A cold chain logistics energy consumption management and control method and system

Abstract

The application relates to the technical field of industrial big data, and discloses a cold-chain logistics energy consumption management and control method and system. The method comprises the following steps: acquiring cold-chain logistics area demand distribution and energy supply point data, performing spatial superposition analysis based on transportation path length and area coverage radius, determining a preliminary logistics center candidate position set, determining candidate position distribution accessibility according to transportation time cost and road conditions, generating an optimized position combination, determining capacity configuration in combination with distribution frequency, cargo characteristics and temperature control requirements, obtaining equipment selection according to capacity parameters, determining energy consumption optimization configuration, and generating a full-cycle operation plan. The application optimizes the preliminary logistics center candidate position set and the optimized position combination, improves the distribution accessibility, reasonably determines the capacity configuration parameters and the equipment selection scheme, reduces the refrigeration energy consumption proportion and the uneven equipment load, generates the full-cycle operation plan based on the target energy consumption optimization configuration, and realizes global energy saving and efficient deployment of the cold-chain logistics network.

Description

Technical Field

[0001] This invention belongs to the field of industrial big data technology, specifically a method and system for energy consumption management in cold chain logistics. Background Technology

[0002] In today's context of global supply chain integration, energy consumption management in cold chain logistics is a crucial support for ensuring food safety, pharmaceutical distribution, and cross-border trade. With the rapid development of fresh food e-commerce and pharmaceutical cold chain demand, the need for efficient and low-energy cold chain logistics management is becoming increasingly urgent. Existing energy consumption management methods can already achieve basic equipment energy saving or transportation route adjustments.

[0003] However, existing methods lack sufficient overall energy consumption control capabilities in the collaborative optimization involving network layout and facility selection. Inappropriate cold chain network layout and improper facility capacity configuration lead to energy waste and inefficiency, making it difficult to achieve users' energy consumption optimization goals. How to scientifically determine the geographical location of logistics centers and rationally allocate cold storage capacity and refrigeration equipment according to regional needs in cold chain logistics energy consumption control, thereby optimizing overall energy consumption, has become a pressing technical problem in this field. Summary of the Invention

[0004] To address the above problems, this invention provides a cold chain logistics energy consumption control method and system, which solves the technical problem that existing methods lack the ability to coordinate and optimize the layout of the cold chain network and the selection of facilities, resulting in the difficulty in matching the location of logistics centers with the configuration of cold storage and refrigeration equipment to regional needs.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Acquire regional demand distribution data and energy supply location data for cold chain logistics. Perform spatial overlay analysis on the regional demand distribution data and energy supply location data based on transportation route length and regional coverage radius to determine a preliminary set of candidate locations for logistics centers. Based on transportation time costs and road conditions, route simulation is performed to determine the delivery accessibility of each candidate location in the set of candidate locations for the logistics center to the demand point, and an optimized location combination is generated based on the delivery accessibility. Based on the optimized location combination, the delivery frequency demand and stored goods characteristic data of the corresponding area are obtained, and the capacity configuration parameters are determined in combination with the cold storage temperature control accuracy requirements. Based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters, a suitable equipment selection scheme is obtained; The operating time distribution and energy consumption peak control data are extracted using the equipment selection scheme to determine the target energy consumption optimization configuration; Based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data, a full-cycle operation plan for the cold chain logistics network is generated.

[0006] In one embodiment, obtaining a suitable equipment selection scheme based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters includes: Acquire ambient temperature data and equipment operation records, and determine the range of impact of temperature interference on equipment load based on the historical fluctuation segmentation results of the ambient temperature data and the real-time load data of the equipment operation records; Based on the aforementioned range of influence, dynamic simulation technology is used to simulate the change process of cooling energy consumption and obtain the correlation distribution between the proportion of cooling energy consumption and the equipment load rate. Determine whether the associated distribution exceeds the preset energy consumption ratio range. If it does, perform a hierarchical analysis on the capacity configuration parameters to determine the direction of parameter adjustment. The capacity configuration is redefined based on the adjustment direction of the parameters, and the adjusted capacity configuration is obtained. Dynamic simulation is then performed based on the adjusted capacity configuration to obtain the adjusted cooling energy consumption ratio. Multiple simulations are performed based on the adjusted cooling energy consumption ratio until the adjusted cooling energy consumption ratio meets the preset energy consumption ratio range. This verifies the matching degree between the corresponding equipment load rate and the ambient temperature, and generates a suitable equipment selection scheme.

[0007] In one embodiment, the parameter adjustment direction includes a capacity reduction direction or a capacity expansion direction. The step of determining whether the associated distribution exceeds a preset energy consumption ratio range, and if so, performing a hierarchical analysis of the capacity configuration parameters to determine the parameter adjustment direction, includes: Based on the aforementioned correlation distribution, when the equipment load rate is lower than the preset load lower limit and the cooling energy consumption ratio is within the preset energy consumption ratio range, the parameter adjustment direction is determined to be the capacity reduction direction. When the cooling energy consumption ratio exceeds the preset energy consumption ratio range, the parameter adjustment direction is determined to be the capacity expansion direction, and when the ambient temperature continues to be higher than the preset temperature threshold, the standby capacity allocation is triggered to temporarily increase the capacity by the preset ratio.

[0008] In one embodiment, the step of performing route simulation based on transportation time cost and road traffic conditions to determine the delivery accessibility from each candidate location in the candidate location set of the logistics center to the demand point, and generating an optimized location combination based on the delivery accessibility, includes: Acquire dynamic path change data, and by analyzing the impact of the dynamic path change data on transportation time costs, determine the target path segments affected by the dynamic path change data; Based on road traffic conditions and vehicle load limits, the transportation cost of the target route segment is determined, and alternative locations are extracted from the candidate location set of the logistics center according to the transportation cost. Based on the candidate locations, location combination optimization is performed with the goal of minimizing transportation length and transportation time costs to obtain a preliminary optimized location combination. Based on the preliminary optimized location combination, path simulation is performed according to the road traffic conditions to determine the delivery accessibility from each candidate location in the logistics center candidate location set to the demand point, and an optimized location combination is generated based on the delivery accessibility.

[0009] In one embodiment, the step of optimizing the location combination based on the candidate locations with the objective of minimizing transportation length and transportation time costs to obtain a preliminary optimized location combination includes: Determine the length-weighted value and the time-weighted value of transportation; determine the length cost based on the length-weighted value and the transportation length of the target route segment; determine the time cost based on the time-weighted value and the transportation time of the target route segment. Based on the length cost and the time cost, the comprehensive cost of each candidate location in the set of candidate locations for the logistics center is determined, and the candidate locations are sorted according to the comprehensive cost to obtain the sorting result; Eliminate candidate positions in the sorting results whose overall cost exceeds a preset cost threshold, and retain the remaining candidate positions as the initial optimized position combination.

[0010] In one embodiment, the acquisition of regional demand distribution data and energy supply point data for cold chain logistics, and the spatial overlay analysis of the regional demand distribution data and energy supply point data based on transportation route length and regional coverage radius to determine a preliminary set of candidate logistics center locations, includes: Obtain regional demand distribution data and energy supply location data for cold chain logistics, construct a spatial distribution model based on the regional demand distribution data, and obtain the spatial mapping results of regional demand; Based on the spatial mapping results, distance calculations are performed on the transportation path length to determine the optimal set of paths between the demand points in each region and the energy supply points. Based on the constraint of the regional coverage radius, spatial coverage analysis is performed on the optimal path set to determine the distribution density of demand points within the coverage area and obtain the path node set that meets the preset coverage conditions. The path node set is matched with the energy supply point data to determine the target node whose distance from the energy supply point is less than a preset distance threshold, thereby obtaining a potential location set. By integrating the regional demand distribution data and the regional coverage radius, a spatial overlay analysis is performed on the potential location set to determine the candidate location set that satisfies multiple constraints as the preliminary candidate location set for logistics centers.

[0011] In one embodiment, the step of obtaining delivery frequency demand and stored goods characteristic data for the corresponding area based on the optimized location combination, and determining capacity configuration parameters in conjunction with cold storage temperature control accuracy requirements, includes: Based on the optimized location combination, the delivery frequency demand for the corresponding area is obtained, and a preliminary resource allocation plan is obtained according to the delivery frequency demand and the preset frequency threshold. Based on the characteristics of the stored goods, analyze the temperature control accuracy requirements of the goods in the cold storage and determine the appropriate temperature control parameter range. Based on the temperature control parameter range and the technical specifications of the cold chain facilities, the initial value of the capacity configuration is determined as the capacity configuration parameter.

[0012] In one embodiment, the step of extracting operating time distribution and peak energy consumption control data through the equipment selection scheme to determine the target energy consumption optimization configuration includes: The operating time distribution and energy consumption peak control data are obtained from the equipment selection scheme to construct the initial energy consumption distribution dataset of the cold chain network; Energy consumption simulation is performed based on the initial energy consumption distribution dataset to determine the range of equipment configuration to be adjusted. The peak energy consumption control data is recalculated based on the range of equipment configuration to be adjusted to obtain the adjusted control strategy. Energy consumption simulation is performed based on the adjusted control strategy to obtain the adjusted peak energy consumption control data and the adjusted overall energy consumption data. If the adjusted peak energy consumption control data meets the preset peak control capability threshold, and the adjusted overall energy consumption data meets the preset energy consumption target, then the device configuration corresponding to the adjusted control strategy is determined as the target energy consumption optimization configuration.

[0013] In one embodiment, generating a full-cycle operation plan for the cold chain logistics network based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data, includes: Based on the target energy consumption optimization configuration and the stored cargo characteristic data, a logistics demand dataset is constructed. By combining geographical environment data and the aforementioned logistics demand dataset, a preliminary layout plan for network deployment is obtained; Based on the energy consumption parameters of the target energy consumption optimization configuration, the transportation path and storage node in the preliminary layout scheme are iteratively optimized to obtain the optimized network deployment structure. Based on the optimized network deployment structure, a logistics scheduling schedule is generated, and a dynamic operation plan based on the logistics scheduling schedule is formed. The matching degree between the delivery frequency of the dynamic operation plan and the characteristic data of the stored goods is analyzed, resource priorities are allocated according to the matching degree, and a full-cycle operation plan is generated based on the resource priorities and the delivery frequency requirements.

[0014] In addition, to achieve the above objectives, this application also proposes a cold chain logistics energy consumption management system, which includes: a spatial location module, a route optimization module, a capacity configuration module, an equipment selection module, an energy consumption optimization module, and a plan generation module; The spatial location module is used to acquire regional demand distribution data and energy supply point data for cold chain logistics. Based on the transportation route length and regional coverage radius, it performs spatial overlay analysis on the regional demand distribution data and the energy supply point data to determine a preliminary set of candidate locations for logistics centers. The route optimization module is used to simulate routes based on transportation time costs and road conditions, determine the delivery accessibility of each candidate location in the set of candidate locations of the logistics center to the demand point, and generate an optimized location combination based on the delivery accessibility. The capacity configuration module is used to obtain the delivery frequency demand and stored goods characteristic data of the corresponding area based on the optimized location combination, and determine the capacity configuration parameters in combination with the cold storage temperature control accuracy requirements. The equipment selection module is used to obtain a suitable equipment selection scheme based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters. The energy consumption optimization module is used to extract operating time distribution and energy consumption peak control data through the equipment selection scheme in order to determine the target energy consumption optimization configuration; The planning generation module is used to generate a full-cycle operation plan for the cold chain logistics network based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data.

