New energy logistics equipment management and control method and system based on real-time order information

Abstract

The application relates to the technical field of logistics management, and particularly discloses a new energy logistics equipment management and control method and system based on real-time order information, which comprises the following steps: based on a delivery distance and an order urgency, an order demand coefficient is calculated and obtained; based on a charging station distance and a charging station queuing time, a charging convenience coefficient is calculated and obtained; based on real-time electricity prices and current electricity under the order demand coefficient and the charging convenience coefficient, a charging opportunity adaptation degree is calculated and obtained; based on a battery health degree and a driving energy consumption efficiency, a vehicle state coefficient is calculated and obtained; and based on the charging opportunity adaptation degree and the vehicle state coefficient, a target dynamic charging threshold is calculated and obtained. Under the premise of guaranteeing on-time performance of orders, the application synchronously optimizes charging economic costs, delays battery health degree attenuation, and improves charging facility utilization, so that multi-objective balance of operation efficiency, cost control and equipment maintenance is achieved.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, specifically a method and system for controlling new energy logistics equipment based on real-time order information. Background Technology

[0002] With the rapid development of new energy technologies and the widespread adoption of green logistics concepts, new energy logistics equipment, represented by electric vehicles, is becoming increasingly prevalent in modern urban delivery systems. These devices offer significant advantages in reducing carbon emissions, minimizing operational noise, and driving energy structure transformation. However, their large-scale application is constrained by limited range and complex charging processes. Currently, the industry generally employs charging control strategies based on fixed battery thresholds, such as triggering a charging request when the battery level drops to a preset percentage. This simple rule is somewhat inconvenient in dynamic and complex real-world operational scenarios. For example, a long-distance, high-urgency order requires vehicles to maintain a higher battery reserve to ensure on-time delivery. Static thresholds cannot be flexibly adjusted based on such parameters, easily leading to vehicles charging prematurely during low-priority tasks, reducing delivery efficiency, or failing to respond to high-priority tasks due to insufficient battery reserves. Therefore, how to provide a dynamic, intelligent control scheme that integrates real-time order flow, charging environment parameters, and vehicle status information to optimize group delivery architecture is the technical problem this invention aims to solve. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for managing new energy logistics equipment based on real-time order information, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for managing new energy logistics equipment based on real-time order information, the method comprising: Calculate the order demand coefficient based on delivery distance and order urgency. The charging convenience coefficient is calculated based on the distance to charging stations and the queuing time at charging stations. Based on the real-time electricity price and current power consumption under the order demand coefficient and charging convenience coefficient, calculate the charging timing suitability. Based on battery health and driving energy consumption efficiency, the vehicle state coefficient is calculated and obtained. The target dynamic charging threshold is calculated based on the charging timing adaptability and vehicle state coefficient.

[0005] As a further aspect of the present invention: the step of calculating and obtaining the order demand coefficient based on delivery distance and order urgency includes: Get delivery distance and order urgency; The delivery distance index is obtained by comparing the current delivery distance with the maximum reference delivery distance. The order urgency index is obtained by comparing the difference between the current order time and the order acceptance time with the difference between the latest delivery time and the order acceptance time. Based on the delivery distance index and the order urgency index, the order demand coefficient is calculated using a nonlinear function; Among them, the larger the delivery distance index and the order urgency index are, the larger the order demand coefficient is, which indicates that the order puts greater pressure on the vehicle for delivery.

[0006] As a further aspect of the present invention: the step of calculating and obtaining the charging convenience coefficient based on the distance to the charging station and the queuing time at the charging station includes: Get the distance to charging stations and the waiting time at charging stations; The current charging station distance and charging station queuing time are compared with the distance reference value and the estimated charging station queuing time, respectively, to obtain the charging station distance index and the charging station queuing time index. Based on the charging station distance index and queuing time index, the charging convenience coefficient is calculated using a ratio function. Among them, the larger the charging station distance index and the queuing time index are, the greater the charging convenience coefficient, indicating that the charging process is less convenient.

[0007] As a further aspect of the present invention: the step of calculating and obtaining the charging timing suitability based on the real-time electricity price and current electricity volume under the order demand coefficient and charging convenience coefficient includes: Obtain order demand coefficient, charging convenience coefficient, real-time electricity price, and current electricity consumption; The real-time electricity price index is obtained by comparing the difference between the current real-time electricity price and the average electricity price of the day with the standard deviation of the electricity price of the day, and then using the min and max functions to limit the upper and lower limits of the ratio to 0-1. The current battery level is compared with the battery's nominal total capacity to obtain the battery level index. Based on the order demand coefficient, charging convenience coefficient, real-time electricity price index, and power index, the charging timing suitability is calculated using a composite function. Among them, an increase in any of the following values—real-time electricity price index, electricity index, order demand coefficient, and charging convenience coefficient—will reduce the suitability of the charging opportunity, indicating that the current charging opportunity is less suitable.

