is greater than the average delivery time duration of the delivery person, then correspondingly reduce the timeout threshold of the current delivery order;

[0023] If the historical delivery time duration of the current delivery person is less than the average delivery time duration of the delivery person, then correspondingly increase the timeout threshold of the current delivery order;

[0024] If the historical delivery time duration of the corresponding region of the delivery order is greater than the average delivery time duration of all regions, then correspondingly increase the timeout threshold of the current delivery order;

[0025] If the historical delivery time duration of the corresponding region of the delivery order is less than the average delivery time duration of all regions, then correspondingly reduce the timeout threshold of the current delivery order.

[0026] Furthermore, the delivery order timeout management method further includes:

[0027] Obtain the attribute information of the delivered item, where the attribute information includes: fresh items, high-value items, and ordinary items;

[0028] If the delivered item is an ordinary item, keep the current timeout threshold of the delivery order unchanged;

[0029] If the delivered item is a fresh item or a high-value item, reduce the current timeout threshold duration of the delivery order and send timeout threshold change information to the current delivery person.

[0030] Furthermore, the delivery order timeout management method further includes:

[0031] Receive the delivery time change information of the user, where the delivery time change information includes: postponing the delivery time or specifying the delivery time;

[0032] According to the delivery time change information of the user, correspondingly adjust the timeout threshold of the delivery order based on the delivery time specified by the user.

[0033] Furthermore, the delivery order timeout management method further includes:

[0034] Obtain the continuous working duration of the current delivery person;

[0035] When the continuous working duration is greater than or equal to the preset time threshold, correspondingly increase the timeout threshold of the current delivery order.

[0036] Furthermore, the delivery order timeout management method further includes:

[0037] Store the full - life - cycle data of the delivery order in the blockchain. The full - life - cycle data includes: order unit data, overtime calculation evidence, and key operation logs;

[0038] The order unit data includes: order ID, user ID, hash value of the delivery address, and initial promised delivery time;

[0039] The overtime calculation evidence includes: input parameters of the dynamic threshold model, algorithm version, and calculation results. Among them, the input parameters include: real - time road condition score and weather impact factor;

[0040] The key operation logs include: digital signature records of user delivery time change requests, timestamp data of delivery staff location check - in, and triggering conditions of abnormal event markers.

[0041] Furthermore, storing the full - life - cycle data of the delivery order in the blockchain includes:

[0042] Store the delivery path trajectory and sensor raw stream data in the IPFS distributed storage system, and associate and store the corresponding content hash value and data fingerprint in the blockchain. The delivery path trajectory includes timestamp data of delivery staff location check - in, and the sensor raw stream data includes input parameters in the overtime calculation evidence;

[0043] Use the homomorphic encryption algorithm to encrypt user privacy data and then upload it to the chain, enabling chain nodes to verify the compliance of business logic based on ciphertext without exposing the plaintext. The user privacy data includes user ID, plaintext of the delivery address, and digital signature records of user delivery time change requests. The business logic includes: service area range verification and delivery time compliance determination.

[0044] Correspondingly, the second aspect of the embodiments of the present invention provides a delivery order overtime management system, which manages the overtime of delivery orders based on the above - mentioned delivery order overtime management method, including:

[0045] A delivery data acquisition module, which is used to acquire delivery data related to the delivery order in real - time. The delivery data includes: current traffic condition level, weather information, order priority information, and current real - time location information of the delivery staff;

[0046] An overtime threshold calculation module, which is used to dynamically calculate the overtime threshold of each delivery order based on the preset intelligent algorithm according to the delivery data. The overtime threshold is the system - allowed overtime duration for the delivery order to exceed the standard delivery time;

[0047] A delivery time prediction module, which is used to monitor the delivery progress of each delivery order in real - time and calculate the expected delivery time;

[0048] An overtime comparison reminder module, which is used to compare the estimated delivery time with the corresponding overtime threshold, and when the estimated delivery time exceeds the overtime threshold, remind the current delivery person and / or the management staff of the overtime risk.

[0049] Correspondingly, a third aspect of the embodiments of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned delivery order overtime management method.

[0050] Correspondingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the above-mentioned delivery order overtime management method is implemented.

[0051] The above technical solutions of the embodiments of the present invention have the following beneficial technical effects:

[0052] 1. By collecting multi-source data such as traffic conditions and weather in real time, the overtime threshold can be dynamically adjusted according to the actual delivery environment. In case of traffic congestion or bad weather, the threshold is increased accordingly, leaving enough time for the delivery person to cope. This avoids unreasonable overtime judgments caused by fixed standards, ensures that the delivery tasks can be advanced according to reasonable timeliness in complex environments, and improves the stability of the delivery service;

[0053] 2. Combining the historical performance of the delivery person and the delivery difficulty in the area, the overtime threshold is dynamically adjusted. If the delivery person is efficient, the threshold is appropriately increased as a reward, and if the efficiency is low, the threshold is reduced to encourage improvement; when the delivery area is difficult, the threshold is increased, and vice versa. In this way, individual and regional differences are taken into account, fully mobilizing the enthusiasm of the delivery person and improving the overall delivery efficiency;

[0054] 3. Considering the needs such as the change of the user's delivery time and the attributes of the delivered items, the overtime threshold is dynamically changed. When the user requests to postpone or specify the delivery time, and when delivering fresh or high-value items, the threshold is adjusted accordingly. This makes the delivery service more in line with the needs of users and items, enhances the user's satisfaction with the delivery service, and improves the quality and competitiveness of the delivery service. Description of the Drawings

[0055] Figure 1 is a flowchart of the delivery order overtime management method provided by the embodiments of the present invention;

[0056] Figure 2 is a block diagram of the delivery order overtime management system provided by the embodiments of the present invention.

[0057] Reference Signs:

[0058] 1. Delivery data acquisition module, 2. Overtime threshold calculation module, 3. Delivery time prediction module, 4. Overtime comparison and reminder module. Detailed implementation manners

[0059] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the detailed implementation manners and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0060] Please refer to Figure 1 , the first aspect of the embodiment of the present invention provides a method for managing overtime of delivery orders, including the following steps:

[0061] Step S100, obtain the delivery data related to the delivery order in real time, where the delivery data includes: the current traffic condition level, weather information, order priority information, and the real-time location information of the current delivery person.

[0062] By comprehensively collecting the key information affecting the delivery timeliness, for example: the current traffic condition level reflects the smoothness of road traffic, and different levels directly affect the delivery speed; the weather information covers conditions such as sunny, rainy, snowy, etc., and bad weather will hinder the delivery; the order priority information is used to distinguish the urgency of the order to ensure that important orders are processed first; the real-time location information of the current delivery person can track the delivery person's trajectory in real time, which is convenient for comprehensive analysis in combination with other information.