[0015] The technical solution proposed in this application determines the set of candidate locations for logistics centers through spatial overlay analysis and generates optimized location combinations based on delivery accessibility, thereby improving the rationality of the cold chain network layout and delivery efficiency. By adapting capacity configuration parameters and equipment selection schemes, it effectively reduces the proportion of refrigeration energy consumption and the problem of uneven equipment load. It extracts operating time distribution and energy consumption peak control data to determine the target energy consumption optimization configuration and finally generates a full-cycle operation plan, realizing full-link collaborative energy saving from planning to operation, significantly reducing energy waste and improving the overall energy efficiency level of cold chain logistics. Attached Figure Description

[0016] Figure 1This is a flowchart illustrating an embodiment of the cold chain logistics energy consumption control method of this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the cold chain logistics energy consumption control method of this application; Figure 3 This is a flowchart illustrating Embodiment 3 of the cold chain logistics energy consumption control method of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solution, the present invention will be described in detail below with reference to embodiments. The description in this part is only exemplary and explanatory, and should not be used to limit the scope of protection of the present invention in any way.

[0018] Existing cold chain logistics energy consumption management methods have the following shortcomings: they lack the ability to coordinate and optimize network layout and facility selection, making it impossible to scientifically determine the geographical location of logistics centers during the planning stage, resulting in an unreasonable set of candidate locations for logistics centers; route simulation does not fully consider the dynamic changes in transportation time costs and road conditions, leading to inaccurate delivery accessibility assessments and making it difficult for optimized location combinations to adapt to regional needs; the determination of capacity configuration parameters ignores the correlation between the proportion of refrigeration energy consumption and equipment load rate, and the equipment selection scheme cannot effectively balance peak energy consumption control, ultimately resulting in a lack of specificity and overall energy-saving effect in the full-cycle operation plan.

[0019] Therefore, this application provides a solution: by using spatial overlay analysis to coordinate and match regional demand with energy supply, reasonable candidate locations for logistics centers are scientifically selected, solving the problem of lack of scientific basis for layout; by introducing transportation time costs and dynamic road conditions for path simulation, delivery accessibility is accurately assessed and location combinations are optimized accordingly, making site selection adaptable to actual road network changes; by combining delivery frequency and cargo characteristics to determine capacity configuration parameters, and by adjusting equipment selection based on the proportion of cooling energy consumption and equipment load rate feedback, capacity redundancy or insufficiency is avoided; by extracting operating time distribution and energy consumption peak data to determine target energy consumption optimization configuration, a full-cycle operation plan is generated accordingly, thereby achieving coordinated optimization of layout, selection and energy consumption in the entire planning and operation chain, effectively overcoming the problems of fragmented links and poor overall energy-saving effect in existing methods.

[0020] It should be noted that the executing entity of each embodiment of this application can be a computing service system with data processing, network communication, and program execution functions, such as an electronic system capable of realizing the above functions, a cold chain logistics energy consumption management system, etc. The following uses a cold chain logistics energy consumption management system (hereinafter referred to as "the system") as an example to describe the following embodiments.

[0021] Based on this, the embodiments of this application provide a method for energy consumption control in cold chain logistics, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the cold chain logistics energy consumption control method of this application.

[0022] In this embodiment, the cold chain logistics energy consumption control method includes steps S10 to S60: Step S10: Obtain regional demand distribution data and energy supply location data for cold chain logistics. Perform spatial overlay analysis on the regional demand distribution data and energy supply location data based on transportation route length and regional coverage radius to determine a preliminary set of candidate locations for logistics centers.

[0023] It should be noted that regional demand distribution data refers to the demand for cold chain logistics services in various geographical units (such as administrative regions, grid blocks, or commercial clusters) within the target area, quantified in weight units (such as tons or kilograms), and can be extracted from historical order data, warehouse inbound and outbound records, or third-party logistics statistical databases. Energy supply point data records the geographical coordinates of various energy stations (such as substations, natural gas stations, or integrated energy supply stations) within the area and their power supply or energy supply capacity, in kilowatts or kilovolt-amperes. This data is imported through a Geographic Information System (GIS) to construct a spatial distribution model.

[0024] In the specific implementation, based on the above data, this step performs spatial overlay analysis using transportation path length and regional coverage radius as constraints. Transportation path length refers to the actual road network distance from a potential logistics center location to various demand points or energy points, calculated using a shortest path algorithm (such as Dijkstra's algorithm or A* algorithm); regional coverage radius is a preset distance threshold, such as 10 kilometers or 15 kilometers, representing the geographical range that the logistics center can effectively serve.

[0025] The specific process of spatial overlay analysis is as follows: For each potential location, calculate the shortest path length to each demand point, and count the number of demand points within the coverage radius and the total demand of these demand points. Overlay the energy supply point data and calculate the path length between the potential location and each energy point. If this length is less than a preset energy access distance threshold (e.g., 5 kilometers), the location is considered to have reliable energy supply conditions. Based on the comprehensive demand coverage and energy accessibility, each potential location is scored. The scoring formula can be set as: Comprehensive Score = Coverage Weight × Coverage + Energy Accessibility Weight × Energy Accessibility. The coverage weight and energy accessibility weight can be set according to actual business priorities, for example, 0.6 and 0.4 respectively. Potential locations with a comprehensive score higher than a preset threshold (e.g., 0.7) are selected to form a preliminary set of candidate logistics center locations. Taking a city as an example, if there are 10 administrative districts, the above analysis can yield 3 to 5 candidate locations. Each candidate location can cover at least three high-demand administrative districts and the distance to the nearest energy station does not exceed 5 kilometers.

[0026] Step S20: Perform route simulation based on transportation time cost and road conditions to determine the delivery accessibility of each candidate location in the candidate location set of the logistics center to the demand point, and generate an optimized location combination based on the delivery accessibility.

[0027] After obtaining an initial set of candidate logistics center locations, the delivery efficiency from each candidate location to various demand points is evaluated, with delivery accessibility as the core indicator. Delivery accessibility refers to the ease with which a logistics center can reach a demand point along the actual road network, quantified by transportation time cost. The calculation of transportation time cost depends not only on path length but also on road conditions, including road class (e.g., highways, national roads, provincial roads), speed limits, number of traffic lights, historical congestion index, and real-time traffic flow. This embodiment employs path simulation technology, such as time-dependent shortest path algorithms, to simulate the vehicle's travel process at different time periods.

[0028] In practical implementation, a road network topology covering the entire target area is constructed. Nodes represent road intersections or demand points, and edges represent road segments. Each edge is assigned multiple attributes: distance (km), design speed (km / h), and congestion coefficient (dimensionless, greater than 1 indicates congestion, less than 1 indicates smooth traffic). The formula for calculating transportation time cost is: Time Cost = Distance / (Design Speed ​​ / Congestion Coefficient). The congestion coefficient can be dynamically set according to historical traffic data for different time periods; for example, the congestion coefficient is 1.5 during morning peak hours and 1.0 during off-peak hours.

[0029] It should be understood that for each candidate location, the shortest time path to each demand point is calculated using path simulation technology, and the corresponding transportation time cost is recorded. Further, the time costs from each candidate location to all demand points are aggregated, and an arithmetic average or weighted average (with different weights assigned based on the importance of the demand points) is used to obtain a comprehensive delivery accessibility index. For example, if the times from candidate location A to five demand points are 0.2 hours, 0.3 hours, 0.25 hours, 0.4 hours, and 0.35 hours respectively, with an average time of 0.3 hours; and another candidate location B has an average time of 0.45 hours, then A has better accessibility than B. Based on the delivery accessibility assessment results, an optimized location combination is generated: if the time cost from a candidate location to a high-priority demand point exceeds a preset threshold (e.g., 2 hours), or its average time cost ranks last among all candidate locations, then that location is removed from the set. Conversely, candidate locations with excellent delivery accessibility are retained, and their combinations are optimized according to actual business needs (e.g., requiring each demand point to be covered by at least two logistics centers), ultimately forming an optimized logistics center location combination. This combination typically includes multiple logistics centers, such as a main center and one or more auxiliary centers, to achieve effective coverage and efficient delivery across all demand areas.

[0030] Step S30: Based on the optimized location combination, obtain the delivery frequency demand and stored goods characteristic data of the corresponding area, and determine the capacity configuration parameters in combination with the cold storage temperature control accuracy requirements.

[0031] It's important to note that delivery frequency demand refers to the number of cold chain deliveries required per unit of time (e.g., daily or weekly). This can be calculated from historical delivery orders or predicted based on the number of customers and the rate of goods consumption within a region. For example, a supermarket in a certain area might need to restock twice a day, while a medical institution might need to deliver vaccines once a day. These data, when aggregated, form the total delivery frequency demand for that area. Storage goods characteristic data includes the type of goods (e.g., frozen meat, refrigerated fruits and vegetables, pharmaceuticals), their required storage temperature ranges (e.g., -18℃ to -15℃, 2℃ to 8℃), the maximum storage capacity per batch, the heat capacity of the goods, and the allowable temperature fluctuation range. This data is stored in the enterprise's warehouse management system (WMS) or product database.

[0032] Cold storage temperature control accuracy requirements refer to the maximum permissible deviation between the storage temperature that cold chain facilities need to maintain and the actual temperature, expressed in degrees Celsius. Different types of goods have significantly different temperature control accuracy requirements: frozen seafood requires an accuracy of ±0.5℃ to prevent ice crystals from damaging cell structure; refrigerated pharmaceuticals may require ±0.3℃ or even higher.

[0033] This step combines delivery frequency requirements and stored goods characteristics data to determine capacity configuration parameters. These parameters primarily include the effective storage volume of the cold storage (cubic meters or tons) and the refrigeration power of the refrigeration equipment (kilowatts). Specifically, the maximum inventory level is calculated based on delivery frequency requirements. For example, if the delivery frequency is twice daily, with each delivery being 10 tons, and the maximum storage period for goods is two days, the maximum inventory level is 40 tons. Considering a 20% safety margin, the cold storage capacity is determined to be 48 tons. Further, based on the temperature requirements and heat load (including heat from goods respiration, heat transfer from the enclosure structure, and heat loss from door opening) in the goods characteristics data, the required refrigeration power is estimated using thermodynamic formulas. Higher temperature control accuracy requirements typically require greater refrigeration power and necessitate a more sophisticated temperature control system. Finally, the cold storage capacity and refrigeration power are combined to form the preliminary capacity configuration parameters for this logistics center. For different optimized locations, the capacity configuration parameters will vary accordingly due to differences in delivery frequency and stored goods characteristics within their service areas, thus achieving on-demand configuration and avoiding resource waste.