[0008] As a further aspect of the present invention: the step of calculating and obtaining the vehicle state coefficient based on battery health and driving energy consumption efficiency includes: Obtain battery health and driving energy efficiency; The current battery health and driving energy consumption efficiency are compared with the maximum battery health value and the best energy consumption efficiency of the vehicle model, respectively, to obtain the battery health index and driving energy consumption efficiency index. Based on the battery health index and driving energy consumption efficiency index, a vehicle state coefficient is generated by weighting them according to preset weights. Among them, the lower the battery health index or the lower the driving energy consumption efficiency index, the higher the vehicle state coefficient, which indicates that the overall condition of the vehicle is worse.

[0009] As a further aspect of the present invention: the step of calculating and obtaining the target dynamic charging threshold based on charging timing adaptability and vehicle state coefficient includes: Obtain charging timing suitability and vehicle status coefficient; Based on the numerical difference between the vehicle state coefficient and the charging timing suitability, the preset benchmark charging threshold is linearly adjusted in the same or opposite direction to obtain the target dynamic charging threshold. Specifically, the larger the vehicle state coefficient or the smaller the charging timing adaptability, the greater the numerical difference between the vehicle state coefficient and the charging timing adaptability, which in turn causes the target dynamic charging threshold to be adjusted upward relative to the baseline charging threshold; conversely, the target dynamic charging threshold is adjusted downward.

[0010] The present invention also provides a new energy logistics equipment management and control system based on real-time order information, the system comprising: The order demand acquisition module is used to calculate and acquire the order demand coefficient based on delivery distance and order urgency. The convenience analysis module is used to calculate and obtain the charging convenience coefficient based on the distance to the charging station and the queuing time at the charging station; The compatibility acquisition module is used to calculate and obtain the charging timing compatibility based on the real-time electricity price and current power consumption under the order demand coefficient and charging convenience coefficient. The state coefficient calculation module is used to calculate and obtain the vehicle state coefficient based on battery health and driving energy consumption efficiency; The threshold acquisition module is used to calculate and obtain the target dynamic charging threshold based on the charging timing adaptability and vehicle state coefficient.

[0011] As a further aspect of the present invention: the order demand acquisition module includes: The order query unit is used to obtain delivery distance and order urgency. The distance analysis unit is used to compare the current delivery distance with the maximum reference delivery distance to obtain the delivery distance index; The urgency analysis unit is used to compare the difference between the current time of the order and the order acceptance time with the difference between the latest delivery time of the order and the order acceptance time to obtain the order urgency index. The calculation output unit is used to calculate the order demand coefficient based on the delivery distance index and the order urgency index using a nonlinear function. Among them, the larger the delivery distance index and the order urgency index are, the larger the order demand coefficient is, which indicates that the order puts greater pressure on the vehicle for delivery.

[0012] As a further aspect of the present invention: the convenience analysis module includes: The charging station information acquisition unit is used to acquire the distance to the charging station and the queuing time at the charging station. The queuing information acquisition unit is used to process the current charging station distance and charging station queuing time with the distance reference value and the estimated charging station queuing time respectively to obtain the charging station distance index and the charging station queuing time index. The comprehensive analysis unit is used to calculate the charging convenience coefficient based on the charging station distance index and queuing time index using a ratio function. Among them, the larger the charging station distance index and the queuing time index are, the greater the charging convenience coefficient, indicating that the charging process is less convenient.

[0013] As a further aspect of the present invention: the adaptability acquisition module includes: The power information acquisition unit is used to acquire order demand coefficient, charging convenience coefficient, real-time electricity price, and current electricity consumption. The normalization processing unit is used to ratio the difference between the current real-time electricity price and the average electricity price of the day to the standard deviation of the electricity price of the day, and then uses the min and max functions to limit the upper and lower limits of the ratio to 0-1 to obtain the real-time electricity price index; The power analysis unit is used to process the ratio of the current power level to the battery's nominal total capacity to obtain the power index; The adaptability calculation unit is used to calculate the charging timing adaptability based on the order demand coefficient, charging convenience coefficient, real-time electricity price index, and power index through a composite function. Among them, an increase in any of the following values—real-time electricity price index, electricity index, order demand coefficient, and charging convenience coefficient—will reduce the suitability of the charging opportunity, indicating that the current charging opportunity is less suitable.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention constructs an adaptive dynamic charging threshold model by integrating real-time order, charging environment, vehicle status and electricity price information, which overcomes the rigidity of traditional static threshold strategies and makes charging decisions more in line with actual operating scenarios; while ensuring timely order fulfillment, it simultaneously optimizes charging economic costs, delays battery health degradation and improves the utilization rate of charging facilities, thus achieving a multi-objective balance between operational efficiency, cost control and equipment maintenance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0016] Figure 1 This is a flowchart of a new energy logistics equipment management method based on real-time order information.

[0017] Figure 2 This is a block diagram of the composition structure of a new energy logistics equipment management and control system based on real-time order information. Detailed Implementation

[0018] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0019] Figure 1 The flowchart below illustrates a method for managing new energy logistics equipment based on real-time order information. In this embodiment of the invention, a method for managing new energy logistics equipment based on real-time order information includes: Step S100: Calculate the order demand coefficient based on delivery distance and order urgency; this coefficient is used to quantify the current order's demand pressure on vehicle battery power. Its value is affected by delivery distance and order urgency; the higher the value, the higher the order's demand pressure on battery power, and the more the vehicle needs to maintain sufficient battery power to complete the task.