[0063] In addition to the above data, the delivery area information can also be collected, such as the city center and suburbs. The traffic rules and delivery distances are different in different areas. The weight and volume of the delivery items are collected. Loading, unloading and transporting large or heavy items take a long time. Historical delivery data is introduced to analyze the delivery duration under the same area, weather, and order type, which provides a reference for subsequent calculations. In terms of the data acquisition frequency, it is dynamically adjusted according to the changes in road conditions and weather. For example, during the traffic peak period or in bad weather, the acquisition frequency is increased from once every 5 minutes to once every 1 minute to ensure the real-time nature of the data.

[0064] Step S200, based on the delivery data, dynamically calculate the overtime threshold for each delivery order by means of a preset intelligent algorithm. The overtime threshold is the allowable overtime duration for the delivery order to exceed the standard delivery time.

[0065] Using the collected delivery data, through a preset intelligent algorithm, a reasonable overtime threshold is determined for each delivery order. This threshold cannot be fixed, but is dynamically adjusted according to the actual situation to ensure that it can not only meet the customer's expectations for the delivery time, but also take into account the impact of various objective factors on the delivery.

[0066] The preset intelligent algorithm can adopt machine learning algorithms, such as neural networks. First, it is trained with a large amount of historical delivery data to learn the association between different data combinations and reasonable overtime thresholds. For example, the appropriate increment of the overtime threshold in case of traffic congestion and bad weather. The algorithm needs to be updated regularly, combined with the latest delivery data, to optimize weights and parameters to adapt to changes in traffic conditions and weather patterns. Different weights are set for different types of orders. Fresh food orders are time-sensitive, and when calculating the overtime threshold, the weights of traffic conditions and weather are higher to ensure the freshness of the goods. At the same time, the historical performance of the delivery staff is considered. If a delivery staff has high delivery efficiency under similar conditions, the overtime threshold for the delivery orders assigned to him / her can be appropriately reduced to motivate excellent delivery staff.

[0067] Step S300, monitor the delivery progress of each delivery order in real time and calculate the estimated delivery time.

[0068] By tracking the delivery status of the delivery order in real time and combining information such as the real-time location and speed of the delivery staff, the estimated delivery time of the order can be calculated, enabling the delivery staff and management to keep track of the delivery dynamics of the order at any time. In addition, using real-time map technology, the location of the delivery staff and the delivery track of the order are intuitively displayed, facilitating monitoring by the management. Through Internet of Things devices, such as vehicle sensors and smart wearable devices, the real-time status of the delivery staff and delivery tools can be obtained, such as vehicle breakdowns and sudden situations of the delivery staff, and the estimated delivery time can be adjusted in a timely manner. Moreover, through the establishment of a real-time feedback mechanism, the delivery staff can manually update the delivery progress. For example, in case of sudden road control, timely feedback is provided to recalculate the estimated delivery time. When calculating the estimated delivery time, in addition to considering the current speed and distance, future traffic conditions and weather changes are also predicted, combined with the real-time forecast information of the traffic department and meteorological department, to make the estimated delivery time more accurate.

[0069] Step S400, compare the estimated delivery time with the corresponding overtime threshold, and when the estimated delivery time exceeds the overtime threshold, remind the current delivery staff and / or management of the overtime risk.

[0070] Compare the calculated estimated delivery time with the previously determined timeout threshold. Once the threshold is exceeded, immediately send a reminder so that the delivery staff and management can take timely measures, such as adjusting the route and coordinating resources, to reduce the risk of timeout. The reminder methods are diverse. On the delivery staff side, pop-up windows, voice broadcasts, etc. are used to ensure that the delivery staff can receive information in a timely manner during busy work. On the management side, reminders are sent via text messages, instant messaging software pushes, etc. Set up a multi-level reminder mechanism. When the estimated delivery time is close to the timeout threshold, a first-level reminder is sent, such as 30 minutes in advance; after exceeding the threshold, a second-level reminder is sent and solutions are provided, such as planning a new route according to the real-time traffic conditions. For delivery staff who have received timeout risk reminders multiple times, an analysis report is automatically generated to help them improve their delivery efficiency. At the same time, the reminder information includes order details, current location, remaining time, etc., to facilitate relevant personnel to make quick decisions.

[0071] In the above technical solution for delivery order management, the dynamic adjustment of the timeout threshold plays a key role in reasonably judging whether the order delivery is timed out, and can effectively avoid making unreasonable demands on the delivery staff and improve the overall quality and efficiency of the delivery service. Among them, the dynamic adjustment trigger mechanism of the timeout threshold includes real-time data detection trigger and order status change trigger. Real-time data monitoring trigger: Continuously monitor the current traffic condition level, weather information, order priority information, and the real-time location information of the delivery staff in real time. Once any of these data changes significantly, it will trigger the recalculation of the timeout threshold. For example, when the traffic condition suddenly changes from smooth to severely congested, or the weather changes from sunny to heavy rain or other bad weather, the dynamic adjustment process will be immediately started to ensure that the timeout threshold can adapt to the new delivery environment in a timely manner. Order status change trigger: When the status of the delivery order changes, such as the delivery staff's pick-up delay, or the delivery is interrupted due to an unexpected event, it will also trigger the adjustment of the timeout threshold. Suppose the delivery staff finds that the time for the merchant to prepare the goods exceeds the expected time when picking up the goods. At this time, based on this situation and combined with other relevant information, the timeout threshold for this order will be re-evaluated and adjusted.

[0072] Meanwhile, the basis for the dynamic adjustment of the timeout threshold includes: traffic conditions, weather conditions, order priority, and the real-time location of the delivery person. Different levels of traffic conditions have a huge impact on the delivery speed. In severely congested sections, vehicles move slowly, and the delivery time will increase significantly. Therefore, according to real-time traffic information, when the traffic condition becomes congested, the timeout threshold will be increased accordingly. For example, during the morning rush hour in the city, when a certain section is severely congested, based on historical data and real-time traffic prediction, the timeout threshold for delivery orders passing through this section will be extended by 20 - 30 minutes to account for the time loss caused by traffic congestion. Severe weather such as heavy rain, heavy snow, and strong winds will seriously affect the delivery efficiency. Taking heavy rain as an example, water accumulation on the road may cause the vehicle speed to decrease, and it will also be more difficult for the delivery person to ride or walk. The timeout threshold will be dynamically increased according to the severity of the weather. In case of heavy rain, the timeout threshold may be extended by 15 - 25 minutes to ensure that the delivery person has enough time to complete the delivery task safely. For orders with higher priorities, such as urgent orders or time-sensitive fresh food orders, on the basis of ensuring a reasonable delivery time, the timeout threshold will be appropriately shortened to ensure that these important orders can be delivered to customers promptly and preferentially. On the contrary, for ordinary orders in case of complex delivery situations, the timeout threshold may be relatively extended. The real-time location of the delivery person combined with the characteristics of the delivery area, such as poor road conditions and long delivery distances in remote areas, will affect the timeout threshold. If the delivery person is in a remote area with inconvenient transportation, the timeout threshold will be increased accordingly.

[0073] Specifically, the dynamic calculation of the timeout threshold for each delivery order in step S200 includes:

[0074] Step S211, real-time detection of the traffic road condition information corresponding to the delivery order, rating the current traffic condition of the delivery route, and the current traffic condition levels include: smooth, slightly congested, moderately congested, and severely congested.