[0034] Step S40: Based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters, obtain a suitable equipment selection scheme.

[0035] It should be noted that the refrigeration energy consumption ratio refers to the proportion of electricity consumed by the refrigeration system to the total electricity consumption of the entire cold chain facility within an operating cycle (such as a day or a month), expressed as a percentage; the equipment load rate refers to the ratio of the actual operating power of the refrigeration equipment to its rated power, also expressed as a percentage. These two indicators reflect the rationality of capacity configuration: if the equipment load rate is consistently low (e.g., below 60%), it indicates that the equipment capacity is too large, resulting in wasted investment and low-load efficiency loss; if the load rate is consistently close to 100%, the equipment may be at risk of overload; if the refrigeration energy consumption ratio is too high (e.g., exceeding 50% of total energy consumption), it suggests that the refrigeration system is inefficient or that the capacity configuration is mismatched with the heat load.

[0036] It should be understood that, based on existing capacity configuration parameters (such as a cold storage volume of 48 tons and a refrigeration power requirement of 35 kilowatts), multiple candidate refrigeration equipment models with rated power close to the required value are retrieved from the equipment database. For each candidate equipment, its technical parameters are read, including rated power, energy efficiency ratio (EER), and part-load performance curve. Furthermore, the operating state of the equipment under the target temperature control accuracy and actual heat load conditions is simulated to calculate the expected refrigeration energy consumption ratio and equipment load rate. Factors such as changes in ambient temperature, number of door openings, and frequency of goods entering and exiting need to be considered during the calculation.

[0037] If the calculated refrigeration energy consumption ratio is higher than the preset upper limit (e.g., 40%), or the equipment load rate is lower than the preset lower limit (e.g., 70%), it indicates that the current capacity configuration parameters do not match the selected equipment, and the capacity configuration parameters need to be adjusted or the equipment model needs to be changed. This embodiment adopts an iterative comparison method: gradually adjust the capacity parameters (e.g., adjust the cold storage volume from 48 tons to 45 tons, or adjust the refrigeration power requirement), and recalculate the corresponding energy consumption ratio and load rate until both fall within the ideal range (e.g., energy consumption ratio 30% to 40%, load rate 75% to 90%). When a combination of capacity parameters and equipment model that meets the conditions is found, a suitable equipment selection scheme is formed. This scheme not only includes the specific model and quantity of the equipment, but also its expected energy consumption performance under different operating conditions.

[0038] Step S50: Extract operating time distribution and energy consumption peak control data through the equipment selection scheme to determine the target energy consumption optimization configuration.

[0039] It should be noted that operating time distribution refers to the start-up and shutdown duration and operating power data of cold chain equipment (such as refrigeration units, fans, condensers, etc.) at different times of the day or week, which can be obtained through the equipment's historical operating logs or simulations. For example, a refrigeration unit might operate for 70% of its time between 8:00 and 18:00 during the day, and only 20% between 22:00 and 6:00 the next day. This distribution pattern reflects the load's variation with the outside temperature. Peak energy consumption control data includes the maximum electrical power (unit: kilowatt) consumed by the equipment per unit time (such as per hour or per 15 minutes), as well as the time period and duration of this peak. Excessive peak energy consumption may lead to insufficient grid capacity, increased electricity costs (in demand-based billing scenarios), or even equipment tripping.

[0040] In practice, an energy consumption simulation model is constructed based on the equipment parameters and expected operating time distribution in the equipment selection scheme. This model takes into account boundary conditions such as ambient temperature curves, cargo entry / exit frequency, and cold storage setpoint temperature, and outputs hourly energy consumption curves for the entire cold chain network within the simulation period. Average and peak energy consumption can be statistically calculated from these curves. If the simulation results show that the peak energy consumption exceeds a preset control capability threshold (e.g., the grid contract capacity is 500 kW, while the simulated peak is 520 kW), or the peak duration is too long, the equipment operation strategy needs to be adjusted. Adjustment methods include, but are not limited to: staggered start / stop of refrigeration equipment, adding cold storage devices to smooth out peaks, and adjusting the temperature setpoint offset range. After each adjustment, the simulation is rerun to observe changes in peak energy consumption and whether total energy consumption increases.

[0041] Through multiple iterations, a combination of operating time distribution and peak energy consumption control parameters is found that keeps peak energy consumption within acceptable limits while minimizing overall energy consumption or maximizing energy efficiency. This set of parameters and the corresponding equipment operation strategy constitute the target energy consumption optimization configuration. The target energy consumption optimization configuration does not change the selection of physical equipment, but rather optimizes the equipment's operation, enabling existing equipment to effectively manage peak energy consumption and improve overall energy efficiency while meeting cold chain temperature requirements.

[0042] Step S60: Based on the target energy consumption optimization configuration, and combined with the delivery frequency requirements and the stored goods characteristic data, generate a full-cycle operation plan for the cold chain logistics network.

[0043] It should be understood that this step integrates the outputs of all the above modules to generate a complete full-cycle operation plan for the cold chain logistics network. A full-cycle operation plan refers to the detailed operational instructions and scheduling schemes for each logistics center, each piece of equipment, and each delivery vehicle throughout the entire time span, from the start of operation of the logistics center until the equipment is decommissioned or network adjustments are completed. This plan needs to simultaneously meet three constraints: First, the equipment operating time distribution and energy peak control strategy determined based on the target energy consumption optimization configuration; second, the regional delivery frequency requirements obtained in previous steps, i.e., when and where to deliver how much goods; and third, storing cargo characteristic data, including temperature requirements, shelf life, batch management rules, etc., for different goods.

[0044] In practical implementation, the equipment operation strategy determined in the target energy consumption optimization configuration is transformed into specific equipment control parameters. For example, defining the power at which the refrigeration unit operates at different times of the day, and the automatic power reduction measures taken when energy consumption approaches the peak threshold. Furthermore, combining delivery frequency requirements with stored goods characteristic data, a daily delivery task sequence and cold storage inbound / outbound schedule are formulated. For instance, if a certain product has a short shelf life and a high delivery frequency, the plan will prioritize its outbound shipment and use a more compact delivery route. Through a time-axis merging algorithm, the equipment operation plan and delivery operation plan are synchronized to avoid peak delivery periods overlapping with peak equipment energy consumption periods, leading to exceeding peak limits. For example, a large number of pre-cooling operations in the cold storage are scheduled during nighttime when electricity prices are low and delivery tasks are fewer, while daytime refrigeration power is maintained at a lower level. After the above integration and optimization, a detailed full-cycle operation plan is finally output. This plan can be a timetable divided by hours or a set of executable control command sequences, covering the daily operation, maintenance cycles, energy consumption monitoring points, and emergency response plans of the entire cold chain logistics network. Implementing a full-cycle operation plan enables the cold chain logistics network to achieve the goals of low overall energy consumption and controllable peak energy consumption while ensuring cargo quality and delivery efficiency.

[0045] It should be noted that this application is widely applicable to cold chain logistics scenarios for temperature-sensitive goods such as fresh food, pharmaceuticals, and biological agents, and is particularly suitable for cross-regional distribution networks, urban cold chain distribution systems, and areas with complex transportation conditions such as remote mountainous areas or islands. By integrating geographic information system analysis, route simulation, and energy consumption simulation in the planning stage, it provides cold chain logistics companies, fresh food e-commerce platforms, and pharmaceutical distribution companies with full-chain technical support from network layout to equipment selection, from operation scheduling to energy consumption management. It is suitable for the planning and deployment of new cold chain networks and the energy-saving renovation and upgrading of existing networks, and has good industry versatility and environmental adaptability.

[0046] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S40 includes steps S401 to S405: Step S401: Obtain ambient temperature data and equipment operation records. Based on the historical fluctuation segmentation results of the ambient temperature data and the real-time load data of the equipment operation records, determine the range of influence of temperature interference on equipment load.

[0047] It should be understood that after determining the initial capacity configuration parameters, this embodiment further obtains a suitable equipment selection scheme by analyzing the proportion of cooling energy consumption and equipment load rate. This process first requires obtaining ambient temperature data and equipment operation records. Ambient temperature data refers to the historical record of the change of the outside air temperature at the geographical location of the cold chain facility over time, with sampling intervals of hours or minutes, and can be obtained from meteorological databases or on-site temperature sensors. Equipment operation records include real-time monitoring data such as the start-up and shutdown time, operating power, current, evaporation temperature, and condensation temperature of the refrigeration equipment.

[0048] Since ambient temperature is the most significant external disturbance affecting cooling energy consumption, this embodiment segments the historical fluctuations of ambient temperature data to divide continuous temperature changes into several typical intervals. For example, periods below 15°C are classified as low-temperature intervals, 15°C to 25°C as medium-temperature intervals, and above 25°C as high-temperature intervals, or more precisely, into intervals of 5°C each. Simultaneously, real-time load data, i.e., the ratio of actual operating power to rated power, is extracted from equipment operation records. By comparing the segmented ambient temperature results with the equipment load data, the range of influence of temperature disturbances on equipment load can be determined. The range of influence refers to the magnitude and distribution characteristics of equipment load rate changes in different temperature ranges. For example, the analysis shows that when the ambient temperature is between 20°C and 25°C, the equipment load rate fluctuates between 65% and 75%; when the ambient temperature rises to between 30°C and 35°C, the equipment load rate rises to 85% to 95%. This analysis quantifies the degree of interference of external temperature on the equipment's operating state, providing boundary conditions for subsequent simulations.

[0049] Step S402: Based on the said range of influence, use dynamic simulation technology to simulate the change process of cooling energy consumption and obtain the correlation distribution between the proportion of cooling energy consumption and the equipment load rate.

[0050] It should be noted that dynamic simulation technology refers to using computer models to simulate the hourly energy consumption output of a refrigeration system under different ambient temperatures and load conditions, based on thermodynamic principles and equipment performance curves, in a time-step manner. The simulation model inputs include: ambient temperature change curves (e.g., a continuous curve showing a gradual increase from 20°C to 35°C and then a decrease within a day), cold storage enclosure parameters (heat transfer coefficient, area), cargo characteristics (heat capacity, respiration heat), equipment performance parameters (rated power, energy efficiency ratio curves as a function of load rate), and control strategies (temperature setpoint, start-stop temperature difference). After the simulation runs, the hourly energy consumption data of the refrigeration system is output, from which the refrigeration energy consumption ratio—the proportion of refrigeration energy consumption to the total energy consumption of the facility—can be calculated, as well as the distribution of equipment load rate over time. By analyzing the correlation between the energy consumption ratio and load rate in the simulation results, the correlation distribution between the two can be obtained.

[0051] Step S403: Determine whether the associated distribution exceeds the preset energy consumption ratio range. If it does, perform a hierarchical analysis on the capacity configuration parameters to determine the direction of parameter adjustment.

