Energy efficiency prediction and energy-saving scheduling optimization system and method of food cold chain system

Abstract

The invention provides an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system, and relates to the technical field of cold chain system energy-saving scheduling, and the system comprises a data recognition module which is used for recognizing cold chain loading state data, and analyzing the space influence characteristics of the loading state data of a cold chain loading space; the strategy obtaining module is used for carrying out energy efficiency optimization on cold chain energy supply according to the space energy efficiency relation between the cold chain space and the link energy supply parameters and the space influence characteristics; the state chain establishment module is used for carrying out loading state dynamic analysis based on the cold chain vehicle path and predicting a path switching node; and the optimization strategy obtaining module is used for carrying out path state node scheduling optimization according to the cold chain path state chain by taking a space energy efficiency strategy as an initial parameter based on the vehicle energy supply coupling relationship and the space energy efficiency relationship of the cold chain system. The technical problem of energy consumption waste in cold chain transportation in the prior art can be solved, and the technical effect of reducing cold chain energy consumption is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy-saving scheduling of cold chain systems, and particularly relates to an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system. BACKGROUND

[0002] With the rapid development of modern food industry and logistics industry, cold chain transportation has become an important link to ensure food safety, extend shelf life and maintain nutritional quality. Especially in the logistics process of perishable foods such as fruits, vegetables, meat, dairy products and seafood, the cold chain system not only undertakes the functions of transportation and storage, but also is directly related to the health of consumers and the economic benefits of enterprises.

[0003] At present, the existing cold chain transportation technology mainly relies on single temperature monitoring and timing energy supply strategy, which usually maintains the cold chain environment by setting fixed refrigeration power, constant air speed and single temperature control point. However, this kind of method is difficult to adapt to the changes of vehicle loading state, external environment fluctuation and temperature distribution difference of different space regions in real time, resulting in problems such as local temperature unevenness, air flow obstruction and unreasonable heat distribution in the cold chain warehouse.

[0004] In summary, in the prior art, there is a technical problem that due to the lack of real-time monitoring and dynamic regulation of the coupling relationship between the loading state in the cold chain warehouse, the spatial distribution of goods, the air flow obstruction condition and the coupling relationship between the vehicle driving energy consumption and the cold chain refrigeration energy consumption, the temperature control is uneven, the local is over-cooled or over-heated, and the energy consumption is wasted seriously in the cold chain transportation process, which further affects the food safety guarantee level, the transportation cost control and the overall operation efficiency of the cold chain system. SUMMARY

[0005] The purpose of the present application is to provide an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system, which solves the technical problem in the prior art that due to the lack of real-time monitoring and dynamic regulation of the coupling relationship between the loading state in the cold chain warehouse, the spatial distribution of goods, the air flow obstruction condition and the coupling relationship between the vehicle driving energy consumption and the cold chain refrigeration energy consumption, the temperature control is uneven, the local is over-cooled or over-heated, and the energy consumption is wasted seriously in the cold chain transportation process, which further affects the food safety guarantee level, the transportation cost control and the overall operation efficiency of the cold chain system.

[0006] In view of the above problems, the present application provides an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system.

[0007] In a first aspect, the application provides an energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system, comprising: a data identification module configured to identify cold chain loading state data, analyze spatial influence characteristics of a cold chain loading space under the influence of the loading state data; a strategy obtaining module configured to perform energy efficiency optimization on cold chain energy supply according to the spatial influence characteristics and according to a spatial energy efficiency relationship between a cold chain space and link energy supply parameters, and obtain a spatial energy efficiency strategy; a state chain establishment module configured to obtain a cold chain vehicle path, perform dynamic analysis of loading states based on the cold chain vehicle path and predict path switching nodes, and establish a cold chain path state chain; and an optimization strategy obtaining module configured to perform path state node scheduling optimization according to the cold chain path state chain with the spatial energy efficiency strategy as an initial parameter based on a vehicle energy supply coupling relationship of the cold chain system and the spatial energy efficiency relationship, and obtain a full-path optimization scheduling strategy, which is a link energy supply parameter strategy that minimizes energy consumption under the premise of meeting cold chain targets.

[0008] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system for the food cold chain system further comprises a state chain establishment unit configured to form a state sequence according to path switching nodes on a preset transportation path, wherein the cold chain path state chain comprises a plurality of link state nodes, and each state node comprises vehicle loading state parameters, environmental parameters and energy supply parameters for describing the running state of the cold chain system on a full-task path segment, and the path switching nodes include loading and unloading points, stopping points and route segmentation points.

[0009] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system for the food cold chain system further comprises an information acquisition unit configured to acquire loading article information in a cold chain warehouse, including article attributes and quantities, location spatial distribution and stacking mode, calculate the distribution space proportion of the loading articles in the cold chain warehouse, and obtain the loading state data; a model construction unit configured to construct a three-dimensional space model of the cold chain warehouse based on the loading state data, and calculate and mark the relative positional relationship between the goods and the warehouse wall and the goods and the air duct; and a feature obtaining unit configured to extract spatial influence factors according to the three-dimensional space model, perform influence weighting identification using the spatial influence factors, and obtain the spatial influence characteristics of the cold chain loading space under the influence of the loading state data, which are used to represent the influence spatial coordinate position and degree of the loading space distribution on the cold chain space temperature distribution.

[0010] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system for the food cold chain system further comprises that the spatial influence factors include cold air flow passage blockage rate, loading uniformity index, heat exchange surface area and convection path length, and air circulation effectiveness coefficient.

[0011] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system of the food cold chain system further comprises: a section establishing channel for establishing a plurality of sections along the center line of the air duct based on the three-dimensional space model, calculating the cavity flow area of each section, comparing with the normal flow area, and obtaining the cold air flow passage blockage rate; an index determining channel for dividing the loading space into grids, calculating the loading occupancy rate of each grid, and determining the loading uniformity index according to the loading occupancy rate variance of the grid unit; a length obtaining channel for screening the effective heat exchange surface in direct contact with air according to the exposed surface area of the goods, analyzing the flow path of the air outlet and the return air outlet based on the effective heat exchange surface, and obtaining the convection path length; and a coefficient identifying channel for measuring the temperature and gas concentration decay time at different positions in the cold chain warehouse by arranging temperature sensors and gas sensors, identifying the air circulation effectiveness coefficient according to the decay concentration change period, and using the air circulation effectiveness coefficient to represent the overall efficiency of the air circulation in the warehouse.

[0012] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system of the food cold chain system further comprises: a grid division unit for dividing the cold chain space into grids according to sample data of the cold chain space; a relationship establishing unit for adjusting the link energy supply parameters based on the cold chain space grid, analyzing the temperature change of each space grid, and establishing a corresponding relationship between the link energy supply parameters and the temperature change of the space grid; a relationship forming unit for calculating the space grid temperature energy consumption under each link energy supply parameter based on the corresponding relationship, forming a mapping relationship between the link energy supply parameters and the energy efficiency of the cold chain space, and taking the space energy efficiency relationship as the mapping relationship.