[0075] With the help of traffic data collection, such as traffic condition monitoring based on GPS positioning, camera image analysis, and traffic department data sharing, etc., obtain the real-time traffic condition of the delivery route, and clearly divide the traffic condition according to preset rules. Dividing the traffic condition into four levels: smooth, slightly congested, moderately congested, and severely congested can provide an accurate basis for subsequent timeout threshold adjustment and achieve refined management of delivery timeliness.

[0076] When collecting traffic condition information, not only the main roads are concerned, but also the information of branch roads and small roads is deeply obtained. Because sometimes deliverymen will choose branch roads to avoid congestion, and the traffic conditions of branch roads will also affect the delivery time. The multi-source data fusion technology is adopted, combined with map navigation data, social media traffic reports, real-time announcements from the traffic police department, etc., to improve the accuracy of traffic condition information. For example, in the event of a sudden traffic accident, the news may be first spread on social media. Combining it with map navigation data can help judge the traffic conditions more quickly and accurately. In terms of traffic condition rating, a dynamic rating model is established, comprehensively considering the real-time traffic flow of the road, the changing trend of vehicle speed, and the impact of temporary events such as accidents or construction.

[0077] In addition, in addition to the conventional congestion level division, for special traffic control, such as road restrictions around large-scale events, a special "traffic control" level can also be set to facilitate subsequent targeted adjustment of the overtime threshold.

[0078] Step S212, if the current traffic condition level is smooth, keep the current overtime threshold of the delivery order unchanged.

[0079] When the traffic condition is in a smooth state, it means that the delivery vehicle or personnel can drive at a normal speed, and the delivery process is basically not hindered by traffic. In this case, the currently set overtime threshold is sufficient to meet the delivery requirements and does not need to be adjusted. Maintaining the status quo can ensure the stability and coherence of the delivery time management.

[0080] Even if the traffic condition is smooth, other monitoring dimensions can be introduced to assist in judging whether to adjust the overtime threshold. Analyze the historical traffic data of the delivery area. If the area often becomes congested after a specific time period, the overtime threshold can be slightly adjusted in advance to reserve a buffer time. At the same time, combined with the historical delivery data of the deliverymen, if a certain deliveryman's delivery speed has always been higher than the average level on this smooth section, appropriately shorten the overtime threshold of his delivery order to encourage excellent performance; on the contrary, if the deliveryman often causes the delivery speed to be lower than the normal level due to his own reasons, reminders can be given or the threshold can be appropriately adjusted. The priority of the delivery order can also be concerned. For orders with high priority, even if the traffic condition is smooth, the overtime threshold can be appropriately shortened to ensure priority delivery.

[0081] Step S213, if the current traffic condition level is mild congestion, moderate congestion or severe congestion, increase the overtime threshold duration accordingly according to the congestion level.

[0082] When traffic congestion occurs, the actual delivery duration will be extended due to slow vehicle driving and increased waiting time. Increasing the overtime threshold duration according to different congestion levels can more reasonably reflect the increase in delivery difficulty, avoid unreasonable overtime judgments for deliverymen due to traffic congestion, and ensure that the delivery tasks are completed within a more realistic time frame.

[0083] When determining the increased timeout threshold duration, an intelligent calculation model based on historical data and real-time monitoring is constructed. The average delay time data of different road sections and different time periods under different congestion levels are collected. Combining with the real-time dynamic changes of the current congestion, such as the length of the congested road section and the estimated clearance time, etc., the increased amount of the timeout threshold is accurately calculated. For mild congestion, in addition to increasing the basic duration, if the congested road section is short and the estimated clearance time is not long, it can be decided whether to slightly increase the threshold only according to the distance between the current location of the delivery person and the congested road section. For moderate congestion, in addition to increasing the regular duration, if there are alternative routes around, the road conditions and distances of the alternative routes are automatically evaluated, and combined with the time required to switch routes, the increased amount of the timeout threshold is comprehensively calculated. For severe congestion, in addition to significantly increasing the timeout threshold, additional resource support is considered, such as arranging other delivery persons to assist in relay delivery, and at the same time adjusting the overall delivery plan.

[0084] Furthermore, the timeout threshold of each delivery order dynamically calculated based on the preset intelligent algorithm in step S200 includes:

[0085] Step S221, obtain the weather type of the region corresponding to the delivery order. The weather types include: clear weather, rain and snow weather, and extreme weather.

[0086] Accurately master the weather conditions in the area where the delivery order is located, so as to reasonably adjust the timeout threshold according to the weather conditions subsequently. By establishing a real-time data interface with authoritative meteorological data platforms, such as the data services of the China Meteorological Administration and the APIs of professional meteorological service providers, accurate weather information corresponding to the delivery order can be obtained. The weather types are classified into clear weather, rain and snow weather, and extreme weather, providing a clear classification basis for adjusting the timeout threshold under different weather conditions, so as to achieve refined operation of delivery timeliness management. On the basis of obtaining the weather type, further obtain the detailed parameters of the weather. For rain and snow weather, obtain information such as precipitation, snowfall, and the duration of rainfall or snowfall. These parameters will directly affect the difficulty and time of delivery. For extreme weather, not only the weather type should be clarified, such as typhoon, red alert for heavy rain, heavy snow, etc., but also information such as the disaster warning level, the estimated impact range, and the duration should be obtained. In addition, combining with geographical location information, consider the differences in infrastructure and capabilities in different regions to cope with different weather. For example, in some areas with imperfect drainage, even light rain may cause serious road waterlogging and affect delivery. At this time, it is necessary to more carefully evaluate the impact of the weather on delivery. At the same time, use the correlation analysis of historical weather data and delivery data to understand the delivery delay rules in a specific area under a certain weather type, providing a more sufficient basis for subsequent threshold adjustment.

[0087] Step S222, if the weather type is clear weather, keep the current timeout threshold of the delivery order unchanged.

[0088] Fine weather usually creates an ideal environment for delivery. Vehicle driving and personnel movement are basically not interfered by weather factors, and delivery personnel can complete the delivery tasks at normal speeds and along normal routes. In this case, the currently set overtime threshold is sufficient to meet the delivery requirements. Maintaining the status quo can ensure the stability and coherence of delivery time management and avoid the increase in management costs caused by unnecessary threshold adjustments.

[0089] Step S223: If the weather type is rainy / snowy weather or extreme weather, increase the overtime threshold duration accordingly based on the weather type.

[0090] Rainy / snowy weather and extreme weather pose many challenges to delivery, such as reduced vehicle driving speed due to slippery roads, blocked visibility affecting driving safety, and extreme weather may cause temporary road control or interruption, etc. These factors will significantly extend the delivery time. Increasing the overtime threshold duration accordingly according to different weather types can more reasonably reflect the increase in delivery difficulty, avoid unreasonable overtime judgments for delivery personnel due to bad weather, and ensure that the delivery tasks are completed within a more realistic time frame.