[0052] The correlation distribution can be represented by a scatter plot or correlation curve, reflecting the changing pattern of cooling energy consumption as a percentage of total energy consumption under different load conditions. For example, simulation results show that when the load rate is 60%, the cooling energy consumption percentage is approximately 35%; when the load rate increases to 85%, the cooling energy consumption percentage drops to 28%. The innovation of this method lies in using this correlation distribution to determine whether the current capacity configuration is reasonable. The preset energy consumption percentage range is an empirical threshold interval, for example, setting the normal range for cooling energy consumption percentage to be 30% to 40% of total energy consumption. If the correlation distribution obtained from the simulation shows that the energy consumption percentage exceeds this range (e.g., reaching 42% or decreasing to below 25%), it indicates that there is a deviation in the capacity configuration, and adjustments are needed.

[0053] Step S404: Adjust the direction according to the parameters to redetermine the capacity configuration, obtain the adjusted capacity configuration, and perform dynamic simulation based on the adjusted capacity configuration to obtain the adjusted cooling energy consumption ratio.

[0054] When the correlation distribution exceeds the preset energy consumption ratio range, a stratified analysis of the capacity configuration parameters is performed to determine the direction of parameter adjustment. Stratified analysis involves examining the impact of each component of the capacity parameter (such as cold storage volume, refrigeration power, evaporator area, etc.) on the energy consumption ratio and load rate, thereby determining the key adjustment direction. For example, if the equipment load rate is low while the refrigeration energy consumption ratio is high, it indicates that the cold storage volume is too large, causing frequent start-ups and shutdowns or inefficient operation of the refrigeration equipment; the adjustment direction should be capacity reduction. If the refrigeration energy consumption ratio is significantly higher than the preset range and the load rate is close to 100%, it indicates insufficient refrigeration power, requiring capacity expansion. The parameter adjustment direction can be summarized as either capacity reduction or capacity expansion. After determining the adjustment direction, the capacity configuration is redefined, for example, adjusting the cold storage volume from 48 tons to 42 tons, or increasing the refrigeration power from 35 kW to 42 kW. Based on the adjusted capacity configuration, dynamic simulation is run again to obtain the new refrigeration energy consumption ratio.

[0055] Step S405: Perform multiple simulations to verify the adjusted cooling energy consumption ratio until the adjusted cooling energy consumption ratio meets the preset energy consumption ratio range, verify the matching degree between the corresponding equipment load rate and the ambient temperature, and generate a suitable equipment selection scheme.

[0056] It should be understood that if the new energy consumption ratio (i.e., the adjusted cooling energy consumption ratio) still does not meet the preset range, the capacity parameters should be adjusted and repeated simulations verified. Each adjustment can be a fixed-step change (e.g., reducing the volume by 5% each time), or a more efficient search algorithm (e.g., binary search or the golden section method) can be used to approximate the optimal value. After multiple simulations, the adjusted cooling energy consumption ratio falls within the preset energy consumption ratio range, and the matching degree between the equipment load rate and the ambient temperature is verified to reach an acceptable level, that is, the load rate can be maintained within the ideal window of 70% to 90% under different ambient temperature ranges. Finally, the equipment model corresponding to the capacity configuration parameters can be confirmed as the suitable equipment selection scheme.

[0057] For example, a data center initially had a capacity of 1000 kWh, with a load rate of only 60% and an energy consumption ratio of 35%, which was within acceptable limits. However, further analysis revealed that the energy consumption ratio exceeded 42% after the temperature rose. Through simulation adjustments, the capacity was reduced to 850 kW, the load rate increased to 85%, and the energy consumption ratio returned to 38%. Therefore, an equipment model with a rated power of 850 kW was selected, forming a suitable equipment selection plan. It should be noted that in extreme cases where the ambient temperature remains above 35°C, the backup capacity allocation mechanism is triggered, temporarily increasing the capacity by 10% to cope with heat wave impacts and ensure that the cooling system always operates within a safe and efficient range.

[0058] In one implementation, step S403 includes: Based on the aforementioned correlation distribution, when the equipment load rate is lower than the preset load lower limit and the cooling energy consumption ratio is within the preset energy consumption ratio range, the parameter adjustment direction is determined to be the capacity reduction direction. When the cooling energy consumption ratio exceeds the preset energy consumption ratio range, the parameter adjustment direction is determined to be the capacity expansion direction, and when the ambient temperature continues to be higher than the preset temperature threshold, the standby capacity allocation is triggered to temporarily increase the capacity by the preset ratio.

[0059] In practice, after obtaining the correlation distribution between cooling energy consumption ratio and equipment load rate through dynamic simulation, the adjustment direction of capacity configuration parameters is determined based on the specific characteristics of this correlation distribution. The adjustment direction includes capacity reduction and capacity expansion. This embodiment determines which adjustment direction should be adopted by analyzing the different intervals in which the correlation distribution falls.

[0060] The first scenario is when the equipment load rate is below the preset lower load limit and the cooling energy consumption ratio is within the preset energy consumption ratio range. In this case, the parameter adjustment direction is capacity reduction. The preset lower load limit is a threshold pre-set based on the equipment's optimal energy efficiency range, and can be 70% or 75%. A load rate below this limit means the refrigeration equipment is operating under light load for an extended period, for example, a load rate of only 50% or 60%. Under these conditions, the frequency of equipment start-ups and shutdowns may increase, and partial load efficiency may decrease, resulting in energy waste and equipment wear. However, the cooling energy consumption ratio is still within the normal range (e.g., 30% to 40%), indicating that the refrigeration system itself is still efficient, but the overall capacity is too large relative to the actual heat load. Therefore, the reasonable adjustment direction is to reduce the capacity configuration. Reduction can be reflected in reducing the cold storage volume (e.g., reducing redundant storage space), reducing the rated power of the refrigeration equipment (e.g., replacing with a smaller unit), or both simultaneously.

[0061] For example, the rated power of the refrigeration equipment in a cold storage is 1000 kilowatts, the actual load rate is only 60%, and the refrigeration energy consumption ratio is 35%, which is still within the preset range. At this time, it is determined that the capacity is excessive and the capacity configuration should be adjusted to reduce it, for example, by reducing the rated power to 850 kilowatts, so that the load rate can be increased to 85%.

[0062] The second scenario involves adjusting parameters to increase capacity when the proportion of cooling energy consumption exceeds the preset energy consumption ratio range. The upper limit of the preset energy consumption ratio range is typically set at 40% or 45%. Exceeding this limit means that cooling energy consumption accounts for an excessively high share of the facility's total energy consumption. This is often due to insufficient cooling capacity leading to prolonged high or even full-load operation of equipment. Under these conditions, the equipment load rate is usually high, even approaching 100%. Capacity expansion can include increasing the effective volume of the cold storage to distribute the heat load, adding refrigeration equipment to increase the total cooling capacity, or replacing the refrigeration unit with a larger one. It should be noted that even if the proportion of cooling energy consumption exceeds the upper limit, if the load rate is significantly low, a comprehensive analysis may be needed to determine the adjustment direction. However, this embodiment prioritizes the energy consumption ratio as the trigger condition because it directly reflects the weight of the refrigeration component in the overall energy consumption and is a key indicator for determining whether the refrigeration system matches the overall load.

[0063] Based on the established adjustment towards capacity expansion, this embodiment also includes a backup capacity allocation mechanism for extreme environmental conditions. Specifically, when the ambient temperature remains above a preset temperature threshold, backup capacity allocation is triggered to temporarily increase the capacity by a preset proportion. The preset temperature threshold can be determined based on local climate conditions and the heat resistance design of the cold chain facilities, for example, set to 35°C. The criterion for continuously exceeding this threshold can be exceeding it for several consecutive hours (e.g., 3 hours) or several consecutive days (e.g., 3 days). Backup capacity can come from pre-configured backup refrigeration units, rapidly deployable mobile refrigeration equipment, or temporary refrigeration capacity obtained through leasing. The preset proportion is typically set to 10% to 20% of the original capacity to ensure that the refrigeration system can maintain the required temperature control accuracy during heatwaves. After triggering backup capacity allocation, the total refrigeration capacity is temporarily increased, avoiding temperature exceedances and cargo loss due to insufficient capacity. Once the ambient temperature drops below the threshold and remains stable for a period of time, the backup capacity can be deactivated, and the normal capacity configuration can be restored. This mechanism demonstrates the adaptability of this application to complex environmental conditions, especially in mountainous areas, deserts and other regions with drastic temperature fluctuations, where it can achieve a balance between energy saving and reliability while ensuring the safety of the cold chain.

[0064] In one implementation, step S20 includes steps S201 to S204: Step S201: Obtain dynamic path change data, and determine the target path segment affected by the dynamic path change data by analyzing the impact of the dynamic path change data on transportation time cost.

[0065] It should be noted that dynamic route change data refers to real-time or predictive information affecting road traffic conditions, including but not limited to: traffic congestion index, road closures due to construction, traffic accidents, weather effects (such as slippery roads due to rain or snow), and time-limited traffic restrictions. This data can be obtained through real-time traffic APIs, roadside sensing devices, historical data analysis, or weather forecasts. Transportation time cost has been defined above as a calculated value based on distance, speed limits, and congestion coefficients, and dynamic route change data directly affects the value of the congestion coefficient.

[0066] By analyzing the impact of dynamic changes in route data on transportation time costs, target route segments significantly affected by this data are identified. For example, if the congestion coefficient of a road increases from 1.0 to 1.8 during the morning rush hour, causing the transportation time cost of that route segment to increase from 0.2 hours to 0.36 hours, then that route segment is marked as a target route segment. Target route segments are typically bottleneck sections in the entire delivery network, and changes in their status significantly alter the accessibility assessment results from the logistics center to the demand point.

[0067] Step S202: Combine road traffic conditions and vehicle load limit data to determine the transportation cost of the target route segment, and extract alternative locations from the candidate location set of the logistics center based on the transportation cost.

[0068] Road traffic conditions include road surface type (asphalt, cement, gravel), slope, curve radius, height and width restrictions, etc. These factors affect the safe driving speed and fuel consumption rate of vehicles. Vehicle load limit data includes the vehicle's rated load (tons), actual load, and the load's impact coefficient on energy consumption and speed. The greater the load, the higher the energy consumption per unit distance, and the more significant the speed drop on slopes. Transportation cost is a comprehensive indicator that includes both time cost and energy cost (or monetized operating cost). In this embodiment, it is defined as the transportation length multiplied by the energy consumption coefficient per unit length plus the transportation time multiplied by the labor and depreciation costs per unit time.

[0069] After calculating the transportation cost for each target route segment, candidate locations are extracted from the set of candidate locations for the logistics center based on this cost. The extraction rule is as follows: for each candidate location, the transportation costs of the target route segments it takes to reach all demand points are summarized. If the cumulative or average transportation cost of the candidate location exceeds a certain preset threshold (e.g., more than 1.2 times the average of all candidate locations), the location is excluded; otherwise, candidate locations with lower costs are retained as candidate locations.

[0070] For example, the initial candidate locations include three points: A, B, and C. Calculations show that starting from point C requires frequently passing through a heavily congested and steep road section, resulting in significantly higher transportation costs than A and B. Therefore, point C is eliminated, and A and B are retained as alternative locations.

[0071] Step S203: Based on the candidate locations, optimize the location combination with the goal of minimizing transportation length and transportation time costs to obtain a preliminary optimized location combination.