[0013] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system of the food cold chain system further comprises: a distribution map establishing unit for establishing a space energy consumption obstacle distribution map based on the space influence characteristics, for representing the obstruction degree of different space regions to cold air circulation and temperature equalization; a heat map establishing unit for establishing an energy efficiency coverage heat map using the space energy efficiency relationship, for representing the temperature control coverage ability of the link energy supply parameters to each space region; a superposition analysis unit for superimposing and analyzing the space energy consumption obstacle distribution map and the energy efficiency coverage heat map to obtain the energy efficiency distribution of the cold chain space; and a strategy obtaining unit for adjusting and compensating the link energy supply parameters based on the energy efficiency distribution to obtain a space energy efficiency strategy that meets the food cold chain demand temperature and minimizes energy consumption.

[0014] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system of the food cold chain system further comprises: a path determination unit configured to obtain task data of the cold chain vehicle, perform task data path identification and extraction, and determine the cold chain vehicle path; a change obtaining unit configured to perform dynamic analysis on the cold chain loading state based on the cold chain vehicle path in combination with the loading and unloading plan and the transportation time sequence of the vehicle, and obtain the loading state change of each path section; a node prediction unit configured to predict path switching nodes according to the loading state change and the path section characteristics, the path switching nodes including at least one of a stop point, a loading and unloading point, and a power supply mode switching point; and a state chain construction unit configured to construct a state sequence of the path section and the corresponding loading state, environmental parameters, and power supply parameters with the path switching nodes as the segmentation points, and obtain the cold chain path state chain.

[0015] Preferably, the energy efficiency prediction and energy-saving scheduling optimization system of the food cold chain system further comprises: a relationship analysis unit configured to analyze the energy consumption coupling relationship between vehicle driving power supply and cold chain system power supply, and establish a power supply energy efficiency evaluation model configured to represent the synergistic effect of the whole vehicle energy consumption and the cold chain energy consumption under different power supply modes; a change amount obtaining unit configured to identify the loading change, vehicle engine shutdown and auxiliary power supply switching, and external environmental parameter change of each path state node based on the cold chain path state chain, obtain the node state change amount, and establish an adjustment state chain; an iterative optimization unit configured to perform time sequence iterative optimization on the adjustment state chain with the space energy efficiency strategy as the initial parameter, and obtain the optimal energy efficiency strategy of each node; and an integration unit configured to take the cold chain temperature control target of each node as the optimization target, perform link energy efficiency evaluation integration on the optimal energy efficiency strategy of each node based on the power supply energy efficiency evaluation model and in combination with the adjustment state chain and the temperature decay and delay change relationship of the cold chain, and obtain the whole path optimization scheduling strategy.

[0016] In a second aspect, the present application also provides an energy efficiency prediction and energy-saving scheduling optimization method of a food cold chain system, comprising: identifying cold chain loading state data, analyzing the spatial influence characteristics of the cold chain loading space under the influence of the loading state data; performing energy efficiency optimization on the cold chain power supply according to the spatial energy efficiency relationship between the cold chain space and the link power supply parameters, and obtaining a space energy efficiency strategy according to the spatial influence characteristics; obtaining a cold chain vehicle path, performing dynamic analysis on the loading state based on the cold chain vehicle path, and predicting path switching nodes to establish a cold chain path state chain; and performing path state node scheduling optimization according to the cold chain vehicle power supply coupling relationship and the spatial energy efficiency relationship with the space energy efficiency strategy as the initial parameter in the cold chain path state chain, and obtaining a whole path optimization scheduling strategy, which is a link power supply parameter strategy that minimizes energy consumption under the premise of meeting the cold chain target.

[0017] The technical solutions provided in the application have at least the following technical effects or advantages: by achieving the intelligent energy supply control technical goal based on cold chain space energy efficiency analysis and full path optimization scheduling, the technical effect of minimizing the energy consumption of the whole vehicle and the cold chain system, improving transportation safety and system operation efficiency under the premise of guaranteeing food temperature control requirements is achieved.

[0018] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the following detailed description can be implemented according to the content of the description, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following detailed description of the application is given. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating laborious work on the basis of the provided drawings.

[0020] Figure 1 It is a structural schematic diagram of the energy efficiency prediction and energy saving scheduling optimization system of the food cold chain system of the application.

[0021] Figure 2 It is a flowchart of the energy efficiency prediction and energy saving scheduling optimization method of the food cold chain system of the application.

[0022] Explanation of reference signs: data identification module 1, strategy obtaining module 2, state chain establishing module 3, optimization strategy obtaining module 4. DETAILED DESCRIPTION

[0023] The application provides an energy efficiency prediction and energy saving scheduling optimization system and method for a food cold chain system, which solves the technical problem in the prior art that due to the lack of real-time monitoring and dynamic regulation of the coupling relationship between the loading state in the cold chain warehouse, the space distribution of goods, the air flow obstruction condition and the energy consumption of vehicle driving and the refrigeration energy consumption of the cold chain, the temperature control is uneven, local overcooling or overheating and energy waste is serious in the cold chain transportation process, which further affects the food safety guarantee level, transportation cost control and the overall operation efficiency of the cold chain system. The intelligent energy supply control technical goal based on cold chain space energy efficiency analysis and full path optimization scheduling is achieved, and the technical effect of minimizing the energy consumption of the whole vehicle and the cold chain system, improving transportation safety and system operation efficiency under the premise of guaranteeing food temperature control requirements is achieved.

[0024] The technical solutions in the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for the convenience of description, only some of the parts related to the present application are shown in the drawings, rather than all the parts.

[0025] Embodiment one, please refer to the attached Figure 1 The present application provides an energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system, specifically comprising: A data recognition module 1 is configured to recognize cold chain loading state data and analyze the spatial influence characteristics of the cold chain loading space under the loading state data.

[0026] Specifically, recognizing the cold chain loading state data means collecting and organizing the attributes, quantity, volume and placement of goods in the cold chain warehouse, so as to obtain data information capable of describing the overall loading condition in the warehouse, including the basic parameters of the goods, and covering the spatial position and stacking layers of the goods in the warehouse.

[0027] Analyzing the spatial influence characteristics of the cold chain loading space under the loading state data means further studying the influence of the distribution of goods on the temperature and air circulation in the cold chain warehouse on the basis of obtaining the loading state data. The spatial influence characteristics can manifest as local blockage, uneven air flow or temperature gradient, etc., and are used to quantify the effect of the placement of goods on energy efficiency and temperature balance.