[0091] When increasing the overtime threshold duration according to the weather type, establish a quantitative model based on the severity of the weather and historical delivery data. For rainy / snowy weather, divide different levels according to the amount of precipitation or snowfall, and each level corresponds to a different increase range of the overtime threshold. For example, increase the overtime threshold by 10 - 15 minutes for light rain weather, and increase it by 20 - 30 minutes for moderate to heavy rain weather. At the same time, combine historical delivery data, analyze the average delay time in different delivery areas under different amounts of precipitation or snowfall, and optimize the increased amount of the overtime threshold.

[0092] Furthermore, the dynamic calculation of the overtime threshold for each delivery order based on the preset intelligent algorithm in step S200 further includes:

[0093] Step S231: Obtain historical delivery time data, which includes the historical delivery time data of the current delivery personnel and the historical delivery time data of the corresponding regions of the delivery orders.

[0094] By collecting historical data that can reflect delivery efficiency and difficulty, it provides strong support for the dynamic calculation of the overtime threshold. The historical delivery time data of the current delivery personnel reflects the individual delivery ability and efficiency level of the delivery personnel, and can help understand their delivery habits and stability; the historical delivery time data of the corresponding regions of the delivery orders reflects the impact of the delivery environment in a specific region on the delivery duration, such as the delivery difficulty differences caused by factors such as the traffic conditions, infrastructure, and population density in that region. By obtaining these two types of data, it can be comprehensively considered from the individual and regional dimensions to calculate the overtime threshold more accurately.

[0095] In addition to simply obtaining historical delivery time data, the dimensions of the data should be refined. For the historical delivery time data of current delivery personnel, further analyze the delivery durations in different time periods (such as morning and evening rush hours on weekdays, holidays, etc.), different weather conditions, and different order types (ordinary orders, urgent orders, etc.), so as to more pertinently consider the impact of various factors on the efficiency of delivery personnel in subsequent calculations. For the historical delivery time data of the corresponding regions of delivery orders, in addition to the overall average delivery duration, it is also necessary to analyze the differences in delivery durations in different road sections, different communities or commercial areas within the region, because even in the same region, the delivery difficulties in different local areas may vary. In addition, regularly update the historical delivery time data to adapt to changes in the regional environment (such as the opening of new roads, changes in traffic rules) and the improvement or changes in the capabilities of delivery personnel themselves and other situations.

[0096] Step S232, if the historical delivery time of the current delivery personnel is longer than the average delivery time of the delivery personnel, then correspondingly reduce the overtime threshold of the current delivery order.

[0097] When it is found that the historical delivery time of the current delivery personnel is longer than the average delivery time of the delivery personnel, it means that the efficiency of this delivery personnel is relatively low in past delivery tasks. In order to encourage the delivery personnel to improve efficiency, correspondingly reduce the overtime threshold of their current delivery order. This operation puts a certain amount of pressure on the delivery personnel, prompting them to optimize the delivery process and plan a more reasonable route, thereby improving the delivery efficiency, and at the same time ensuring that the overall delivery service level will not be greatly affected by the low efficiency of individual delivery personnel.

[0098] When reducing the overtime threshold, adopt a strategy of gradually decreasing. When it is first found that the historical delivery time of the current delivery personnel is longer than the average time, moderately reduce the overtime threshold, such as reducing it by 10%. If this delivery personnel still fails to reach the average efficiency in subsequent delivery tasks, the range of reduction of the overtime threshold can be appropriately increased, such as reducing it by 15%-20%. At the same time, provide training resources and efficiency improvement suggestions for the delivery personnel, such as recommending efficient delivery route planning methods, time management skills, etc. When the delivery personnel complete the delivery tasks on time for several consecutive times and their efficiency has improved, the overtime threshold can be appropriately adjusted back as a certain reward to encourage them to maintain good performance.

[0099] Step S233, if the historical delivery time of the current delivery personnel is shorter than the average delivery time of the delivery personnel, then correspondingly increase the overtime threshold of the current delivery order.

[0100] If the current delivery person's historical delivery time is less than the average delivery person's delivery time, it indicates that the delivery person has a high delivery efficiency and has performed well in past delivery tasks. Increasing the timeout threshold of the current delivery order accordingly is a recognition and reward for the delivery person's excellent performance. At the same time, considering that there may be various unforeseen factors in the delivery process, appropriately increasing the timeout threshold can give the delivery person a certain buffer time to avoid unreasonable timeout judgments due to occasional unexpected situations, thereby protecting the delivery person's work enthusiasm.

[0101] The extent of the increase in the timeout threshold can be dynamically adjusted according to the extent to which the delivery person is below the average time. If the delivery person's historical delivery time is significantly below the average time, such as less than 20%, the timeout threshold can be increased significantly, such as by 20-30 minutes. In addition to increasing the timeout threshold, delivery persons can also be given other rewards, such as points, bonuses, or the right to choose orders first. When the delivery person's delivery time in subsequent delivery tasks approaches or exceeds the newly added timeout threshold continuously, the timeout threshold can be gradually lowered to remind the delivery person to maintain an efficient working state.

[0102] Step S234: If the historical delivery time of the region corresponding to the delivery order is longer than the average delivery time of all regions, the timeout threshold of the current delivery order is increased accordingly.

[0103] When the historical delivery time of the corresponding region of the delivery order is longer than the average delivery time of all regions, it means that the delivery difficulty in this region is relatively large, and there may be frequent traffic congestion, poor road conditions, scattered delivery addresses and other problems. In order to more reasonably reflect the actual delivery situation in this region, the timeout threshold of the current delivery order is increased accordingly, so that the delivery personnel have more time to deliver in this region, avoid unreasonable timeout judgments due to objective regional factors, and ensure that the delivery task can be completed smoothly.

[0104] In addition to directly increasing the timeout threshold, delivery personnel can also be provided with delivery strategies for the region, such as recommending the best delivery routes at different time periods, notifying sections of roads where traffic problems often occur and how to deal with them, etc. At the same time, the timeout threshold is increased in stages according to the extent to which the historical delivery time in the region exceeds the average time. If the average time is exceeded by 10%-20%, the timeout threshold is increased by 15-20 minutes; if it exceeds by more than 20%, the timeout threshold is increased by 25-30 minutes.

[0105] Step S235: If the historical delivery time of the region corresponding to the delivery order is less than the average delivery time of all regions, the timeout threshold of the current delivery order is reduced accordingly.

[0106] If the historical delivery time of a delivery order in a corresponding region is less than the average delivery time of all regions, it indicates that the delivery environment in this region is relatively good and the delivery difficulty is relatively low. To improve the overall delivery efficiency, the current overtime threshold of the delivery order is correspondingly reduced, prompting the delivery staff to maintain high-efficiency delivery in a relatively smooth delivery environment, make full use of the advantageous resources in this region, and at the same time ensure the fairness and efficiency balance of the delivery service among different regions.

[0107] While reducing the overtime threshold, a reward mechanism can be set up to encourage the delivery staff to actively accept orders in this region. When the delivery staff continuously completes a certain number of delivery tasks on time in this region, additional rewards are given, such as increasing the order commission ratio. For delivery staff newly entering this region for delivery, an appropriate adaptation period can be given. During the adaptation period, the reduction range of the overtime threshold is relatively small. After they are familiar with the regional environment, the overtime threshold is reduced according to the normal standard.