[0020] Step S200: Calculate the charging convenience coefficient based on the charging station distance and charging station queuing time; this coefficient is used to assess the convenience of the current charging environment. Its value is affected by the charging station distance and charging station queuing time; the larger the value, the more inconvenient charging is, and it may take longer or take a longer distance to complete charging.

[0021] Step S300: Calculate the charging timing suitability based on the real-time electricity price and current power level under the order demand coefficient and charging convenience coefficient. This suitability is used to comprehensively evaluate the appropriateness of performing the charging operation at this time. Its value is calculated based on the order demand coefficient, charging convenience coefficient, real-time electricity price, and current power level. A higher value indicates a more suitable charging time, such as when the electricity price is low, charging is convenient, and order pressure is not high.

[0022] Step S400: Calculate the vehicle status coefficient based on battery health and driving energy efficiency. This coefficient reflects the health status and energy consumption performance of the new energy logistics equipment. Its value is affected by battery health and driving energy efficiency; a higher value indicates a worse vehicle status, which may require earlier or more frequent charging to maintain normal operation.

[0023] Step S500: Calculate and obtain the target dynamic charging threshold based on charging timing suitability and vehicle state coefficient. The target dynamic charging threshold is a charging trigger point dynamically adjusted according to the real-time operating environment and vehicle state. When the vehicle's battery level is below this threshold, the system will suggest or trigger charging behavior. This threshold is calculated by comprehensively considering charging timing suitability and vehicle state coefficient, aiming to achieve intelligent and flexible charging decisions.

[0024] This application further proposes the following method for calculating and obtaining the target dynamic charging threshold: The system obtains charging timing suitability and vehicle status coefficient. Charging timing suitability (A) is an indicator that comprehensively considers external factors such as order demand, charging convenience, real-time electricity prices, and current battery level; a higher value indicates a more suitable charging time. Vehicle status coefficient (K3) reflects the internal status of the new energy logistics equipment, including battery health and driving energy efficiency; a higher value indicates a worse vehicle status.

[0025] Based on the numerical difference between the vehicle state coefficient and the charging timing adaptation degree, a preset benchmark charging threshold is linearly adjusted in the same or opposite direction to obtain the target dynamic charging threshold. Specifically, the larger the vehicle state coefficient or the smaller the charging timing adaptation degree, the greater the numerical difference between the two, thus causing the target dynamic charging threshold to be adjusted upwards relative to the benchmark charging threshold; conversely, the target dynamic charging threshold is adjusted downwards. The specific calculation method involves importing the charging timing adaptation degree and the vehicle state coefficient into a formula. Obtain the target dynamic charging threshold Target dynamic charging threshold It can adjust in real time according to the actual operating conditions of the vehicle and changes in the external environment, among which, The baseline charging threshold represents a preset, relatively stable lower limit for charging capacity, serving as the starting point for dynamic adjustments. For vehicle state coefficients For charging timing adaptation.

[0026] This application provides a method for quantitatively and dynamically determining the charging threshold of new energy logistics equipment. This method introduces a benchmark charging threshold. As a basis, and combined with vehicle state coefficients Compatibility with charging timing The differences are dynamically adjusted, effectively solving the problem of the difficulty in accurately quantifying and combining multiple dimensions of factors. Specifically, when the vehicle condition is poor ( (High value) or poor charging timing ( When the value is low, the formula in The value will increase, thereby raising the target dynamic charging threshold T and prompting the device to charge earlier to avoid operational risks or damage to battery health due to insufficient power. Conversely, when the vehicle is in good condition ( (The value is low) and the charging time is appropriate ( When the value is high, the target dynamic charging threshold T will decrease, allowing equipment to charge at lower power levels. This provides greater flexibility for equipment scheduling and the opportunity to utilize more favorable electricity prices or more convenient charging resources, enabling refined management of charging strategies and optimization of operating costs. This dynamic adjustment mechanism makes charging decisions more intelligent and flexible, better adapting to complex logistics operating environments and improving the overall operating efficiency and reliability of new energy logistics equipment.

[0027] This application further proposes the following method for calculating and obtaining the vehicle state coefficients: Acquiring battery health and driving energy efficiency: Through the onboard system of new energy logistics equipment or external testing devices, real-time or periodic data on battery health and vehicle energy consumption performance are acquired. Battery health typically refers to the percentage of the battery's actual usable capacity relative to its nominal capacity, reflecting the degree of battery degradation. Driving energy efficiency refers to the amount of electricity consumed per unit distance during actual operation, reflecting the vehicle's energy utilization efficiency, which may be affected by various factors such as load, road conditions, and driving habits.

[0028] The battery health index and driving energy efficiency index are obtained by comparing the current battery health level with the maximum battery health level and the optimal energy efficiency for this vehicle model, respectively. The maximum battery health level is usually set to the health level of a new battery (e.g., 100%). The current battery health level is then compared with this maximum value to obtain the battery health index. The index typically ranges from 0 to 1, with values ​​closer to 1 indicating better battery health. The optimal energy efficiency for this vehicle model refers to the lowest energy consumption level achievable by a specific type of new energy logistics equipment under ideal operating conditions (e.g., unloaded, flat road, constant speed). The energy efficiency index is calculated by comparing the current driving energy efficiency with the optimal energy efficiency. The index typically ranges from 0 to 1, with values ​​closer to 1 indicating better energy efficiency. This ratio processing unifies raw data of different dimensions and ranges onto a comparable scale, facilitating subsequent comprehensive calculations.