[0028] A strategy obtaining module 2 is configured to perform energy efficiency optimization on the cold chain energy supply according to the spatial energy efficiency relationship between the cold chain space and the link energy supply parameters, and obtain a spatial energy efficiency strategy according to the spatial influence characteristics.

[0029] Specifically, the space energy efficiency relationship is constructed according to the cold chain space and link energy supply parameters, that is, the temperature change of the cold chain warehouse internal space is associated with the power of the refrigeration equipment, the fan speed and the energy supply time sequence and other parameters, so as to determine the corresponding relationship between energy supply and energy efficiency. For example, when the refrigeration power increases from 5 kW to 6 kW, the overall temperature drop speed increases by 25%, but the energy consumption increases by 15%. According to the space energy efficiency relationship and the space influence characteristics, the energy efficiency of the cold chain energy supply is optimized, that is, the energy supply mode with the lowest energy consumption under the condition of meeting the cold chain temperature control requirements is found by combining the airflow blockage rate, loading uniformity, effective heat exchange surface area and air circulation effectiveness and other characteristics of the cold chain warehouse interior, and the space energy efficiency strategy is obtained. For example, under the condition that the blockage rate increases by 10%, if the fan speed is not adjusted, the energy consumption may increase by 20%, but by increasing the fan speed by 5%, the energy consumption increase can be controlled to 8%. The space energy efficiency strategy can guide how to allocate power, adjust the air duct and optimize the energy supply time sequence of the refrigeration system to reduce unnecessary energy consumption while ensuring that the temperature meets the standard.

[0030] The state chain establishment module 3 is configured to obtain a cold chain vehicle path, perform dynamic analysis on a loading state based on the cold chain vehicle path, predict a path switching node, and establish a cold chain path state chain.

[0031] Specifically, before the cold chain transportation task starts, the complete driving route of the vehicle from the starting point to the destination is determined according to the transportation demand and navigation information, and the cold chain vehicle path is obtained. The cold chain vehicle path may include highway sections, urban roads, and possible stopping points and loading and unloading points.

[0032] After the transportation path is determined, the loading proportion and change trend of the goods are analyzed in real time based on the loading and unloading plans and operation conditions of the cold chain vehicle in different road sections, and the dynamic analysis result of the loading state is obtained. At the same time, the path switching nodes including the loading and unloading points, stopping points and energy supply mode switching points are predicted according to the dynamic analysis result of the loading state. The entire transportation path is segmented according to the predicted path switching nodes, and the loading state, environmental conditions and energy supply parameters corresponding to each segment are connected in series to form an ordered state sequence, and the cold chain path state chain is established, which can record the running state of the vehicle throughout the journey.

[0033] The optimization strategy obtaining module 4 is configured to perform path state node scheduling optimization according to the cold chain path state chain with the space energy efficiency strategy as the initial parameter based on the vehicle energy supply coupling relationship of the cold chain system and the space energy efficiency relationship, and obtain a full-path optimization scheduling strategy, which is a link energy supply parameter strategy that minimizes energy consumption under the premise of meeting the cold chain target.

[0034] Specifically, the vehicle energy supply coupling relationship and the space energy efficiency relationship of the cold chain system are obtained, that is, the mutual influence between the energy required for vehicle driving and the energy consumption of the cold chain refrigeration system, and the influence of energy supply distribution on overall energy consumption and temperature control is determined by combining the temperature control energy efficiency characteristics of different areas in the cold chain warehouse. For example, in the vehicle acceleration stage, the traction energy consumption increases, which may cause the cold chain refrigeration power to need to be adjusted to maintain the temperature target.

[0035] Based on the vehicle energy supply coupling relationship and the space energy efficiency relationship, the space energy efficiency strategy is used as the initial parameter, the preliminary energy efficiency strategy obtained by the space influence characteristics and energy supply optimization, which has formed an energy supply distribution scheme in different areas in the cold chain warehouse, is taken as the starting point, and the path state node scheduling optimization is performed according to the cold chain path state chain. That is, the energy supply scheme of each node is optimized and calculated by combining the loading state, environmental parameters and energy supply parameters of the vehicle in each path state node of the transportation task, so that the energy consumption is as low as possible while maintaining the temperature control target, and then the full-path optimization scheduling strategy is obtained. The full-path optimization scheduling strategy is a link energy supply parameter strategy that minimizes energy consumption under the premise of meeting the cold chain target, so as to ensure that the temperature of the goods is maintained within the target range, and the total energy consumption of the whole transportation process is minimized.

[0036] Further, the application also includes a state chain establishment unit for the cold chain path state chain, which is a state sequence formed according to path switching nodes on a preset transportation path, includes a plurality of link state nodes, and each state node contains vehicle loading state parameters, environmental parameters and energy supply parameters, for describing the running state of the cold chain system on the whole task path segment, wherein the path switching nodes include loading and unloading points, stopping points and route segmentation points.

[0037] Specifically, the cold chain path state chain is a state sequence formed according to path switching nodes on a preset transportation path, that is, a transportation path planned in advance according to the driving route of the vehicle before the cold chain transportation task starts, and the key positions are marked in the pre-planned transportation path. When the vehicle drives to the key position, it is recorded as a new state node. For example, the section from the starting point to the first loading and unloading point is a path segment, and a new state node is formed when the vehicle reaches another loading and unloading point.

[0038] The cold chain path state chain includes a plurality of link state nodes, that is, a series of nodes are generated during the transportation process, and the whole route is divided into several sections, and each link state node represents a specific running stage. For example, a transportation task may include 10 loading and unloading points and 5 stopping points, and at least 15 nodes will be formed in the cold chain path state chain, and each link state node describes the running characteristics of the cold chain system.

[0039] Each link status node includes vehicle loading status parameters, environmental parameters, and power supply parameters. Vehicle loading status parameters refer to the quantity, weight, and distribution of goods inside the vehicle; environmental parameters refer to external temperature, humidity, and weather conditions during transportation; and power supply parameters represent the power, operating mode, and energy consumption of the refrigeration equipment. These parameters reflect the operating status of the refrigerated vehicle at a given moment.

[0040] Vehicle loading status parameters, environmental parameters, and energy supply parameters are used to describe the operating status of the cold chain system throughout the entire mission path. That is, through continuous link status nodes, the cold chain operation can be completely depicted from departure to arrival at the destination, gradually showing the full picture of temperature control, energy consumption changes, and environmental interference. For example, energy consumption is low but temperature fluctuations are large in the first half of the transportation, while energy consumption increases but temperature control tends to stabilize in the second half of the transportation.

[0041] Route switching nodes include loading / unloading points, stopping points, and route segmentation points. Loading / unloading points refer to the locations where goods are loaded onto or unloaded from the vehicle, stopping points are places where vehicles stop en route, and route segmentation points are key locations where the transportation route is naturally divided, such as highway exits or administrative boundary points.