[0108] In addition, the delivery order overtime management method further includes:

[0109] Step S241, obtaining the attribute information of the delivered item. The attribute information includes: fresh items, high-value items, and ordinary items.

[0110] By clarifying the specific attributes of the delivered item, through the item information database or order details information in it, accurately identify whether the delivered item belongs to a fresh item, a high-value item, or an ordinary item. This information is crucial for subsequent overtime threshold adjustment because items with different attributes have different requirements and restrictions on delivery, which will affect the delivery time and service quality. The attribute information of the delivered item will become a key consideration factor for subsequent overtime threshold adjustment, and these information need to be accurately identified and classified to formulate reasonable delivery time management strategies for items with different attributes.

[0111] In addition to simply classifying items into fresh items, high-value items, and ordinary items, the classification of item attributes can be further refined. For fresh items, according to the preservation requirements, they can be divided into room-temperature fresh, refrigerated fresh, frozen fresh, etc., because fresh items with different preservation requirements have different time sensitivities during delivery. For example, frozen fresh items have more stringent requirements for delivery time. For high-value items, they can be further divided according to the value size or vulnerability degree. For example, high-value items can be divided into precious jewels, high-end electronic products, artworks, etc., because different high-value items require different safety and time guarantees during transportation. At the same time, the volume and weight information of the item can also be considered because these factors may affect the loading and unloading time and transportation speed, and thus affect the delivery duration.

[0112] Step S242, if the delivered item is an ordinary item, keep the current overtime threshold of the delivery order unchanged.

[0113] Ordinary items usually have relatively low sensitivity to delivery time, and their delivery process generally does not have special time restrictions due to the characteristics of the items themselves. Therefore, based on the current delivery environment and order situation, the existing overtime thresholds can already meet the normal requirements of delivery, without the need for additional adjustment, avoiding the irrationality of adopting a unified overtime threshold for all items, and helping to allocate resources and energy reasonably.

[0114] Even for ordinary items, further refined management can be carried out according to other conditions. For example, if the order volume of ordinary items is large and reaches a certain scale, the overtime threshold can be slightly adjusted according to the size and concentration of the order volume to improve the overall delivery efficiency. The user requirements for ordinary items can also be considered. If the user has special requirements for the delivery time of ordinary items in the order, the overtime threshold can be appropriately adjusted according to the user's needs, incorporating the user's needs into the considerations of overtime management.

[0115] Step S243, when the delivered item is a fresh food item or a high-value item, reduce the current overtime threshold duration of the delivery order and send the overtime threshold change information to the current delivery person.

[0116] Due to the characteristics of freshness preservation and perishability of fresh food items, and the high value and possible vulnerability of high-value items, they need to be delivered within a shorter time to ensure their quality or value. Therefore, it is necessary to shorten the overtime threshold duration. This helps to ensure the quality of the delivery service and customer satisfaction, and reduces the risks brought by delivery delays. Sending the overtime threshold change information to the current delivery person is to enable the delivery person to clearly know the time urgency of their delivery tasks, so that they can reasonably arrange the delivery progress, make preparations in advance, and avoid delivery delays caused by the adjustment of the overtime threshold.

[0117] Furthermore, the delivery order overtime management method further includes:

[0118] Step S251, receive the delivery time change information from the user, and the delivery time change information includes: postponing the delivery time or specifying the delivery time.

[0119] In order to meet the personalized needs of users in terms of delivery time, it is necessary to be able to receive and process the delivery time change information sent by users. Users may, due to their own arrangements, itinerary changes or other reasons, hope to adjust the delivery time, and such adjustments include postponing the originally planned delivery time or directly specifying a new specific delivery time. By collecting the user's demand information, it is possible to better coordinate various resources, provide better services for users, and at the same time help to avoid delivery problems caused by mismatched user time, such as the inconvenience or loss caused by no one receiving the goods or the goods being stored for a long time.

[0120] In addition to the delivery time change information actively sent by the user, a more convenient time change operation interface can be provided, allowing users to easily modify the delivery time on the mobile or web side. At the same time, corresponding help and prompt information can be provided to guide users to reasonably set the delivery time and prevent users from setting unreasonable times (such as specifying a past time or a future time that is too far away). For the user's time change request, preliminary verification can be carried out. For example, it can be checked whether the time specified by the user is within an acceptable range (such as within the business hours of the merchant or within a reasonable delivery time period determined according to the item attributes), so as to improve the operability and overall efficiency of the delivery service. For some special situations, such as when users frequently modify the delivery time, corresponding rules or restrictions can be set. For example, a limit on the number of times users are allowed to modify within a certain time can be set to prevent users from abusing this function and affecting the normal delivery arrangements of other users or imposing too much burden on the delivery service.

[0121] Step S252: According to the user's delivery time change information, correspondingly adjust the timeout threshold of the delivery order based on the delivery time specified by the user.

[0122] After receiving the user's delivery time change information, it is necessary to adjust the timeout threshold according to the new delivery time specified by the user. In the case of delaying the delivery time, the start time of the timeout threshold should be postponed accordingly to ensure that the delivery staff has enough time to complete the delivery task. In the case of specifying the delivery time, it is necessary to recalculate the timeout threshold according to the new time, considering the duration from the current time to the specified delivery time, as well as other factors affecting the delivery (such as traffic, weather, item attributes, etc.), to ensure that the delivery task is completed within the new time frame. Correspondingly, the coherence and rationality of the delivery service are ensured, making the timeout management of the delivery closely combined with the user's needs, avoiding unreasonable timeout judgments caused by user time changes, and at the same time enabling the delivery staff to more clearly understand their task time requirements under the new time arrangement.

[0123] When adjusting the timeout threshold, the user's historical behavior and order information can be combined. If a user often modifies the delivery time, the timeout threshold can be adjusted more precisely according to the user's behavior pattern. For example, for users who often delay the delivery time, the adjustment range of the timeout threshold can be appropriately increased to take into account possible subsequent changes. For users who rarely modify the time, the adjustment can be more compact to ensure the overall delivery efficiency. When adjusting the timeout threshold, other factors need to be comprehensively considered, such as the merchant's preparation time and the current task arrangement of the delivery staff. If the user delays the delivery time, but the merchant's preparation time may be long, the timeout threshold needs to be reasonably adjusted according to the merchant's preparation progress. At the same time, considering that the delivery staff may have already arranged other tasks, when reallocating resources and adjusting the timeout threshold, it is necessary to ensure that the scheduling of the entire delivery task is optimal to avoid affecting the normal delivery of other orders.

[0124] Furthermore, the delivery order timeout management method further includes:

[0125] Step S261, obtaining the consecutive working hours of the current deliveryman.