[0029] Based on the battery health index and driving energy consumption efficiency index, a vehicle state coefficient is generated by weighting them according to preset weights. The lower the battery health index or the lower the driving energy consumption efficiency index, the higher the vehicle state coefficient, indicating a worse overall vehicle condition. Specifically, the calculation method involves importing the battery health index and driving energy consumption efficiency index into a formula... Obtain vehicle state coefficients , Output range 0-1, A higher value indicates a worse vehicle condition. This is the battery health weight, ranging from 0 to 1, used to adjust the relative importance of battery health and driving energy efficiency in vehicle condition assessment. For example, when battery life is a more critical consideration, this weight can be appropriately increased. The value can be assigned through a preset strategy or a dynamic algorithm. Battery health index This is the driving energy efficiency index. In this formula, This indicates the degree of degradation in battery health; the worse the battery health, the lower the degradation level. The smaller, the better The larger; similarly, This indicates the degree of degradation in driving energy efficiency; the worse the energy efficiency, the lower the energy consumption efficiency. The smaller, the better The larger the value, the greater the vehicle state coefficient becomes. Through this weighted summation method, the vehicle state coefficient... It can comprehensively reflect the health status of the battery and the energy consumption performance of the vehicle. The output range is 0-1, and A higher value indicates a worse vehicle condition, which makes... It can intuitively and quantitatively characterize the overall operating status of new energy logistics equipment.

[0030] Through the above technical solution, this application provides a refined and quantitative method for vehicle state assessment. When calculating the target dynamic charging threshold, it no longer relies solely on the abstract concept of "vehicle state," but instead acquires two key indicators: battery health and driving energy efficiency, and standardizes them into a battery health index and a driving energy efficiency index. These indices are then weighted and summed to generate a comprehensive vehicle state coefficient. This method can more accurately reflect the actual degradation and energy efficiency of batteries in new energy logistics equipment, thereby avoiding potential range risks or increased operating costs due to decreased battery health or reduced energy efficiency. When the vehicle state coefficient... When the value is large (i.e., the vehicle condition is poor), the dynamic charging threshold formula mentioned above should be used as a reference. This will make the target dynamic charging threshold This improvement allows the system to schedule charging earlier when vehicles are in poor condition, effectively mitigating the risk of order delays or inability to complete delivery tasks due to degraded vehicle performance, and ensuring the reliable operation of logistics equipment and on-time order delivery.

[0031] This application further proposes the following method for calculating and obtaining the charging timing suitability: Obtaining the order demand coefficient, charging convenience coefficient, real-time electricity price, and current battery level is crucial. The order demand coefficient and charging convenience coefficient are important indicators reflecting the external operating environment and the condition of charging infrastructure, respectively quantifying the urgency of order delivery and the accessibility and waiting time of charging stations. The real-time electricity price directly affects charging costs and is an important factor in economic considerations. The current battery level directly reflects the vehicle's own energy reserves, determining the vehicle's range and the necessity of charging. Accurately obtaining these parameters is the foundation for a comprehensive assessment of the suitability of charging timing.

[0032] The real-time electricity price index is obtained by comparing the difference between the current real-time electricity price and the daily average electricity price with the daily standard deviation of electricity prices. The ratio is then limited to 0-1 using the min and max functions. This step aims to standardize the real-time electricity price, generating a dimensionless real-time electricity price index. By calculating the deviation between the current real-time electricity price and the daily average electricity price, and dividing it by the daily standard deviation, the relative level of the current electricity price relative to daily fluctuations can be reflected. Limiting the ratio to 0-1 using the min and max functions ensures the standardization of the index, making electricity price data comparable across different time periods or regions, and avoiding the impact of extreme values ​​on subsequent calculations. For example, when the real-time electricity price is significantly lower than the average price, the real-time electricity price index value approaches 0, indicating a very favorable electricity price; when the real-time electricity price is significantly higher than the average price, the real-time electricity price index value approaches 1, indicating a very unfavorable electricity price.

[0033] The current battery level is compared to the battery's nominal total capacity to obtain a battery capacity index. This step converts the vehicle's current battery level into a standardized, dimensionless index. By dividing the current battery level by the battery's nominal total capacity, the remaining percentage of battery charge can be visually represented. For example, when the battery is fully charged, the battery capacity index value is close to 1; when the battery is low, the battery capacity index value is close to 0. This index accurately reflects the vehicle's energy reserves, providing a crucial basis for charging decisions.

[0034] Import the order demand coefficient, charging convenience coefficient, real-time electricity price index, and electricity index into the formula. Obtain the charging timing suitability , Output range 0-1, A higher value indicates a more suitable time to charge. This is the real-time electricity price index. This refers to the power index. This is the order demand coefficient. For charging convenience factor.