[0042] Furthermore, this application also includes: an information acquisition unit, used to acquire information on loaded items in the cold chain warehouse, including item attributes and their quantity, spatial distribution of location, stacking method, calculate the spatial proportion of the loaded items in the cold chain warehouse, and obtain the loading status data; a model construction unit, used to construct a three-dimensional spatial model of the interior of the cold chain warehouse based on the loading status data, and calculate and mark the relative positional relationships between goods and warehouse walls, and goods and air ducts; and a feature acquisition unit, used to extract spatial influence factors based on the three-dimensional spatial model, use the spatial influence factors for influence weighted identification, and obtain the spatial influence features of the cold chain loading space on the loading status data, used to characterize the spatial coordinate position and degree of influence of the loading space distribution on the temperature distribution of the cold chain space.

[0043] Specifically, the data collection process involves acquiring information about the items loaded in the cold chain warehouse. This includes obtaining the specific attributes of each item, such as its type, weight, and volume. The quantity of each item is also counted, the spatial distribution of each item within the cold chain warehouse is recorded, and the stacking method of each item is analyzed, such as single-layer or multi-layer staggered stacking. Then, based on the actual volume occupied by each item, the spatial proportion of the items within the entire cold chain warehouse is calculated, forming a complete set of loading status data to reflect the overall distribution of items within the warehouse.

[0044] Then, based on the loading status data, a three-dimensional spatial model of the cold chain warehouse can be constructed. That is, the three-dimensional layout of the cold chain warehouse can be simulated in the computer, and the size, position and relative height of each loaded item can be represented. At the same time, the specific positions of the warehouse walls and air ducts are considered, so as to calculate the distance between the goods and the warehouse walls and the gap between the goods and the air ducts, and mark the positions that may affect the air circulation or the cold air coverage effect. For example, if the goods are close to the warehouse walls, they may obstruct the airflow, while if they are too far away from the air ducts, the cold air may not be able to reach evenly.

[0045] Next, based on the constructed three-dimensional spatial model, spatial influencing factors can be extracted, including key indicators that affect the temperature distribution of the cold chain, such as the cold airflow channel blockage rate, loading uniformity index, heat exchange surface area and convection path length, and air circulation effectiveness coefficient. By weighting the spatial influencing factors, the degree and specific location of their influence on the temperature of the cold chain space can be identified, thereby obtaining the spatial influence characteristics of the cold chain loading space on the loading status data. This can clearly represent the degree of obstruction and influence of different areas in the cold chain warehouse on temperature balance.

[0046] Furthermore, this application also includes: the spatial influence factors include: cold airflow channel blockage rate, loading uniformity index, heat exchange surface area and convection path length, and air circulation effectiveness coefficient.

[0047] Specifically, the cold airflow channel blockage rate refers to the proportion of reduced airflow area caused by the obstruction of air ducts in the cold chain warehouse by goods or stacking methods. It reflects whether the airflow is smooth during the transportation process. If the blockage rate is too high, the cold air will not be able to evenly cover the space inside the warehouse.

[0048] The loading uniformity index is a standard measure derived from the statistical distribution of goods in the warehouse. It indicates whether the goods are stacked evenly in different areas. When the goods are concentrated in a certain area, the local space will be overcrowded, while other areas will be relatively empty, resulting in uneven temperature distribution. For example, if the warehouse is divided into 10 grids, and the ideal occupancy rate of each grid is 50%, if some grids have an occupancy rate of 80% while some grids have only 20%, the uniformity index is too low.

[0049] Heat exchange surface area and convection path length refer to the exposed surface area of ​​goods that can directly contact the air and the path length required for airflow from the air outlet, through the goods, and to the air return outlet. A larger heat exchange surface area means more opportunities for cold air to come into contact with the goods, resulting in a better cooling effect. The path length, on the other hand, affects the efficiency of airflow circulation within the warehouse. For example, if the effective heat exchange surface area is 20 square meters and the airflow path is 6 meters, the cooling process will be more complex and delayed than if the path is only 4 meters.

[0050] The air circulation effectiveness coefficient refers to the cold air circulation efficiency parameter calculated by measuring the air flow speed, temperature change and gas concentration decay in the chamber through sensors. It can reflect the uniformity and speed of air flow in the entire chamber. The higher the value, the smoother the air flow and the more balanced the temperature distribution. For example, when the gas concentration decay time is shortened from 15 seconds to 10 seconds, the air circulation effectiveness coefficient increases by about 50%.

[0051] Furthermore, this application also includes: a cross-section establishment channel, used to establish multiple cross-sections along the centerline of the air duct based on a three-dimensional spatial model, calculate the cavity flow area of ​​each cross-section, and compare it with the normal flow area to obtain the cold airflow channel blockage rate; an index determination channel, used to divide the loading space into grids, calculate the loading occupancy rate of each grid, and determine the loading uniformity index according to the variance of the loading occupancy rate of the grid unit; a length acquisition channel, used to screen effective heat exchange surfaces that are in direct contact with the air based on the exposed surface area of ​​the goods, and analyze the flow path of the air outlet and return air outlet based on the effective heat exchange surfaces to obtain the convection path length; and a coefficient identification channel, used to measure the temperature and gas concentration decay time at different locations in the cold chain warehouse by deploying temperature sensors and gas sensors, and identify the air circulation effectiveness coefficient according to the decay concentration change period to characterize the overall efficiency of the cold air circulation in the warehouse.

[0052] Specifically, multiple cross-sections are established along the centerline of the air duct based on a three-dimensional spatial model. In the process of simulating the air duct in the cold chain warehouse, the centerline of the air duct is used as a reference line, and multiple cross-sections are cut at certain intervals on the reference line. Then, the flowable area of ​​the cavity on each cross-section is calculated, which is the actual space through which cold air can pass. This is then compared with the normal flow area under the design state to obtain the cold air flow channel blockage rate. For example, if the design cross-sectional area is 2 square meters, but the flowable area of ​​a certain cross-section is only 1.4 square meters, then the blockage rate at that point is 30%.

[0053] Next, the loading space is divided into grids, that is, the entire three-dimensional space of the cold chain warehouse is divided into multiple small unit grids of equal volume. Each grid is used to represent the loading situation of a local area in the warehouse. Then, the loading occupancy rate of each grid is calculated, that is, the ratio of the volume occupied by the goods in the grid to the total volume of the grid. Then, the variance of the occupancy rates of all grids is used to measure the uniformity of the goods distribution. The smaller the variance of the loading occupancy rate, the more uniform the goods are distributed. The larger the variance of the loading occupancy rate, the more crowded some areas are and the more empty some areas are, indicating that the unevenness of the goods stacking is enhanced.