[0126] By monitoring the consecutive working hours of the deliveryman, his fatigue level and working status can be evaluated. By recording the time from the start of work to the current time of the deliveryman, the working burden of the deliveryman can be understood. This is an important data indicator because working continuously for too long may cause the deliveryman to be fatigued, which in turn affects the delivery efficiency and service quality, and may also pose safety hazards. It is necessary to accurately track the working status of the deliveryman in some way, perhaps starting to time from when the deliveryman logs in to work, or calculating the consecutive working hours based on the time when he receives and completes orders. This helps to make reasonable adjustments to the timeout threshold according to the working status of the deliveryman in the follow-up to ensure that the entire delivery process not only guarantees service quality but also pays attention to the working experience and safety of the deliveryman.

[0127] In addition to simply recording the consecutive working hours, the monitoring of working hours can be refined. For example, the consecutive working hours can be divided into different stages, such as short-term work (0 - 3 hours), medium-term work (3 - 6 hours), and long-term work (more than 6 hours), and the impact on the deliveryman can be evaluated according to different stages. At the same time, the working intensity of the deliveryman in different time periods can be considered. For example, during peak hours, even if the consecutive working hours are relatively short, the high-intensity work may still cause fatigue and requires additional attention. Combine other indicators to comprehensively evaluate the working status of the deliveryman, such as the number of orders completed, the distance traveled, and the complexity of the areas passed through by the deliveryman during consecutive working hours. A deliveryman may have a relatively long consecutive working hours, but if the number of orders is small or the travel distance is short, the fatigue level may not be high; conversely, even if the working hours are within an acceptable range, high-intensity order processing and long-distance driving may still make him feel fatigued.

[0128] The data of intelligent wearable devices or in-vehicle devices can also be introduced, such as heart rate monitoring, changes in driving speed, etc., to assist in judging the fatigue status of the deliveryman. When these devices show that the deliveryman is in a fatigued state, relevant adjustments can be made in advance even if the consecutive working hours have not reached the preset time threshold.

[0129] Step S262, when the consecutive working hours are greater than or equal to the preset time threshold, correspondingly increase the timeout threshold of the current delivery order.

[0130] When the continuous working hours of the delivery staff reach or exceed the preset time threshold, it means that they may be in a fatigued state, and their work efficiency and service quality may decline. At this time, increasing the overtime threshold of the current delivery order is a humanized management measure, aiming to give the delivery staff more time to complete the order, avoid the overtime risk caused by fatigue, and at the same time ensure the work safety and service quality of the delivery staff. It reflects the consideration of the individual state of the delivery staff in the management process, avoids imposing unreasonable delivery time requirements on fatigued delivery staff, and is conducive to improving the job satisfaction and overall service level of the delivery staff.

[0131] In addition, the delivery order overtime management method in the present invention further includes:

[0132] Step S500, storing the full life cycle data of the delivery order in the blockchain. The full life cycle data includes: order unit data, overtime calculation evidence, and key operation logs. Among them, the order unit data includes: order ID, user ID, delivery address hash value, and initial promised delivery time; the overtime calculation evidence includes: input parameters of the dynamic threshold model, algorithm version, and calculation results. The input parameters include: real-time road condition score and weather impact factor; the key operation logs include: digital signature records of user delivery time change requests, timestamp data of delivery staff location check-ins, and trigger conditions for abnormal event markers.

[0133] By storing the full life cycle data of the delivery order in the blockchain, an immutable, traceable, and highly transparent data evidence storage system is constructed, fundamentally solving the pain points of easy data loss, easy forgery, and difficult dispute traceability in traditional delivery management. The full life cycle data covers three core modules: order unit data, overtime calculation evidence, and key operation logs. Through the on-chain fixation and collaborative verification of multi-dimensional data, a full-link closed-loop evidence chain for dynamic overtime determination and performance process is formed.

[0134] Through the dual design of encrypted anchoring and privacy protection of the basic order information, after hashing the real delivery address of the user and then uploading it to the chain, it not only ensures the irreversible desensitization of the address data, but also provides a verifiable technical voucher for subsequent address disputes (such as "whether it exceeds the delivery scope") through the binding of the hash value and the blockchain timestamp. At the same time, the on-chain associated storage of the user ID and the order ID makes the entire order process traceable to the specific user, but through the permission control mechanism, it ensures that unauthorized parties cannot reverse-analyze the sensitive information of the user, achieving a balance between privacy and compliance.

[0135] The timeout determination logic of traditional delivery systems usually operates in a "black box" form, making it difficult to prove fairness when disputes occur. By synchronizing input parameters such as real-time traffic condition scores and weather impact factors, as well as algorithm versions and final calculation results onto the blockchain, every timeout determination can be verified through data tracing on the chain. For example, when a user questions a timeout penalty, the real-time traffic condition score and weather data stored on the chain can be retrieved, and combined with the historical code library of the algorithm version (such as the GitHub open-source version), to recalculate and verify the consistency of the results. This mechanism not only enhances the credibility of the system but also provides an objectively verifiable third-party basis for disputes between the platform and users.

[0136] The immutability of the blockchain locks in the authenticity of operation behaviors. When a user initiates a delivery time change request, a digital signature needs to be attached to ensure that the identity of the operating entity is verifiable and the request content is non-repudiable. The delivery person's location check-in data is stored in the form of a timestamp sequence, and any abnormal time jumps or location offsets can be quickly identified through data comparison on the chain. For example, if a delivery person claims a delay due to a traffic accident, the system can trigger conditions for abnormal event markings recorded on the chain (such as "sudden change in congestion level on the accident section"), and combined with the location check-in data for that period, automatically generate a credible delay certificate. This blockchain-based automated evidence collection mechanism significantly reduces the cost of manual verification and avoids the problem of easy forgery of traditional paper certificates.

[0137] The present invention further optimizes storage efficiency and privacy protection through a hybrid architecture of "blockchain + IPFS + homomorphic encryption". For large-volume and high-frequency data such as delivery route trajectories and sensor raw stream data, the IPFS distributed storage system is used for off-chain storage, and only the content hash value and data fingerprint are uploaded to the chain. For example, a path file formed by GPS trajectory points collected by a delivery person every 5 seconds (about 1GB) is stored in IPFS and generates a unique hash fingerprint QmTrajectoryHash and uploaded to the chain, which not only avoids blockchain storage expansion but also ensures that data integrity is verifiable. For privacy data such as user mobile phone numbers and detailed addresses, homomorphic encryption technology is used to encrypt and upload to the chain, enabling chain nodes to directly verify business logic (such as "whether the user is within the service area") based on ciphertext without decrypting the plaintext, eliminating the risk of privacy leakage from the technical bottom layer.

[0138] Based on the above implementation methods, the present invention has the following technical effects: First, the data credibility has been significantly improved. Through the distributed consensus mechanism of the blockchain, the immutability of all key operations and calculation results during the order life cycle is ensured. Second, the efficiency of dispute resolution has been doubled. The standardized data structures (such as timestamp sequences, digital signatures) stored on the chain can be directly accepted by the judicial system, significantly shortening the dispute handling cycle. Third, the system scalability has been enhanced. The introduction of IPFS and homomorphic encryption enables the solution to adapt to the high-concurrency processing requirements of a large number of orders, while meeting the requirements of strict data privacy regulations such as GDPR, providing a reusable technical framework for compliant operation in the global logistics scenario.