[0072] It should be understood that after obtaining candidate locations, location combination optimization is performed with the goal of minimizing transportation length and time costs to obtain a preliminary optimized location combination. This optimization process typically employs genetic algorithms or integer programming algorithms because the location combination problem involves discrete decision variables (selecting or not selecting a location) and a continuous objective function (total transportation cost). The basic algorithm flow is as follows: Each combination of candidate locations is encoded as a chromosome. The fitness function is set as the weighted sum of the total transportation length and total transportation time cost of all delivery tasks under that combination, with weight coefficients set according to business preferences (e.g., distance weight 0.4, time weight 0.6). Through iterative evolution via genetic operations such as selection, crossover, and mutation, the fitness value converges to a minimum or reaches a preset number of iterations. The final output is the location combination that minimizes the total transportation cost, serving as the preliminary optimized location combination. For example, applying a genetic algorithm, the optimal combination calculated is point B as the main center and point A as the auxiliary center, reducing the total transportation cost by 17% compared to the initial combination. It should be noted that the preliminary optimized location combination may still include multiple logistics centers, which together constitute a collaborative delivery network.

[0073] Step S204: Based on the preliminary optimized location combination, perform path simulation according to the road traffic conditions, determine the delivery accessibility from each candidate location in the logistics center candidate location set to the demand point, and generate an optimized location combination based on the delivery accessibility.

[0074] It should be understood that the reason for resimulating is that some of the original candidate locations in the optimized location combination are removed, and the original delivery responsibility areas need to be redistributed, which may cause changes in the paths from each retained location to the demand point (for example, the demand point originally delivered by point A is now delivered by point B).

[0075] During the re-route simulation, the same transportation time cost calculation formula and real-time road traffic condition data are used to calculate the shortest time path from each reserved location to the demand point it is responsible for, and the results are aggregated to obtain a new delivery accessibility assessment. If the new assessment results show that the delivery accessibility of all demand points meets the preset requirements (e.g., the maximum transportation time does not exceed 2 hours), then this combination directly becomes the final optimized location combination. If there are still cases where the requirements are not met, the location combination can be further fine-tuned according to the new accessibility indicators, such as adding an auxiliary center near areas with poor accessibility, or adjusting the service radius division of each location. The optimized location combination generated after this closed-loop verification and adjustment takes into account both dynamic changes in the route and vehicle load constraints, and ensures delivery accessibility under the goal of minimizing global transportation costs, laying the spatial layout foundation for subsequent capacity configuration and energy consumption optimization.

[0076] As one implementation, step S203 includes: Determine the length-weighted value and the time-weighted value of transportation; determine the length cost based on the length-weighted value and the transportation length of the target route segment; determine the time cost based on the time-weighted value and the transportation time of the target route segment. Based on the length cost and the time cost, the comprehensive cost of each candidate location in the set of candidate locations for the logistics center is determined, and the candidate locations are sorted according to the comprehensive cost to obtain the sorting result; Eliminate candidate positions in the sorting results whose overall cost exceeds a preset cost threshold, and retain the remaining candidate positions as the initial optimized position combination.

[0077] In practice, after extracting candidate locations from the set of logistics center locations, these candidate locations need to be quantitatively evaluated and screened to obtain a preliminary optimized combination of locations. The core of this process is the introduction of two parameters: a transportation length weighting value and a transportation time weighting value. The transportation length weighting value reflects the weight of transportation distance in the overall cost calculation, while the transportation time weighting value reflects the weight of transportation time. The sum of the two weighting values ​​is usually equal to 1, but a non-normalized ratio can also be set according to business needs.

[0078] The specific weighting value depends on the core concerns of the cold chain logistics operator: if fuel costs and vehicle wear and tear are the primary concerns, the transport length weighting value should be set higher (e.g., 0.7); if timeliness and customer satisfaction are the primary objectives, the transport time weighting value should be set higher (e.g., 0.6). In medium- and long-distance cold chain delivery scenarios, a transport length weighting value of 0.4 and a transport time weighting value of 0.6 can be set to balance distance and timeliness.

[0079] Based on the aforementioned weighted values, length cost and time cost are calculated separately. Length cost equals the transport length of the target route segment multiplied by the transport length weighted value. Transport length refers to the actual road network distance from the candidate logistics center location to each demand point it serves, measured in kilometers. Time cost equals the transport time of the target route segment multiplied by the transport time weighted value. Transport time refers to the time spent by the vehicle traveling on that route segment, measured in hours, and its calculation depends on road conditions (speed limits, congestion coefficients, etc.). For a given candidate location, the sum of its length costs to all demand points constitutes the total length cost for that location, and the sum of its time costs constitutes the total time cost. Adding the total length cost to the total time cost yields the comprehensive cost of that candidate location. The comprehensive cost is a dimensionless value used to uniformly compare the economics of different candidate locations.

[0080] For example, suppose a city has three candidate locations A, B, and C, with a transportation length weighting of 0.6 and a transportation time weighting of 0.4. The total transportation length from point A to all demand points is 50 kilometers, and the total transportation time is 2.0 hours. Therefore, the comprehensive cost of A is 50 × 0.6 + 2.0 × 0.4 = 30 + 0.8 = 30.8. Point B has a total length of 45 kilometers and a total time of 1.5 hours, with a comprehensive cost of 45 × 0.6 + 1.5 × 0.4 = 27 + 0.6 = 27.6. Point C has a total length of 60 kilometers and a total time of 2.2 hours, with a comprehensive cost of 60 × 0.6 + 2.2 × 0.4 = 36 + 0.88 = 36.88. After calculating the comprehensive cost of each candidate location, they are sorted in ascending order. A lower comprehensive cost indicates a better overall performance in terms of transportation length and time.

[0081] After obtaining the ranking results, unqualified candidate locations are eliminated based on a preset cost threshold. The preset cost threshold is a pre-set upper limit, which can be determined based on historical data or industry experience. For example, it can be set to 1.2 times the average comprehensive cost of all candidate locations, or a direct absolute value (such as 35.0). In the example above, if the preset cost threshold is 32.0, then point C's comprehensive cost of 36.88 exceeds the threshold and is eliminated; points A (30.8) and B (27.6) are both below the threshold and are retained. Another approach is to retain the top K locations in the ranking results, where K can be determined based on the number of logistics centers planned to be built.

[0082] After the elimination process, the remaining candidate locations constitute the preliminary optimized location combination. Each location in this combination is an optimal set based on a weighted comprehensive evaluation of transportation length and time, having filtered out candidate points with excessively high costs. It should be noted that the preliminary optimized location combination may include multiple logistics center locations, which will serve as the basis for subsequent route simulation and delivery accessibility assessment. If the number of retained locations still exceeds the actual planned construction quantity, a secondary screening can be performed in subsequent steps through further delivery accessibility simulation or capacity constraints. In this way, rapid optimization of the location combination is achieved through quantitative weighting and threshold comparison, avoiding the arbitrariness of relying on experience-based decisions and ensuring the objectivity and repeatability of the optimization results.

[0083] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S10 may include steps S101 to S105: Step S101: Obtain regional demand distribution data and energy supply location data for cold chain logistics, construct a spatial distribution model based on the regional demand distribution data, and obtain the spatial mapping result of regional demand.

[0084] It should be noted that spatial distribution models are a data processing method that transforms discrete demand data into continuous or semi-continuous spatial representations. Regional demand distribution data is presented as discrete geographical units (such as administrative regions, grid blocks, and customer locations), each unit accompanied by a demand value, which can be in tons, units, or order quantities. When constructing a spatial distribution model, kernel density estimation or inverse distance weighted interpolation can be used to diffuse the demand from these discrete points into the surrounding space, forming a continuous demand density map covering the entire target area.

[0085] For example, if an administrative region has an area of ​​100 square kilometers and a demand of 500 tons, its average demand density is 5 tons / square kilometer. This density value is assigned to each raster cell within the region, and then smoothed by combining it with the density values ​​of neighboring regions, ultimately yielding a spatial mapping result of the region's demand. This mapping result is stored in the form of a raster layer in a geographic information system, where each raster (pixel) represents the estimated demand within a unit area (e.g., 1 square kilometer). The spatial mapping result is typically visualized using a color gradient map, with areas of high demand displayed in red and areas of low demand displayed in green, facilitating intuitive identification of demand hotspots.

[0086] Step S102: Based on the spatial mapping results, perform distance calculations for the transportation path length to determine the optimal path set between the demand points in each region and the energy supply points.

[0087] The length of a transport route refers to the actual road network distance from the point of demand (or the centroid of the demand area) to the point of energy supply, rather than the straight-line Euclidean distance. Distance calculation uses shortest path algorithms, such as Dijkstra's algorithm or A* algorithm.

[0088] In its implementation, the high-density grid center points in the spatial mapping of regional demand are extracted as discrete demand representative points, each assigned a demand weight. Simultaneously, energy supply point data is used as the target point input. The algorithm uses a road network topology as the base map, storing distance attributes for each edge, calculating the shortest path length from each demand representative point to each energy supply point, and recording the sequence of nodes traversed and the cumulative distance along the path. For the same demand representative point, if multiple energy supply points exist, the shortest path is selected as the optimal path; for the same energy supply point, multiple demand representative points may be connected. Ultimately, the shortest paths from all demand representative points to their respective optimal energy points constitute the optimal path set. For example, assuming 10 administrative regions as demand representative points and 5 energy stations as supply points, Dijkstra's algorithm can calculate 10 × 5 (50 paths in total). The shortest path is selected for each administrative region, forming the optimal path set of 10 optimal paths.

[0089] Step S103: Based on the constraint of the area coverage radius, perform spatial coverage analysis on the optimal path set to determine the distribution density of demand points within the coverage area and obtain a path node set that meets the preset coverage conditions.

[0090] The area coverage radius is a preset straight-line distance threshold, such as 10 kilometers or 15 kilometers, representing the maximum geographical range that the logistics center can effectively serve. For each path node (i.e., a demand representative point) in the optimal path set, a circle is drawn with that node as the center and the area coverage radius as the radius (or a buffer is generated in the GIS). The number of other demand representative points contained within this circle and the sum of the demand of these points are counted.

[0091] Distribution density refers to the number of demand points or the total demand per unit area, which can be calculated as "the total demand within the coverage radius circle divided by the circle area". If a node can cover a sufficient amount of demand or a sufficiently high demand density within its coverage radius (e.g., the total covered demand exceeds the preset minimum single-point service capacity, or the number of covered demand points exceeds 3), then the node is considered to meet the preset coverage conditions. All path nodes that meet the coverage conditions are extracted to form a path node set, which is used to filter out nodes with advantageous geographical locations that can radiate to a large demand area, and to eliminate isolated or marginalized nodes.

[0092] Step S104: Match the path node set with the energy supply point data to determine the target node whose distance from the energy supply point is less than a preset distance threshold, and obtain a potential location set.