[0054] Then, based on the exposed surface area of ​​the goods, the parts that are in direct contact with the air are selected as the effective heat exchange surfaces. Only the surface area directly exposed to the airflow can exchange heat with the cold air. Then, combined with the location of the effective heat exchange surfaces, the airflow path from the air outlet to the air return outlet is analyzed, and the airflow distance over the surface of the goods is calculated to obtain the convection path length. For example, if the effective heat exchange area of ​​a batch of goods is 15 square meters, and the airflow needs to travel 5 meters around the goods to reach the air return outlet, then the convection path length is 5 meters.

[0055] Finally, by deploying temperature and gas sensors, the temperature and gas concentration at different locations within the cold chain warehouse can be measured over time. For example, the time required for the carbon dioxide concentration to drop from 1000 mg / m³ to 500 mg / m³ can be measured. The smoothness of air circulation can then be assessed based on the periodic characteristics of the concentration changes, thereby identifying the air circulation effectiveness coefficient. A higher air circulation effectiveness coefficient indicates higher efficiency of the cold air circulation.

[0056] Furthermore, this application also includes: a grid division unit, used to divide the cold chain space into grids based on sample data of the cold chain space; a relationship establishment unit, used to adjust the link power supply parameters based on the cold chain space grid, analyze the temperature changes of each space grid, and establish a correspondence between the link power supply parameters and the temperature changes of the space grid; and a relationship formation unit, used to calculate the temperature energy consumption of the space grid under the adjustment of each link power supply parameter based on the correspondence, and form a mapping relationship between the link power supply parameters and the energy efficiency of the cold chain space, as the space energy efficiency relationship.

[0057] Specifically, the cold chain space is divided into grids based on sample data. This involves using historical data such as temperature, humidity, and airflow distribution to divide the internal space of the cold chain warehouse into several small units. Each grid unit represents an independent computing area, which allows for more precise analysis of local differences in temperature and energy consumption.

[0058] Based on the cold chain spatial grid, the link power supply parameters are adjusted to change the power supply mode and intensity of cold chain equipment, such as adjusting the power of refrigeration units or the speed of fans, so as to observe the temperature change in each grid unit over time, and then establish the correspondence between power supply parameters and grid temperature changes.

[0059] Based on the correspondence, the spatial grid temperature consumption under the adjustment of the power supply parameters of each link is calculated. The temperature change and energy consumption of each grid under different power supply conditions are comprehensively analyzed to obtain the mapping relationship between power supply and energy efficiency. This can reveal which power supply mode is the most energy-efficient under the premise of meeting the temperature target. For example, when the power supply increases by 20%, the overall energy consumption increases by 15%, but the temperature drop rate increases by 25%, indicating that there is an optimal point that can achieve a balance between energy consumption and temperature control.

[0060] Furthermore, this application also includes: a distribution map establishment unit, used to establish a spatial energy consumption obstacle distribution map based on the spatial influence characteristics, used to represent the degree of obstacle of different spatial regions to cold air circulation and temperature balance; a heat map establishment unit, used to establish an energy efficiency coverage heat map using the spatial energy efficiency relationship, used to represent the temperature control coverage capability of the link energy supply parameters for each spatial region; an overlay analysis unit, used to overlay and analyze the spatial energy consumption obstacle distribution map and the energy efficiency coverage heat map to obtain the energy efficiency distribution of the cold chain space; and a strategy acquisition unit, used to adjust and compensate the link energy supply parameters based on the energy efficiency distribution with the food cold chain demand temperature as the target, to obtain a spatial energy efficiency strategy that meets the food cold chain demand temperature and minimizes energy consumption.

[0061] Specifically, a spatial energy consumption obstruction distribution map is established based on spatial impact characteristics. This involves visually marking areas within the cold chain warehouse where factors such as cargo stacking, airflow obstruction, or uneven temperature lead to a decrease in cold air circulation efficiency. The spatial energy consumption obstruction distribution map can clearly show spatial areas with significant airflow obstruction or heat accumulation. For example, in a refrigerated truck, the area near the door experiences 40% greater obstruction than the central area due to frequent opening and closing.

[0062] By utilizing spatial energy efficiency relationships to establish an energy efficiency coverage heat map, that is, based on the mapping relationship between the parameters of the energy supply equipment and the temperature control effect, an image is generated showing the space that the air can cover and the strength of the coverage effect. This can then show the cooling capacity of different areas under different energy supply conditions. For example, when the power of the chiller is set to 8 kilowatts, the temperature of 80% of the area can be stabilized below 5 degrees Celsius, while the temperature of the remaining 20% ​​of the area is still maintained above 7 degrees Celsius.

[0063] By overlaying the spatial energy consumption obstruction distribution map with the energy efficiency coverage heat map, the area that obstructs the flow of cold air is combined with the effect of energy supply coverage, thereby obtaining the overall energy efficiency distribution. This can reveal areas that need additional compensation under the same energy supply conditions, as well as areas that have been over-supplied with energy. For example, after overlaying, it was found that the temperature of the upper shelf dropped by 2 degrees Celsius too quickly, while the temperature of the lower shelf dropped by only 0.5 degrees Celsius due to obstruction.

[0064] Under the condition of meeting the constant temperature required for goods, and with the temperature requirement of the food cold chain as the target, the energy supply parameters of the link are adjusted and compensated based on the energy efficiency distribution. That is, by precisely adjusting the power of the refrigeration unit, the fan speed, or the energy supply sequence, the overall energy consumption is minimized. For example, while maintaining the average temperature of the entire warehouse at 4 degrees Celsius, by optimizing the energy supply strategy, the total energy consumption is reduced from 100 kWh to 85 kWh, achieving an energy saving rate of 15%.

[0065] Furthermore, this application also includes: a path determination unit, used to acquire task data of cold chain vehicles, perform task data path identification and extraction, and determine the path of the cold chain vehicles; a change acquisition unit, used to dynamically analyze the loading status of the cold chain based on the cold chain vehicle path and the vehicle's loading and unloading plan and transportation sequence, and obtain the loading status changes of each path segment; a node prediction unit, used to predict path switching nodes based on the loading status changes and path segment characteristics, wherein the path switching nodes include at least one of stopping points, loading and unloading points, and energy supply mode switching points; and a state chain construction unit, used to construct a state sequence of path segments and corresponding loading status, environmental parameters, and energy supply parameters using the path switching nodes as dividing points, to obtain the cold chain path state chain.

[0066] Specifically, all data involved in a single cold chain transportation mission is collected and analyzed to obtain the mission data of the cold chain vehicles. By identifying and extracting the mission data path, the complete route that the cold chain vehicles will actually travel is determined, including information such as the origin, destination, route, mission duration, and required temperature of the goods. For example, the entire journey from the warehouse to the supermarket is 200 kilometers, passing through highways and urban roads.