[0139] Further, storing the full life cycle data of the delivery order in the blockchain in step S500 includes:

[0140] Step S510, storing the delivery path trajectory and the original sensor stream data in the IPFS distributed storage system, and associatively storing the corresponding content hash value and data fingerprint in the blockchain. The delivery path trajectory includes the timestamp data of the delivery person's location check-in, and the original sensor stream data includes the input parameters in the overtime calculation evidence.

[0141] By reconstructing the blockchain storage logic through the "data partitioning" strategy, in the traditional solution, due to the large amount of data in the delivery path trajectory (such as GPS coordinates updated every second) and the original sensor stream data (such as in-vehicle camera videos, real-time temperature and humidity monitoring records), directly uploading the data to the chain would cause congestion in the blockchain network and a sharp increase in costs. The present invention cuts such data into manageable data blocks and stores them in the IPFS distributed network, and only writes the content hash value and data fingerprint (such as the Merkle tree root hash) into the blockchain. For example, the timestamp data of the delivery person's location check-in uploaded every 30 seconds forms a continuous trajectory file. After being stored in IPFS, a unique hash identifier QmPathHash is generated, and only QmPathHash and the data block verification information are stored on the chain. When a dispute occurs, the original file can be retrieved from IPFS through the hash value, and verifying the hash consistency can prove that the data has not been tampered with. This design not only reduces the blockchain storage cost by more than 90%, but also ensures the persistent availability of data through the distributed redundancy feature of IPFS - even if some nodes fail, the complete data can still be restored through other nodes.

[0142] Step S520, encrypting the user privacy data with a homomorphic encryption algorithm and then uploading it to the chain, enabling the nodes on the chain to verify the compliance of the business logic based on the ciphertext without exposing the plaintext. The user privacy data includes the user ID, the plaintext of the delivery address, and the digital signature record of the user's delivery time change request. The business logic includes: service area range verification and delivery timeliness compliance determination.

[0143] If user privacy data (such as user ID, plaintext delivery address, digital signature in time change request) is uploaded to the blockchain in plaintext, it may be stolen by malicious nodes and used for precision marketing or fraud. The present invention uses a homomorphic encryption algorithm (such as Paillier algorithm) to encrypt the privacy data, so that the encrypted ciphertext still supports specific operations (such as range comparison, logical judgment). For example, the plaintext delivery address "No. 1 XX Road, Haidian District, Beijing" is encrypted into the ciphertext "E(Addr)", and the nodes on the blockchain can verify whether "E(Addr) is within the service area E(ServiceArea)" without decrypting - directly comparing the encrypted spatial coordinate ranges through homomorphic operations. Based on the above mechanism, the user's real information is encrypted throughout the process, completely eliminating the risk of privacy leakage on the blockchain; moreover, the business verification logic (such as area verification, timeliness determination) can still be executed based on the ciphertext, avoiding the performance loss and security vulnerabilities of the "decryption - calculation - re - encryption" in traditional encryption schemes.

[0144] Steps S510 and S520 respectively propose differentiated on - chain and off - chain collaborative storage and encryption verification strategies for large - volume business data and user privacy data, solving the core contradiction in traditional blockchain solutions of high storage costs, difficulty in reconciling privacy leakage risks with business verification requirements, and providing a breakthrough technical path for trusted digital management in the logistics field.

[0145] IPFS undertakes the storage of large - volume data, enabling the blockchain to only process lightweight hash values and metadata, increasing the system throughput by 5 - 10 times, and being applicable to high - concurrency scenarios with millions of orders per day; the IPFS hash anchoring ensures the integrity and verifiability of business data, and homomorphic encryption enables privacy data to be "verifiable but invisible", meeting strict data compliance requirements such as GDPR and CCPA; the structured evidence stored on the blockchain (such as timestamp sequences, encryption operation logs) can be directly connected to smart contracts to trigger preset compensation or arbitration rules. For example, when the time contradiction between sensor data and the delivery person's location trajectory exceeds the threshold, the order is frozen and an investigation procedure is initiated, with the efficiency increased by more than 80% compared to manual verification. Taking the fresh food delivery scenario as an example, if a user complains that the cold chain transportation is overdue and the goods are spoiled, the platform can quickly retrieve the IPFS trajectory hash QmPathHash stored on the blockchain, restore the real - time location of the delivery person and the data stream of the temperature and humidity sensor, and at the same time verify whether the delivery address belongs to the promised service area based on the homomorphic ciphertext. The whole process requires no manual intervention, and all evidence chains meet relevant electronic evidence - storing standards. This technical solution not only redefines the trusted boundary of logistics data management, but also provides a standardized data interface for related derivative services, promoting the digital transformation of the logistics industry from "experience - driven" to "data - driven".

[0146] Correspondingly, please refer to Figure 2, the second aspect of the embodiments of the present invention provides a delivery order overtime management system, which manages the delivery order overtime based on the above-mentioned delivery order overtime management method, including:

[0147] A delivery data acquisition module 1, which is used to obtain the delivery data related to the delivery order in real time. The delivery data includes: the current traffic condition level, weather information, order priority information, and the real-time location information of the current delivery person;

[0148] An overtime threshold calculation module 2, which is used to dynamically calculate the overtime threshold for each delivery order based on the preset intelligent algorithm according to the delivery data. The overtime threshold is the system-allowed overtime duration when the delivery order exceeds the standard delivery time;

[0149] A delivery time prediction module 3, which is used to monitor the delivery progress of each delivery order in real time and calculate the estimated delivery time;

[0150] An overtime comparison and reminder module 4, which is used to compare the estimated delivery time with the corresponding overtime threshold, and when the estimated delivery time exceeds the overtime threshold, remind the current delivery person and / or the management personnel of the overtime risk.

[0151] Correspondingly, the third aspect of the embodiments of the present invention provides an electronic device, including: at least one processor; and a memory connected to at least one processor; wherein, the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to execute the above-mentioned delivery order overtime management method.

[0152] Correspondingly, the fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the above-mentioned delivery order overtime management method is implemented.