[0035] This formula takes into account economic factors (real-time electricity price index). ), vehicle status (battery level) ) and the external operating environment (order demand coefficient) and charging convenience This is analyzed from multiple dimensions, including numerator and descent, to quantify the overall advantages and disadvantages of the current charging opportunity. This reflects the economics and necessity of charging; that is, when electricity prices are low and electricity consumption is low, the numerator will be larger, leading to the perception that charging is more appropriate. The denominator part... This reflects external obstacles and operational pressures related to charging. Specifically, when order pressure is high or charging is inconvenient, the denominator is larger, tending to indicate that charging timing is less suitable. The final charging timing suitability score is... The value is between 0 and 1. The higher the value, the more suitable the current charging time is, and vice versa.

[0036] Through the above technical solution, this application overcomes the shortcomings of traditional charging timing assessment methods that fail to consider multiple dimensions. By introducing real-time electricity price and power indexes, and combining them with existing order demand and charging convenience coefficients, this application can more comprehensively and precisely quantify the suitability of charging timing. Specifically, when electricity prices are low and vehicle power is insufficient, both the economics and necessity of charging are high, and the suitability of charging timing is thus enhanced. The value will increase accordingly; conversely, when electricity prices are high or electricity supply is sufficient, The value will decrease. At the same time, the solution also considers the constraints of order pressure and charging convenience on charging decisions, ensuring that in situations where orders are urgent or charging is inconvenient, The value will decrease, thus avoiding inappropriate charging behavior. This multi-factor comprehensive evaluation mechanism ensures that the calculated charging timing suitability is accurate. This allows for a more accurate reflection of the charging quality under the current environment, providing a more reliable and precise input for subsequent calculations of dynamic charging thresholds. This not only helps new energy logistics equipment seize the optimal charging window and reduce operating costs while ensuring operational efficiency, but also effectively avoids logistics delays or additional expenses caused by indiscriminate charging or insufficient charging, thereby significantly improving the intelligent management level and economic benefits of the entire logistics system.

[0037] This application further proposes the following method for obtaining the order demand coefficient: Obtaining delivery distance and order urgency: Delivery distance refers to the length of the delivery route for the current order to be executed. It can be obtained through route planning calculations using the vehicle's Global Positioning System (GPS) combined with map data, or directly from pre-set delivery route information in the Order Management System (OMS). This distance is an important indicator for assessing order delivery pressure; the longer the distance, the higher the requirements for vehicle range and time. Order urgency refers to the timeliness requirement for the current order's delivery. This can be calculated using information such as the order creation time, promised delivery time, and current time in the Order Management System. For example, the urgency of the order can be judged based on the difference between the current time and the latest delivery time; the smaller the difference, the higher the urgency.

[0038] The current delivery distance is compared with the maximum reference delivery distance to obtain a delivery distance index. The maximum reference delivery distance can be preset based on factors such as the type of logistics equipment, battery capacity, and typical delivery areas. For example, it can be set as the maximum delivery distance that a vehicle of this type can cover when fully charged, or the maximum delivery distance for orders within a certain area. By comparing the distances of different orders, the delivery distances can be standardized to a comparable scale, facilitating subsequent calculations.

[0039] The order urgency index is calculated by comparing the difference between the current order time and the order acceptance time with the difference between the latest delivery time and the order acceptance time. The difference between the current order time and the order acceptance time represents the time already spent on the order, while the difference between the latest delivery time and the order acceptance time represents the total allowed delivery time for the order. The ratio of these two values ​​provides an index reflecting the time pressure and progress of the order; a larger ratio indicates a higher proportion of time already spent on the order relative to the total allowed time, and thus a more urgent order.

[0040] Based on the delivery distance index and order urgency index, an order demand coefficient is calculated using a nonlinear function. The larger the delivery distance index and order urgency index, the larger the order demand coefficient, indicating greater delivery pressure on vehicles. Specifically, the calculation method involves importing the delivery distance index and order urgency index into a formula... Obtain the order demand coefficient , Output range 0-0.95 A larger value indicates greater order demand pressure. It is a natural constant. This is the pressure accumulation coefficient. For delivery distance index, This represents the order urgency index. In this formula, It is a natural constant. This is a pressure accumulation coefficient used to adjust the rate of exponential growth, and can be optimized based on actual operational experience or through machine learning methods. The characteristic of this formula is that when... or When it increases, Increase Reduce, thus Reduce, ultimately making Increase. This aligns with the reality that order demand pressure increases with distance and urgency. Order demand coefficient. The output range is 0-0.95, and A larger value indicates greater order demand pressure, which provides a quantitative input of order pressure for the subsequent calculation of charging timing suitability.

[0041] Through the above technical solution, this application is able to dynamically and precisely calculate the order demand coefficient. This allows it to accurately reflect the actual operational pressure brought about by real-time delivery distance and order urgency. This quantitative assessment based on real-time order information ensures the appropriateness of charging timing. The calculation no longer relies solely on static or generalized assumptions, but fully considers immediate business needs. When order delivery distances are long or timeliness requirements are high, the calculated order demand coefficient... This will increase accordingly, thus affecting the compatibility of charging timing. The assessment process encourages the system to prioritize charging when necessary, ensuring order fulfillment. This effectively prevents premature or delayed charging due to inaccurate order demand assessments, thereby optimizing the operational efficiency of new energy logistics equipment, ensuring timely order delivery, and balancing energy management optimization. The introduction of this non-linear formula makes the system more sensitive and nuanced in responding to delivery pressures, resulting in more robust overall charging decisions that are highly aligned with business priorities.