[0067] The loading and unloading plan represents the arrangement of goods being loaded and unloaded at different locations, while the transportation sequence represents the time sequence of each stage in the task. Based on the cold chain vehicle route combined with the vehicle's loading and unloading plan and transportation sequence, the cold chain loading status is dynamically analyzed. That is, based on the determined driving route, combined with the changes in goods and transportation arrangements at different time periods, the changes in the loading status of goods can be dynamically tracked. For example, the goods are fully loaded for the first 50 kilometers, then 20% is unloaded at the first loading and unloading point, and the remaining 80% continues to be transported.

[0068] Based on changes in loading status and characteristics of route segments, predict route switching nodes. That is, by analyzing changes in cargo during vehicle travel and environmental characteristics of the segment, identify key nodes that cause changes in the cold chain operation status in advance as route switching nodes. These include stops, loading and unloading points, and energy supply mode switching points. For example, stops are locations where vehicles briefly stop when there is traffic congestion in urban areas, or energy supply mode switching points are locations in mountainous areas where the energy supply mode needs to be adjusted.

[0069] Using path switching nodes as dividing points, a state sequence is constructed that connects path segments with corresponding loading status, environmental parameters, and energy supply parameters. In other words, the entire transportation path is divided into multiple continuous segments according to the path switching nodes. Each segment is bound to specific cargo loading status, external environmental conditions, and energy supply parameters, forming an ordered sequence as a cold chain path state chain. For example, in a 300-kilometer transportation task, 15 segments may be generated, and each segment has independent temperature control requirements and energy consumption records.

[0070] Furthermore, this application also includes: a relationship parsing unit, used to parse the energy consumption coupling relationship between vehicle driving energy supply and cold chain system energy supply, and establish an energy supply efficiency evaluation model to characterize the synergistic effect of vehicle energy consumption and cold chain energy consumption under different energy supply modes; a change quantity acquisition unit, used to identify the loading changes, vehicle shutdown and auxiliary energy supply switching, and external environmental parameter changes of each path state node based on the cold chain path state chain, obtain the node state change quantity, and establish an adjustment state chain; an iterative optimization unit, used to perform time-series iterative optimization with the spatial energy efficiency strategy as the initial parameter and in combination with the adjustment state chain to obtain the optimal energy efficiency strategy for each node; and an integration unit, used to perform link energy efficiency evaluation and integration of the optimal energy efficiency strategy for each node with the cold chain temperature control target of the loaded items at each node as the optimization target, based on the energy supply efficiency evaluation model, and in combination with the temperature decay and delay change relationship of the adjustment state chain and the cold chain, to obtain the full-path optimized scheduling strategy.

[0071] Specifically, this study analyzes the energy consumption coupling relationship between vehicle driving energy supply and cold chain system energy supply, and establishes an energy efficiency evaluation model. This involves studying the mutual influence between the fuel or electrical energy consumed by the vehicle during driving and the energy consumed by the cold chain system to maintain a low-temperature environment. The energy consumption coupling relationship reflects the competition and synergy between the two under the same energy source. For example, when the vehicle accelerates, traction energy consumption increases, leading to a reduction in the energy supply available to the cold chain system, which may consequently decrease the cold chain system's energy efficiency. By establishing an energy efficiency evaluation model based on the energy consumption coupling relationship, the synergistic effect of vehicle energy consumption and cold chain energy consumption under different energy supply modes can be mathematically quantified. For instance, in a parallel energy supply mode, vehicle energy efficiency may improve by 15%.

[0072] Throughout the cold chain transportation process, based on the cold chain path state chain, the loading changes, vehicle shutdown and auxiliary power switching, and changes in external environmental parameters of each path state node are identified to obtain the node state change amount and establish an adjustment state chain. That is, the path state chain is used to re-analyze the key nodes and identify the differences of each node in actual operation, such as the node state change amount caused by differences such as a 20% reduction in loading ratio, vehicle shutdown causing the cold chain system to switch to backup power, or an increase in external ambient temperature of 3 degrees. These differences are recorded and connected in series to obtain the adjustment state chain.

[0073] Using spatial energy efficiency strategy as initial parameters, and combining iterative optimization of the state chain, the optimal energy efficiency strategy for each node is obtained. That is, based on the spatial energy efficiency strategy, the dynamic changes of the nodes caused by the adjustment of the state chain are considered. Through continuous iterative calculation, the energy supply mode and energy consumption distribution of each node are optimized, making the spatial energy efficiency strategy more refined in the time dimension. For example, in the transportation task, after 5 rounds of iterative optimization, the cold chain energy demand of a certain node is reduced from 10 kWh to 8 kWh, while maintaining the target temperature stability.

[0074] With the cold chain temperature control target of the goods loaded at each node as the optimization objective, after clarifying the temperature control requirements of the goods loaded at each node, the energy supply synergy effect of the nodes is analyzed based on the energy supply efficiency evaluation model. This is combined with adjusting the relationship between the temperature decay and delay changes in the state chain and the cold chain, i.e., considering the laws of temperature decay over time and system delay, the optimal energy efficiency strategies of each node are integrated and evaluated for link energy efficiency. In other words, the optimal strategies of each node are summarized and integrated to obtain the overall path optimization scheduling strategy. For example, in a 400-kilometer transportation path, by integrating the strategies of each node, the overall energy consumption can be reduced from 120 kWh to 95 kWh, while still ensuring that all goods are maintained within the target range of 4 to 6 degrees Celsius.

[0075] In summary, the energy efficiency prediction and energy-saving scheduling optimization system for the food cold chain system provided in this application has the following technical effects: by realizing the intelligent energy supply control technology goal based on cold chain space energy efficiency analysis and full-path optimization scheduling, it achieves the technical effect of minimizing the energy consumption of the whole vehicle and cold chain system, improving transportation safety and system operating efficiency while ensuring food temperature control requirements.

[0076] Example 2: Based on the same inventive concept as the energy efficiency prediction and energy-saving scheduling optimization system for the food cold chain system in the foregoing examples, this application also provides a method for energy efficiency prediction and energy-saving scheduling optimization of the food cold chain system. Please refer to the appendix. Figure 2 The process includes: identifying cold chain loading status data and analyzing the spatial influence characteristics of the cold chain loading space on the loading status data; optimizing the energy efficiency of the cold chain energy supply based on the spatial energy efficiency relationship between the cold chain space and the link energy supply parameters, and obtaining a spatial energy efficiency strategy; acquiring cold chain vehicle paths, dynamically analyzing the loading status based on the cold chain vehicle paths and predicting path switching nodes, and establishing a cold chain path state chain; and optimizing the path state node scheduling according to the cold chain path state chain based on the vehicle energy supply coupling relationship of the cold chain system and the spatial energy efficiency relationship, using the spatial energy efficiency strategy as initial parameters, to obtain a full-path optimization scheduling strategy, wherein the full-path optimization scheduling strategy is a link energy supply parameter strategy that minimizes energy consumption while meeting the cold chain objectives.