[0093] It should be understood that location matching refers to calculating the actual road network distance (or straight-line distance) from each path node to each energy supply point. If this distance is less than a preset distance threshold, the node is marked as a successfully matched target node. The preset distance threshold is a configurable parameter, such as 5 kilometers or 8 kilometers. Its physical meaning is the engineering feasibility distance for the logistics center to access energy from the energy station. If the distance exceeds this, the energy access cost is too high or technically infeasible. After a successful match, all path nodes marked as target nodes constitute a potential location set. Each node in this set can serve enough demand within its coverage radius and can easily access energy supply, thus meeting the basic conditions to become a candidate location for a logistics center.

[0094] Step S105: By integrating the regional demand distribution data and the regional coverage radius, a spatial overlay analysis is performed on the potential location set to determine the candidate location set that satisfies multiple constraints as the preliminary candidate location set for logistics centers.

[0095] Spatial overlay analysis refers to the process of logically overlapping and calculating multiple spatial layers in a GIS environment. The elements that need to be integrated in this process include: regional demand distribution data (providing demand weights), regional coverage radius (providing radiation range constraints), energy supply point data (providing access feasibility), and node locations in the potential location set.

[0096] In practice, for each node in the potential location set, the weighted sum of demand from all demand points within its coverage radius is calculated, while the access cost from that node to the energy point is evaluated. A comprehensive score is calculated for each node according to a preset scoring rule, for example, comprehensive score = total coverage demand weight factor × normalized total coverage demand value - energy access cost weight factor × normalized energy access cost value. Nodes with comprehensive scores higher than a preset threshold are retained, while nodes with scores lower than the threshold are removed.

[0097] Furthermore, a minimum distance constraint between nodes is introduced to avoid over-concentration of candidate locations (e.g., the distance between any two candidate locations must not be less than 15 kilometers), ensuring a uniform distribution of site selection. After the above multi-layered constraint analysis, one or more nodes constitute a preliminary set of candidate locations for logistics centers. Each location in the set has passed quantitative evaluation in terms of demand coverage, energy access, and spatial distribution, providing a scientifically reliable input for subsequent path simulation and accessibility analysis. Taking a certain city as an example, after the above six-step analysis, three candidate locations, C, D, and E, were selected from the initial hundreds of potential grid points, with coverage rates of 80%, 75%, and 70%, respectively, and comprehensive scores of 0.76, 0.71, and 0.68, respectively, all higher than the preset threshold of 0.65, thus forming a preliminary set of candidate locations.

[0098] In one implementation, step S30 includes steps S301 to S303: Step S301: Based on the optimized location combination, obtain the delivery frequency demand for the corresponding area, and obtain a preliminary resource allocation plan according to the delivery frequency demand and the preset frequency threshold.

[0099] Delivery frequency demand refers to the total number of times a cold chain delivery vehicle is dispatched from a logistics center to its covered demand points within a certain time period (usually daily or weekly). This data can be extracted from historical order systems, transportation management systems (TMS), or customer contract data. For each logistics center in the optimized location combination, its service area is clearly defined, for example, by dividing it into nearest Thiessen polygons or service radii. The delivery frequency demand for that logistics center is obtained by summing the number of deliveries to all demand points within that area.

[0100] After obtaining the required delivery frequency, it is compared with a preset frequency threshold. The preset frequency threshold is an empirical value or a limit set based on transportation resource capacity, such as 30 times per day. Below this threshold, the delivery pressure is relatively low, while above it, the high-frequency delivery area needs to be prioritized for more resources. Based on the comparison results, a preliminary resource allocation plan is obtained, which mainly includes: the number of delivery vehicles required for the logistics center (calculated based on the maximum number of deliveries per vehicle per day), the number of inbound and outbound channels for the cold storage, and the shift arrangements for sorting and loading / unloading personnel.

[0101] For example, the delivery frequencies of five demand points in a certain area are 10 times, 8 times, 6 times, 5 times and 3 times per day, respectively, totaling 32 times / day, which exceeds the preset frequency threshold of 30 times / day. Therefore, the area is marked as high priority, and the initial resource allocation plan is to configure 9 high-frequency delivery vehicles (assuming that each vehicle can complete a maximum of 4 deliveries per day) and add an additional sorting port.

[0102] Step S302: Based on the characteristic data of the stored goods, analyze the temperature control accuracy requirements of the goods for cold storage and determine the appropriate temperature control parameter range.

[0103] It should be noted that the data on the characteristics of stored goods typically consists of parameters such as the type of goods, the storage temperature range, the allowable temperature fluctuation range (i.e., temperature control accuracy), respiration heat, and freezing point. Different goods have significantly different requirements for temperature control accuracy: frozen seafood requires an accuracy of ±0.5℃, because excessive temperature fluctuations will cause ice crystals to repeatedly melt and recrystallize, damaging the cell structure; ice cream requires an accuracy of ±0.3℃ to ensure its taste; and refrigerated medicines (such as vaccines) may require ±0.2℃ or even ±0.1℃.

[0104] In practice, the temperature control accuracy requirements for each type of cargo are extracted from the cargo characteristic data, and the most stringent value among all stored cargo is taken as the upper limit of the overall temperature control parameter range. For example, if a logistics center stores both seafood (±0.5℃) and regular frozen meat (±1.0℃), the appropriate temperature control parameter range should meet ±0.5℃. The temperature control parameter range also includes the set temperature point, such as -18℃ for frozen products and 4℃ for refrigerated products. Unless there are special requirements, the median temperature value from the cargo characteristic data or industry-recommended values ​​can be used. After determining the appropriate temperature control parameter range, this range will serve as the core constraint for subsequent refrigeration equipment selection and capacity calculation, because stricter temperature control accuracy usually requires larger refrigeration power, more frequent start-stop control, and more precise sensor configuration.

[0105] Step S303: Determine the initial value of the capacity configuration as the capacity configuration parameter based on the temperature control parameter range and the technical specifications of the cold chain facility.

[0106] The technical specifications of cold chain facilities may include the building dimensions (length, width, height) of the cold storage, the heat transfer coefficient of the enclosure structure, the model parameters of the evaporator and condenser, the energy efficiency ratio (EER) of the refrigeration unit, etc.; the capacity configuration parameters mainly include two core values: the effective storage volume of the cold storage (unit: cubic meters or tons) and the rated refrigeration power of the refrigeration equipment (unit: kilowatts).

[0107] The process of calculating the initial capacity configuration is as follows: First, the maximum inventory level is calculated based on delivery frequency requirements. The maximum inventory level equals the total daily delivery volume multiplied by the longest storage turnover days (e.g., 3 days for frozen meat). Taking a high-frequency delivery area as an example, the total daily delivery demand is 32 times. Assuming an average delivery volume of 2 tons per delivery, the total daily delivery volume is 64 tons. If the longest turnover days are 2 days, the maximum inventory level is 128 tons. Considering a safety redundancy factor typically between 1.1 and 1.2, here we use 1.15, so the effective cold storage volume requirement is approximately 147 tons, converted to cubic meters based on cargo density. Next, combining the temperature control parameter range (set temperature -18℃, accuracy ±0.5℃) and enclosure structure parameters, the maximum refrigeration power required to maintain the set temperature under the most unfavorable ambient temperature (e.g., 35℃) is calculated using the heat balance equation. The calculation formula is: Refrigeration power = (Envelope heat transfer load + Cargo cooling load + Door opening heat load + Breathing heat) / System energy efficiency ratio. Each load item is calculated based on engineering thermodynamic formulas, and the final calculated cooling power value is the cooling power portion of the initial capacity configuration value.

[0108] Combining the cold storage volume and refrigeration power values ​​yields the complete capacity configuration parameters. It should be noted that these are preliminary initial values; subsequent steps will dynamically adjust them based on energy consumption ratio and load rate to achieve optimal energy efficiency.

[0109] In one implementation, step S50 includes steps S501 to S505: Step S501: Obtain the operating time distribution and energy consumption peak control data from the equipment selection scheme to construct the initial energy consumption distribution dataset of the cold chain network.

[0110] Operating time distribution refers to the start-up and shutdown times and continuous operating durations of various devices in the cold chain system (such as refrigeration compressors, condenser fans, evaporator fans, defrost heaters, etc.) within a typical operating cycle (usually 24 hours or one week). This data can be extracted from the technical manuals accompanying the equipment selection plan, historical operating logs, or actual monitoring data from similar projects. For example, a certain model of refrigeration compressor with a rated power of 50 kilowatts exhibits the following operating time distribution under standard operating conditions: continuous operation from 8:00 to 20:00 during the day with a load rate of 85%; intermittent operation from 20:00 to 8:00 the next day, with 20 minutes on and 40 minutes off per hour.

[0111] Peak energy consumption control data includes the maximum power consumption of the equipment per unit time, as well as the duration and occurrence pattern of the peak. For example, when goods are being moved in and out of a cold storage facility and defrosting is being performed simultaneously, the combined effect of multiple devices may result in an instantaneous power consumption of 80 kilowatts for 15 minutes.

[0112] The operating time distribution and energy consumption peak control data are organized to form an initial energy consumption distribution dataset, which is stored in the form of a time series. At each time point (e.g., every 15 minutes), the expected power of all devices is recorded, as well as the working status identifier of each device.

[0113] Step S502: Perform energy consumption simulation based on the initial energy consumption distribution dataset to determine the range of equipment configuration to be adjusted.

[0114] It should be understood that the purpose of energy consumption simulation is to simulate the hourly energy consumption curves and peak distribution of the cold chain network under actual operating conditions, and to evaluate whether the existing equipment selection scheme will lead to excessively high peak energy consumption or excessive overall energy consumption. The simulation process uses discrete event simulation or a thermodynamic simulation engine based on a physical model. The input boundary conditions include ambient temperature curves, goods entry and exit plans, and cold storage set temperatures. The output is the total energy consumption curve of the entire cold chain network during the simulation period and the individual energy consumption of each piece of equipment.

[0115] By analyzing simulation results, the time periods during which energy consumption peaks exceeded limits and the main equipment causing these peaks were identified. For example, the simulation showed that between 14:00 and 15:00, the total power reached 95 kW, exceeding the preset peak control threshold (e.g., 90 kW). Further analysis revealed that the compressor and defrost heater were operating simultaneously during this period, and frequent opening of the cold storage door caused a surge in heat load. Based on this identification, the range of equipment configurations to be adjusted was determined, i.e., the set of equipment whose operating strategies needed adjustment, such as compressors, defrost controllers, and the opening and closing logic of the cold storage door. The range of equipment configurations to be adjusted can be limited to specific types or locations of equipment to avoid unnecessary large-scale adjustments.

[0116] Step S503: Recalculate the peak energy consumption control data according to the configuration range of the equipment to be adjusted, and obtain the adjusted control strategy.

[0117] The adjusted control strategy involves specific modifications to the equipment operating rules, aiming to reduce peak energy consumption or optimize overall energy consumption. For example, defrosting operations might be moved from 2:00 PM to 10:00 PM to avoid overlapping with compressor peak hours; compressor power might be briefly reduced when the cold storage door is open, relying on the thermal inertia of the cold storage to maintain temperature; or a "staggered operation" strategy might be adopted, offsetting the start-up times of multiple compressors by a few seconds to reduce instantaneous current surges. When recalculating peak energy consumption control data, it is necessary to re-simulate the equipment's operating time distribution based on the adjusted control rules, recalculate the total power at each time point, and extract the new peak values ​​and their durations. The adjusted control strategy should be specific and executable, for example, specifying that "the defrost heater only operates between 10:00 PM and 6:00 AM the next day, for no more than 20 minutes each time" or "when the total power exceeds 85 kilowatts, the lowest priority refrigeration unit automatically reduces its frequency."