[0042] This application further proposes the following method for calculating and obtaining the charging convenience coefficient: The system obtains information on charging station distance and queuing time. Charging station distance refers to the distance from the current location of the new energy logistics equipment to the nearest or target charging station. Its purpose is to assess the accessibility of charging stations. This can be achieved by using the vehicle's current location via GPS, combined with map services or a pre-set charging station database, to calculate the straight-line distance or actual driving distance to each charging station. Charging station queuing time refers to the waiting time required to charge at the target charging station. Its purpose is to assess the immediate availability of the charging station. This can be obtained through real-time data interfaces provided by the charging station operator, or predicted through historical data analysis combined with current charging pile occupancy.

[0043] The charging station distance index and charging station queue time index are obtained by comparing the current charging station distance and queuing time with the distance reference value and the estimated charging station queuing time, respectively. This ratio processing standardizes the raw distance and time data, converting them into dimensionless indices, facilitating subsequent formula calculations and comparisons between different factors. The distance reference value can be set based on operational experience to determine a reasonable maximum acceptable charging distance; for example, exceeding this distance is considered inconvenient for charging. The estimated charging station queuing time can be based on the average queuing time from historical data or predicted queuing time based on real-time charging station load. The charging station distance index reflects the relative magnitude of the charging station distance; a higher value indicates a greater distance and greater charging inconvenience. The charging station queue time index reflects the relative magnitude of the charging station queuing time; a higher value indicates a longer queuing time and greater charging inconvenience.

[0044] Based on the charging station distance index and queuing time index, a charging convenience coefficient is calculated using a ratio function. The larger the charging station distance index and queuing time index, the larger the charging convenience coefficient, indicating a less convenient charging process. Specifically, the calculation method involves importing the charging station distance index and charging station queuing time index into a formula. Obtain the charging convenience factor , Output range 0-1, A higher value indicates greater inconvenience in charging. This refers to the distance index to the charging station. This refers to the queuing time index at charging stations. This is a minimal constant to prevent the denominator from being zero. This formula is a mathematical model for comprehensively evaluating charging convenience. It integrates the two independent factors of charging station distance and queuing time into a unified charging convenience coefficient. The characteristic of this formula is that when either the distance to the charging station or the queuing time is large, The value will increase accordingly, indicating an increase in the inconvenience of charging. In the denominator... It is a very small constant, used to prevent when A division-by-zero error occurs when the value is zero, ensuring calculation stability. Charging convenience factor. The output range is 0-1, and A larger value indicates greater inconvenience in charging. Clarifying the physical meaning and range of values ​​for this coefficient facilitates subsequent comprehensive calculations with other coefficients.

[0045] Through the above technical solution, this application further considers the impact of charging station distance and charging station queuing time on charging convenience. This is achieved by obtaining the charging station distance and queuing time and standardizing them into a charging station distance index. Charging station queue time index Then, the charging convenience factor is calculated using a specific formula. This coefficient quantifies the accessibility and immediate availability of charging stations, ensuring that when assessing the suitability of charging timing, factors such as order demand, electricity price, and capacity are considered, as well as the actual operational difficulty of charging are fully taken into account. When charging stations are far away or waiting times are long, the charging convenience coefficient... This will increase accordingly, thus reflecting the inconvenience of charging when calculating the suitability of charging timing, and avoiding charging decision errors caused by ignoring actual charging conditions. This makes the charging strategy for new energy logistics equipment closer to actual operational needs, improves the accuracy and rationality of charging decisions, and helps optimize vehicle scheduling and energy management efficiency.

[0046] With the increasing prevalence of new energy logistics equipment in urban delivery systems, existing charging management methods, relying on static threshold strategies, struggle to cope with dynamic and complex operating environments. This results in an inability to achieve dynamic balance among multiple objectives, such as ensuring on-time order fulfillment, optimizing charging costs, managing battery health, and efficiently utilizing charging network resources, thus hindering the overall efficiency and resilience of the logistics system. To address this technical challenge, this application proposes a new energy logistics equipment management system based on real-time order information, comprising: an order demand assessment module, which calculates an order demand coefficient based on delivery distance and order urgency; a charging convenience assessment module, which calculates a charging convenience coefficient based on charging station distance and queuing time; a charging timing suitability assessment module, which calculates a charging timing suitability degree based on real-time electricity price and current battery level under the order demand coefficient and charging convenience coefficient; a vehicle status assessment module, which calculates a vehicle status coefficient based on battery health and driving energy efficiency; and a dynamic charging threshold adjustment module, which calculates a target dynamic charging threshold based on the charging timing suitability degree and the vehicle status coefficient.