[0077] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: the cold chain path state chain is a state sequence formed on a preset transportation path based on path switching nodes, including multiple link state nodes, and each state node includes vehicle loading state parameters, environmental parameters, and energy supply parameters, used to describe the operating status of the cold chain system on the entire task path segment, wherein the path switching nodes include loading and unloading points, stopping points, and route segmentation points.

[0078] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: collecting information on the loaded items in the cold chain warehouse, including item attributes and quantities, spatial distribution of locations, and stacking methods; calculating the proportion of the loaded items in the cold chain warehouse's distribution space to obtain the loading status data; constructing a three-dimensional spatial model of the interior of the cold chain warehouse based on the loading status data; calculating and marking the relative positional relationships between goods and warehouse walls, and goods and air ducts; extracting spatial influence factors based on the three-dimensional spatial model; using the spatial influence factors for influence weighting identification to obtain the spatial influence characteristics of the cold chain loading space on the loading status data, which is used to characterize the spatial coordinate position and degree of influence of the loading space distribution on the temperature distribution of the cold chain space.

[0079] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: the spatial influencing factors include: cold airflow channel blockage rate, loading uniformity index, heat exchange surface area and convection path length, and air circulation effectiveness coefficient.

[0080] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: establishing multiple cross-sections along the centerline of the air duct based on a three-dimensional spatial model, calculating the cavity flow area of ​​each cross-section, and comparing it with the normal flow area to obtain the cold airflow channel blockage rate; dividing the loading space into grids, calculating the loading occupancy rate of each grid, and determining the loading uniformity index according to the variance of the loading occupancy rate of the grid unit; selecting effective heat exchange surfaces that are in direct contact with the air based on the exposed surface area of ​​the goods, and performing air outlet and return air flow path analysis based on the effective heat exchange surfaces to obtain the convection path length; measuring the temperature and gas concentration decay time at different locations in the cold chain warehouse by deploying temperature sensors and gas sensors, and identifying the air circulation effectiveness coefficient based on the decay concentration change cycle to characterize the overall efficiency of the cold air circulation in the warehouse.

[0081] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: dividing the cold chain space into grids based on sample data of the cold chain space; adjusting the link energy supply parameters based on the cold chain space grids, analyzing the temperature changes of each space grid, and establishing a correspondence between the link energy supply parameters and the temperature changes of the space grids; based on the correspondence, calculating the temperature energy consumption of the space grids under the adjustment of each link energy supply parameter, forming a mapping relationship between the link energy supply parameters and the energy efficiency of the cold chain space, as the space energy efficiency relationship.

[0082] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: establishing a spatial energy consumption obstacle distribution map based on the spatial influence characteristics to represent the degree of obstruction of different spatial regions to cold air circulation and temperature balance; establishing an energy efficiency coverage heat map using the spatial energy efficiency relationship to represent the temperature control coverage capability of the link energy supply parameters for each spatial region; superimposing and analyzing the spatial energy consumption obstacle distribution map and the energy efficiency coverage heat map to obtain the energy efficiency distribution of the cold chain space; and adjusting and compensating the link energy supply parameters based on the energy efficiency distribution, with the food cold chain demand temperature as the target, to obtain a spatial energy efficiency strategy that meets the food cold chain demand temperature and minimizes energy consumption.

[0083] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: acquiring task data of cold chain vehicles, performing task data path identification and extraction, and determining the cold chain vehicle path; dynamically analyzing the cold chain loading status based on the cold chain vehicle path combined with the vehicle's loading and unloading plan and transportation sequence to obtain the loading status changes of each path segment; predicting path switching nodes based on the loading status changes and path segment characteristics, wherein the path switching nodes include at least one of stopping points, loading and unloading points, and energy supply mode switching points; and constructing a state sequence of path segments and corresponding loading status, environmental parameters, and energy supply parameters using the path switching nodes as dividing points to obtain the cold chain path state chain.

[0084] Furthermore, the energy efficiency prediction and energy-saving scheduling optimization method for the food cold chain system also includes: analyzing the energy consumption coupling relationship between vehicle driving energy supply and cold chain system energy supply, establishing an energy supply efficiency evaluation model to characterize the synergistic effect of vehicle energy consumption and cold chain energy consumption under different energy supply modes; based on the cold chain path state chain, identifying the loading changes, vehicle shutdown and auxiliary energy supply switching, and external environmental parameter changes of each path state node, obtaining the node state change amount, and establishing an adjustment state chain; using the spatial energy efficiency strategy as the initial parameter, combining the adjustment state chain for time-series iterative optimization to obtain the optimal energy efficiency strategy for each node; using the cold chain temperature control target of the loaded items at each node as the optimization target, based on the energy supply efficiency evaluation model, and combining the adjustment state chain with the temperature decay and delay change relationship of the cold chain, performing link energy efficiency evaluation and integration of the optimal energy efficiency strategy for each node to obtain the full-path optimization scheduling strategy.

[0085] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The energy efficiency prediction and energy-saving scheduling optimization system and specific examples of the food cold chain system in the aforementioned embodiment 1 are also applicable to the energy efficiency prediction and energy-saving scheduling optimization method of the food cold chain system in this embodiment. Through the foregoing detailed description of the energy efficiency prediction and energy-saving scheduling optimization system of the food cold chain system, those skilled in the art can clearly understand the energy efficiency prediction and energy-saving scheduling optimization method of the food cold chain system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0086] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0087] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. An energy efficiency prediction and energy-saving scheduling optimization system for food cold chain systems, characterized in that, include: The data identification module is used to identify cold chain loading status data and analyze the spatial influence characteristics of cold chain loading space on loading status data. The strategy acquisition module is used to optimize the energy efficiency of cold chain energy supply based on the spatial energy efficiency relationship between cold chain space and link energy supply parameters, and to obtain a spatial energy efficiency strategy based on the spatial influence characteristics. The state chain establishment module is used to obtain the cold chain vehicle route, dynamically analyze the loading status based on the cold chain vehicle route, predict the route switching node, and establish the cold chain route state chain. The optimization strategy acquisition module is used to optimize the scheduling of path state nodes according to the cold chain path state chain based on the vehicle energy supply coupling relationship of the cold chain system and the spatial energy efficiency relationship, using the spatial energy efficiency strategy as the initial parameter, to obtain the full path optimization scheduling strategy. The full path optimization scheduling strategy is the link energy supply parameter strategy that minimizes energy consumption under the premise of satisfying the cold chain target.

2. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 1, characterized in that, The state chain establishment module includes: The state chain establishment unit is used to describe the cold chain path state chain as a state sequence formed on a preset transportation path based on path switching nodes. It includes multiple link state nodes, and each state node contains vehicle loading status parameters, environmental parameters, and energy supply parameters, which are used to describe the operating status of the cold chain system on the entire task path segment. The path switching nodes include loading and unloading points, stopping points, and route segmentation points.

3. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 1, characterized in that, The data recognition module includes: The information acquisition unit is used to collect information on the items loaded in the cold chain warehouse, including the attributes and quantity of the items, their spatial distribution, and stacking methods; calculate the proportion of the space in which the loaded items are located in the cold chain warehouse; and obtain the loading status data. The model building unit is used to build a three-dimensional spatial model of the interior of the cold chain warehouse based on the loading status data, and to calculate and mark the relative positional relationships between the goods and the warehouse walls, and between the goods and the air ducts. The feature acquisition unit is used to extract spatial influence factors based on the three-dimensional spatial model, and to perform influence weighting identification using the spatial influence factors to obtain the spatial influence features of the cold chain loading space on the loading status data, which are used to characterize the spatial coordinate position and degree of influence of the loading space distribution on the temperature distribution of the cold chain space.

4. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 3, characterized in that, The spatial influencing factors include: cold airflow channel blockage rate, loading uniformity index, heat exchange surface area and convection path length, and air circulation effectiveness coefficient.

5. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 4, characterized in that, The feature-derived unit includes: Cross-section channel establishment is used to establish multiple cross-sections along the centerline of the air duct based on a three-dimensional spatial model, calculate the flowable area of ​​the cavity of each cross-section, and compare it with the normal flow area to obtain the cold airflow channel blockage rate. The index determines the channel, which is used to divide the loading space into grids, calculate the loading occupancy rate of each grid, and determine the loading uniformity index according to the variance of the loading occupancy rate of the grid cells. The length acquisition channel is used to screen the effective heat exchange surface that is in direct contact with the air based on the exposed surface area of ​​the goods, and to analyze the flow path of the air outlet and return air outlet based on the effective heat exchange surface to obtain the convection path length. The coefficient identification channel is used to measure the temperature and gas concentration decay time at different locations in the cold chain warehouse by deploying temperature sensors and gas sensors. Based on the decay concentration change cycle, the air circulation effectiveness coefficient is identified to characterize the overall efficiency of cold air circulation in the warehouse.

6. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 1, characterized in that, The strategy acquisition module includes: Grid division unit, used to divide the cold chain space into grids based on sample data of the cold chain space; The relationship establishment unit is used to adjust the link power supply parameters based on the cold chain spatial grid, analyze the temperature changes of each spatial grid, and establish the correspondence between the link power supply parameters and the temperature changes of the spatial grid. The relationship forming unit is used to calculate the spatial grid temperature energy consumption under the adjustment of each link power supply parameter based on the correspondence relationship, and form a mapping relationship between the link power supply parameter and the cold chain space energy efficiency as the space energy efficiency relationship.

7. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 6, characterized in that, The strategy acquisition module further includes: The distribution map establishment unit is used to establish a spatial energy consumption obstacle distribution map based on the spatial influence characteristics, which is used to represent the degree of obstruction of different spatial regions to cold air circulation and temperature balance. The heat map establishment unit is used to establish an energy efficiency coverage heat map using the spatial energy efficiency relationship, which is used to represent the temperature control coverage capability of the link power supply parameters for each spatial region; The overlay analysis unit is used to overlay the spatial energy consumption obstruction distribution map with the energy efficiency coverage heat map to obtain the energy efficiency distribution of the cold chain space. The strategy acquisition unit is used to adjust and compensate the link power supply parameters based on the energy efficiency distribution, with the target temperature of food cold chain as the objective, to obtain a spatial energy efficiency strategy that meets the temperature requirements of food cold chain and minimizes energy consumption.

8. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 1, characterized in that, The state chain establishment module includes: The path determination unit is used to acquire task data of cold chain vehicles, perform task data path identification and extraction, and determine the path of the cold chain vehicles. The change acquisition unit is used to dynamically analyze the cold chain loading status based on the cold chain vehicle route and the vehicle's loading and unloading plan and transportation sequence, and obtain the loading status changes of each route segment. The node prediction unit is used to predict path switching nodes based on the changes in loading status and path segment characteristics. The path switching nodes include at least one of the following: stopping point, loading / unloading point, and power supply mode switching point. The state chain construction unit is used to construct a state sequence of path segments and corresponding loading states, environmental parameters and energy supply parameters, with the path switching nodes as the dividing points, to obtain the cold chain path state chain.

9. The energy efficiency prediction and energy-saving scheduling optimization system for a food cold chain system according to claim 8, characterized in that, The optimization strategy acquisition module includes: The relation parsing unit is used to analyze the energy consumption coupling relationship between vehicle driving energy supply and cold chain system energy supply, and to establish an energy supply efficiency evaluation model to characterize the synergistic effect of vehicle energy consumption and cold chain energy consumption under different energy supply modes. The change acquisition unit is used to identify the loading changes, vehicle shutdown and auxiliary power supply switching, and external environmental parameter changes of each path state node based on the cold chain path state chain, obtain the node state change amount, and establish an adjustment state chain. The iterative optimization unit is used to perform time-series iterative optimization with the spatial energy efficiency strategy as the initial parameter and the adjusted state chain to obtain the optimal energy efficiency strategy for each node. The integration unit is used to evaluate and integrate the optimal energy efficiency strategies of each node based on the energy supply efficiency evaluation model, taking the cold chain temperature control target of the loaded items at each node as the optimization target, and combining the temperature decay and delay change relationship between the adjusted state chain and the cold chain, so as to obtain the full path optimization scheduling strategy.

10. A method for energy efficiency prediction and energy-saving scheduling optimization of a food cold chain system, characterized in that, The energy efficiency prediction and energy-saving scheduling optimization system for the food cold chain system as described in any one of claims 1 to 9 is executed, including: Identify cold chain loading status data and analyze the spatial influence characteristics of cold chain loading space on loading status data; Based on the spatial energy efficiency relationship between cold chain space and link energy supply parameters, energy efficiency optimization of cold chain energy supply is performed based on the spatial influence characteristics to obtain spatial energy efficiency strategy. Obtain the cold chain vehicle route, dynamically analyze the loading status based on the cold chain vehicle route and predict the route switching node, and establish a cold chain route status chain. Based on the vehicle energy supply coupling relationship of the cold chain system and the spatial energy efficiency relationship, the path state node scheduling optimization is performed according to the cold chain path state chain using the spatial energy efficiency strategy as the initial parameter to obtain the full path optimization scheduling strategy. The full path optimization scheduling strategy is the link energy supply parameter strategy that minimizes energy consumption under the premise of satisfying the cold chain objective.

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