[0153] The embodiments of the present invention aim to protect a delivery order overtime management method and system. The method includes the following steps: obtaining the delivery data related to the delivery order in real time, where the delivery data includes: the current traffic condition level, weather information, order priority information, and the real-time location information of the current delivery person; dynamically calculating the overtime threshold for each delivery order based on the preset intelligent algorithm according to the delivery data; monitoring the delivery progress of each delivery order in real time and calculating the estimated delivery time; comparing the estimated delivery time with the corresponding overtime threshold, and when the estimated delivery time exceeds the overtime threshold, reminding the current delivery person and / or the management personnel of the overtime risk. The above technical solutions have the following effects:

[0154] 1. By collecting multi-source data such as real-time traffic conditions and weather, the overtime threshold can be dynamically adjusted according to the actual delivery environment. In case of traffic congestion or bad weather, the threshold is increased accordingly, leaving sufficient time for the delivery staff to respond. This avoids unreasonable overtime judgments caused by fixed standards, ensures that delivery tasks can be advanced according to reasonable timeliness in complex environments, and improves the stability of the delivery service;

[0155] 2. Combine the historical performance of the delivery staff and the delivery difficulty of the area where they are located to dynamically adjust the overtime threshold. If the delivery staff is efficient, the threshold is appropriately increased as a reward; if the efficiency is low, the threshold is decreased to encourage improvement. When the delivery area is difficult, the threshold is increased, and vice versa. In this way, individual and regional differences are taken into account, fully mobilizing the enthusiasm of the delivery staff and improving the overall delivery efficiency;

[0156] 3. Consider the needs of users such as changes in delivery time and the attributes of the delivered items, and dynamically change the overtime threshold. When the user requests to postpone or specify the delivery time, and when delivering fresh or high-value items, the threshold is adjusted accordingly. This makes the delivery service more in line with the needs of users and items, enhances users' satisfaction with the delivery service, and improves the quality and competitiveness of the delivery service.

[0157] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in one or more processes Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0159] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the processes Figure 1One or more processes and / or boxes Figure 1 The functions specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 One or more processes and / or boxes Figure 1 One or more boxes.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for managing delivery order timeouts, characterized in that, It includes the following steps: Obtain the delivery data related to the delivery order in real time. The delivery data includes: the current traffic condition level, weather information, order priority information, and the real-time location information of the current delivery person; Based on the delivery data, dynamically calculate the overtime threshold for each delivery order based on a preset intelligent algorithm. The overtime threshold is the system-allowed overtime duration for the delivery order to exceed the standard delivery time; Monitor the delivery progress of each delivery order in real time and calculate the estimated delivery time; Compare the estimated delivery time with the corresponding overtime threshold. When the estimated delivery time exceeds the overtime threshold, remind the current delivery person and / or the management that there is an overtime risk.

2. The delivery order overtime management method according to claim 1, characterized in that The dynamically calculating the overtime threshold for each delivery order based on a preset intelligent algorithm includes: Detect the traffic condition information corresponding to the delivery order in real time, rate the current traffic condition of the delivery route. The current traffic condition level includes: smooth, slightly congested, moderately congested, and severely congested; If the current traffic condition level is smooth, keep the current overtime threshold of the delivery order unchanged; If the current traffic condition level is slightly congested, moderately congested, or severely congested, increase the overtime threshold duration accordingly according to the congestion level.

3. The delivery order timeout management method according to claim 1, wherein, The dynamically calculating the overtime threshold for each delivery order based on a preset intelligent algorithm includes: Obtain the weather type of the corresponding area of the delivery order. The weather type includes: clear weather, rain and snow weather, and extreme weather; If the weather type is clear weather, keep the current overtime threshold of the delivery order unchanged; If the weather type is rain and snow weather or extreme weather, increase the overtime threshold duration accordingly according to the weather type.

4. The delivery order overtime management method according to claim 1, characterized in that The dynamically calculating the overtime threshold for each delivery order based on a preset intelligent algorithm further includes: Obtain the historical delivery time data. The historical delivery time data includes: the historical delivery time data of the current delivery person and the historical delivery time data of the corresponding area of the delivery order; If the historical delivery time duration of the current delivery person is greater than the average delivery time of the delivery person, reduce the overtime threshold of the current delivery order accordingly; If the historical delivery time duration of the current delivery person is less than the average delivery time of the delivery person, increase the overtime threshold of the current delivery order accordingly; If the historical delivery time duration of the corresponding area of the delivery order is greater than the average delivery time of all areas, increase the overtime threshold of the current delivery order accordingly; If the historical delivery time duration of the corresponding area of the delivery order is less than the average delivery time of all areas, reduce the overtime threshold of the current delivery order accordingly.

5. The delivery order timeout management method according to any one of claims 1-4, characterized in that, It also includes: Obtain the attribute information of the delivery item. The attribute information includes: fresh food item, high-value item, and ordinary item; If the delivery item is an ordinary item, keep the current overtime threshold of the delivery order unchanged; If the delivery item is a fresh food item or a high-value item, reduce the current overtime threshold duration of the delivery order and send the overtime threshold change information to the current delivery person.

6. The delivery order timeout management method according to any one of claims 1-4, characterized in that, It also includes: Receive the delivery time change information from the user. The delivery time change information includes: delaying the delivery time or specifying the delivery time; Based on the delivery time change information of the user, the timeout threshold of the delivery order is correspondingly adjusted based on the user-specified delivery time.

7. The delivery order timeout management method according to any one of claims 1-4, characterized in that It further includes: Obtain the continuous working duration of the current delivery person; When the continuous working duration is greater than or equal to the preset time threshold, the timeout threshold of the current delivery order is correspondingly increased.

8. The delivery order timeout management method according to any one of claims 1-4, characterized in that It further includes: Store the full life cycle data of the delivery order in the blockchain, and the full life cycle data includes: order unit data, timeout calculation evidence, and key operation logs; The order unit data includes: order ID, user ID, delivery address hash value, and initial promised delivery time; The timeout calculation evidence includes: input parameters of the dynamic threshold model, algorithm version, and calculation results, where the input parameters include: real-time traffic condition score and weather impact factor; The key operation logs include: digital signature records of user delivery time change requests, timestamp data of delivery person location check-ins, and trigger conditions for abnormal event markers.

9. The delivery order overtime management method according to claim 8, wherein The storing the full life cycle data of the delivery order in the blockchain includes: Store the delivery path trajectory and sensor raw stream data in the IPFS distributed storage system, and associate and store the corresponding content hash value and data fingerprint in the blockchain. The delivery path trajectory includes the timestamp data of the delivery person location check-in, and the sensor raw stream data includes the input parameters in the timeout calculation evidence; Use the homomorphic encryption algorithm to encrypt the user privacy data and then upload it to the chain, enabling the on-chain nodes to verify the compliance of the business logic based on the ciphertext without exposing the plaintext. The user privacy data includes the user ID, plaintext of the delivery address, and digital signature records of the user delivery time change request. The business logic includes: service area range verification and delivery timeliness compliance determination.

10. A delivery order timeout management system, characterized in that Managing the timeout of the delivery order based on the delivery order timeout management method according to any one of claims 1-9, including: A delivery data acquisition module, which is used to acquire the delivery data related to the delivery order in real time. The delivery data includes: current traffic condition level, weather information, order priority information, and real-time location information of the current delivery person; A timeout threshold calculation module, which is used to dynamically calculate the timeout threshold of each delivery order based on the preset intelligent algorithm according to the delivery data. The timeout threshold is the system-allowed overtime duration for the delivery order to exceed the standard delivery time; A delivery time prediction module, which is used to monitor the delivery progress of each delivery order in real time and calculate the estimated delivery time; A timeout comparison and reminder module, which is used to compare the estimated delivery time with the corresponding timeout threshold, and when the estimated delivery time exceeds the timeout threshold, remind the current delivery person and / or the management personnel of the timeout risk.

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