[0118] Step S504: Perform energy consumption simulation based on the adjusted control strategy to obtain adjusted peak energy consumption control data and adjusted overall energy consumption data.

[0119] After obtaining the adjusted control strategy, a second energy consumption simulation is performed based on this strategy to obtain adjusted peak energy consumption control data and adjusted overall energy consumption data. The second simulation uses the same boundary conditions as the first (ambient temperature, cargo entry and exit patterns, etc.), but the equipment's control logic is executed according to the new strategy. The simulation outputs a new hourly energy consumption curve, from which the adjusted peak energy consumption (maximum value) and adjusted overall energy consumption data (such as daily total power consumption) are extracted. These two sets of data are then compared with the preset peak control capability threshold and the preset energy consumption target, respectively.

[0120] Step S505: If the adjusted peak energy consumption control data meets the preset peak control capability threshold and the adjusted overall energy consumption data meets the preset energy consumption target, then the device configuration corresponding to the adjusted control strategy is determined as the target energy consumption optimization configuration.

[0121] The preset peak control capability threshold can be determined based on the grid contract capacity or the safety limit of the distribution system, for example, set to 85 kW; the preset energy consumption target is the daily total power consumption limit set based on industry benchmarks or the operator's own energy-saving indicators, for example, set to 1200 kWh / day. If the adjusted peak energy consumption is lower than or equal to the preset threshold, and the adjusted overall energy consumption data is lower than or equal to the preset energy consumption target, then the adjusted control strategy is feasible, and the corresponding equipment configuration (i.e., the equipment selection scheme plus the control strategy) can be determined as the target energy consumption optimization configuration. If it is still not satisfied, the control strategy needs to be further modified, such as further adjusting the defrosting time window or adding a cold storage device, and then simulated and verified again until both conditions are met simultaneously. For example, by shifting defrosting operations from daytime to nighttime, peak energy consumption drops from 95 kW to 82 kW, below the 85 kW threshold, while total daily energy consumption drops from 1280 kWh to 1150 kWh, below the target of 1200 kWh. Therefore, the equipment configuration corresponding to this adjusted control strategy is determined as the target energy consumption optimization configuration, which includes not only the equipment model and quantity, but also specific operation control rules.

[0122] In one implementation, step S60 includes steps S601 to S605: Step S601: Construct a logistics demand dataset based on the target energy consumption optimization configuration and the stored cargo characteristic data.

[0123] After determining the target energy consumption optimization configuration, the energy consumption optimization results are combined with delivery frequency requirements and stored goods characteristic data to generate an executable full-cycle operation plan for the cold chain logistics network.

[0124] It should be noted that the target energy consumption optimization configuration includes equipment operation control strategies, such as cooling power setpoints for each time period and energy consumption peak management rules; the stored goods characteristic data records the temperature requirements, shelf life, batch information, and storage quantity of each type of goods; the logistics demand dataset is the result of linking and integrating the above two types of data with the delivery task.

[0125] In practice, for each batch of goods, the system records its arrival time, storage location, estimated departure time, destination, delivery time window, required transportation temperature range, and the heat load contribution of that batch of goods to the refrigeration system during storage. Simultaneously, the control strategies in the target energy consumption optimization configuration are translated into constraints in the dataset, such as "the refrigeration power must not exceed 80% of the rated value between 14:00 and 16:00 each day." The logistics demand dataset can be stored in a relational database table or in JSON format, containing fields such as goods batch number, storage node ID, planned delivery time, priority, and heat load coefficient.

[0126] For example, in a cold chain logistics operation in a remote mountainous area, a batch of 500 kg of frozen seafood arrives at the warehouse at 10:00 AM on the first day, has a shelf life of 30 days, requires storage at -18℃, and is expected to be delivered to five villages between 8:00 AM and 10:00 AM on the third day. The heat load coefficient of this batch, calculated based on the seafood's respiration heat and packaging method, is 0.2 kW / ton. Entering this information constitutes a record in the logistics demand dataset.

[0127] Step S602: Combine the geographical environment data and the logistics demand dataset to obtain a preliminary layout plan for network deployment.

[0128] Geographic environmental data includes digital elevation models of the target area, road network vector maps, river and mountain distribution, climate zones, and other information. This data can be obtained from a geographic information system database. The initial layout plan for network deployment refers to the spatial arrangement of logistics centers (cold storage nodes) and delivery routes.

[0129] The locations of the storage nodes in the logistics demand dataset (i.e., the logistics centers in the optimized location combination) are used as fixed facility points. Geographical environment data is used to determine the impact of road connectivity and terrain undulations between nodes on transportation energy consumption. For example, if there is a mountain road with an elevation difference exceeding 500 meters between two nodes, the path energy consumption coefficient needs to be increased by 20%. The initial layout plan can use the Thiessen polygon or service radius method to divide the demand area responsible for each logistics center, while simultaneously marking the main transportation routes between nodes on the map.

[0130] Step S603: Based on the energy consumption parameters of the target energy consumption optimization configuration, iteratively optimize the transportation path and storage nodes in the preliminary layout scheme to obtain the optimized network deployment structure.

[0131] Energy consumption parameters may include the ton-kilometer power consumption of transport vehicles (kWh / ton·km), the energy efficiency ratio of refrigeration equipment under different ambient temperatures, and the standby energy consumption coefficient of cold storage. The goal of iterative optimization is to minimize the total energy consumption of the entire network while satisfying the constraints of delivery frequency and goods shelf life. The optimization process uses genetic algorithms or particle swarm optimization algorithms.

[0132] Taking a genetic algorithm as an example: the selection of each transportation route and the activation status of each storage node are encoded as chromosomes. The fitness function is set as the total network energy consumption, including transportation energy consumption (path length × load × ton-kilometer power consumption) and storage energy consumption (refrigeration power of each cold storage × running time). During the iteration process, each time the allocation of delivery routes is changed (for example, transferring the delivery task of a certain village from logistics center A to logistics center B), the total energy consumption is recalculated. If the total energy consumption of the new solution is reduced, the mutation is retained. At the same time, storage nodes can also be adjusted, for example, shutting down an inefficient cold storage in case of excessive energy consumption and transferring its goods to a neighboring cold storage. After several generations (such as 500 generations), when the fitness value converges, the optimized network deployment structure is obtained.

[0133] Step S604: Generate a logistics scheduling schedule based on the optimized network deployment structure, and form a dynamic operation plan based on the logistics scheduling schedule.

[0134] The logistics scheduling schedule refers to the specific work schedule of each logistics center, each delivery vehicle, and each cold storage equipment on each day. The schedule is based on time on the horizontal axis and resources (vehicles, cold storage doors, refrigeration units, personnel) on the vertical axis, indicating the start and end times and resource usage of each task.

[0135] When generating the scheduling schedule, the required delivery frequency (e.g., twice a week to village A) needs to be translated into specific departure times, while matching the equipment operating time distribution in the target energy consumption optimization configuration (e.g., cooling power can be reduced by 30% at night). For example, if the delivery task from the logistics center to village A departs at 8:00 AM every Tuesday and Friday, the corresponding cold storage will perform pre-cooling operations from 5:00 AM to 7:00 AM on the same day to ensure that the goods reach the optimal temperature when leaving the storage.

[0136] Dynamic operation plans, based on fixed schedules, add response mechanisms to real-time factors (such as weather, road congestion, and equipment failure). For example, the plan stipulates that when the real-time temperature exceeds a preset threshold, subsequent delivery tasks will be automatically executed two hours earlier to avoid increased energy consumption due to transportation during high-temperature periods; when a road becomes congested, the system will dynamically switch to an alternative route and shift the subsequent delivery time window accordingly. Dynamic operation plans are stored in the form of rule engines or decision trees, enabling them to automatically adjust scheduling instructions based on sensor data and external interface information during operation.

[0137] Step S605: Analyze the matching degree between the delivery frequency of the dynamic operation plan and the stored cargo characteristic data, allocate resource priorities according to the matching degree, and generate a full-cycle operation plan based on the resource priorities and the delivery frequency requirements.

[0138] It should be noted that matching degree refers to the degree to which the delivery time window matches the optimal time for goods to leave the warehouse, and whether the delivery frequency is synchronized with the rate at which the goods are consumed. For example, if a certain product has a shelf life of only 3 days, but the delivery frequency is set to once a week, the matching degree is low, which will cause the product to expire in cold storage.

[0139] The matching degree is quantified by calculating the deviation between the actual outbound time and the ideal outbound time for each batch of goods, as well as the ratio between inventory turnover rate and delivery frequency. When the matching degree is lower than the threshold (e.g., 0.7), the resource priority of the goods needs to be adjusted, and the delivery tasks of such goods are marked as high priority, with priority allocation of vehicle and cold storage aisle resources.

[0140] Once resource priorities are allocated, a full-cycle operation plan can be generated. This plan covers the complete operational schedule of the logistics network from the current moment until several months or even a year in the future, including: daily delivery task lists, start-up and shutdown schedules for each refrigeration unit, execution times for each defrosting operation, regular maintenance dates for cold storage, energy consumption data monitoring cycles (e.g., data collected every 15 minutes), and contingency plans for handling abnormal situations. This plan is output digitally and can be directly imported into logistics management platforms or building automation systems for execution. For example, the full-cycle operation plan includes an annual delivery schedule (20 deliveries per month), a 7-day adjustment cycle for cold storage operating parameters, an energy consumption monitoring threshold of 300 kWh / week, and real-time monitoring via the Internet of Things to ensure execution deviations are less than 5%. Executing this plan can reduce the total annual energy consumption of the cold chain logistics network by an average of 18% while meeting cargo quality requirements, achieving a low-energy and highly targeted network deployment result.

[0141] This application also provides a cold chain logistics energy consumption management system, the system comprising: a spatial location module, a route optimization module, a capacity configuration module, an equipment selection module, an energy consumption optimization module, and a plan generation module; The spatial location module is used to acquire regional demand distribution data and energy supply point data for cold chain logistics. Based on the transportation route length and regional coverage radius, it performs spatial overlay analysis on the regional demand distribution data and the energy supply point data to determine a preliminary set of candidate locations for logistics centers. The route optimization module is used to simulate routes based on transportation time costs and road conditions, determine the delivery accessibility of each candidate location in the set of candidate locations of the logistics center to the demand point, and generate an optimized location combination based on the delivery accessibility. The capacity configuration module is used to obtain the delivery frequency demand and stored goods characteristic data of the corresponding area based on the optimized location combination, and determine the capacity configuration parameters in combination with the cold storage temperature control accuracy requirements. The equipment selection module is used to obtain a suitable equipment selection scheme based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters. The energy consumption optimization module is used to extract operating time distribution and energy consumption peak control data through the equipment selection scheme in order to determine the target energy consumption optimization configuration; The planning generation module is used to generate a full-cycle operation plan for the cold chain logistics network based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data.