[0047] Figure 2 The diagram shows the structural composition of a new energy logistics equipment management and control system based on real-time order information. As a preferred embodiment of the technical solution of this invention, a new energy logistics equipment management and control system based on real-time order information is also provided. The new energy logistics equipment management and control system 10 based on real-time order information includes: The order demand acquisition module 11 is used to calculate and acquire the order demand coefficient based on delivery distance and order urgency. Convenience analysis module 12 is used to calculate and obtain the charging convenience coefficient based on the distance to the charging station and the queuing time at the charging station; The adaptability acquisition module 13 is used to calculate and acquire the charging timing adaptability based on the real-time electricity price and current power consumption under the order demand coefficient and charging convenience coefficient. The state coefficient calculation module 14 is used to calculate and obtain the vehicle state coefficient based on battery health and driving energy consumption efficiency. The threshold acquisition module 15 is used to calculate and acquire the target dynamic charging threshold based on the charging timing adaptability and vehicle state coefficient.

[0048] Furthermore, the order demand acquisition module 11 includes: The order query unit is used to obtain delivery distance and order urgency. The distance analysis unit is used to compare the current delivery distance with the maximum reference delivery distance to obtain the delivery distance index; The urgency analysis unit is used to compare the difference between the current time of the order and the order acceptance time with the difference between the latest delivery time of the order and the order acceptance time to obtain the order urgency index. The calculation output unit is used to calculate the order demand coefficient based on the delivery distance index and the order urgency index using a nonlinear function. Among them, the larger the delivery distance index and the order urgency index are, the larger the order demand coefficient is, which indicates that the order puts greater pressure on the vehicle for delivery.

[0049] Specifically, the convenience analysis module 12 includes: The charging station information acquisition unit is used to acquire the distance to the charging station and the queuing time at the charging station. The queuing information acquisition unit is used to process the current charging station distance and charging station queuing time with the distance reference value and the estimated charging station queuing time respectively to obtain the charging station distance index and the charging station queuing time index. The comprehensive analysis unit is used to calculate the charging convenience coefficient based on the charging station distance index and queuing time index using a ratio function. Among them, the larger the charging station distance index and the queuing time index are, the greater the charging convenience coefficient, indicating that the charging process is less convenient.

[0050] Furthermore, the adaptation degree acquisition module 13 includes: The power information acquisition unit is used to acquire order demand coefficient, charging convenience coefficient, real-time electricity price, and current electricity consumption. The normalization processing unit is used to ratio the difference between the current real-time electricity price and the average electricity price of the day to the standard deviation of the electricity price of the day, and then uses the min and max functions to limit the upper and lower limits of the ratio to 0-1 to obtain the real-time electricity price index; The power analysis unit is used to process the ratio of the current power level to the battery's nominal total capacity to obtain the power index; The adaptability calculation unit is used to calculate the charging timing adaptability based on the order demand coefficient, charging convenience coefficient, real-time electricity price index, and power index through a composite function. Among them, an increase in any of the following values—real-time electricity price index, electricity index, order demand coefficient, and charging convenience coefficient—will reduce the suitability of the charging opportunity, indicating that the current charging opportunity is less suitable.

[0051] 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 within the protection scope of the present invention.

Claims

1. A method for managing new energy logistics equipment based on real-time order information, characterized in that, The method includes: Calculate the order demand coefficient based on delivery distance and order urgency. The charging convenience coefficient is calculated based on the distance to charging stations and the queuing time at charging stations. Based on the real-time electricity price and current power consumption under the order demand coefficient and charging convenience coefficient, calculate the charging timing suitability. Based on battery health and driving energy consumption efficiency, the vehicle state coefficient is calculated and obtained. The target dynamic charging threshold is calculated based on the charging timing adaptability and vehicle state coefficient.

2. The method for managing new energy logistics equipment based on real-time order information according to claim 1, characterized in that, The steps for calculating the order demand coefficient based on delivery distance and order urgency include: Get delivery distance and order urgency; The delivery distance index is obtained by comparing the current delivery distance with the maximum reference delivery distance. The order urgency index is obtained by comparing the difference between the current order time and the order acceptance time with the difference between the latest delivery time and the order acceptance time. Based on the delivery distance index and the order urgency index, the order demand coefficient is calculated using a nonlinear function; Among them, the larger the delivery distance index and the order urgency index are, the larger the order demand coefficient is, which indicates that the order puts greater pressure on the vehicle for delivery.

3. The method for managing new energy logistics equipment based on real-time order information according to claim 2, characterized in that, The steps for calculating the charging convenience coefficient based on the distance to the charging station and the queuing time at the charging station include: Get the distance to charging stations and the waiting time at charging stations; The current charging station distance and charging station queuing time are compared with the distance reference value and the estimated charging station queuing time, respectively, to obtain the charging station distance index and the charging station queuing time index. Based on the charging station distance index and queuing time index, the charging convenience coefficient is calculated using a ratio function. Among them, the larger the charging station distance index and the queuing time index are, the greater the charging convenience coefficient, indicating that the charging process is less convenient.

4. The method for managing new energy logistics equipment based on real-time order information according to claim 3, characterized in that, The step of calculating and obtaining the charging timing suitability based on the real-time electricity price and current electricity volume under the order demand coefficient and charging convenience coefficient includes: Obtain order demand coefficient, charging convenience coefficient, real-time electricity price, and current electricity consumption; The real-time electricity price index is obtained by comparing the difference between the current real-time electricity price and the average electricity price of the day with the standard deviation of the electricity price of the day, and then using the min and max functions to limit the upper and lower limits of the ratio to 0-1. The current battery level is compared with the battery's nominal total capacity to obtain the battery level index. Based on the order demand coefficient, charging convenience coefficient, real-time electricity price index, and power index, the charging timing suitability is calculated using a composite function. Among them, an increase in any of the following values—real-time electricity price index, electricity index, order demand coefficient, and charging convenience coefficient—will reduce the suitability of the charging opportunity, indicating that the current charging opportunity is less suitable.

5. The method for managing new energy logistics equipment based on real-time order information according to claim 1, characterized in that, The steps for calculating and obtaining the vehicle state coefficient based on battery health and driving energy consumption efficiency include: Obtain battery health and driving energy efficiency; The current battery health and driving energy consumption efficiency are compared with the maximum battery health value and the best energy consumption efficiency of the vehicle model, respectively, to obtain the battery health index and driving energy consumption efficiency index. Based on the battery health index and driving energy consumption efficiency index, a vehicle state coefficient is generated by weighting them according to preset weights. Among them, the lower the battery health index or the lower the driving energy consumption efficiency index, the higher the vehicle state coefficient, which indicates that the overall condition of the vehicle is worse.

6. The method for managing new energy logistics equipment based on real-time order information according to claim 1, characterized in that, The step of calculating and obtaining the target dynamic charging threshold based on charging timing adaptability and vehicle state coefficient includes: Obtain charging timing suitability and vehicle status coefficient; Based on the numerical difference between the vehicle state coefficient and the charging timing suitability, the preset benchmark charging threshold is linearly adjusted in the same or opposite direction to obtain the target dynamic charging threshold. Specifically, the larger the vehicle state coefficient or the smaller the charging timing adaptability, the greater the numerical difference between the vehicle state coefficient and the charging timing adaptability, which in turn causes the target dynamic charging threshold to be adjusted upward relative to the baseline charging threshold; conversely, the target dynamic charging threshold is adjusted downward.

7. A new energy logistics equipment management and control system based on real-time order information, characterized in that, The system includes: The order demand acquisition module is used to calculate and acquire the order demand coefficient based on delivery distance and order urgency. The convenience analysis module is used to calculate and obtain the charging convenience coefficient based on the distance to the charging station and the queuing time at the charging station; The compatibility acquisition module is used to calculate and obtain the charging timing compatibility based on the real-time electricity price and current power consumption under the order demand coefficient and charging convenience coefficient. The state coefficient calculation module is used to calculate and obtain the vehicle state coefficient based on battery health and driving energy consumption efficiency; The threshold acquisition module is used to calculate and obtain the target dynamic charging threshold based on the charging timing adaptability and vehicle state coefficient.

8. The new energy logistics equipment management and control system based on real-time order information according to claim 7, characterized in that, The order demand acquisition module includes: The order query unit is used to obtain delivery distance and order urgency. The distance analysis unit is used to compare the current delivery distance with the maximum reference delivery distance to obtain the delivery distance index; The urgency analysis unit is used to compare the difference between the current time of the order and the order acceptance time with the difference between the latest delivery time of the order and the order acceptance time to obtain the order urgency index. The calculation output unit is used to calculate the order demand coefficient based on the delivery distance index and the order urgency index using a nonlinear function. Among them, the larger the delivery distance index and the order urgency index are, the larger the order demand coefficient is, which indicates that the order puts greater pressure on the vehicle for delivery.

9. The new energy logistics equipment management and control system based on real-time order information according to claim 8, characterized in that, The convenience analysis module includes: The charging station information acquisition unit is used to acquire the distance to the charging station and the queuing time at the charging station. The queuing information acquisition unit is used to process the current charging station distance and charging station queuing time with the distance reference value and the estimated charging station queuing time respectively to obtain the charging station distance index and the charging station queuing time index. The comprehensive analysis unit is used to calculate the charging convenience coefficient based on the charging station distance index and queuing time index using a ratio function; Among them, the larger the charging station distance index and the queuing time index are, the greater the charging convenience coefficient, indicating that the charging process is less convenient.

10. The new energy logistics equipment management and control system based on real-time order information according to claim 9, characterized in that, The compatibility acquisition module includes: The power information acquisition unit is used to acquire order demand coefficient, charging convenience coefficient, real-time electricity price, and current electricity consumption. The normalization processing unit is used to ratio the difference between the current real-time electricity price and the average electricity price of the day to the standard deviation of the electricity price of the day, and then uses the min and max functions to limit the upper and lower limits of the ratio to 0-1 to obtain the real-time electricity price index; The power analysis unit is used to process the ratio of the current power level to the battery's nominal total capacity to obtain the power index; The adaptability calculation unit is used to calculate the charging timing adaptability based on the order demand coefficient, charging convenience coefficient, real-time electricity price index, and power index through a composite function. Among them, an increase in any of the following values—real-time electricity price index, electricity index, order demand coefficient, and charging convenience coefficient—will reduce the suitability of the charging opportunity, indicating that the current charging opportunity is less suitable.

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