[0142] The cold chain logistics energy consumption management system provided in this application, employing the cold chain logistics energy consumption management method described in the above embodiments, can solve the technical problem that existing methods lack the ability to coordinate and optimize the layout of the cold chain network and the selection of facilities, leading to difficulties in matching the location of logistics centers with the configuration of cold storage and refrigeration equipment to regional needs. Compared with the prior art, the beneficial effects of the cold chain logistics energy consumption management system provided in this application are the same as those of the cold chain logistics energy consumption management method provided in the above embodiments, and other technical features of the cold chain logistics energy consumption management system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0143] It should be noted that, in this document, the terms "comprising," "including," and any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Specific examples have been used in this document to illustrate the principles and implementation methods of the present invention. These examples are only intended to aid in understanding the method and core ideas of the present invention. The above descriptions are merely preferred embodiments of the present invention. It should be pointed out that, due to the limitations of written expression and the objective existence of infinite specific structures, those skilled in the art can make various improvements, modifications, or variations without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner. These improvements, modifications, variations, or combinations, or the direct application of the concept and technical solution of the present invention to other situations without modification, should all be considered within the scope of protection of the present invention.

Claims

1. A method for energy consumption control in cold chain logistics, characterized in that, The method includes: Acquire regional demand distribution data and energy supply location data for cold chain logistics. Perform spatial overlay analysis on the regional demand distribution data and energy supply location data based on transportation route length and regional coverage radius to determine a preliminary set of candidate locations for logistics centers. Based on transportation time costs and road conditions, route simulation is performed to determine the delivery accessibility of each candidate location in the set of candidate locations for the logistics center to the demand point, and an optimized location combination is generated based on the delivery accessibility. Based on the optimized location combination, the delivery frequency demand and stored goods characteristic data of the corresponding area are obtained, and the capacity configuration parameters are determined in combination with the cold storage temperature control accuracy requirements. Based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters, a suitable equipment selection scheme is obtained; The operating time distribution and energy consumption peak control data are extracted using the equipment selection scheme to determine the target energy consumption optimization configuration; Based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data, a full-cycle operation plan for the cold chain logistics network is generated.

2. The cold chain logistics energy consumption control method according to claim 1, characterized in that, The step of obtaining a suitable equipment selection scheme based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters includes: Acquire ambient temperature data and equipment operation records, and determine the range of impact of temperature interference on equipment load based on the historical fluctuation segmentation results of the ambient temperature data and the real-time load data of the equipment operation records; Based on the aforementioned range of influence, dynamic simulation technology is used to simulate the change process of cooling energy consumption and obtain the correlation distribution between the proportion of cooling energy consumption and the equipment load rate. Determine whether the associated distribution exceeds the preset energy consumption ratio range. If it does, perform a hierarchical analysis on the capacity configuration parameters to determine the direction of parameter adjustment. The capacity configuration is redefined based on the adjusted direction of the parameters, and the adjusted capacity configuration is obtained. Dynamic simulation is then performed based on the adjusted capacity configuration to obtain the adjusted cooling energy consumption ratio. Multiple simulations are performed based on the adjusted cooling energy consumption ratio until the adjusted cooling energy consumption ratio meets the preset energy consumption ratio range. This verifies the matching degree between the corresponding equipment load rate and the ambient temperature, and generates a suitable equipment selection scheme.

3. The cold chain logistics energy consumption control method according to claim 2, characterized in that, The parameter adjustment direction includes either capacity reduction or capacity expansion. The step of determining whether the associated distribution exceeds a preset energy consumption ratio range, and if so, performing a hierarchical analysis of the capacity configuration parameters to determine the parameter adjustment direction, includes: Based on the aforementioned correlation distribution, when the equipment load rate is lower than the preset load lower limit and the cooling energy consumption ratio is within the preset energy consumption ratio range, the parameter adjustment direction is determined to be the capacity reduction direction. When the cooling energy consumption ratio exceeds the preset energy consumption ratio range, the parameter adjustment direction is determined to be the capacity expansion direction, and when the ambient temperature continues to be higher than the preset temperature threshold, the standby capacity allocation is triggered to temporarily increase the capacity by the preset ratio.

4. The cold chain logistics energy consumption control method according to claim 1, characterized in that, The process of performing route simulation based on transportation time costs and road conditions to determine the delivery accessibility from each candidate location in the candidate location set of the logistics center to the demand point, and generating an optimized location combination based on the delivery accessibility, includes: Acquire dynamic path change data, and by analyzing the impact of the dynamic path change data on transportation time costs, determine the target path segments affected by the dynamic path change data; Based on road traffic conditions and vehicle load limits, the transportation cost of the target route segment is determined, and alternative locations are extracted from the candidate location set of the logistics center according to the transportation cost. Based on the candidate locations, location combination optimization is performed with the goal of minimizing transportation length and transportation time costs to obtain a preliminary optimized location combination. Based on the preliminary optimized location combination, path simulation is performed according to the road traffic conditions to determine the delivery accessibility from each candidate location in the logistics center candidate location set to the demand point, and an optimized location combination is generated based on the delivery accessibility.

5. The cold chain logistics energy consumption control method according to claim 4, characterized in that, The step of optimizing the location combination based on the candidate locations, with the objective of minimizing transportation length and time costs, to obtain a preliminary optimized location combination includes: Determine the length-weighted value and the time-weighted value of transportation; determine the length cost based on the length-weighted value and the transportation length of the target route segment; determine the time cost based on the time-weighted value and the transportation time of the target route segment. Based on the length cost and the time cost, the comprehensive cost of each candidate location in the set of candidate locations for the logistics center is determined, and the candidate locations are sorted according to the comprehensive cost to obtain the sorting result; Eliminate candidate positions in the sorting results whose overall cost exceeds a preset cost threshold, and retain the remaining candidate positions as the initial optimized position combination.

6. The cold chain logistics energy consumption control method according to claim 1, characterized in that, The process involves acquiring regional demand distribution data and energy supply location data for cold chain logistics, and then performing spatial overlay analysis on the regional demand distribution data and energy supply location data based on transportation route length and regional coverage radius to determine a preliminary set of candidate logistics center locations, including: Obtain regional demand distribution data and energy supply location data for cold chain logistics, construct a spatial distribution model based on the regional demand distribution data, and obtain the spatial mapping results of regional demand; Based on the spatial mapping results, distance calculations are performed on the transportation path length to determine the optimal set of paths between the demand points in each region and the energy supply points. Based on the constraint of the regional coverage radius, spatial coverage analysis is performed on the optimal path set to determine the distribution density of demand points within the coverage area and obtain the path node set that meets the preset coverage conditions. The path node set is matched with the energy supply point data to determine the target node whose distance from the energy supply point is less than a preset distance threshold, thereby obtaining a potential location set. By integrating the regional demand distribution data and the regional coverage radius, a spatial overlay analysis is performed on the potential location set to determine the candidate location set that satisfies multiple constraints as the preliminary candidate location set for logistics centers.

7. The cold chain logistics energy consumption control method according to claim 1, characterized in that, Based on the optimized location combination, the corresponding region's delivery frequency demand and stored goods characteristic data are obtained, and the capacity configuration parameters are determined in conjunction with the cold storage temperature control accuracy requirements, including: Based on the optimized location combination, the delivery frequency demand for the corresponding area is obtained, and a preliminary resource allocation plan is obtained according to the delivery frequency demand and the preset frequency threshold. Based on the characteristics of the stored goods, analyze the temperature control accuracy requirements of the goods in the cold storage and determine the appropriate temperature control parameter range. Based on the temperature control parameter range and the technical specifications of the cold chain facilities, the initial value of the capacity configuration is determined as the capacity configuration parameter.

8. The cold chain logistics energy consumption control method according to claim 7, characterized in that, The step of extracting operating time distribution and peak energy consumption control data through the equipment selection scheme to determine the target energy consumption optimization configuration includes: The operating time distribution and energy consumption peak control data are obtained from the equipment selection scheme to construct the initial energy consumption distribution dataset of the cold chain network; Energy consumption simulation is performed based on the initial energy consumption distribution dataset to determine the range of equipment configuration to be adjusted. The peak energy consumption control data is recalculated based on the range of equipment configuration to be adjusted to obtain the adjusted control strategy. Energy consumption simulation is performed based on the adjusted control strategy to obtain the adjusted peak energy consumption control data and the adjusted overall energy consumption data. If the adjusted peak energy consumption control data meets the preset peak control capability threshold, and the adjusted overall energy consumption data meets the preset energy consumption target, then the device configuration corresponding to the adjusted control strategy is determined as the target energy consumption optimization configuration.

9. The cold chain logistics energy consumption control method according to claim 8, characterized in that, The process of generating a full-cycle operation plan for the cold chain logistics network based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data, includes: Based on the target energy consumption optimization configuration and the stored cargo characteristic data, a logistics demand dataset is constructed. By combining geographical environment data and the aforementioned logistics demand dataset, a preliminary layout plan for network deployment is obtained; Based on the energy consumption parameters of the target energy consumption optimization configuration, the transportation path and storage node in the preliminary layout scheme are iteratively optimized to obtain the optimized network deployment structure. Based on the optimized network deployment structure, a logistics scheduling schedule is generated, and a dynamic operation plan based on the logistics scheduling schedule is formed. The matching degree between the delivery frequency of the dynamic operation plan and the characteristic data of the stored goods is analyzed, resource priorities are allocated according to the matching degree, and a full-cycle operation plan is generated based on the resource priorities and the delivery frequency requirements.

10. A cold chain logistics energy consumption control system, characterized in that, The system includes: a spatial location module, a path optimization module, a capacity configuration module, an equipment selection module, an energy consumption optimization module, and a plan generation module; The spatial location module is used to acquire regional demand distribution data and energy supply point data for cold chain logistics. Based on the transportation route length and regional coverage radius, it performs spatial overlay analysis on the regional demand distribution data and the energy supply point data to determine a preliminary set of candidate locations for logistics centers. The route optimization module is used to simulate routes based on transportation time costs and road conditions, determine the delivery accessibility of each candidate location in the set of candidate locations of the logistics center to the demand point, and generate an optimized location combination based on the delivery accessibility. The capacity configuration module is used to obtain the delivery frequency demand and stored goods characteristic data of the corresponding area based on the optimized location combination, and determine the capacity configuration parameters in combination with the cold storage temperature control accuracy requirements. The equipment selection module is used to obtain a suitable equipment selection scheme based on the cooling energy consumption ratio and equipment load rate corresponding to the capacity configuration parameters. The energy consumption optimization module is used to extract operating time distribution and energy consumption peak control data through the equipment selection scheme in order to determine the target energy consumption optimization configuration; The planning generation module is used to generate a full-cycle operation plan for the cold chain logistics network based on the target energy consumption optimization configuration, combined with the delivery frequency requirements and the stored goods characteristic data.

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